Background/Objectives: The global burden of colorectal cancer (CRC) is projected to increase by 2040, highlighting the need for prevention strategies, early detection and adequate follow. The aim of this study was to provide an overview of the progress in personalized medicine of CRC in Serbia, with results and insights from the Institute for Oncology and Radiology of Serbia (IORS), and to propose guidance for tackling observed challenges in the future. Methods: Epidemiological data was derived from official global and national cancer registries and IORS electronic medical records. Germline genetic testing for Lynch syndrome was performed by Next Generation Sequencing. RAS and BRAF mutation analyses were performed using qPCR diagnostic kits. Results: Epidemiology and risk factors, prevention and early detection programs, as well as treatment options and scientific advances have been described in detail. Out of 103 patients who underwent germline testing for Lynch syndrome, 19 (18.4%) had a pathogenic or likely pathogenic mutation in one of the MMR genes, and 8 (7.8%) in other CRC-associated genes (APC, CHEK2, MUTYH). Of 6369 tested patients, 50.43% had a mutation in KRAS or NRAS genes, while 9.54% had the V600 mutation in the BRAF gene. Conclusions: Although significant improvements in CRC management have occurred globally in recent years, the high incidence of young-onset CRC and the growing elderly population due to a rise in life expectancy require a strategic approach leading to population-based systemic solutions, especially important for countries with limited health care resources as Serbia.

This retrospective study aimed to assess coronary artery calcium (CAC) progression in serial computed tomography measurements according to risk factor changes. In 448 asymptomatic adults who underwent CAC measurements with more than one-year intervals, CAC progression was assessed according to age, sex, variable traditional risk factors (diabetes mellitus, hypertension, hyperlipidemia, and smoking), and initial CAC score (0, 0.1–100, and >100). Univariate and multivariate logistic regression analyses were assessed for independent predictors of rapid CAC progression (△CAC/year>20). During the 3.5-year follow-up, coronary artery calcifications occurred in 43 (12.8%) of 336 individuals with an initial CAC score of zero. Of 112 individuals with initial CAC presence, 60 (53.6%) had △CAC/year>20. Age, male sex, body mass index, and all risk factors were significantly associated with △CAC/year>20, but recently diagnosed hypertension (odds ratio [OR], 11.3) and initial CAC score (OR, 1.05) were significant independent predictors in multivariate regression analyses. CAC progression was affected by demographic and traditional risk factors, but adjusting for these factors, recently diagnosed hypertension and initial CAC score were the most influential factors for rapid CAC progression. These findings suggest that patients with higher initial CAC scores may benefit from more frequent follow-up scans and checks regarding risk factor changes.

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

Traditional photodynamic therapy (PDT) is often limited in its efficacy by insufficient light penetration, inefficient photosensitizer energy transfer, and inadequate target tissue oxygenation. X-ray activated PDT (XPDT), using nanoparticle (NPs) photosensitizers comprised of europium-doped yttrium oxide cores encased within silica shells (Y₂O₃:Eu@SiO₂), offers the potential of overcoming most of these limitations. Aimed at maximizing the generation of reactive oxygen species (ROS), this study reports methodologies for investigation of the effects of synthesis conditions on the characteristics of Y₂O₃:Eu@SiO₂ NP. Transmission electronic microscope (TEM) revealed that the NP core diameter was linearly, positively correlated with condensation time (R2 = 0.7357, p < 0.05) and linearly, negatively correlated with urea concentration (R2 = 0.958, p < 0.05). The addition of cetyltrimethylammonium bromide (CTAB) during core synthesis demonstrated enhanced particle dispersion. Silica coating via APTES-1 and TEOS-3 conditions resulted in Y₂O₃:Eu@SiO₂ NPs that generated the highest ROS, with 8.7-fold and 8.9-fold increase respectively in relative fluorescence intensity at 8 Gy irradiation compared to a 5.2-fold increase in PBS under the same condition. In vivo evaluation using 18F-fluorothymidine positron emission tomography (18F-FLT PET) of mouse xenografts of a human prototypical cancer (ovarian) revealed a 55% reduction in tumor proliferation of radiation-sensitive CAOV3 tumors treated with Y₂O₃:Eu@SiO₂ NPs compared to a 19% reduction in tumor proliferation of radiation-resistant SKOV3 tumors on the fourth day post-treatment. Taken together, these findings suggest Y₂O₃:Eu@SiO₂ NPs can serve as effective photosensitizers for XPDT and their performance characteristics can be optimized by selecting synthesis conditions and parameters to deliver the desirable therapeutic effects, offering novel approaches to circumventing many of the limitations of conventional PDT in the treatment of cancer.

Traumatic vascular injuries consist in direct or indirect damage to arteries and/or veins and accounts for 3% of all traumatic injuries. Typical consequences are hemorrhage and ischemia. Extremities vascular injuries can occur isolated or in association in major trauma with other organ injuries. They account for 1-2% of patients admitted to Emergency Department, for approximately 50% of all arterial injuries. Lower extremities are more frequently injured than the upper ones in adult population. The outcome of vascular injuries is strictly correlated to the environment and the time background. Treatment can be challenging notably in polytrauma because of the dilemma of which injury should be prioritized and treatment delay can cause disability or even death especially for limb vascular injury. Our purposes are to discuss the role of Computed Tomography Angiography (CTA) in the diagnosis of vascular trauma and its optimized protocol to achieve the definitive diagnosis, and to assess the radiological signs of vascular injuries and the possible pitfalls.

Cystic artery bleeding (CAB), a relatively uncommon entity, can arise from both traumatic and non-traumatic etiologies. This pictorial essay aims to elucidate the computed tomography (CT) findings associated with CAB in these distinct contexts. In traumatic CAB, particularly blunt or penetrating trauma, a cystic artery lesion is the culprit behind the hemoperitoneum characteristically located within the gallbladder fossa. Prompt diagnosis is crucial. Fortunately, trans-arterial embolization offers a successful treatment option for traumatic cystic artery lesions, especially in settings where non-operative management is preferred. Non-traumatic CAB, on the other hand, often stems from inflammation in the hepatobiliary and pancreatic system, gallbladder tumours, or iatrogenic injuries. The underlying mechanism typically involves cystic artery wall damage, either due to inflammation or procedural complications, leading to the formation of a cystic artery pseudoaneurysm (CAP). Notably, CAP bleeding can manifest long after the causative event, posing a diagnostic challenge with a variable clinical presentation. This pictorial essay serves to illustrate the CT signs and features of CAB in both traumatic and non-traumatic scenarios. Additionally, it will delve into the characteristic CT appearance of CAP.

The generation of forensic DNA profiles consisting of single nucleotide polymorphisms (SNPs) is now being facilitated by wider adoption of next-generation sequencing (NGS) methods in casework laboratories. At the same time, and in part because of this advance, there is an intense focus on the generation of SNP profiles from evidentiary specimens for so-called forensic or investigative genetic genealogy (FGG or IGG) applications. However, FGG methods are constrained by the algorithms for genealogical database searches, which were designed for use with single-source profiles, and the fact that many forensic samples are mixtures. To enable the use of two-person mixtures for FGG, we developed a workflow, MixDeR, for the deconvolution of mixed SNP profiles. MixDeR, a flexible and easy to use R package and Shiny app, processes ForenSeq Kintelligence® (QIAGEN, Inc.) SNP genotyping results and directs deconvolution of the profiles in EuroForMix (EFM). MixDeR then filters the EFM outputs to produce inferred single-source genotypes in reports formatted for use with GEDmatch® PRO. An optional MixDeR output includes metrics that assist with testing and validation of the workflow. As the Shiny app provides a graphical user interface and the software is designed to be run offline, MixDeR should be suitable for use by any laboratory developing FGG capabilities, no matter their bioinformatic resources or expertise.

Purpose: The purpose of this study is to apply the Composite Radiographic Index modified (CRIm) in CBCT scans of patients with Medication related osteonecrosis of the jaws (MRONJ) in order to detect its validity, and corelate the radiologic findings with the clinical staging of the disease. Methods: This study included 43 patients with MRONJ diagnosis and CBCT scans performed in the School of Dentistry and approved by the Research Ethics Committee. Clinical staging for every patient (0-3) was provided according to AAOMS 2022 guidelines. In cases of patients with both jaws affected, each jaw was evaluated independently resulting to 52 CBCT scans. The 52 CBCT scans were assessed under standardized viewing conditions by 2 observers who evaluated independently the following 8 radiologic features: lytic changes, sclerosis, periosteal reaction, sequestration, non-healing extraction sockets and other findings (sinus implication, inferior alveolar canal involvement, jaw fracture). The CRIm index was constructed to assess the extent of osseous changes. The radiologic features were scored as absent (0), localized/single (1), extensive/multiple (2), yielding a theoretical range of 0–12. For statistical analysis Fisher’s exact test and Spearman’s correlation coefficient were performed. Results: A statistically significant association of clinical stage with lytic changes, sequestration and inferior alveolar canal involvement was found. Also, a statistically significant correlation of clinical stage with CRIm index classification was obtained. Conclusions: The CRIm index tends to increase as the clinical stages of MRONJ advance, suggesting a correlation between the clinical stage of MRONJ and the CRIm index.

: Shear-wave elastography (SWE) has become popular in clinical practice for many diseases.However, there are not adequate research for spleen-related diseases.This study aimed to investigate the potential of quantitative values obtained through SWE in evaluating spleen pathologies in the pediatric population and to demonstrate its perfomance to differentiate splenomegaly-related diseases.Research group retrospectively included children with pathological diagnoses related to spleen from November 2016 to April 2021 and they were categorized three groups as portal hypertension (PH), benign lymphoid hyperplasia (BLH), malignant infiltration (MI).Spleen sizes and parenchymal stiffness were also calculated for each group.Subsequently, mean spleen stiffness in each compared with normal values within the same age group.In total 2781 children (1379 children for study group, 1402 children for control group) were enrolled to the study.The highest stiffness were observed in the PH group, which is statistically higher than others (p&lt;0.05). Although the mean spleen stiffness in the group with BLH was higher than the control and MI group, the difference was not statistically significant (p=0.08).The mean stiffness in the group with MI was significantly lower than both the control group (p=0.005) and PH (p=0.01).In conclusion, using SWE in the differential diagnosis of etiologies causing splenomegaly, could make important contribution.

Due to its time effective, joint friendly and highly customizable character whole-body-electromyostimulation (WB-EMS) is regarded as a feasible solution for a large variety of training issues particularly in the area of non-athletic application. To identify gaps in research and summarize prevalent evidence we conducted an evidence map on outcomes addressed by WB-EMS trials in middle-aged to older, non-athletic adults. Based on a comprehensive systematic search in five databases and two study registers according to PRISMA, 54 projects published in 80 articles were included. More than 90% of the studies reported outcomes related to the physical fitness or function domain. Body composition parameters were addressed by two thirds of the projects. Health-related outcomes addressed by WB-EMS included cancer/neoplasm (n=7), endocrine regulation (n=8), glucose metabolism (15), nervous system diseases (n=2), cardiovascular system diseases (n=22), non-specific chronic low back pain (n=4), osteopenia (n=5) and diseases of the renal system (n=12). Outcomes related to inflammation were addressed 18 times. Of importance, no studies reported clinically relevant adverse effects related to the WB-EMS intervention. In summary, while considerable evidence on outcomes related to fitness/function and body composition is prevalent, evidence gaps of WB-EMS research were particular evident for diseases of the nervous and cerebrovascular system.

Whole body electromyostimulation (WB-EMS) is frequently presented as a safe and attractive training technology, however evidence for this description is vague. Thus, the present study aimed to provide an overview of different aspects related to safety and attractiveness of WB-EMS in non-athletic cohorts. A systematic review of the literature according to PRISMA searched five electronic databases, two study registers and google scholar without language restrictions. Briefly, WB-EMS studies that reported adverse effects, loss to follow, withdrawal or attendance rates in non-athletic cohorts were included. Finally, 58 eligible studies were eligible. All studies applied low-frequency WB-EMS predominately 1-2x 20-25 min/week. Seventeen studies provided a superimposed protocol. While no study (n=56) reported serious adverse effects, four studies observed abnormal laboratory findings, albeit without clinical relevance. Loss to follow-up (10±11%) and withdrawal rate (5±6%) of WB-EMS trials were low, but did not differ from data for the non-training (10±12% and 4±6%) or exercise control groups (12±11% and 7±8%). In parallel, we observed high attendance rates (94±7%) in the WB-EMS groups that again did not relevantly vary from findings for the exercising control groups (n=20; 91±7%). WB-EMS can indeed be considered as a safe and attractive training technology for non-athletic cohorts, be it with or without health problems.

Hypoxia is a hallmark of solid tumors, including hepatocellular carcinoma (HCC). Hypoxia has proved to be involved in multiple tumor biological processes and associated with malignant progression and resistance to therapy. Transarterial chemoembolization (TACE) is a well-established locoregional therapy for patients with unresectable HCC. However, TACE-induced hypoxia regulates tumor angiogenesis, energy metabolism, epithelial-mesenchymal transition (EMT), and immune processes through hypoxia-inducible factor 1 (HIF-1), which may have adverse effects on the therapeutic efficacy of TACE. Hypoxia has emerged as a promising target for combination with TACE in treatment of HCC. This review summarizes impact of hypoxia on HCC tumor biology, adverse effects of TACE-induced hypoxia on its therapeutic efficacy, highlighting the therapeutic potential of hypoxia-targeted therapy in combination with TACE for HCC.

In vivo proton (1H) magnetic resonance spectroscopy (MRS) is a powerful noninvasive method which can measure Alzheimer’s disease (AD) related neuropathological alterations at the molecular level. AD biomarkers include amyloid-beta (Aβ) plaques and hyperphosphorylated tau neurofibrillary tangles. These biomarkers can be detected via postmortem analysis, but also in living individuals through positron emission tomography (PET) or biofluid biomarkers of Aβ and tau. This review offers an overview of biochemical abnormalities detected by 1H MRS within the biologically defined AD spectrum. It includes a summary of earlier studies that explored the association of 1H MRS metabolites with biofluid, PET, and postmortem AD biomarkers, and examined how apolipoprotein e4 allele carrier status influences brain biochemistry. Studying these associations is crucial for understanding how AD pathology affects brain homeostasis throughout the AD continuum and may eventually facilitate to develop potential novel therapeutic approaches.

Recently, Medical Radiology has undergone significant improvements in patients’ management due to advancements in image acquisition by last generation machines, data processing, and integration of artificial intelligence. In this way, cardiovascular imaging is one of the fastest growing radiological subspecialty. A compressive review was focused on addressing how and why CT and MR have gained a I class indication in most cardiovascular diseases, and the potential impact of the tissue and functional characterization by CT photon counting, quantitative MR mapping, and 4-D flow. Regarding rectal imaging, advances in cancer imaging by using diffusion-weighted MRI sequences for identifying residual disease after neoadjuvant chemoradiotherapy and [18F] FDG PET/MRI were provided for high-resolution anatomical and functional data in oncological patients. We offered a large overview on the approach to the imaging of diffuse and focal liver diseases by US elastography, contrast-enhanced US, quantitative MRI, and CT for the patient’s risk stratification. Italy is riding the wave of these improvements. The development of large networks will be crucial to create high-quality databases for a patient-centered precision medicine by artificial intelligence. dedicated radiologists with specific training and a close relationship with the referring clinicians will be the essential human factor.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteopancreatic neuroendocrine tumors (GEP-NETs), and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n=83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n=123 subjects). Set1, the data set from Scanner1 was reconstructed with standard clinical parameters (5 minutes; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-minutes). Set2, data from Scanner2, representing out-of-domain clinical scans, used standard iterative reconstruction (5 minutes; OSEM). Reconstructed datasets were divided into training, validation, and testing datasets in a 60%, 20%, 20% proportion, and a deep neural network was trained with each: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50%, and 75%) of Set1 for training. Scanner1, list-mode 2-minute reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1=0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1=0.755; p-value=0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1=0.478) to 100% training data (F1=0.713; p&lt;0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets, and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with ClariCT. AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p<0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p<0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusion: AI-based noise reduction using ClariCT. AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

In the last decade, the role of Computed Tomography Pulmonary Angiography (CTPA) in diagnosing and managing pulmonary embolism (PE) has significantly evolved. Once a purely diagnostic tool, CTPA now plays a crucial role in initial emergency department assessments and treatment planning tailored to the patient’s condition. Advances in CTPA technology, including image quality enhancements and artificial intelligence (AI) applications, necessitate a reassessment of its current utility. This narrative review updates on new CTPA tools and techniques and focuses on data extraction challenges in emergency settings. CTPA studies, often conducted with multidetector scanners, provide essential information for risk stratification and treatment planning. The quantification of thrombotic burden using CTPA is vital for predicting mortality and determining appropriate treatments. Classical scoring systems, such as those developed by Qanadli, Mastora, and Ghanima, convert CTPA findings into quantifiable data to assist clinical decision-making. Despite CTPA not being a standard mortality predictor, significant research suggests including CTPA data, especially RV dysfunction indicators, for prognostic purposes. Recent studies highlight automated techniques for quantifying pulmonary perfusion and thrombus composition, enhancing the accuracy of PE severity assessment. The integration of AI in CTPA, particularly through deep learning algorithms, shows promise in automating thrombus load assessment and improving risk stratification. The continuous development of imaging techniques positions CTPA as a potential tool for comprehensive PE management, aiding in diagnosis, prognosis, and treatment decisions.

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study optimizes the CO2 huff-n-puff process for multistage-fractured horizontal wells in Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO2 sequestration simultaneously was addressed using a coupled geomechanics-reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. Fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO2 injection. After the primary production phase, various CO2 injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking, and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO2 storage efficiency. The simulation approach proposed by this study provides a systematic workflow for evaluating and optimizing CO2 huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.

This review focuses on the pivotal role of radiotracers in breast cancer imaging, emphasizing their importance in accurate detection, staging, and treatment monitoring. Radiotracers, labeled with radioactive isotopes, are integral to various nuclear imaging techniques, including positron emission tomography (PET) and positron emission mammography (PEM). The most widely used radiotracer in breast cancer imaging is 18F-fluorodeoxyglucose (18F-FDG), which highlights areas of increased glucose metabolism, a hallmark of many cancer cells. This allows for the identification of primary tumors, metastatic sites, and assessment of tumor response to therapy. In addition to 18F-FDG, this review will explore newer radiotracers targeting specific receptors, such as estrogen receptors or HER2, which offer more personalized imaging options. These tracers provide valuable insights into the molecular characteristics of tumors, aiding in tailored treatment strategies. By integrating radiotracers into breast cancer management, clinicians can enhance early disease detection, monitor therapeutic efficacy, and guide interventions, ultimately improving patient outcomes. Ongoing research aimed at developing more specific and sensitive tracers will also be highlighted, underscoring their potential to advance precision medicine in breast cancer care.

The migration of gallstones from the gallbladder into the gastrointestinal tract has been recognized for over 100 years. The most common way gallstone reaches the digestive tract is through a fistula between the gallbladder and the duodenum. Large stones become impacted in the duodenum and result in gastric outlet obstruction (Bouveret's syndrome). In this report, we present a 81-years old patient with the cholecystoduodenal fistula, which was diagnosed by ultrasound and computed tomography and effectively surgically managed. Even Bouveret syndrome is rare, with a timely diagnosis followed with an appropriate treatment maybe we could decrease morbidity and mortality rates of this complication.

Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis

Non-targeted effects (NTE) such as bystander effects or genomic instability have been known for many years but their significance for radiotherapy or medical diagnostic radiology are far from clear. Central to the issue are reported differences in response of normal and tumour tissues to signals from directly irradiated cells. This review will discuss possible mechanisms and implications of these different responses and will then discuss possible new therapeutic avenues suggested by the analysis. Finally, the importance of NTE for diagnostic radiology and nuclear medicine which stems from the dominance of NTE in the low dose region of the dose response curve will be presented. Areas such as second cancer induction and microenvironment plasticity will be discussed.

Background and Objectives: High job strain is worsening the outcome in coronary artery disease, CAD. Severe dissatisfaction on workplace, imbalance between high demands , and low rewards, are affecting the quality of life, and diminishing the duration of life, especially in CAD patients. Our study included two samples : CAD patients (experimental group), and without CAD patients, but with traditional risk factors (control group). The main objective of the study was the role played by high sensitivity C reactive protein, hs CRP, and microalbuminuria, as mediators of job strain and CAD relationship. Secondary objectives were the following: identification of the most vulnerable patients( with significant job strain) and intervention towards this category, for cardiovascular events prevention (primary or secondary). Materials and Methods: The utilized methods were the following : clinical exam; electrocardiogram; echocardiogram; non contrast enhanced computed chest tomography; laboratory findings ( lipid profile, glycemia, hs CRP, microalbuminuria); type D scale, DS 14; depression questionnaire, PHQ-9; and satisfaction with work scale, SWWS. Results: The highest values for hs CRP and microalbuminuria were noticed in patients with the lowest Likert score, for both samples CAD, and without CAD patients. The highest job strain was noticed in females. More than half of CAD patients ,with low Likert scores, revealed myocardial infarction. Conclusions: Microalbuminuria and hs CRP were statistical significant in the study as intercessors, in job strain and CAD relationship. The most vulnerable patients for vascular events were females, in both groups : CAD and “CAD waiting desk”. In CAD sample, the vascular vulnerability was also increased by high job strain.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Background: Although several studies have been conducted on the use of Artificial Intelligence (AI) in mammography (MG), research focusing on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose, remains limited. This study assessed whether AI could enhance the detection of BC, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. Methods: The study included patients who underwent unilateral BC surgery and subsequently developed contralateral BC during follow-up. We evaluated the capability of the AI-supported MG diagnostic system FxMammo™ (FathomX Pte Ltd, Singapore) to diagnose BC more accurately or earlier than radiologists' assessments. This evaluation was supplemented by reviewing MG readings by radiologists. Results: Out of 1,101 patients who underwent surgery, 10 patients who had initially received a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in 6 cases (60%), while radiologists identified 5 cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year prior to the conventional diagnosis. Conclusions: This study highlights the AI system's effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer at earlier than radiological assessments.

French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic pathogens that are of considerable medical or veterinary importance. Until now, there are few data available on the description of parasites circulating in this area, especially on protozoan belonging to the phy-lum Apicomplexa; conversely, the neighbouring countries describe a high parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new potentially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis. However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based on Oxford Nanopore Technologies sequencing platform using the 18S gene of fourteen Apicomplexa-positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTIIME pipeline. Thanks to a scoring rule, we were able to classify as Apicom-plexa-positive 10 samples out of 14 and reveal the presence of co-carriages. The same samples were also sequenced with Illumina platform, for validation purposes. On samples identified as Apicomplexa-positive by both platforms, a strong positive correlation at up to genus level was reported. Overall, the presented workflow represents a reliable method for Apicomplexa detec-tion, which may pave the way for more comprehensive biomonitoring of zoonotic pathogens.

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine of melanoma patients. Using noninvasive 1H magnetic resonance spectroscopy (1H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels at early stages of effective therapy and could be construed as metabolic imaging biomarkers for drug response. The present work aimed to demonstrate if these metabolic changes and those assessed by 31P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1H and 31P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for early predicting melanoma response to MEK inhibition.

Artificial intelligence (AI) tools, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have great potential in various fields, such as healthcare, education, and industry. This review highlights the applications and challenges of the ChatGPT versions 3.5 and 4.0 in radiology. ChatGPT can process medical texts, analyze medical images, and generate reports and summaries for different audiences. ChatGPT can also answer patients' questions and translate complex medical information into a plain language. ChatGPT has shown impressive results in medical tests and quizzes, indicating its potential to enhance diagnostic accuracy and medical education. These models show promise for reducing diagnosis times and healthcare costs. However, ChatGPT also faces challenges and limitations, such as a lack of visual content, ethical and legal issues, data quality, and error detection. This review highlights the benefits and future directions of ChatGPT and artificial intelligence (AI) in radiology.

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at project level impact the emergence of systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analyzing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system, and to that end, mitigate the impacts of climate change. This paper demonstrates a workflow using drone-based photogrammetry, 3D modeling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modeled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management.

Pulmonary artery stenosis represents a group of disorders involving main, branch or peripheral pulmonary arteries resulting either in pain, dyspnea, hemoptysis or even no symptoms. Early diagnosis and timely intervention are crucial for reducing mortality, while timely diagnosis is challenging due to the nonspecific symptoms. Computed tomography pulmonary angiography (CTPA) is useful in the diagnosis because it can provide more details about the abnormal changes in the lumen, vessel wall and adjacent mediastinal structures. Congenital and acquired pulmonary artery anomalies have some characteristics on CTPA, which can be useful for differential diagnosis. Awareness of these conditions and is important for the radiologists. This pictorial review provides an overview of CTPA imaging features of pulmonary artery stenosis.

There is little doubt that the efficacy of MnDPDP and its therapeutic improved counterpart Ca4Mn(DPDP)5 mainly depends on their MnSOD-mimetic activity when it comes to their potential use as rescue medicines, during e.g., acute myocardial infarction. However, pharmacokinetic considerations suggest that the efficacy of MnDPDP on Pt2+-associated neurotoxicity depends on another action of this drug. Electron Paramagnetic Resonance (EPR) studies have demonstrated that Pt2+ outcompetes Mn2+ and endogenous Zn2+ from binding to fodipir (DPDP), and hence suggest that the previously reported CIPN protective efficacy of MnDPDP is a result of chelation and elimination of Pt2+ by DPDP, which in turn suggests that Mn2+ is unnecessary for efficacy when it comes to oxaliplatin-associated CIPN.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions. This IRB approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. We used a General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) to acquire two DTI sequences of the left knee on each child at 3T: an in-plane 2.0 x 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel, neither had an inter-section gap. We used a multi-band DTI acquisi-tion with fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 sec/mm2). The MR vendor-provided a commercially available DL model applied with 75% noise reduction settings to same subject DTI sequences at different spatial res-olutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for femur and tibia, at each spatial resolution. Differences were evaluated using Wilcox-on-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffu-sion metrics in femur and tibia using the 2 mm x 2mm x 3 mm voxel dimension there were no significant differences between tract count (p = 0.1, p =0.14) tract volume (p = 0.14, p = 0.29), or tibial tract length (p=0.16); femur tract length exhibited a significant difference (p

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields, including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure comprehensiveness and reliability of the results. We discuss the role of AI techniques used before, during, and after intervention treatments and the advantages of robotics in achieving precise operations and reducing intra-operative risks. Challenges faced in the implementation of these techniques are also addressed. Finally, we explore the future trends and potential applications of AI and robotics in interventional therapy, highlighting their potential to improve patient treatment outcomes and quality of life.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

RNA-seq faces persistent challenges due to the ongoing expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Shannon entropy serves as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, employing RPKM and median normalization on paired samples of 475 TCGA RNA-seq datasets spanning eight different cancer types with 5-year overall survival rates ranging from 30% to 98%. Our analysis revealed that the RPKM normalization coupled with a threshold of log2 fold change ≥1 for indentifying differentially expressed genes, yielded the best results with a correlation coefficient of 0.91. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows.

The results of applying drilling engineering tools in planned deviated and horizontal wells of oil and gas condensate fields have been examined. The basis for the implementation of a comprehensive approach was the information obtained from drilling horizontal wells in the West and East Siberia, Volga-Ural region, and Iraqi Kurdistan. A synthetic model is considered as an example of the data, incorporating geomechanical and geological properties. As a result of the conducted audit of the geomechanical model and drilling process, the methodologies used were further developed to obtain optimal drilling fluid densities, aiming to prevent cavity formation on well walls, hydraulic fractures, and other related issues. The implementation of the Python programming language was introduced in the stages of geomechanical model preparation, resulting in a time acceleration of up to 20%. During the pre-drilling stage, technological operations were synchronized with the geomechanical model, increasing the involvement of process participants through the synchronization of knowledge areas. The interrelation between drilling technology, drilling parameters, properties of flushing fluids, and rock stability analysis was established, ensuring a 5% increase in construction speed (through NPT reduction and modification of existing tools and technological operations) and accident-free drilling of complex deviated and horizontal wells, provided that the recommended drilling parameters are followed.

The transition from Building Information Modelling (BIM) to Heritage Building Information Modelling (H-BIM) is intended to pursue an adequate knowledge of the artefact that is to be preserved, progressively replacing the traditional methods of the restoration and structural reinforcement projects with new tools for managing both existing information and new interventions. The aim of the paper is to show the application of the H-BIM method to a stone pavement road located in the Archaeological Site of Pompeii. In detail, starting from a Laser Scanner based survey, juxtaposed by coordinated points georeferenced through a total station, points clouds were handled by means of several BIM-based tools to perform the road design process, starting from the digital elevation model (DEM) to the corridor representation. Subsequently, a visual programming application based on Python language was adopted to update corridor information by means of the object property set. As preliminary results, a tool, complete with graphical and non-graphical information, is proposed to be used in conservation, maintenance and restoration project.

This review is focused on the current status of quantitative MR (qMR) of skeletal muscle. The first section covers the techniques of qMR in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/ pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1 weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, diffusion tensor imaging to emerging qMR features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMR of muscle; the intent is to demonstrate the utility qMR in different disease states of the muscle rather than a complete comprehensive survey.

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It sheds light on the journey of radiology, from the pioneering discovery of X-rays to today’s intricate imaging technologies, infused with machine learning and deep learning in medical image analysis. At the crux of this review lies an in-depth study of AI applications in radiology, elucidating its seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology — data quality, the ’black box’ enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. It concludes by firmly cementing the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

D-dimer testing in combination with clinical assessment is commonly used to diagnose suspected DVT and pulmonary embolism. However, as D-dimer levels are physiologically higher in elderly patients, this test loses clinical specificity in the setting of geriatric patients. The aim of our study was to investigate the changes in D-dimer levels with age and the specificity of the D-dimer test in the diagnosis of pulmonary embolism. 1617 elderly patients consecutively admitted to the Geriatric Emergency Care Department of the INRCA-IRCCS hospital between January 2013 and February 2014 and for whom D-dimer results were available were included. The D-dimer immunoturbidimetric assay was performed on a cs-2100i Sysmex Siemens using a conventional cut-off level of 500 µg/L. The median D-dimer level was 1200 [550-2990] µg/L FEU, and the percentage of patients with D-dimer levels below the conventional cut-off was only 22.1%. As expected, there was a significant age-related increase in D-dimer, even when sex was considered, and a significant decrease in the specificity of D-dimer assay with age. The most established use of D-dimer testing is in the diagnosis of deep vein thrombosis or pulmonary embolism. Our study confirms the need for an age-adjusted cut-off for D-dimer testing in geriatric patients.

The aim of this study was to evaluate the quality of legends associated with diagnostic images in the published oral and maxillofacial radiology literature using a novel rating scale. Images and their corresponding legends were randomly selected from published manuscripts over the last ten years in the Oral Radiology journals, namely Dentomaxillofacial Radiology, Imaging Science in Dentistry, Oral Radiology, and Oral Surgery Oral Medicine Oral Pathology Oral Radiology. An Image Quality Rating Scale (IQRS) was introduced to assess the quality of the legends associated with images. A program was developed for the rating scale form using Google Apps Script API to gather and analyze the data. The rating scale ranged from 1 to 5, with 5 as the highest rating. The highest average IQRS rating for one journal was 3.04. The overall IQRS rating average across all four journals was 2.87, which is 2.13 rating scores lower than the ideal score of 5. There is room for improvement in the legends that accompany diagnostic images and figures in the oral and maxillofacial radiology literature. A proper legend provides an excellent diagnostic teaching opportunity for the reader and enhances the quality of a publication.

A series of conditions can mimic musculoskeletal infections on imaging, complicating their diagnosis and affecting the treatment. Depending on the anatomical location, different conditions can manifest with clinical and imaging findings that mimic infections. Herein we present a wide spectrum of the musculoskeletal disorders of the axial skeleton, long bones, peripheral joints and soft-tissue which may manifest as an infectious process, besides focusing on the potential mimics of osteomyelitis, septic arthritis and infectious spondylodiscitis that are common in clinical practice. We present the typical imaging characteristics of each musculoskeletal infection, followed by mimicking conditions.

Aneurysmal wall enhancement (AWE) of non-ruptured sacular intracranial aneurysms (IA) after endovascular treatment (ET) is a frequently observed imaging finding. So far, its value remains unclear. We aimed to investigate the effect of AWE on aneurysm reperfusion rates in a longitudinal cohort. This is a retrospective MRI study over the timespan of up to 5 years, assessing the correlation of increased AWE of non-ruptured IAs and events of aneurysm reperfusion and retreatment, PHASES Score and grade of AWE. T1 SPACE fat saturation (FS) and T1 SE FS blood suppression sequences after contrast administration were used for visual interpretation of increased AWE. The IAs’ sizes were assessed via the biggest diameter. The grade of enhancement was defined in a grading system from grade 1 to grade 3. 127 consecutive non ruptured IA-patients (58.9 ± 9.0 years, 94 female, 33 male) who underwent elective aneurysm occlusion were included. AWE was observed in 40.2% of patients (51/127) after ET, 6 patients already showed AWE before treatment. In large IAs (which were defined as a single maximum diameter of over 7.5mm,) AWE was significantly associated with aneurysm reperfusion in contrast to large aneurysm without AWE (p = 000.1). All grades of AWE were significantly associated with reperfusion. Our data suggests that in patients with initially large IAs, AWE is correlated with aneurysm reperfusion.

With nearly 1,700 species, Microsporidia represent a group of obligate intracellular eukaryotes with veterinary, economic and medical impacts. To help in understanding the biological functions of these microorganisms, complete genome sequencing is used routinely. Nevertheless, the proper prediction of their gene catalogue is challenging due to taxon-specific evolutionary features. As innovative genome annotation strategies are needed to obtain a representative snapshot of the overall lifestyle of these parasites, the MicroAnnot tool, a dedicated workflow for microsporidian sequence annotation using data from curated databases of accurately annotated microsporidian genes, has been developed. Furthermore, specific modules have been implemented to perform small genes (&lt; 300bp) and transposable element identification. Finally, functional annotation was performed using the signature-based InterProScan software. MicroAnnot’s accuracy has been verified by the re-annotation of four microsporidian genomes for which structural annotation had previously been validated. With its comparative approach and transcriptional signal identification method, MicroAnnot provides accurate prediction of translation initiation sites, efficient identification of transposable elements, as well as high specificity and sensitivity for microsporidian genes including those under 300 bp. Thanks to its web interface (https://microannot.org), MicroAnnot is available to the community to ensure high quality annotation of microsporidian genomes.

Epidemiological surveillance and phylogenetic studies rely nowadays on processing and analysing huge volumes of data. Processing tasks consist on running and refining a series of intertwined computational tasks. And, despite of existing several web applications for data processing and interactive visualization for phylogenetic studies, integrating many different tools and algorithms, their execution is total or partially on the client side, making them unsuitable for dealing with huge volumes of data. Studies are often also not easy to reproduce. On the other hand, in recent years, data-centric workflow systems have been proposed, allowing to deal better with increasingly larger datasets. The integration of these systems within phylogenetic tools will allow to scale them as required, and will contribute also to promote studies reproducibility. We propose then the FLOWViZ middleware for facilitating the integration of a state of the art data-centric workflow system, Apache Airflow, within web applications for phylogenetic analyses. This framework abstracts contracts and a core API for defining tools and workflows, where tools are assumed to be containerized. FLOWViZ has been tested and evaluated within the PHYLOViZ web application, a tool supporting phylogenetic inference and data visualization.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. Initial results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound Workflow net is trivial; however, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, when the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree re-discoverability.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics, in order to provide coherent and high-quality data that enables discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce NoTaMe, an analytical workflow for non-targeted metabolic profiling approaches utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research, and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data, and finally to identify and interpret the compounds that have emerged as interesting.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. The results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

Freezing of Gait (FoG) is a disabling motor complication in Parkinson’s disease (PD), affecting approximately 50% of advanced PD patients. It is characterized by sudden and transient inability to initiate or maintain walking, leading to falls and reduced quality of life. This study investigates the relationship between white matter integrity and step initiation impairments in FoG. Forty-three PD patients (26 with FoG) underwent diffusion-weighted imaging to assess white matter DTI metrics and kinematic analysis during step initiation tasks. Multiple regression analyses were conducted to explore the connection between white matter tract integrity and step initiation behavior. The multiple regression analysis shows that (1) Right Inferior Longitudinal Fasciculus RD explained 31% of APA duration’s variance; (2) Corpus Callosum Forceps Major explained 25% of the step length's variance. There was a positive correlation between the Right Inferior Longitudinal Fasciculus RD and APA duration and a negative correlation between Corpus Callosum Forceps Major RD and step length. The findings suggest that compromised white matter integrity in RILF and CC contributes to impaired motor control during step initiation in FoG. These regions are critical for inhibitory control and interhemispheric coordination, crucial for APA execution and step length regulation.

COVID-19 has brought unprecedented challenges and exacerbated existing work-related struggles resulting in heightened occupational stress and impacted work performance among health caregivers. This study utilised the transactional model of stress and coping to explore the coping strategies adopted by radiology health caregivers during the pandemic with the aim to provide baseline data for enhancing preparedness and development of interventions to support staff during future health crises. A qualitative, interpretative phenomenological analysis (IPA) methodology coupled with constructivist and interpretivist philosophical views were utilised in a multi-method context to conduct semi-structured interviews and focus group discussions among 24 radiology health caregivers in the eThekwini district of KwaZulu-Natal, South Africa. Data were transcribed verbatim and manually analysed using the IPA framework. The radiology health caregivers highlighted resilience, adaptability and identified emotion-focused and problem-focused coping as their mostly preferred strategies of dealing with COVID-19 related mental distress. These included accepting professional responsibilities and duties, social coping, spiritual therapy, adaptive behavioural coping, escape-avoidance and solution-oriented approaches. The findings can guide radiology departments in developing bespoke mental health interventions to support radiology health caregivers during any future health crises.

Chest trauma is a critical medical condition that encompasses a range of injuries and can result from various causes. Prompt and accurate diagnosis is crucial in managing chest trauma patients and radiology plays a pivotal role in this aspect. Imaging modalities in chest trauma aid in precise diagnosis and treatment planning. Especially, X-rays remain the initial imaging modality of choice due to their accessibility, speed, and ability to detect acute life-threatening conditions. Computed Tomography (CT) Scan offers detailed cross-sectional images allowing for comprehensive evaluation of chest trauma and surgical planning when necessary. Ultrasound (US) is increasingly valuable tool due to its portability and real-time imaging capabilities, aiding in rapid assessment at the bedside. Magnetic Resonance Imaging (MRI) is less commonly used due to time constraints and limited availability. Radiological imaging serves various purposes in the diagnosis and management of chest trauma such as early detection and triage, characterization and severity assessment, surgical planning, follow-up and monitoring of chest trauma. Reduction of radiation exposure, advancements in imaging technology (dual-energy CT, contrast-enhanced imaging) and Artificial Intelligence (AI) algorithms are crucial challenges and deserve attention. A multidisciplinary approach integrating radiological expertise remains fundamental in ensuring optimal outcomes for patients with chest trauma.

Radiomics represents the science of extraction and analysis of a multitude of quantitative features from medical imaging, unrevealing quantitative potentiality of Radiologic images. This scientific review aims to provide radiologists a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and future prospects. Limits of standardization of actual scientific production are analyzed with the possible solutions proposed by some papers reported and commented. As the continuous evolution of medical images is ongoing, radiologists must be aware of new perspective to perform a central role in patients management.

Background and Objectives: The existing recommendations for female pelvic magnetic susceptibility weighted imaging MRI (FP-SWI) have not been optimized to capture the disease characteristics of the female pelvis. Therefore, it is imperative to explore acquisition parameters before conducting larger-scale studies. To establish optimized flip angle (FA) for acquiring FP-SWI to improve the image quality of female pelvic lesion sites. Materials and Methods: To evaluate signal quality and lesion conspicuity, regions of interest (ROIs) were manually drawn within the lesion on SWI sequences. Signal intensity was measured within the ROIs. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for SWI sequences acquired at three FA: 10°, 15°, and 20°. Finally, a qualitative assessment of anatomical detail visualization, geometric distortion extent, artifacts, and lesion clarity was performed using a four-point scale. The Wilcoxon rank sum test was employed to compare the qualitative evaluation parameters. The intra-class correlation coefficient (ICC) was calculated to determine the consistency of subjective ratings between two observers. Results: FA=10° showed significantly higher SNR than FA=15° and 20° in most anatomical regions (P&lt;0.05), while the CNR was significantly higher than FA=15° and 20° (P&lt;0.05). Compared with FA=15° and 20°, the image quality of FA=10° was significantly higher, with less distortion and ghosting, and better image contrast (P&lt;0.05). Lesions with FA=10° exhibited higher clarity compared to FA=15° and 20° (P&lt;0.05). Conclusions: The image quality of FP-SWI images at FA=10° exceeds that at FA=15° and 20°. We recommend that when conducting SWI examination of the female pelvic cavity, the FA value should be set at 10° to stabilize the SWI sequence and improve image quality.

Background: Whole-body computed tomography (WBCT) is commonly employed for primary screening in pediatric patients experiencing out-of-hospital cardiac arrest (OHCA) without prehospital return of spontaneous circulation (ROSC). This study aimed to evaluate the cause of OHCA on WBCT and compare WBCT findings between ROSC and non-ROSC groups in non-traumatic pediatric OHCA cases in an emergency department (ED) setting. Methods: A retrospective analysis was conducted on 27 pediatric patients (mean age: 32.4 months; median age: 10 months) who experienced non-traumatic OHCA without prehospital ROSC and were transported to our tertiary care hospital between January 2013 and December 2023. WBCT scans were performed to investigate the cause of OHCA, with recorded findings in the head, chest, abdomen, and subcutaneous tissues. Results: In all cases, the direct causes of OHCA were undetermined, and WBCT identified no fatal findings. Statistical comparisons of CT findings between the ROSC and non-ROSC groups revealed significant differences. The non-ROSC group had a higher incidence of brain swelling, loss of cerebral gray-white matter differentiation, symmetrical lung consolidation/ground-glass opacity, cardiomegaly, hyperdense aortic walls, narrowed aorta, gas in the mediastinum, and hepatomegaly compared to the ROSC group. Conclusions: Although WBCT did not provide the direct cause of OHCA, it clarified several indicative findings for non-ROSC. These imaging findings could support clinical decision-making, offering objective criteria for discontinuing resuscitation in pediatric OHCA cases where prehospital ROSC is absent.

Background: Alzheimer's disease (AD) is a leading cause of dementia, significantly influenced by the Apolipoprotein E4 (APOE4) gene and gender. This study aims to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 AD patients. We applied three ML regression models—XGBoost, Random Forest (RF), and Linear Regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, Brain Age Difference (BAD) and Integrated Difference (ID), to evaluate model performance and analyze the influence of APOE4 genotype and gender on brain aging. Results: AD patients displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing APOE4 carriers with non-carriers, the models showed enhanced ID values and consistent brain age predictions, improving overall performance. Gender-specific analyses indicated slight enhancements, with models performing equally well on both genders. It indicates that APOE4 may be a more robust predictor of brain age than gender. Conclusion: Robust ML models for brain age prediction can be pivotal in the early detection of AD risk via MRI brain structural imaging, especially for APOE4 carriers. Such early identification may facilitate timely preventive interventions for AD.

Incidental findings (IFs) depicted in imaging tests during diagnostic evaluation of patients with cleft lip and/or palate (CL/P) can guide clinicians during treatment-planning procedures for effective, comprehensive cleft care. Evidence regarding IFs in different anatomical regions distant from the dentition are scarce. The aim of this study was to compare the prevalence of IFs in various anatomical areas between patients with non-syndromic CL/P and unaffected subjects. Orthopantomographs (OPT), lateral cephalometric (LC) radiographs and cone-beam computed tomography (CBCT) scans of 120 subjects (case group: n = 40, 18 females, age 14.5 ± 5.0 years; control group: n = 80, 36 females, age 14.6 ± 4.9 years) were examined and IFs were assessed by four observers. Significantly higher prevalence of IFs (p &lt; 0.001) was reported in the case group (97.5%) compared with the control group (62.5%). The most prevalent location of IFs in the case group was the maxilla (92.5%), followed by the nasal cavity (75.8%) and the mandible (63.2%), while in the control group, the highest rates of IFs occurred in the maxilla (43.8%), mandible (41.3%) and temporomandibular joint (TMJ) (14.7%). No gender-dependent pattern for IF occurrence was detected overall. Orofacial clefts may affect the mandible. However, the reported IFs were limited compared to those in the maxilla. In conclusion, individuals with CL/P are more likely to present with a range of IFs than their unaffected peers. Understanding the association between CL/P and IFs is critical for successful interdisciplinary treatment, raising awareness of the potential need for future dental care for cleft patients, and managing extra-dental aberrations. A systematic assessment of diagnostic records is required to eliminate the risk of overlooking clinically significant IFs.

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g. enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from NMR spectroscopy time courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python API. Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

Genome-scale metabolic models (GEMs) have been widely used for phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space inaccessible. Inspired by previous studies that take allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviors under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

COVID-19 has a severe risk of spreading rapidly, the quick identification of which is essential. In this regard, chest radiology images have proven to be a practical screening approach for COVID-19 affected patients. This study proposes a deep learning-based approach using Densenet-121 to detect COVID-19 patients effectively. We have trained and tested our model on the COVIDx dataset and performed both 2-class and 3-class classification, achieving 96.49% and 93.71% accuracy, respectively. By successfully utilizing transfer learning, we achieve comparable performance to the state-of-the-art method while using 15x fewer model parameters. Moreover, we performed an interpretability analysis using Grad-CAM to highlight the most significant image regions at test time. Finally, we developed a website that takes chest radiology images as input and detects the presence of COVID-19 or pneumonia and a heatmap highlighting the infected regions. Source code for reproducing results and model weights are available.

The advent of Large Language Models like Generative Pre-trained Transformer 4 (GPT-4) has introduced a revolutionary approach to bridging the communication gap between complex radiology reports and patient comprehension. This review article explores the pivotal role of GPT-4 in generating patient-friendly summaries of radiology reports, aiming to demystify medical jargon and enhance patient engagement and understanding. Through an examination of the technological underpinnings of GPT-4, this article highlights its capability to accurately interpret and simplify radiological findings into layman's terms. It assesses the impact of such advancements on patient satisfaction, adherence to treatment plans, and overall healthcare outcomes. Furthermore, the review delves into the potential challenges and ethical considerations associated with implementing AI-driven summaries in clinical practice, including accuracy, privacy, and the need for human oversight. By offering insights into the current applications, benefits, and limitations of GPT-4 in radiology, this short review underscores the transformative potential of AI in fostering a more inclusive and patient-centric healthcare communication paradigm.

This retrospective study examines 248 test-bolus examinations preceding contrast-enhanced magnetic resonance angiography (CE-MRA) to extract clinically relevant data for critical limb ischemia (CLI) management. Methods involved a retrospective review of test-bolus exams, analysing 60 graphs for time-to-peak (TTP), full-width half-maximum (FWHM) time, and time to continual rise in signal intensity. These values were correlated with heart function parameters (ejection fraction, ASA-Classification, Lee-Index, and MET-Score). Results indicate a mean TTP of 31.2 ± 7.3s, showing a correlation between ejection fraction and ASA classification. Patients with atrial fibrillation exhibited prolonged TTP compared to those without. Despite population heterogeneity, findings facilitate risk stratification for limb-saving interventions in CLI. TTP emerges as a potential clinical cardiovascular parameter and a risk factor for vascular interventions. Given the variation in injection protocols across centres, this study underscores the importance of precise bolus arrival time documentation for future multicentre studies.

Accurate and timely testing has become an essential measure in combatting the COVID-19 global pandemic. Currently, polymerase chain reaction (PCR) based assays are the most relied on methods for SARS-CoV-2 detection. This traditional workflow involves a viral RNA extraction from the viral transport media storing nasopharyngeal swabs collected from patients, followed by PCR based detection. While accurate, this methodology is time consuming and resource heavy, causing for delays in receiving results or limited access to testing. Herein, we demonstrate a validated method for SARS-CoV-2 detection from viral transport media using a two-step, direct-to-PCR workflow revolving around shaker-mill homogenization. This method completely bypasses the extraction steps of the traditional workflow, replacing it with 30 seconds of mechanical disruption sufficient to allow for COVID-19 detection with a 96.43% sensitivity and 100% specificity when compared to traditional extraction to PCR based methods.

The Parecis Basin is one of the largest intracratonic basins in Brazil. Despite its considerable size, the basin has yet to be extensively explored, with only five wildcat wells drilled for hydrocarbons. So far, no commercial discoveries have been announced. Regional studies have suggested Paleozoic sedimentation, while recent studies indicate a Neoproterozoic fill. However, no tectonic model accurately describes the basin’s development, and no detailed structural map for the entire basin has been published. The present work presents a new detailed structural map of the Parecis Basin based on a four-step interpretation workflow integrating seismic and gravimetric data. The first step includes converting the public 2D seismic lines to the depth domain. The second step is estimating the residual Bouguer anomaly, where the computed residual anomalies should relate to the basin’s tectonic features. The third step comprises the 2D forward modeling of the gravimetric anomalies using the 2D seismic interpretation as a constraint. The final step compiled all the interpreted features into our new structural map. This map reveals the top of the basement, forming a complex framework of horsts and grabens. Normal faults define the main structural style in the basin. Further, we could recognize thick, high-density bodies embedded in the crystalline basement. These bodies consist of Orosian-Calimian (1.8-1.6 Ga) mafic and ultramafic rocks, which may be a potential source for hydrogen exploration in the basin. Subsequent geophysical and geochemical surveys will assess the hydrogen potential in the area.

Outbreaks of coronavirus disease 2019 (COVID-19) have been recorded in different countries across the globe. The virus is highly contagious, hence early detection, isolation, and quarantine of infected patients will play an important role in containing the viral spread. Diagnosis in a mobile lab can aid to find infected patients in time. Here, we develop a field-deployable diagnostic workflow that can reliably detect COVID-19. Instruments used in this workflow could easily fit in a mobile cabin hospital and also be installed in the community. Different steps from sample inactivation to detection were optimized to find the fastest steps and portable instruments in detection of COVID-19. Each step was compared to that of the normal laboratory diagnosis set-up. From the results, our proposed workflow (80 min) was two times faster compared to that of the normal laboratory workflow (183 min) and a maximum of 32 samples could be detected at each run. Additionally, we showed that using 1% Rewocid WK-30 could inactivate the novel coronavirus directly without affecting the overall detection results. Comparison of our workflow using an in-house assay to that of a commercially acquired assay produced highly reliable results. From the 250 hospital samples tested, there was a high concordance 247/250 (98.8%) between the two assays. The in-house assay sensitivity and specificity were 116/116 (100%) and 131/134 (97.8%) compared to that of the commercial assay. Based on these results, we believe that our workflow is fast, reliable, adaptable and most importantly, field deployable.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer-review and increases the impact of datasets by enhancing their visibility, accessibility, and re-usability. We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivise researchers to publish high-quality data sets, we created a workflow for streamlined import of omics metadata directly into a data paper manuscript. An omics data paper template was designed by defining key article sections which encourage the description of omics datasets and methodologies. The workflow was based on REpresentational State Transfer services and Xpath to extract information from the European Nucleotide Archive, ArrayExpress and BioSamples databases, which follow community-agreed standards. The workflow for automatic import of standard-compliant metadata into an omics data paper manuscript facilitates the authoring process. It demonstrates the importance and potential of creating machine-readable and standard-compliant metadata. The omics data paper structure and workflow to import omics metadata improves the data publishing landscape by providing a novel mechanism for creating high-quality, enhanced metadata records, peer reviewing and publishing of these. It constitutes a powerful addition for distribution, visibility, reproducibility and re-usability of scientific data. We hope that streamlined metadata re-use for scholarly publishing encourages authors to improve the quality of their metadata to achieve a truly FAIR data world.

The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant diversity in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this paper proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity, as it is suitable regardless of the data volume (small/big) and regardless of the nature of the variables (continuous/qualitative/mixed), (2) ease of implementation, as it is based on few easy-to-use software packages, and (3) robustness, through the stability evaluation of the final clusters and through recognized algorithms and implementations. This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.

1) Introduction: Brain parcellation is an important processing step in the analysis of structural brain MRI. Existing software implementations are optimized for fully developed adult brains, and provide inadequate results when applied to neonatal brain imaging. 2) Methods: We developed a semi-automated pipeline, NeBSS, for extracting 50 discrete brain structures from neonatal brain MRI, using an atlas registration method that leverages the existing ALBERT neonatal atlas 3) Results: We demonstrate a simple linear workflow for neonatal brain parcellation. NeBSS is robust to variation in imaging acquisition protocol and magnet field strength. 4) Conclusion: NeBSS is a robust pipeline capable of parcellating neonatal brain MRIs using a simple processing workflow. NeBSS fills a need in clinical translational research in neonatal imaging, where existing automated or semi-automated implementations are too rigid to be successfully applied to multi-center neuroprotection studies and clinically heterogeneous cohorts. The software is open source and freely available.

Traditional painting artefacts study and documentation face the limitations of well-known recording tools and methods, such as handwritten or digital descriptions, scaled 2D drawings in situ, measurements with calipers, rulers or tape measures, sketches, tracings, as well as conventional or technical photographs. This paper advocates Close-Range Photogrammetry (SfM-MVS) and Reflectance Transformation Imaging (RTI) for recording vital morphological and 2.5D geometric features of painted surfaces with high accuracy. These digital tools, widely accessible due to increased computing power, enhance art observation, material study and conservation assessment. Despite specialized alternatives like 3D laser scanners, the focus here is on the cost-effective SfM-MVS and RTI, which offer advanced pixel colour accuracy. The ability to reproduce photorealistic textures proves essential for accurate documentation of painted artefacts. The paper showcases promising results from applying these methods to a mural from the Palace of Tiryns, justifying their fundamental role in an integrated scientific art study methodology.

To allow for a broad survey of subtle metabolic shifts in wine caused by rootstock and irrigation, an integrated metabolomics-based workflow followed by quantitation was developed. This workflow was particularly useful when applied to a poorly studied variety cv. Chambourcin. Allowing volatile metabolites that otherwise may have been missed with a targeted analysis to be included, this approach allowed deeper modeling of treatment differences which then could be used to identify important compounds. Wines produced on a per vine basis, over two years, were analyzed using SPME-GC-MS/MS. From the 382 and 221 features that differed significantly among rootstocks in 2017 and 2018 respectively, we tentatively identified 94 compounds by library search and retention index, with 22 confirmed and quantified using authentic standards. Own-rooted Chambourcin differed from other root-systems for multiple volatile compounds with fewer dif-ferences among grafted vines. For example, the average concentration of β-Damascenone present in own-rooted vines (9.49 µg/L) was significantly lower in other rootstocks (8.59 µg/L), whereas mean Linalool was significantly higher in 1103P rootstock compared to own-rooted. β-Damascenone was higher in regulated deficit irrigation (RDI) than other treatments. The workflow outlined not only was shown to be useful for scientific investigation, but also in creating a protocol for analysis that would ensure differences of interest to industry are not missed.

(1) Background: the NGS based mutational study of hereditary cancer genes is crucial to design tailored prevention strategies in subjects with different hereditary cancer risk. The ease of amplicon-based NGS library construction protocols contrasts with the greater uniformity of enrichment provided by capture-based protocols and so with greater chances for detecting larger genomic rearrangements and copy-number variations. Capture-based protocols, however, are characterized by a higher level of complexity of sample handling, extremely susceptible to human bias. Robotics platforms may definitely help dealing with these limits, reducing hands-on time, limiting random errors and guaranteeing process standardization. (2) Methods: We implemented and validated the complete automation of the SOPHiA GENETICS’ CE-IVD Hereditary Cancer Solution™ (HCS) libraries preparation workflow on the Hamilton’s STARlet platform. (3) Results: We demonstrate that this automated workflow, used for more than 1000 samples achieved the same performances of manual setup in terms of coverages and reads uniformity, with extremely lower variability of reads mapping rate onto the regions of interest. (4) Conclusions: This automated solution offers same reliable and affordable NGS data, but with the essential advantages of a flexible, automated and integrated framework, minimizing possible human errors and depicting a laboratory’s walk-away scenario.

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. A comfortable GUI allows error corrections not only in the final output, but already in early stages to minimize error propagations. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. Experiments showed that users with minimal or no experience were able to capture the text of even the earliest printed books with manageable effort and great quality, achieving excellent character error rates (CERs) below 0.5%. The fully automated application on 19th century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.

Background and Objectives: Fibrillinopathies have different phenotypic expression: Marfan- like skeleton features, MLSF; mitral valve prolapse syndrome, MVPS; MASS phenotype, M= mitral valve prolapse, MVP, A= aortic root dilation, S= skeletal features, and the second S= (cutaneous) striae; Marfan syndrome, MS. These are inherited connective tissue diseases, provoked by a mutation of the encoding gene for a protein(fibrillin 1, FBN1) which has major importance in the formation of elastic fibers.The study had the following main objectives: the correlation between disc displacement, DD( a major sign for temporomandibular joint dysfunction, TMD) on magnetic resonance imaging, MRI, and aortic Z score(a major sign for aortic root dilation) on echocardiography; the predictive value of DD towards aortic Z score. DD had 2 types of severity: DD with reduction(the mild type, DDwR) , and DD without reduction( the severe type, DDwoR). Materials and Methods: The type of fibrillinopathy was precised by clinical exam( systemic score), ophthalmic exam(for ectopia lentis), and echocardiography(aortic Z score, MVP). Medical treatment consisted in betablockers, BB(for patients with tachycardia), or angiotensin II receptor blockers, ARB. Surgical treatment was addressed to aortic root aneurysm, and severe MR. DD benefited by dental conservatory treatment, or surgical restoration.Results: DD- Z score had a powerful correlation in MASS(Rho= 0.787, p&lt; 0.01), and in MS patients(Rho=0.819, p&lt; 0.01). For the entire sample, both DDwR- Z score, and DDwoR- Z score had a moderate correlation(Rho= 0.469, and respectively 0.669). Muchmore, DD was an important predictor for Z score. DDwoR had a double power of prediction for Z score, comparative with DDwR(B coefficient= 1.661 for DDwR, and 3.281 for DDwoR). Conclusions: TMD had a powerful correlation with aortic root dilation, in MASS, and MS patients from the sample. Likewise, TMD was a major predictor for aortic root dilation, in the entire sample. We can utilize in clinical practice, an extracardiac finding, TMD, obtained by a non-invasive technique, MRI, for cardiac severity stratification of fibrillinopathies.

Objective: Interstitial fibrosis / tubular atrophy (IFTA) is a common, irreversible, and progressive form of chronic kidney allograft injury, and it is considered a critical predictor of kidney allograft outcomes. The extent of IFTA is estimated through a graft biopsy, while a non-invasive test is lacking. The aim of this study was to evaluate the feasibility and accuracy of an MRI radiomic-based machine learning (ML) algorithm in estimating the degree of IFTA in a cohort of transplanted patients. Approach: Patients who underwent MRI and renal biopsy within a 6-month interval from 1/1/2012 to 1/3/2021 were included. Stable MRI sequences were selected, and renal parenchyma, renal cortex and medulla were segmented. After image filtering and pre-processing, we computed radiomic features which were subsequently selected through a LASSO algorithm for their highest correlation with the outcome and lowest intercorrelation. Selected features and relevant patients’ clinical data were used to produce ML-algorithms using 70% of the study cases for feature selection, model training and validation with a 10-fold cross-validation, and 30% for model testing. Performances were evaluated using AUC with 95% confidence interval. Main results: 70 coupled tests (63 patients, 35.4% females, mean age 52.2 years) have been included and subdivided into a wider cohort of 50 for training and a smaller cohort of 20 for testing. For IFTA ≥ 25%, AUCs in test cohort were 0.60, 0.59, and 0.54 for radiomic features only, clinical variables only, and combined radiomic-clinical model, respectively. For IFTA ≥ 50%, AUCs in training cohort were 0.89, 0.84, 0.96, and in test cohort were 0.82, 0.83, and 0.86, for radiomic features only, clinical variables only, and combined radiomic-clinical model, respectively. Significance: An ML-based MRI radiomic algorithm showed promising discrimination capacity for IFTA>50%, especially when combined with clinical variables. These results need to be confirmed in larger cohorts.

Epigenetic mechanisms, including histone post-translational modifications (PTMs), play a critical role in regulating pain perception and the pathophysiology of burn injury. However, the epigenetic regulation and molecular mechanisms underlying burn injury-induced pain remain insufficiently explored. Spinal dynorphinergic (Pdyn) neurons contribute to heat hyperalgesia induced by severe scalding-type burn injury through p-S10H3-dependent signaling. Beyond p-S10H3, burn injury may impact various other histone H3 PTMs. Double immunofluorescent staining and histone H3 protein analyses demonstrated significant hypermethylation at H3K4me1 and H3K4me3 sites and hyperphosphorylation at S10H3 within the spinal cord. By analyzing Pdyn neurons in the spinal dorsal horn, we found evidence of chromatin activation with a significant elevation in p-S10H3 immunoreactivity. We used RNA-seq analysis to compare the effects of burn injury and forma-lin-induced inflammatory pain on spinal cord transcriptomic profiles. We identified 98 DEGs for burn injury and 86 DEGs for formalin-induced inflammatory pain. A limited number of shared differentially expressed genes (DEGs) suggest distinct central pain processing mechanisms between burn injury and formalin models. KEGG pathway analysis supported this divergence, with burn injury activating Wnt signaling. This study enhances our understanding of burn injury mechanisms and uncovers converging and diverging pathways in pain models with different origins.

Marine invertebrates are model organisms in several areas of biological sciences, being a source of massive biological information. Although, the scientific relevance of marine invertebrates, the research with them can be limited for their tissue characteristics and troubles for the replication of physical and chemical properties of seawater. Thence, the main goal of this laboratory workflow is to provide a useful methodological approach to reduce the experimental limitations during the study of marine invertebrates. The present study describes experimental methodologies for the collection, transport, and maintenance of sessile tunicates. Also, an approach to observe and characterize, a diverse population of blood cells in marine invertebrates, by several cytological stains and electron microscopy. Lastly, suggestions and protocols to extract quality RNA from samples with high concentrations of salts, pigments, secondary metabolites, and polysaccharides. This methodological approach can be easily adapted to other marine invertebrates, moreover uses low-cost reagents and widely available laboratory equipment. Making possible the study of different types of marine animals in diverse locations.

To identify factors associated with interstitial lung disease (ILD) in patients with systemic sclerosis (SSc) and build an algorithm to better define this association, for a personalized application in clinical practice. Methods. 78 SSc patients underwent HRCT to assess ILD. Demographic, clinical and laboratory variables were collected, focusing on those associated either directly or indirectly with lung involvement. The discriminant value of each variable was determined using the operating characteristic curves (ROC) and included in a model to estimate the strength of ILD association in SSc. Results. Thirty-three (42.31%) patients showed ILD on HRCT. DLco, M-Borg, GERD-Q, and capillary density were significantly associated with the presence of ILD-SSc. A model including these variables had a coefficient of determination (R2) of 0.697. DLco had an AUC of 0.861 (p&lt;0.001) with a cut-off ≤72.3% (sensitivity 78.8%, specificity 91.1%, +LR 8.86). The m-Borg scale showed an AUC of 0.883 (p&lt;0.001) with a cut-off &gt;2 (sensitivity 84.8%, specificity 82.2%, +LR 4.77), GERD-Q had an AUC of 0.815 (p&lt;0.001) with a cut-off &gt;7 (sensitivity 72.7%, specificity 86.7%, +LR 5.45). The capillary density showed an AUC of 0.815 (p&lt;0.001) with a cut-off ≤4.78 (sensitivity 87.9%, specificity 68.9%, +LR 2.82). Based on the pre-test probability values, these four variables were applied to Fagan's nomogram to calculate the post-test probability of this association. Conclusions. Our study identified four associated clinical factors of ILD in SSc patients. Moreover, their inclusion in an algorithm for the post-test probability, tailored to the specific patients’ characteristics, significantly increases the ability to find out the presence of SSc-ILD.

Background: Clinical trails have revealed that patients with depression generally accompanied with learning and memory impairment, which critically impact on individual’s health and development. Danggui-Shaoyao-San (DSS), a famous Chinese complex prescription, significantly overcame depression and relieved cognitive disorder based on previous research and publication. However, its effectsand potential mechanism against chronic restraint stress (CRS) remained unknown. Methods: CRS animal model was established and mice were divided to six groups while they were oral administrated with Danggui-Shaoyao-San at doses of 1.25, 2.5 and 5 g/kg for 14 days. Emotional and cognitive performances were detected by behavior tests, meanwhile neural plasticity and its molecular mechanism were examined by Dil staining, western blot and immunofluorescence. Results: DSS treatment dose-dependently improved locomotion ability in open filed test, overcame depression behavior in forced swimming test and elevated plus maze test, enhanced learning and memory ability in Morris water maze test. CRS decreased number of total spines and mushroom spines, while DSS treatment dose-dependently restored these by Dil staining. Expression of BDNF and GluR1 were significantly down-regulated in CRS group, which were significantly normalized by DSS. Conclusions: DSS treatment dose-dependently reversed CRS-induced cognitive impairments by inducing structural remodeling of neurons.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Then, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

Data generated by metagenomic and metatranscriptomic experiments is both enormous and inherently noisy [1]. When using taxonomy-dependent alignment-based methods to classify and label reads, such as MEGAN [2], the first step consists in performing homology searches against sequence databases. To obtain the most information from the samples, nucleotide sequences are usually compared to various databases (i.e., nucleotide and protein) using local sequence aligners such as BLASTN and BLASTX [3]. Nevertheless, the analysis and integration of these results can be problematic because the outputs from these searches usually show differences, which can be notorious when working with RNA-seq (Personal observation; Graphical abstract). These inconsistencies led us to develop the HoSeIn workflow to determine the unequivocal taxonomic and functional profile of environmental samples, based on the assumption that the sequences that correspond to a certain taxon are composed of (Graphical abstract): 1) sequences that were assigned to the same taxon by both homology searches, plus 2) sequences that were assigned to that taxon by one of the homology searches but returned no hits in the other one, and vice versa.

Hamman’s syndrome is a rare condition that mostly affects young males, often with a predisposition of asthma. It includes the presence of free air in the mediastinum and subcutaneous emfysema with no other underlying cause such as trauma or infection. It occurs spontanously and often in association with prolonged valsalva manouvre. This might explain why there are some cases of Hamman syndrome among young females giving birth. Here we present a case report of a 24-year- old primigravida with Hamman’s syndrome. She presented with symptoms a few hours after an uncomplicated vaginal delivery at 40+1 pregnancy week. The symptoms resolved spontanously after a few days of observation. We also give a systemic review of reported cases since 2000 to provide overview on pathomechanism, symptoms, diagnostics, treatment and management of this condition. Hamman’s syndrome is a rare, usually benign, but potentially serious complication that can occur during the second stage of labour. Diagnostics include inquiring about typical symptoms, clinical examination and chest x-ray or CT scan. Treatment is usually conservative with oxygen, bronchodilators and pain relief. The reccurence rate is low and there is no contraindication to vaginal delivery in future pregnancies. However, it is suggested that physicians and midwives be cautious and consider low threshold for instrumental delivery or caesarean section to avoid excessive valsalva manoeuvre.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

Viral integration within the host genome plays a pivotal role in carcinogenesis. Various disruptive mechanisms are involved, leading to genomic instability, mutations, and DNA damage. With next-generation sequencing (NGS), we can now precisely identify viral and host genomic breakpoints and chimeric sequences, which are useful for integration site analysis. In this study, we evaluated a commercial hybrid-capture NGS panel specifically designed for detecting three key viruses: HPV, HBV, and HIV-1. We also tested workflows for Viral Hybrid Capture (VHC) and Viral Integration Site (VIS) analysis, leveraging customized viral databases in CLC Microbial Ge-nomics. By analyzing sequenced data from virally infected cancer cell lines (including SiHa, HeLa, CaSki, C-33A, DoTc2, 2A3, SCC154 for HPV; 3B2, SNU-182 for HBV; and ACH-2 for HIV-1), we precisely pinpointed viral integration sites. The workflow also highlighted disrupted and neigh-boring human genes that may play a crucial role in tumor development. Our results included in-formative virus-host read mappings, genomic breakpoints, and integration circular plots. These visual representations enhance our understanding of the integration process. In conclusion, our seamless end-to-end workflow bridges the gap in understanding viral contributions to cancer development, paving the way for improved diagnostics and treatment strategies.

This paper aims to present the method of producing visual media for communicating the Asinara Island National Park plan. These products are landscape visualizations able to foster landscape preservation both from the point of view of the management of the landscape and of the citizen's involvement in the communication of the historical and environmental values of the landscape. Starting from landscape information gathering, the research has experimented with an operative method for processing different graphic representations from the same geographic database, calibrating the outputs to different audiences, their needs, objectives, and literacy skills. Three different types of products are presented: the first is a digital, dynamic and multisectoral decision-making GIS tool for park management. The second is a 3D model, aimed to the virtual fruition. The third is a map of the zoning park plan drawn to be easily readable to the non-expert public. This case study emerges as a replicable method to process the landscape information to make different visualization tools from a single geographic model, to meet the different requirements that arise from a complex landscape planning process.

Barcode medication administration (BCMA) is advocated as a technology that reduces medication errors relating to incorrect patient identity, drug or dose. Little is known, however, about the impact it has on nursing workflow. Our aim was to investigate the impact of BCMA on nursing activity and workflow. A comparative study was conducted on two similar surgical wards within an acute UK hospital. We observed nurses during drug rounds on a non-BCMA ward and a BCMA ward. Data were collected on drug round duration, timeliness of medication administration, patient identification, medication verification and general workflow patterns. BCMA appears not to alter drug round duration, although it may reduce the administration time per dose. Workflow was streamlined, with a reduction in use of the medicines room. The rate of patient identification increased from 74% (of 47) patients to 100% (of 43), with 95% of 255 scannable medication doses verified using the system. This study suggests that BCMA does not affect drug round duration; further research is required to determine the impact it has on timeliness of medication administration. There was reduced variability in the medication administration workflow of nurses, along with an increased patient identification rate and high medication scan rate, representing potential benefits to patient safety.

In the literature, the question of the amount of data necessary and sufficient to validate different models of the occurrence of risk of adverse events for patients or the classification of the presence or absence of pathological features has been repeatedly raised. In the presented study, we propose a new approach to determine the necessary and enough studies for validation of medical software based on artificial intelligence technology, whose main task is to classify medical X-rays according to the presence of normality and pathology. It is shown that for several studies in a dataset, when AUC ROC has maximum heterogeneity, it varies depending on the balance of "norm"/"pathology" classes. Thus, for a balance of "normal"/"abnormality", where 90% is "normal" and 10% is "abnormality", maximum heterogeneity is achieved for 190 studies, for a balance of 80% ("normal")/20% ("abnormality") for 80 studies, for a balance of 70% ("norm")/30% ("abnormality") - 120 trials, for a balance of 60% ("norm")/40% ("abnormality") the maximum heterogeneity is reached at 110 trials, and for a balance of 50% ("norm")/50% ("abnormality") - at 70 trials. The obtained data are in good agreement with the previous results. They allow us to determine a sufficient (necessary) number of studies in the dataset to perform an unbiased assessment of AUC ROC.

Transarterial embolization (TAE) for high-flow pancreaticoduodenal artery (PDA) aneurysms in patients with celiac-trunk stenosis by the median arcuate ligament (MAL) has been found effective both after rupturing and to prevent rupture. The objective was to describe the TAE techniques used and their effectiveness in excluding PDA aneurysms due to MAL syndrome. This single-center retrospective study done at the Dijon-Bourgogne University Hospital included all patients treated by TAE in 2010–2022 for ruptured or unruptured high-flow PDA aneurysms caused by MAL syndrome. We identified 14 patients (7 women and 7 men; mean age, 64 years). Packing and trapping techniques were used alone or together. Occlusion was with microcoils, co-polymer, or cyanoacrylate glue, used separately or combined. Technical success was achieved in 13 (93%) patients. Clinical success was achieved in 12 (86%) patients. One major and two minor complications were recorded within the first 30 days. No complications occurred after 30 days. Follow-up ranged from 1 to 84 months. No cases of aneurysm recanalization were recorded to date. TAE had high technical and clinical success rates in our patients with unruptured or ruptured PDA aneurysms due to MAL syndrome.

Introduction Blunt carotid injuries (BCI) in pediatric trauma are quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/ BCVI on pediatric patients after blunt trauma is, what routine diagnostics look like, if a CT/ CTA scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects? Methods This literature review includes reviews, systematic reviews, case reports and original studies in english language between 1999 and 2020 that have dealt with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure on children were included in the study. For literature research, Medline (PubMed) and the Cochrane library were used. Results Pediatric BCI/ BCVI, shows an overall incidence between 0.03 – 0.5% of confirmed BCI/ BCVI cases due to pediatric blunt trauma. 1.1 – 3.5% of pediatric blunt trauma patients underwent CTA to detect BCI. Only 0.17 – 1.2% of all CTA scans shows a positive diagnosis for BCI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligray, a computed tomography angiography needs at least 138 mGy. A cumulative doses of about 50 mGy almost triples the risk of leukemia, and doses of about 60 mGy triples the risk of brain cancer. Discussion Knowing that a BCI could have extensive neurologic consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. Computed tomography scans (CT) and computed tomography angiography (CTA) are mostly used in routine BCI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are reflect established BCI screening options, as well as using duplex ultrasound.

The paper presents the final results from the research project “Urban Abacus of Building Energy performances (Abaco Urbano Energeticodegli Edifci – AUREE)” aimed at supporting the renovation and energy efficiency enhancement of urban building heritage financed by the “Ricerca Sistema Elettrico (MISE) program”. The crux of AUREE project is a Web – GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on urban built environment and promoting the participation of local stakeholders to its improvement. As the latest development of this research, a workflow integrating the AUREE portal with BIM authoring and an open source IoT platform has been implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the sustainable energy research center Sotacarbo was selected as case study. The presented results proved that it was possible to create an open system, which could be easily consulted by "non-expert" users as well, to make available real-time monitoring data obtained from IoT sensors measuring the energy consumption, the indoor environmental conditions and the usage profiles of public buildings. The proposed open system could also be suitable to be later used as an effective tool to support the rising “energy communities”.

Dirofilariosis is an infectious disease caused by species of the Dirofilaria genus. It is manifested by the appearance of a subcutaneous swelling, especially in the eye region. We present the case of a 29-year-old patient that accused facial asymmetry in the right genian region. Following clinical and paraclinical evaluations, the diagnosis of parasitic cyst was established in the context of dirofilariosis with Dirofilaria repens (D. repens). Treatment consisted of surgical excision of the formation associated with prophylactic antibiotic medication. Macroscopic analysis of the excision piece revealed a structure that contains a cystic cavity and a filamentous form with a length of approximately 10 mM and a diameter of 1 mM. This is the first case of dirofilariosis located in the genian region reported in Romania. The overview of this pathology is important to raise awareness among physicians about its presence and its clinical variations.

Implementing High-Performance Computing to solve In-Memory Data Grid (IMDG)-based energy infrastructure resilience research problems in a heterogeneous environment presents a challenge to workflow management systems. Large-scale energy infrastructure needs multi-variant planning and tools to allocate and dispatch resources in IMDG taking into account the subject domain specificity, resource characteristics, and constraints and quotas for resource use. To that end, we propose a workflow management system using our Orlando Tools framework. To scale computing resources, we provide their integration and use corresponding software to determine key application parameters that can significantly impact the processed data size and the required number of allocated resources. We automated the IMDG cluster launch for the workflow executions. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our approach explores large infrastructures by modeling many simultaneously failed elements of different types up to the number of network elements. In terms of problem-solving efficiency and resource use, we achieve near-linear speedup with increasing the number of nodes of each resource.

Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.

Abstract: Background and objectives: Symptomatic walled-off pancreatic necrosis is a serious local complication of acute necrotising pancreatitis. The endoscopic step-up approach is the standard treatment for symptomatic walled-off pancreatic necrosis; however, adjunctive radiologic percu-taneous drainage for this condition is controversial. This study compared the clinical and radio-logic resolution of walled-off pancreatic necrosis achieved with the endoscopic step-up approach with or without radiology-guided percutaneous drainage. Material and Methods: This retrospective, single-center cohort study enrolled patients with symp-tomatic walled-off pancreatic necrosis who underwent endoscopic transmural drainage (ETD) followed by directed endoscopic necrosectomy (DEN) with or without radiology-guided drainage. A total of 34 patients (endoscopic approach, n=22; combined modality approach, n=12) underwent the endoscopic step-up approach (ETD followed by DEN). Baseline characteristics, clinical success, and resolution of necrosis were compared between groups. Results: All patients achieved symptom resolution from walled-off pancreatic necrosis. The mean patient age was 58.4 years, and 21 (61.8%) were men. After treatment with the endoscopic approach and combined modality approach, clinical success was achieved in 90.9% of patients within 11.5 days, and 66.7% of patients within 16.5 days, respectively. Both the total hospital stay (55 days vs 71 days; p=0.071) and time to complete radiologic resolution were shorter (93 days vs 124 days; p=0.23) in the endoscopic approach group. Conclusion: The endoscopic step-up approach resulted in the clinical resolution of symptomatic walled-off pancreatic necrosis comparable to that of the combined modality drainage. However, the endoscopic approach alone allows higher clinical success, early clinical and radiologic resolution, and a shorter hospital stay.

Background/Objectives Bowel endometriosis (BE) is defined as the presence of endometrial-like tissue in the muscular layer of the bowel wall. When BE does not cause severe obstruction to fecal transit, not leading to (sub)occlusive symptoms, the decision to operate becomes challenging as intestinal surgery is associated with higher complication rates and long-term bowel dysfunction. The aim of this cross-sectional study is to evaluate the quality of life (QoL) in patients with BE undergoing medical therapy or expectant management, investigating the impact of characteristics of BE nodules on QoL and intestinal function. Methods A retrospective cross-sectional analysis was conducted on 580 patients with BE who did not undergo surgery but were treated conservatively by medical therapy or expectant management between January 2017 and August 2022. The diagnosis of BE was done by transvaginal ultrasound and confirmed at double contrast barium enema. After at least one year of follow-up, QoL and intestinal function of patients were evaluated by the Endometriosis Health Profile-5 (EHP-5) questionnaire and Bowel Endometriosis Symptom score (BENS), whereas pain symptoms were assessed by the Visual Analogic Scale (VAS 0-10). Statistical analysis was performed to investigate possible associations between QoL and characteristics of bowel nodules (dimension, location, and evidence of stenosis) as well as type and length of eventual medical therapy. Results Patients with BE had a satisfying overall QoL with a mean EHP-5 score of 105.42 ± 99.98 points and VAS lower than 3 for all pain domains; additionally, they did not show a significant impartment of bowel function with mean BENS score of 4.89 ± 5.28 points. Notably, patients under medical therapy had a better QoL than patients without therapy (p

Patients who survived critical COVID-19 frequently reported post-acute sequelae of COVID-19 (PASC) such as psychosomatic and neurocognitive health problems. The goal of this study was to identify clinical risk factors and other predictors for such long-term consequences in severely ill COVID-19 patients. Adult COVID-19 intensive care unit (ICU) survivors from August 2020 to May 2021 were enrolled. A broad range of clinical, laboratory and chest computed tomography (CT) data was collected during ICU stay. The association between ICU predictors and psychosomatic, respiratory and neurocognitive assessments 12 months after ICU discharge was analyzed using univariate regression analysis. In 17 patients (mean age 58.9 ± 11.4 years), laboratory markers (CRP, lymphocytes, hemoglobin), ICU severity (SOFA, SAPS II, need for mechanical ventilation), complications (ARDS), and lung CT data (ground-glass opacity) were promising predictors of depressive and anxiety symptoms, fatigue, and sleep problems. Recovery of psychosomatic health such as fatigue, depression and anxiety correlated with lower levels of inflammation and high hemoglobin levels. ARDS, mechanical ventilation and worse SOFA and SAPS II scores were further risk factors for depressive and anxiety symptoms. Our study identified novel associations such as pulmonary ground-glass opacity being positively associated with depression, anxiety, fatigue, and insomnia levels.