Studies on the possible association between bacteria and oral squamous cell carcinoma (OSCC) remain inconclusive, largely due to methodological variations/limitations. The objective of this study was to characterize the species composition as well as functional attributes of the bacteriome associated with OSCC. DNA obtained from 20 fresh OSCC biopsies (cases) and 20 deep-epithelium swabs (matched control subjects) were sequenced for the V1-V3 region using Illumina’s 2x300 bp chemistry. High quality, non-chimeric merged reads were classified to species level using a prioritized BLASTN-algorithm. Downstream analyses were performed using QIIME, PICRUSt, and LEfSe. Fusobacterium nucleatum subsp. polymorphum was the most significantly overrepresented species in the tumors followed by Pseudomonas aeruginosa and Campylobacter sp. Oral taxon 44, while Streptococcus mitis, Rothia mucilaginosa and Haemophilus parainfluenzae were the most significantly abundant in the controls. Functionally, genes involved in bacterial mobility, flagellar assembly, bacterial chemotaxis and LPS synthesis were enriched in the tumors while those responsible for DNA repair and combination, purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, ribosome biogenesis and glycolysis/gluconeogenesis were significantly associated with the controls. This is the first epidemiological evidence for association of F. nucleatum and P. aeruginosa with OSCC. Functionally, an “inflammatory bacteriome” is enriched in OSSC.

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

Selenium is essential for the synthesis and function of various selenoenzymes, such as glutathione peroxidases, selenoprotein P, and thioredoxin reductase, which play an important role in both antioxidant defense and limiting oxidative damage. Associations between serum selenium concentration and obstetric complications as well as pregnancy outcomes has been reported in many studies. The aim of this study was to determine whether the dietary intake of selenium, its concentration in serum and the activity of glutathione peroxidase in subsequent trimesters of pregnancy affect the birth condition of newborns assessed on the basis of the Apgar score in the 1st and 5th minute of life, birth weight, body length and head and chest circumference in physiological and complicated pregnancy course. Twenty-seven pregnant women at a mean age 29.6 ± 4.8 years from the Lower Silesia region of Poland were recruited to the study, 55% of studied group had pregnancy complications. The mean reported Se intake and serum selenium content for Polish pregnant women was in the first trimester – 56.53 μg/day and 43.75 μg/l, the second trimester – 61.79 μg/day and 41.77 μg/l and the third trimester – 58.37 μg/day and 42.13 μg/l, respectively. In the subgroup of pregnant women with a physiological pregnancy course, a weak, although statistically significant, positive correlation was found in the first trimester between Se intake and the length (R=0.48, p=0.019) and birth weight of newborns (R=0.472, p=0.022), and in the second trimester with the APGAR score at 1 (R=0.680, p=0.005) and 5 minutes (R=0.55, p=0.033), as well as in the third trimester with th APGAR score at 1 minut (R=0.658, p=0.019). The GPX value had a strong positive correlation with the APGAR score at 1 min. (R=0.650, p=0.008) in the second trimester and with the birth weight of the newborns (R=0.598, p=0.039) in the third trimester. There was no correlation between newborns' birth measurements and serum selenium concentration. In the subgroup of pregnant women with pregnancy complications, a strong, statistically significant, negative correlation was found between Se intake in the second trimester and gestational age (R=-0.618, p=0.032), and in the third trimester, a positive correlation between Se concentration in serum and head circumference (R=0.587, p=0.021). The obtained results indicate that there is a relationship between the maternal selenium status during pregnancy, both in terms of the intake of this element, its serum concentration as well as the glutathione peroxidase activity, and the anthropometric parameters of the newborn, such as birth weight, length and APGAR score, especially in the group with physiological course of pregnancy, but when pregnancy complications occur, these relationships lose their importance.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Background: A new access technique was developed to reduce postoperative adverse events after the extraction of impacted maxillary wisdom teeth. Hence, this study aimed to assess the occurrence of adverse events after extraction of maxillary impacted wisdom teeth using traditional or modified access. Methods: Two different surgical incision designs were used for bilateral wisdom tooth extractions in 30 patients. The traditional incision was performed distal to the second molar in the center of the tuberosity, followed by a buccal releasing incision. After tooth extraction the wound was secured by sutures. The new technique consists of an oblique incision from the distal palatal aspect of the tuberosity towards the buccal aspect of the second molar. After tooth extraction, cyanoacrylate glue was used on the wound. Results: Lower pain was reported by patients at the site treated with the new technique (p<0.01). Edema, postsurgical bleeding, and hematoma were similar in both groups. The surgical time was shorter for the new technique (p<0.01). Conclusions: The new technique applied for the extraction of impacted maxillary wisdom teeth reduced postsurgical pain and the duration of surgery.

The prevalence of obesity or metabolic syndrome is increasing worldwide (“Globally metabodemic”). Approximately 25% of adult general population is suffering from nonalcoholic fatty liver disease (NAFLD) which has become serious health problem. Hepatic fibrosis is the most significant determinant of all cause and liver -related mortality in NAFLD. Noninvasive test (NIT) should be urgently required to evaluate hepatic fibrosis in NAFLD. Fibrosis-4 (FIB-4) index is the 1st triaging tool for excluding advanced fibrosis because of its accuracy, simplicity, and cheapness especially for general physicians or endocrinologists, although FIB-4 index has several drawbacks. Accumulating evidence has suggested that vibration controlled transient elastography (VCTE) and the enhanced liver fibrosis (ELF) test may become useful as the 2nd step after triaging by FIB-4 index. The leading cause of mortality in NAFLD is cardiovascular disease (CVD), extrahepatic malignancy, and liver-related diseases. NAFLD often complicates chronic kidney disease (CKD), resulting in increased simultaneous liver kidney transplantation (SLKT). FIB-4 index could be a predictor of not only liver-related mortality and incident hepatocullar carcinoma (HCC) but also prevalent and incident CKD, CVD, and extrahepatic malignancy. Although NITs as milestones for evaluating treatment efficacy have never been established, FIB- 4 index is expected to reflect histological hepatic fibrosis after treatment in several longitudinal studies. We here review the role of FIB-4 index as 1st step NIT in management of NAFLD.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

Background: Peritoneal sclerosis (PS) and its most severe form, encapsulating PS (EPS), are rare entities that can occur in various procedures (liver transplantation, intraperitoneal chemotherapy), secondary to medications ( beta-blockers); however, PS or EPS typically occur in patients undergoing peritoneal dialysis as a form of renal function substitution. Medical or surgical treatments can be applied, but morbidity and mortality have high rates . This condition typically presents clinically as an intestinal obstruction caused by the inclusion of the intestinal loops in the peritoneal fibrous membrane. Methods: Herein we present data from a single tertiary surgery center that has dedicated teams for patients receiving dialysis. In 12 years we analyzed a group of 63 patients admitted for catheter replacement/removal or for acute surgical pathology. In 5 cases (7,9%) we diagnosed EPS . Two patients with EPS presented with atypical abdominal pathologies requiring emergency surgery: one case of hemoperitoneum caused by a ruptured ovarian cyst and one case of uterine fibroids and metrorrhagia. Results: The definitive diagnoses were established intraoperatively and by analyzing the morpho-pathological changes of the peritoneum. The possible intraoperative challenges included laborious dissection, difficulties in restoring the correct anatomical landmarks, increased duration of the surgical intervention and a high rate of incidents and accidents Conclusions: The aim of the present study was to emphasize the possibility of other surgical pathologies overlapping with EPS, increasing the complexity of the surgical intervention. Also, we sought to determine whether there is a correlation between the morpho-pathological results of these patients who underwent emergency surgery and the common EPS morpho-pathological changes.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% [email protected], which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Background: Risk tratification based on pre-test probability may improve the diagnostic accuracy of temporal artery high-resolution compression sonography (hrTCS) in the diagnostic workup of cranial giant cell arteriitis (cGCA). Methods: A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA (n = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort (n = 114) by receiver operator characteristics (ROC)-analysis. The clinical items were composed to a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with CRP-values and hrTCS-values. Results: The model consisted of 4 clinical variables (age > 70, headache, jaw claudication, anterior ischemic optic neuropathy). The diagnostic accuracy of the model for discrimination of patients with and without a final clinical diagnosis of cGCA was excellent in both cohorts (AUC 0.96 and AUC 0.92, respectively). The diagnostic algorithm improved the positive predictive value of hrCTS substantially. Within the algorithm, 32.8% of patients (derivation cohort) and 49.1% (validation cohort) would not have been tested by hrtCS. None of these patients had a final diagnosis of cGCA. Conclusion: A diagnostic algorithm based on a clinical prediction rule improves the diagnostic accuracy of hrTCS.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

Insulated Gate Bipolar Transistor (IGBT) may convey high current and sustain high breakdown voltages; it may provide high power control. In a sense, how IGBT demonstrates the capability of power control was not specifically described, and the equivalent bipolar junction transistors (BJT) activated by an applied bias to the insulated gate is intriguing. As seen in Figure 1, IGBT shows an equivalent circuit structure containing two types of BJT, (NPN-type Bipolar transistor and PNP-type Bipolar transistor), which can be modeled as diode-associated transistor. Apparently, as a bias applies to the controlling gate, the channel beneath the gate oxide gets strongly inversed and turns out to be conductive just like what happens on metal-oxide-silicon field effect transistor (MOSFET), which provides necessary igniting currents to both BJTs and thus causes high current gain flowing through them-selves from the bottom. A proposed and renewed formula provides addressing the above scenario and successfully fitting the measured data that forms the characteristic curves. Some parameters in the proposed formula, which includes size and mobility associated constant, threshold voltage, and Early-Voltage-related lambda for MOSFET, explain and explore the underlying physics. IGBT thus enjoy high input impedance and high current gain due to MOSFET (Insulated Gate) and BJT, respectively. This formula combines both merits of MOSFET and BJT in order to fit the measured data. Indeed, the formula intimately fits IGBT electrical characteristic curves, and thus may be qualified and served as a model. The new model is then advisable for designing power integrated circuit to exactly achieve power saving, power control, and power efficiency.

Postprandial glucose levels between 4 and 7.9 hours (PPG4–7.9h) correlate with mortality from various diseases including hypertension, diabetes, cardiovascular disease, and cancer. This study aimed to assess if predicted PPG4–7.9h could diagnose diabetes. Two groups of participants were involved: Group 1 (4420 participants) had actual PPG4–7.9h, while Group 2 (8422 participants) lacked this measure but had all the diabetes diagnostic measures. Group 1 underwent multiple linear regression to predict PPG4–7.9h using 30 predictors, achieving accuracy within 11.1 mg/dL in 80% of the participants. Group 2 had PPG4–7.9h predicted using this model. Receiver operating characteristic curve analysis showed that predicted PPG4–7.9h could diagnose diabetes with an accuracy of 87.3% in Group 2, with a sensitivity of 75.1% and specificity of 84.1% at the optimal cutoff of 102.5 mg/dL. Simulation on 10,000 random samples from Group 2 revealed that 175 participants may be needed to investigate PPG4–7.9h as a diabetes diagnostic marker with a power of at least 80%. In conclusion, predicted PPG4–7.9h appears to be a promising diagnostic indicator for diabetes. Future studies seeking to ascertain its definitive diagnostic value might require a minimum sample size of 175 participants.

In Brazil, Brazilian borreliosis (BB), characterized molecularly, exhibits symptoms akin to Lyme disease (LD) but presents unique epidemiological, clinical, and morphological features. This study explores the incidence, diagnostic challenges, and awareness initiatives regarding this disease in Brazil. Employing a narrative review methodology, data were sourced from Pubmed, Ebsco, Google Scholar, and Web of Science without temporal constraints. Results reveal diagnostic complexities in Brazil due to the low sensitivity of conventional tests and the use of North American B. burgdorferi antigens. Despite challenges, epidemiological studies indicate an upswing in cases in humans and animals. Therefore, there is an urgent need to define new biomarkers, molecular strategies, or new antigenic targets to advance the development of vaccines and diagnostics. Early and accurate pathogen identification is crucial for active surveillance to comprehend and manage this zoonosis.

In chronic myeloid leukemia (CML), a clonal proliferative disorder of granulocytic lineage, the 90%‐95% of cases are characterized by t(9;22) (q34.1;q11.2) translocation, leading to the Philadelphia chromosome (1). Rarely, CML patients directly present a blast crisis, mostly of myeloid origin (2, 3). Herein, we describe one rare CML case, showing an unusual early T lymphoid blastic crisis.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Dipeptidyl peptidase 9 (DPP9) is a multifunctional intracellular protease with roles in tumour growth, inflammation and mitochondrial function. We developed a hepatocyte-specific DPP9 knockout mouse (DPP9-KO) to explore DPP9 in a mouse model of hepatocellular carcinoma (HCC). DPP9-KO mice were generated by crossing Albumin-Cre mice (Wt/Wt Cre+/+) with DPP9 floxed mice (Fl/Fl Cre-/-). Mice were treated with Diethylnitrosamine and Thioacetamide then an atherogenic High Fat Diet until 28 weeks of age. DPP9-KO mice had reduced liver and subcuta-neous adipose tissue mass and lower fasting plasma glucose, fewer small macroscopic liver nod-ules compared to DPP9-WT mice. However, there were no differences in the total number of mac-roscopic liver nodules, tumour burden, inflammation score and steatosis score. Consistent with the known ability of DPP9 to suppress NLRP1 activation, activated caspase-1 protein was ele-vated in DPP9-KO mouse liver. Additionally, Nfkbib, Cxcl10 and Ccl5 mRNA and protein levels of autophagy marker beclin1 and tumour suppressor p53 were increased. In conclusion, DPP9 de-pletion in hepatocytes may reduce liver cancer initiation, via mechanisms that may include in-creased autophagy and innate tumour suppression in this experimental model. Finally, the data supports DPP9 having a role in glucose regulation by the liver.

Hyperplastic nodules (HNs) are benign polypoid lesions of the colorectal mucosa reportedly caused by hyperplasia of the crypt epithelial cells. They differ from hyperplastic polyps because the crypts do not have histologically serrated changes. HNs often occur in the rectal mucosa and tend to be multifocal. We have proposed a new colitis nucleomigrans (CN) concept as the third type of microscopic colitis. Our histopathological criteria of CN include: i) chained nuclear migration to the middle part of the surface-lining columnar epithelium, ii) apoptotic nuclear debris scattered in the eosinophilic cytoplasm underneath the nuclei, and iii) mild to moderate chronic inflammation in the lamina propria. Immunostaining for cleaved-caspase 3 revealed apoptotic bodies. In this study, we report that most HN lesions share the histological features with CN, representing polypoid CN. We evaluated 108 lesions of HN in total, and 94 (87%) were reclassified as polypoid CN. In the polypoid CN, the lamina propria mucosae were uniformly edematous, suggesting that accelerated apoptosis of the mucosal surface epithelium may cause osmotic dysregulation to form localized mucosal elevations.

Deadlock is a real problem that can cause expensive, time-dependent hang-ups or failures in distributed computing systems. Some studies have been conducted with deadlock detection and resolution in generalized request computing models, most of these studies are based on the diffusing computation scheme, where forward and backward propagated probe and reply messages respectively are diffused between dependent processes. The replies carry the dependency information between processes for the initiator. Using this dependency information initiator can detect and resolve the deadlock cycle. It has been proven in the literature that a deadlock cycle length distribution is skewed with a large majority of cycles (90%) having length two. Based on this fact an improved algorithm is proposed for handling deadlock detection in distributed computing systems under the distributed request model where each process has several requests at a time. The improved algorithm depends mainly on message propagation and timeout policy. Probe and reply messages are propagated between the initiator and its successors only to detect deadlock cycles between them. Global timeout policy is used to detect potential deadlock cycles with large length distribution. The proposed algorithm can handle concurrent executions. Simulation experiments are performed to see the effectiveness of the improved algorithm. It is found that the improved algorithm compares favorably with other existing algorithms and it shows better results for several performance metrics especially in reducing deadlock latency and execution time.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Inflammation plays a key role in the induction of choroidal neovascularization (CNV). Inflammatory choroidal neovascularization (iCNV) is a severe but uncommon complication of both infectious and non-infectious uveitides. It is hypothesized that its pathogenesis is similar to that of wet age-related macular degeneration (AMD), and involves hypoxia as well as the release of vascular endothelial growth factor, stromal cell-derived factor 1-alpha, and other mediators. Inflammatory CNV develops when inflammation or infection directly involves the retinal pigment epithelium (RPE)–Bruch’s membrane complex. Inflammation itself can compromise perfusion, generating a gradient of retinal-choroidal hypoxia that additionally promotes the formation of choroidal neovascularization in the course of uveitis. The development of choroidal neovascularization is extremely common in conditions such as punctate inner choroidopathy, multifocal choroiditis, serpiginous choroiditis, and presumed ocular histoplasmosis syndrome. Although the majority of iCNV cases are well defined and appear as the “classic” type on fluorescein angiography, the diagnosis of iCNV is challenging due to difficulties in differentiating between inflammatory choroiditis lesions and choroidal neovascularization. Modern multimodal imaging, particularly the recently introduced technology of optical coherence tomography (OCT) and OCT angiography (noninvasive and rapid imaging modalities) can reveal additional features that aid the diagnosis of iCNV. However, more studies are needed to establish their role in the diagnosis and evaluation of iCNV activity. Keywords: uveitis; inflammatory choroidal neovascularization; multimodal imaging; fluorescein angiography; indocyanine green angiography; optical coherence tomography; optical coherence tomography angiography; near-infrared autofluorescence

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

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.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

It is increasingly common for experiments in biology and medicine to involve large numbers of hypothesis tests. A natural graphical method for visualizing these tests is to construct a histogram from the p-values of these tests. In this article, we examine the shapes, both normal and abnormal, that these histograms can take on, as well as present simple inferential procedures that help to interpret the shapes in terms of diagnosing potential problems with the experiment. We examine potential causes of these problems in detail, and discuss potential remedies. Throughout, examples of abnormal-looking p-value histograms are provided and based on case studies involving real biological experiments.

Tuberculosis (TB) remains a global health threat, necessitating innovative strategies for control and prevention. This comprehensive review explores the Mycobacterium tuberculosis Lysine Exporter (LysE) gene, unveiling its multifaceted roles and potential uses in controlling and preventing tuberculosis (TB). As a pivotal player in eliminating excess L-lysine and L-arginine, LysE contributes to the survival and virulence of M. tuberculosis. This review synthesizes findings from different electronic databases and includes 13 studies focused on the LysE of M. tuberculosis. The research unveils that LysE can be a potential drug target, a diagnostic marker for TB, and a promising candidate for vaccine development. The absence of LysE in the widely used BCG vaccine underscores its uniqueness and positions it as a novel area for TB prevention. In conclusion, this review underscores the significance of LysE in TB pathogenesis and its potential as a drug target, diagnostic marker, and vaccine candidate. The multifaceted nature of LysE positions it at the forefront of innovative approaches to combat TB, calling for sustained research efforts to harness its full potential in the global fight against this infectious disease.

Introduction: The repertoire of microRNAs (miRNAs) in thyroid carcinomas starts to be elucidated. Among differentiated thyroid carcinomas (DTC), papillary thyroid carcinoma (PTC) is the most common. The assessment of miRNAs expression may contribute to refine the pre-surgical diagnosis, in order to obtain a personalized and more effective treatment of patients. Aims: This study aims to evaluated 1) miRNAs expression in a series of DTC, and the association with the presence of selected genetic mutations, in order to improve diagnosis and predict biologic behavior of DTC/PTC. 2) the reliability of molecular tests in Ultrasound-guided Fine Needle Aspiration Cytology (US-FNAC) for a more precise preoperative diagnosis. Material and methods: This series includes 176 samples (98 cytology samples and 78 formalin-fixed paraffin embedded (FFPE) tissues) obtained from 106 patients submitted to surgery, being 13 benign lesions (controls) and 93 DTCs (cases). The microRNA expression was assessed for miR-146b, miR-221, miR-222, and miR-15a by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and the results were analyzed using the 2- ΔΔCT method, using miR16 as an endogenous control. The discriminative ability of miRNAs expression regarding PTC diagnosis was evaluated using the area under the Receiver Operating Characteristic Curve (AUC). The association of miRNAs expression, clinicopatological features and genetic alterations (BRAF, RAS and TERTp) was evaluated in PTCs. Results/Discussion: All the analyzed miRNAs showed a tendency to be over expressed in DTCs/PTCs when compared with benign lesions, both in cytology and histology samples. In cytology, miRNAs expression levels were higher in malignant than in benign tumors. In histology, corresponding discriminative abilities regarding PTC diagnosis were: miR-146b (AUC0.94, 95%CI 0.87-1), miR-221 (AUC0.79, 95%CI 0.68-0.9), miR-222 (AUC0.76, 95%CI 0.63-0.89) and miR-15a (AUC0.85, 95%CI 0.74-0.97). miR-146b showed 89% sensitivity (se) and 87% specificity (sp); mi-R221 se=68.4, sp=90; miR222 se=73, sp=70, and mi-R15a se=72, sp=80. MicroRNAs were associated with aggressive clinicopathological features and tumor progression in PTCs (p < 0.05), particularly for miR-222. Our data reveals a significant association between higher expression levels of miR-146b, miR-221 and miR-222, with the presence of BRAF mutation (p<0.001), and miR-146b (p=0.016) and miR-221 (p=0.010) with the presence of RAS mutation, suggesting an interplay of these mutations with miRNAs expression. Although this study has a relatively small sample size, over expression of miRNAs in cytology may contribute to a more precise preoperative diagnosis. The miRNAs showed a good discriminative ability in PTC diagnosis. The association between the miRNAs expression profile and genetic alterations results could be useful for an accurate diagnosis of DTCs/PTCs in FNAC.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

Smokeless tobacco (ST) products vary significantly in their oral carcinogenicity. Much is known about the differences in chemical, but not bacterial, constituents of these products. In this study, we explore the composition and function of the bacteriome in ST products from 4 countries using q-PCR and 16S rRNA-based next generation sequencing. The bacterial load (16S rRNA copies/gram) was lowest in Swedish snus (3.4E+6) and highest in Yemeni shammah (6.6E+11). A total of 491 species-level taxa, many of which are potentially novel, belonging to 178 genera and 11 phyla were identified. Species richness and diversity were highest for Swedish snus and lowest for Yemeni shammah. Bacillus, Paenibacillus, and Oceanobacillus spp. were the most abundant in American snuff; species of Pseudomonas, Massilia, Propionibacterium, Puniceispirillum and Gloeothece predominated in Swedish snus. In Sudanese toombak, Facklamia, Desemzia, Atopostipes and Lysinibacillus spp. accounted for the majority of the bacteriome. Yemeni shammah exclusively contained Bacillus spp. PICRUSt functional prediction showed that genes encoding cadmium/zinc and nickel transport systems were enriched in the presumptively “high carcinogenicity” products. The bacteriome of ST products thus differed qualitatively, quantitatively and functionally. The relevance of these differences, particularly with respect to nickel and cadmium, to oral carcinogenesis warrants further investigation.

We presented unusual case of double left ventricular false aneurysm complicating late presentation AMI successfully treated with the combination of urgent surgical drainage of tamponade, staged PCI of non-culprit lesion, preventive implantation of ICD-DR, guideline directed heart failure medical therapy and delayed but effective reconstructive operation.

Wheat, a component of the staple diet globally, is a common food allergen in children. The symptoms of wheat allergy (WA) range from skin rash to shortness of breath, significantly impairing quality of life. Following initial clinical suspicion, individuals may undergo wheat allergen-specific skin prick test (SPT), blood test for serum immunoglobulin E (sIgE) levels or oral food challenge. Conventional management of WA lies in wheat avoidance, yet accidental consumption may be inevitable owing to the ubiquity of wheat in various food products. This article aims to provide an overview of the immunologic pathway of WA, followed by its novel diagnostic methods, namely alcohol-soluble SPT extracts, component-resolved diagnosis and basophil activation test (BAT). The mechanisms underlying wheat allergen-specific oral immunotherapy (OIT) as well as a summary of the efficacy, tolerability and safety of related clinical trials will then be discussed.

Scrub typhus (ST) is one of the most neglected tropical diseases, a leading cause of acute undifferentiated febrile illness in areas of the ‘tsutsugamushi triangle’, diagnosed frequently in South Asian countries. The bacteria Orientia tsutsugamushi is the causative agent of the disease, which enters to human body through the bite of trombiculid mite (Chigger) of the genus Leptotrombidium deliense.The diagnosis of the disease becomes challenging as its early symptoms mimic other febrile illnesses like dengue, influenza, and corona viruses. Lack of rapid, reliable, and cost-effective diagnostic methods further complicates the identification process. Northeast India, a mountainous region with a predominantly rural tribal population, has witnessed a resurgence of scrub typhus cases in recent years. Various ecological factors, including rodent population, habitat characteristics, and climatic conditions, influence its prevalence. Entomological investigations have confirmed the abundance of vector mites, highlighting the importance of understanding their distribution and the probability of transmission of scrub typhus in the region. Proper diagnosis, awareness campaigns, and behavioral interventions are essential for controlling scrub typhus outbreaks and reducing its impact on public health in Northeast India. Further research and community-based studies are necessary to accurately assess the disease burden and implement effective prevention strategies.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Endometriosis is a disease that affects women of reproductive age and has a significant impact on their well-being. The main symptoms are dysmenorrhea, chronic pelvic pain and infertility. The diagnostic process in many cases is very long and can take up to 8-12 years. Laparoscopy, which is an invasive method, is still necessary to confirm final identification. Therefore, the development of diagnostic markers seems to be crucial for the diagnosis and proper treatment of women affected by endometriosis as soon as possible. Still the most frequently studied and used marker is Cancer Antigen 125 (CA-125). Other glycoproteins, growth factors and immune markers seem to play an important role. However, the search for the ideal endometriosis marker is still ongoing. Developing researches on endometriosis pathogenesis help to identify potential biomarkers or sets of biomarkers in order to improve and speed up the diagnostic process in a non-invasive way.

Saliva, as a diagnostic medium, offers a promising alternative to blood by virtue of its non-invasive collection, which enhances patient compliance, especially in paediatric and geriatric populations. In this study, we assessed the utility of saliva as a non-invasive medium for measuring V. cholerae-specific serum antibodies in naturally infected individuals. We tested paired serum and saliva samples obtained from a total of 63 cholera patients enrolled in a cohort study. Vibriocidal antibodies assay (IgM/IgG) as markers for accurate determination was used to determine cholera specific antibody levels. Using receiver operating characteristics (ROC) curve, we found that the best cut-off that maximizes (sensitivity + specificity) is 10 titers. At this saliva titer, the sensitivity is 76.9% (95%CI: 60.9%, 87.7%) and specificity is 80.0% (95%CI: 56.6%, 92.5%). Using Spearman’s correlation coefficient, we also found evidence of a positive correlation between Vibrio Cholerae saliva and serum antibodies (rho=0.66, p<0.001). In conclusion, saliva-based diagnostic cholera tests has high diagnostic accuracy, and would be advantageous, cheaper, and quicker for early diagnosis of severe cholera outcomes.

DKA represents an acute, severe complication of relative insulin deficiency and a common presentation of Type 1 Diabetes Mellitus (T1DM) primarily and, occasionally, T2DM in children and adolescents. It is characterized by the biochemical triad of hyperglycaemia, ketonaemia and/or ketonuria, and acidaemia. Clinical symptoms include dehydration, tachypnoea, gastrointestinal symptoms, and reduced level of consciousness, precipitated by a variably long period of polyuria, polydipsia and weight loss. The present review aims to summarize potential pitfalls in the diagnosis and management of DKA. A literature review was conducted using the Pubmed/Medline databases including articles published from 2000 onwards. Diagnostic challenges include differentiating between T1DM and T2DM, DKA and hyperosmolar hyperglycaemic state (HHS), or between DKA and alternative diagnoses presenting with overlapping symptoms, such as pneumonia, asthma exacerbation, urinary tract infection, gastroenteritis, acute abdomen and central nervous system infection. The mainstays of DKA management include careful fluid resuscitation, timely intravenous insulin administration and restoration of shifting electrolyte disorders and underlying precipitating factors. However, evidence suggests that optimal treatment remains a therapeutic challenge. Accurate and rapid diagnosis, prompt intervention and meticulous monitoring are of major importance to break the vicious cycle of life-threatening events and prevent severe complications during this potentially fatal medical emergency.

Advances in gastric cancer screening and endoscopic technology have made it possible to detect and treat gastric cancer at an early stage. As a result, about 40% of all gastric cancers have now been resected by endoscopic treatment in Japan. In endoscopic diagnosis, magnifying endoscopic observation using band-limited light such as narrow band imaging (NBI) has significantly improved the accuracy of gastric cancer diagnosis. At the same time, in endoscopic treatment, endoscopic submucosal dissection (ESD) has contributed to increase explosively the number of gastric cancers which could be resected endoscopically. In recent years, the construction of algorism for endoscopic diagnosis and treatment with a view to personalized medicine has been explored, and endoscopic use for gastric cancer managements is expected to develop further in the future. Keywords (5 words):

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

This study aimed to evaluate the diagnostic roles of various immunohistochemical markers in urothelial carcinoma in situ (uCIS) through a meta-analysis and review of diagnostic test accuracy. Immunohistochemical markers CK20, CD44, AMACR, and p53 were evaluated in the present study. We analyzed the expression rates of immunohistochemical markers and compared their diagnostic accuracies. The estimated expression rates were 0.803 (95% confidence interval [CI]: 0.726–0.862), 0.142 (95% CI: 0.033–0.449), 0.824 (95% CI: 0.720–0.895), and 0.600 (95% CI: 0.510–0.683) for CK20, CD44, AMACR, and p53, respectively. In the comparison between uCIS and reactive/normal urothelium, the expression of CK20, AMACR, and p53 in uCIS was significantly higher than in reactive/normal urothelium. CD44 showed significantly lower expression in uCIS than in the reactive/normal urothelium. Among the markers, AMACR had the highest sensitivity, specificity, and diagnostic odds ratio. The AUC on SROC was the highest for CK20. In conclusion, immunohistochemical markers, such as CK20, CD44, AMACR, and p53, can be useful in differentiating uCIS from reactive/normal urothelium.

Leukodystrophies, also known as demyelinating diseases, mainly affect the CNS. Clinical presentation of different types of leukodystrophies can be nonspecific and thus, imaging techniques like MRI can be used for a more definitive diagnosis. These diseases are characterized as cerebral lesions with characteristic demyelinating patterns, which can be used as differentiating tools. In this review, we talk about these MRI imaging findings for each leukodystrophy, associated genetics, blood work that can help differentiate, emerging diagnostics, and follow up imaging strategy. The leukodystrophies discussed in this paper include X-linked adrenoleukodystrophy, Metachromatic leukodystrophy, Krabbe’s disease, Pelizaeus-Merzbacher disease, Alexander’s disease, Canavan disease, and Aicardi-Goutières Syndrome.

Abstract: Recent years demonstrate the increased attention to radon from two scientific directions. After neglecting radon as earthquake precursors in 1990-th it becomes again the subject of earthquake-forecast papers discussions due to growing networks of the radon monitoring in different countries, especially, the technologies of real-time radon measurements where the gamma-spectrometers become the leader of interest as the sources of 222Rn identification. The second fast developing direction is including the radon in the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) models as a source of the boundary layer ionization. And here we encounter with second direction which is not connected with the earthquake forecast problems. It is the role of air ionization by radon as a source of the Global Electric Circuit (GEC) modification. In this publication we try to unite all these problems to present more complex view on radon as important element of our environment.

Neutropenia refers to a decrease in the absolute neutrophil count according to age and race norms and poses a common concern in pediatric practice. Neutrophils serve as host defenders and act crucially in acute inflammation procedures. In this narrative review we systematically present causes of neutropenia in childhood mainly adopting the pathophysiological classification of Frater, thereby studying: (1) neutropenia with reduced bone marrow reserve, (2) secondary neutropenia with reduced bone marrow reserve, and (3) neutropenia with normal bone marrow reserve. Different conditions in each category are thoroughly discussed and practically approached from the clinician point of view. Secondary mild to moderate neutropenia is usually benign due to childhood viral infections and is expected to resolve in 2- 4 weeks. Bacterial and fungal agents are also associated with transient neutropenia although fever with severe neutropenia constitutes a medical emergency. Drug-induced and immune neutropenias should be suspected following a careful history and a detailed clinical examination. Cytotoxic chemotherapies treating malignancies are responsible for severe neutropenia and neutropenic shock. Rare genetic neutropenias usually manifest with major infections early in life. Our review taxonomies clinical findings and associates them to specific neutropenia disorders. We consequently propose a practical diagnostic algorithm for managing neutropenic children.

This study aimed to evaluate the diagnostic and prognostic roles of GATA3 immunohistochemistry in urothelial carcinoma (UC) through a meta-analysis. We investigated GATA3 immunohistochemical expression rates and performed a subgroup analysis based on tumor site, study location, and histological subtypes. The overall survival rates of patients with GATA3-positive and negative UC were compared. The estimated GATA3 expression rate was 0.748 (95% confidence interval [CI]: 0.704–0.787). GATA3 expression rates in the urinary bladder and urinary tract were 0.775 (95% CI: 0.727–0.818) and 0.614 (95% CI: 0.426–0.774), respectively. The GATA expression rates of noninvasive and invasive UCs were 0.965 (95% CI: 0.938–0.980) and 0.644 (95% CI: 0.581–0.702), respectively. In invasive UCs, there was a significant difference in GATA3 expression between non-muscular invasion and muscular invasion subgroups (0.937, 95% CI: 0.883–0.967 vs. 0.753, 95% CI: 0.645–0.836). GATA3 expression was the highest in the microcytic subtype among the histologic subtypes (0.952, 95% CI: 0.724–0.993). There was a significant correlation between GATA3 expression and better prognosis (hazard ratio: 0.402, 95% CI: 0.311–0.521). Taken together, GATA3 expression significantly correlated with low-stage and better prognosis in UC. GATA3 expression is highly variable across histological subtypes and it is careful interpreting GATA3 expression.

Feline infectious peritonitis (FIP), caused by the feline coronavirus (FCoV), is a devastating dis-ease in cats. Based on its antigenicity, FCoV can be divided into two serotypes: FCoV-I and FCoV-II. Furthermore, according to its pathogenicity, FCoV can be divided into feline enteric coronavirus (FECV) and feline infectious peritonitis virus (FIPV). There are numerous factors that affect the pathogenesis of FIP, among which host immunity and viral genetics can play an es-sential role in the development of FIP. Owing to the lack of specific symptoms, existing individual diagnostic methods can only support the suspicion of FIP, and multiple diagnostic methods and test data need to be combined to make a diagnosis. Although there are still no effective FIPV vaccines or commercial drugs available in the market, various studies have shown that some compounds can be used for treatment. Therefore, FIP is no longer incurable in cats

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). We present results for sensor validation and disease classification via Machine Learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches and within a 15-min examination. Symptoms and medical history were captured on the paired smartphone. A broad range of different ML classifiers were cross-validated. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. The most advanced task of distinguishing PD vs DD was evaluated with 74,1% balanced accuracy, 86,5% precision and 90,5% recall by Multilayer Perceptrons. Deep Learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle-tremor signs with low noise. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers but it remains challenging for distinguishing similar disorders.

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: clubfoot is a common congenital deformity. The Ponseti technique, involving early corrective manipulations followed by the application of long leg casts and Achilles tenotomy, is widely accepted as the preferred treatment. Rapid healing of the tendon after surgery has been documented, but the aspect regarding tendon structure and properties is not known. Three cases of Achilles tendon rupture in adolescent previously treated for clubfoot have been described in the literature. Since rupture is a rare event in this age group a possible correlation with previous surgery has been hypothesized. The primary aim of the study was to compare the ultrasound findings of the Achilles tendon, in patients treated for clubfoot, between patients treated with casting alone and the ones who underwent surgery (percutaneous tenotomy or Z-plasty lengthening). Methods: 22 asymptomatic patients, (34 feet) median age 12 years, previously treated for clubfoot, were recruited for this study; the patients underwent an Achilles tendon ultrasound examination during a follow-up outpatient visit. Results: a greater thickness and structural alterations with presence of hypoechoic areas of the operated tendons compared to those treated with plaster alone were observed (p value: 0.0498 and

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Infectious keratitis (IK) is among the top 5 leading causes of blindness globally. Early diagnosis is key to guide an appropriate therapy to avoid complications such as vision impairment and blindness. Culture of corneal scrapes is the initial diagnostic test to grow and identify the causal organism. Alternative diagnostic tools include imaging such as with optical coherence tomography (OCT) and in vivo confocal microscopy (IVCM). OCT’s advantage is its ability to accurately determine the depth and extent of the corneal ulceration, infiltrates and haze; therefore, characterizing the severity and progression of the infection. However, it is not a preferred choice in the diagnostic tool package for infectious keratitis. IVCM is a great aid in the diagnosis of fungal and Acanthamoeba keratitis with overall sensitivities of 66-74% and 80-100%, and specificity of 78-100% and 84-100%, respectively. Recently the use of deep learning (DL) models in IK has been shown to aid diagnosis via image recognition. Most of the studies that have developed DL models to diagnose the different types of IK have utilised photographs from digital camaras, slit-lamp images, or IVCM images. Some studies have used extremely efficient single DL algorithms to train their models and others used ensemble approaches. This technology is likely to assist in the diagnosis of IK in some years. Further work is needed to examine and validate the clinical performance of the DL models in the real-world setting and to evaluate whether this technology improves patient clinical outcomes. The scope of this review was to provide a recent update on the diagnostic imaging tools in IK

A patient in her early 20s presented with constant and progressive lower abdominal and back pain, mainly on the right side of the abdomen, purulent vaginal discharge and pyrexia. Radio-logical assessment revealed a possible tuboovarian abscess and incidental diagnosis of ipsilateral renal agenesis. The patient was treated for pelvic inflammatory disease (PID), however after antibiotic administration and since the symptoms were not resolving, an abdominal MRI was requested which revealed a uterus didelphys with two cervices, an obstructed haemivagina and evidence of haematocolpos. The diagnosis of OHVIRA syndrome was confirmed and the patient underwent an excision of the vaginal septum, drainage of the haematopyocolpos and laparoscopic drainage of the tuboovarian abscess. She achieved a good recovery.

Background: Neurosarcoidosis is a great mimic, and its distinction from multiple sclerosis and other disorders can be particularly troublesome. We investigated whether cerebrospinal fluid (CSF) examination could help distinguish these two conditions and propose new diagnostic criteria. Methods: Stringent hierarchical diagnostic criteria were formulated. These were then applied prospectively to 531 neurological patients in whom neurosarcoidosis entered the differential diagnosis. The CSF total protein (TP), white cell count (WCC) and angiotensin-converting enzyme (ACE) levels were measured. The CSF and serum IgG patterns after isoelectric focusing were compared. Not all CSF parameters were measured in every patient. Results: 85/531 (16%) cases examined were diagnosed with neurosarcoidosis. Only 22 of these 85 (26%) met stringent diagnostic criteria. Intrathecal synthesis of oligoclonal IgG was evident in only 1/19 (5%) who fulfilled these criteria compared to 19/59 (32%) cases who did not (p = 0.03). CSF TP levels > 1g/L were more common in patients meeting the stringent criteria compared to the remainder of the patients, i.e. 11/22 (50%) vs 14/58 (24%) (p<0.05). Eleven out of 20 cases (55%) fulfilling stringent criteria had elevated CSF WCCs, and 6/12 cases (50%) had raised CSF ACE, neither significantly different from the remaining cases. Mean CSF TP, WCC, and ACE levels did not differ significantly between any groups of patients. Interpretation: Large elevations in CSF total protein, white cell count, and ACE occur in neurosarcoidosis but are rare in multiple sclerosis. However, the diagnostic use of these tests is limited since minimal changes may occur in both conditions. In contrast, intrathecal synthesis of oligoclonal IgG is a powerful discriminator because it is very uncommon in neurosarcoidosis whilst occurring in 95-98% of cases of multiple sclerosis. This observation implies that the immunopathogenesis of the two disorders is quite different. We suggest caution in diagnosing neurosarcoidosis when intrathecal oligoclonal IgG synthesis is detected. We propose new diagnostic criteria for neurosarcoidosis for future research.

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.

Diagnostics and decomposition of atmospheric disturbances in a planar flow are considered and applied to numerical modeling results with the direct possibility to use in atmosphere monitoring especially in such strong events which follow magnetic storms. The study examines a situation in which the stationary equilibrium temperature of a gas may depend on a vertical coordinate, that seriously complicates the problem solution. The relations connecting perturbations for acoustic and entropy modes are analytically established and led to the solvable diagnostic equations. These perturbation structures, found as the equation solutions specify acoustic and entropy modes in an arbitrary stratified gas under the condition of stability. These time-independent diagnostic relations link gas perturbation variables of the acoustic and the entropy modes. Hence, they provide the ability to decompose the total vector of perturbations into acoustic and non-acoustic (entropy) parts uniquely at any instant within the all accessible heights range. As a prospective model, we consider the diagnostics at the height interval [120;180] km, where the equilibrium temperature of a gas depends linearly on the vertical coordinate. For such a heights range it is possible to proceed with analytical expressions for pressure and entropy perturbations of gas variables. Individual profiles of acoustic and entropy parts for some data, obtained by numerical experiment, are illustrated by the plots for the pure numerical data against ones obtained by the model. The total energy of a flow is determined for both approaches and its height profiles are compared.

Familial dysbetalipoproteinemia (FD) is a highly atherogenic genetically-based lipid disorder with the underestimated actual prevalence. In the recent years, several biochemical algorithms have been developed to diagnose FD using available laboratory tests. However, there is not enough data on their use in real-world clinical implementation. We studied the applicability of the most accessible biochemical algorithms to diagnose FD in clinical practice. We also investi-gated the prevalence of FD in one of the European regions of Russia based on a population sample. In this study there was detected a high prevalence of FD: 1 in 151. We demonstrated that the diagnostic algorithms of FD including a diagnostic apoB levels require correction, taking into account the characteristics of the distribution of apoB levels in the population. At the same time a triglycerides cutoff ≥1.5 mmol/L may be a useful tool in identifying subjects with FD. We also analyzed the presence and pathogenicity of APOE variants associated with the autosomal dominant FD in a large research sample.

Advances in nanomedicine bring the attention of researchers to the molecular targets which can play a major role in the development of novel therapeutic and diagnostic modalities for cancer management. The choice of a proper molecular target can decide on the efficacy of the treatment and endorse the personalized medicine approach. Gastrin-releasing peptide receptor (GRPR) is a G-protein-coupled membrane receptor, well known to be overexpressed in numerous malignancies including pancreatic, prostate, breast, lung, colon, cervical and gastrointestinal cancers. Therefore, many research groups express a deep interest in targeting GRPR with their nanoformulations. A broad spectrum of the GRPR ligands has been described in the literature, which allows tuning of the properties of the final formulation, particularly in the field of the ligand affinity to the receptor and internalization possibilities. Hereby the recent advances in the field of applications of various nanoplatforms which are able to reach the GRPR expressing cells are reviewed.

Typhoid fever is a major public health burden which causes substantial global morbidity and mortality due to lack of decisive diagnostic protocols. The capacity of commonly use diagnostic test to validate the absence of typhoid fever is controversial. This study explores to evaluate new techniques for typhoid diagnosis and proposed a harmonised suitable standardized composite reference to be adopted. Published peer-reviewed articles indexed in PubMed, MEDLINE and Google scholar were reviewed for hospital-based studies. This study reveals new typhoid diagnostic techniques such as proteomics, serology, Rapid Diagnostic tests (RDTs), transcriptomics, genomics, and metabolomics. 34.4% of the studies use prospective study design. The study result establishes that, Widal test has a moderate diagnostic accuracy with average percentage sensitivity (52.9%), specificity (54%), positive predictive value (PPV) (56.8%) as well as negative predictive value (NPV) (55.6%) when compared with 29.4%, 28%, 29.5%, and 27.8% of Typhidot respectively. The findings showed a statistically significant difference on the sensitivity between Widal and Typhidot t (40) = 2.639, p = 0.012 at p<0.05 using independent sample t-test. When there is no perfect reference standard that has an optimal diagnostic accuracy, the need for a harmonised suitable standardized composite reference is essential. Hence, this study recommends that, peripheral blood culture with established sensitivity of 60% and Widal test with average sensitivity of 52.9% be adopted as a consensus composite reference standard for typhoid fever diagnosis in other to improve confidence in prevalence estimates.

Metabolic-associated fatty liver disease (MASLD) is not only associated with traditional cardiovascular risk factors such as T2DM and obesity, but it is also an independent risk factor for the development of cardiovascular disease. MASLD has been shown to be independently related to endothelial dysfunction and atherosclerosis. MASLD is characterized by a chronic proinflammatory response that, in turn, may induce a prothrombotic state. Several mechanisms such as endothelial and platelet dysfunction, changes in the coagulation cascade, decreased fibrinolytic activity can contribute to induce the prothrombotic state. Platelets are players and addresses of metabolic dysregulation; obesity and insulin resistance are related to platelet hyperactivation. Furthermore, platelets can exert a direct effect on liver cells, particularly through the release of mediators from granules. Growing data in literature support the use of antiplatelet agent as a treatment for MASLD. The use of antiplatelets drugs seems to have potential clinical implication for the prevention of HCC patients with MASLD, since platelets contribute to fibrosis progression and cancer development. This review aims to summarize the main data on the role of platelets in the pathogenesis of MAFLD and its main complications such as cardiovascular events and the development of liver fibrosis. Furthermore, we will examine the role of antiplatelet therapy not only in the prevention and treatment of cardiovascular events but also as a possible anti-fibrotic and anti-tumor agent.

The diagnosis and treatment of autoimmune diseases are major challenges. Their signs, symptoms, and their clinical severity change throughout the course of the disease. The available treatments for these conditions do not result in complete remission, and their long-term use generates several side effects. The main challenges in this field are the development of more sensitive diagnostic tools, and effective treatments. Studies in patients and animal models reported alterations in monocyte phenotype, activation, and function. These cells play an essential role in the pathogenesis of chronic inflammatory states and have become a target for extracorporeal monitoring and specific intervention. Nanoparticles (NPs) are ultrafine particles that can be used as contrast agents in diagnostic imaging techniques to detect specific cells in inflammatory infiltrates in tissues that are not easily accessible by biopsy. In addition, NPs can be designed to deliver drugs to a cell population or tissue. This review describes several experimental pieces of evidence demonstrating that monocytes and macrophages can specifically uptake various types of NPs, thus becoming detectable in vivo by magnetic resonance imaging and susceptible to be modulated by therapeutic agents. Hence, nanoparticles targeting mononuclear phagocytes represent promising tools for diagnosing and treatment.

Mesenteric ischemia diagnosis is challenging with an overall mortality in up to 50% of cases despite advances in treatment. The main problem that affects the outcome is the delayed diagnosis because of non-specific clinical presentation. Multi Detector CT Angiography (MDCTA) is the first line investigation for the suspected diagnosis of vascular abdominal pathologies and is the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extension and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases helping clinicians and surgeons for the proper management. CT can differentiate forms that are still susceptible to pharmacological or interventional treatment (NOM= non-operative management) from advanced disease with transmural necrosis in which a surgical approach is required. Knowledge of CT imaging patterns and corresponding vascular pathways is mandatory in Emergency settings to reach a prompt and accurate diagnosis. The aims of this paper are 1. to provide technical information about the optimal CTA protocol; 2. To empathize the CTA arterial and venous supply to gastrointestinal tract and relative ischemic pattern; 3. To describe vascular, bowel and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia.

Cardiac amyloidosis (CA) results mainly from infiltration of myocardium by either immunoglobulin light chain fibrils (AL) or transthyretin fibrils (ATTR), causing restrictive cardiomyopathy and eventually death if untreated (3). AL derives from monoclonal immunoglobulin light chain produced by plasma cell clone in the bone marrow, while ATTR is the misfolded form of hepatically-derived transthyretin (TTR) protein, and could be hereditary (hATTR) or wild-type (wtATTR). Over the last decade, improvement in diagnostic imaging and better clinical awareness has unleashed a notable presence of CA in the community, especially ATTR in the elderly population. These multimodality imaging modalities include echocardiography, cardiac magnetic resonance and radionuclide scintigraphy with bone-avid tracers. There has been remarkable progress in the therapeutic landscape as well, and there are disease-modifying therapies available now that can alter the course of the disease and improve survival if initiated at an early stage in the disease. There remains an unmet need for detecting this disease accurately and early, so that these patients could benefit the most from newly-emerging therapies.

Post-acute COVID-19 vaccination syndrome (PACVS) is distinguished from the normal post-vaccination state by altered receptor antibodies (Semmler et al. Vaccines 2023, 11, 1642). Here, we explore the symptoms registry and standard serum markers acquired in the above study to delineate the disease phenotype of PACVS. Symptoms reported by the participants overlapped with established complex dysautonomia syndromes (ME/CFS, POTS, MCAS, SFN). However, many participants were qualified for several (131/191) or none (31/191) of these syndromes. Unbiased clustering (modified Jaccard index) revealed a prevalent cross-cohort symptomatology of malaise and chronic fatigue (&gt; 80% of cases). Overlapping clusters of (i) peripheral nerve dysfunction, dysesthesia, motor weakness, pain, vasomotor dysfunction, (ii) cardiovascular impairment, and (iii) cognitive impairment, headache, optic, oculo-motoric and acoustic dysfunctions were also frequently represented. Conspicuous serum markers encompassed increases in interleukins 6 and 8 and low fT3 levels (&gt; 80%), IgG-subclass imbalances and impaired iron-storage (&gt; 50%), and increases in sNFL (&gt; 30%). Serum markers were not correlated to specific symptoms or symptom clusters. A single and unique cause (i. e. SARS-CoV-2 vaccination) is improbable to trigger several distinct etiologies. Therefore, the symptoms and serological features observed in our study cohort probably represent facets of a single complex syndrome.

Amid the deepening imbalance in the supply and demand of allogeneic organs, xenotransplanta-tion can be a practical alternative because it makes an unlimited supply of organs possible. How-ever, to perform xenotransplantation on patients, the source animals to be used must be free from infectious agents. This requires breeding of animals using assisted reproductive techniques, such as somatic cell nuclear transfer, embryo transfer, and cesarean-section, without colostrum de-rived in designated pathogen-free (DPF) facilities. Most infectious agents can be removed from animals produced by these methods, but several viruses known to pass through the placenta are not easy to remove, even with these methods. Therefore, in this review, we look at the characteris-tics of several viruses known to cross the placenta and determine how they can be detected. This review is intended to help maintain DPF facilities by preventing animals infected with the virus from entering DPF facilities and to help select pigs suitable for xenotransplantation.

Cancer have been known as a most important global mortality reason, which in spite of novel therapeutic ways, the leading death is still considerable. Molecular targeted diagnosis and therapy using aptamers with high affinity have converted to a popular technique for pathological angiogenesis and cancer therapy scientists. In this paper several aptamer-based diagnostic and therapeutic techniques such as aptamer-nanomaterial conjugation, aptamer-drug conjugation (physically or covalently) and biosensors, which successfully designed for biomarkers, are critically reviewed. The results obviously demonstrated that aptamer have potentially incorporated with targeted delivery systems and biosensors for detection of biomarkers which expressed by cancer cells. Aptamer-based therapeutic and diagnostic methods, as the main field of medical sciences, possess high potential in cancer therapy, pathological angiogenesis and increase of community health. The clinical use of aptamer is so limited due to unreliability resulted from targets impurities, inaccuracy in SELEX stages process, and in vitro synthesis which led to the lower selectivity for in vivo targets. Moreover, size behavior, probable toxicity, low-distribution and unpredictable behavior of nanomaterials in in vivo media make critical their usage in clinical assays. This review can be helpful for implementation and designing the future studies which are effective and applicable for clinical use.

The metastatic disease is a complex and sequential process that involves the migration of tumor cells from the primary site to distant areas. This metastatic pathway is not always predictable. Therefore, this paper presents three rare cases of unusual metastases, due to their primary site: two metastases of a clear cell renal cell carcinoma, one gingival and one nasal, as well as a mandibular metastasis of a hepatocellular carcinoma. In all cases, an incisional biopsy was performed in order to sign out the diagnosis. After microscopical examination of morphological Hematoxylin and Eosin stained slides, for all cases, immunohistochemical reactions were performed to support the primary tumor site. Two cases had a previous histopathological diagnosis of a primary tumor, while for the third case, the metastatic lesion represented the first manifestation of the neoplastic disease, with an unfavorable prognosis.

In order to provide a new direction for the diagnosis and treatment of gastric cancer, the differentially expressed blood-based miRNAs in gastric carcinoma were obtained and analyzed from the datasets GSE113486, GSE112264 and GSE113740 by the SangerBox, the miRNA - target genes prediction, GO and KEGG functional enrichment analyses with DAVID 6.8, and the STRING database with Cytoscape, respectively. The correlation between the key miRNAs expression and the prognosis and diagnostic value of gastric cancer patients were detected by survival prognosis and ROC curve. QRT-PCR was used to detect the possible key miRNAs expression in human blood species. The dataset GSE108307 which shows the miRNAs affected by H. pylori infection was analyzed by GEO2R. The expression level of CagA and the clinically used gastric cancer markers were detected in human blood species for contrast. Five key blood-based miRNAs were determined, including hsa-miR-124-3p, hsa-miR-125a-3p, hsa-miR-29b-3p, hsa-miR-4276, and hsa-miR-575.The detection in human blood species as well as the cross analysis with H. pylori infection and clinically used markers expression suggested the importance and efficacy of the specific miRNAs in the early diagnosis and monitoring of gastric carcinoma. The study identified potential novel blood miRNA biomarkers of gastric cancer by bioinformatics and human blood species detection, which may give a new possibility of the important biomarkers for the diagnosis and prognosis of gastric cancer.

Fever and rash as manifestations of infection by microorganisms are collectively known as febrile exanthem. Viruses are more frequently associated with febrile exanthema, and often these symptoms are thus impetuously termed viral exanthem. However, bacteria represent a frequently overlooked infectious etiology causing rash in humans. In addition, certain microbes may exhibit pathognomonic features that erupt during illness and facilitate clinical diagnosis. Conversely, coinfections often obscure the clinical characteristics of the primary disease and further challenge clinicians attempting to reach a diagnosis. We retrospectively looked at de-identified clinical data of a patient who presented to the Hospital for Tropical Diseases in Bangkok in July 2019 with complaints of fever and rash. The case involved a 35-year-old who presented with a 3-day history of fever, respiratory symptoms, myalgia, conjunctivitis, diarrhea, and a generalized maculopapular rash. On examination, the patient was febrile, tachycardic, and tachypneic, with a mean arterial pressure of 95 mmHg. A differential white blood cell count showed: leukocytes, 5800/µL; neutrophils, 4408/µL; lymphocytes, 406/µL; and platelets, 155,000/µL. Striking findings involving the integumentary system included Koplik’s spots and generalized maculopapular rash. Further serology revealed positive immunoglobulin (Ig)M and IgG for both measles and rubella virus, including reactive serology for Treponema pallidum. Here we describe the clinical course and management of this patient.

Li-ion battery packs are the heart of modern electric vehicles. Due to their perishable nature, it is crucial to supervise them closely. In addition to on-board supervision over safety and range, insights into the battery’s degradation are also becoming increasingly important, not only for the vehicle manufacturers but also for vehicle users. The concept of digital twins has already emerged on the field of automotive technology, and can also help to digitalize the vehicle’s battery. In this work, we set up a data pipeline and digital battery twin to track the battery state, including state of charge (SOC), state of health (Capacity) (SOHc ) and state of health (Resistance) (SOHr ). To achieve this goal, we reverse-engineer the diagnostics interface of a 2014 e-Golf to query for Unified Diagnostic Services (UDS) messages containing both battery pack and cell-individual data. An on-board diagnosis (OBD) logger records the data with edge-processing capability. Pushing this data into the cloud twin system using IoT-technology, we can fit battery models to the data and infer cell individual internal resistance from them. We find that the resistances of the cells differ by a magnitude of two. Furthermore, we propose an architecture for the battery twin in which the twin fleet shares resources like models by encapsulating them in Docker containers run on a cloud stack. By using web technology, we present the analyzed results on a web interface.

We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for prediction of the longitudinal phase space along the linac, spectral reconstruction of the bunch profile and non-destructive inference of transverse beam quality (emittance) using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded in to adaptive feedbacks and ML-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results based on simulation data and discuss progress towards implementation in regular operations.

Endometrial Cancer (EC) is the sixth most commonly occurring cancer in women with 380 000 cases in 2018. Sadly, EC morbidity and mortality are continuously increasing, therefore the medical society have a substantial need for an accurate and inexpensive diagnostic test for EC early detection and a prognostic tool for treatment planning and evaluation. Considering experience with different types of cancers C-reactive protein (CRP) appears to be a promising diagnostic and prognostic factor. Aiming to investigate its potential and in view of EC authors, this paper reviewed the following databases for metanalysis, randomized controlled trials and review articles published up to June 2020: Pubmed, Scopus, Google scholar and ClinicalKey. Studies indicate CRP >3.33 mg/l correlate with EC incidence with HR = 2.29 (p<0.05). Moreover, High-sensitivity CRP assay allows to detect CRP in very low concentrations and distinguish patients with endometriosis, soft tissue sarcomas and possibly EC. Preoperational and postoperational CRP, as well as its dynamic change are independent prognostic factors for EC and are more reliable if analyzed together. However, CRP-to-albumin ratio as well as Glasgow Prognostic Scale have greater prognostic value that CRP alone. Additionally, CRP is possibly a mediator of carcinogenesis and cancer progression through activation of inter alia FcgRs/MAPK/ERK, FcgRs/IL-6/AKT/STAT3 and FcgRs/NF-κB/NLRP3 pathways.

(1) Background Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. Screening for DR is important but many patients do not attend; (2) Methods: We validated an algorithm for predicting DR and its progression by conducting a retrospective study of 11 children in a population of 120,389 T2DM (3) Results: By applying the algorithm to the population showed an AUC of 0.93 (95% CI, 0.92–0.94) for any-DR and 0.90 (95% CI, 0.89–0.91) for referable-DR(4) Conclusions: The algorithm is useful for predicting patients who could develop referable forms of DR and also for any-DR. This would allow a personalized screening plan to be drawn up for each patient.

We wanted to investigate the current knowledge on the impact of diet on anti-TNF response in inflammatory bowel diseases (IBD), to identify dietary factors that warrant further investigations in relation to anti-TNF treatment response, and, finally, to discuss potential strategies for such investigations. PubMed was searched using specified search terms. One small prospective study on diet and anti-TNF treatment in 56 patients with CD found similar remission rates after 56 weeks among 32 patients with good compliance that received concomitant enteral nutrition and 24 with poor compliance that had no dietary restrictions (78% versus 67%, p = 0.51). A meta-analysis of 295 patients found higher odds of achieving clinical remission and remaining in clinical remission among patients on combination therapy with specialised enteral nutrition and Infliximab (IFX) compared with IFX monotherapy (OR 2.73; 95% CI: 1.73–4.31, p < 0.01, OR 2.93; 95% CI: 1.66–5.17, p < 0.01, respectively). In conclusion, evidence-based knowledge on impact of diet on anti-TNF treatment response for clinical use is scarce. Here we propose a mechanism by which Western style diet high in meat and low in fibre may promote colonic inflammation and potentially impact treatment response to anti-TNF drugs. Further studies using hypothesis-driven and data-driven strategies in observational, animal and interventional studies are warranted.

In reservoir operations rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This paper proposes an approach to optimize the reservoir operation rule curves (RORC) using intelligent optimization techniques from the Firefly Algorithm (FA) and a unique combination method utilizing the Artificial Bees Colony and Cultural Algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORC for flood control. The proposed model was used to determine the Nam-Oon Reservoir (NOR) is optimal flood control RORC in Northeastern Thailand. The minimum frequency and a minimum average of ex-cess water were given as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORC involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data syn-thesized from 1000 events. The results demonstrate that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, situations of excess water were smaller than those using the current RORC. The results indicated that situations of excess water using the obtained RORC from the proposed model were smaller than those using the current RORC situations. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

Community-acquired pneumonia (CAP) is among the most common causes of death and one of the leading healthcare concerns worldwide. It can evolve into sepsis and septic shock, which have a high mortality rate, especially in critical patients and comorbidities. The definitions of sepsis were revised in the last decade as “life-threatening organ dysfunction caused by a dysregulated host response to infection”. Procalcitonin (PCT), C-reactive protein (CRP), and complete blood count including white blood cells are among the most commonly analyzed sepsis-specific biomarkers also used in pneumonia in a broad range of studies. It appears to be a reliable diagnostic tool to expedite care of these patients with severe infections in the acute setting. PCT was found superior to most other acute phase reactants and indicators, including CRP as a predictor of pneumonia, bacteremia, sepsis and poor outcome. In addition, PCT use is beneficial to judge timing for cessation of antibiotic treatment in most severe infectious states. The clinicians should be aware of strengts and weaknesses of known and potential biomarkers in expedient recognition and management of severe infections. This manuscript is intended to present an overview of the definitions, complications and outcomes of CAP and sepsis, with special regard to PCT and other important markers.

Maximum likelihood estimation (MLE) is a popular method for parameter estimation in both applied probability and statistics but MLE cannot solve the problem of incomplete data or hidden data because it is impossible to maximize likelihood function from hidden data. Expectation maximum (EM) algorithm is a powerful mathematical tool for solving this problem if there is a relationship between hidden data and observed data. Such hinting relationship is specified by a mapping from hidden data to observed data or by a joint probability between hidden data and observed data. In other words, the relationship helps us know hidden data by surveying observed data. The essential ideology of EM is to maximize the expectation of likelihood function over observed data based on the hinting relationship instead of maximizing directly the likelihood function of hidden data. Pioneers in EM algorithm proved its convergence. As a result, EM algorithm produces parameter estimators as well as MLE does. This tutorial aims to provide explanations of EM algorithm in order to help researchers comprehend it. Moreover some improvements of EM algorithm are also proposed in the tutorial such as combination of EM and third-order convergence Newton-Raphson process, combination of EM and gradient descent method, and combination of EM and particle swarm optimization (PSO) algorithm.

MAACS is a software architecture which layers multiple standard cipher algorithms together to increase the overall strength of the encrypted data stream, without modifying the standard algorithms, which have been heavily vetted from a cryptanalysis perspective and are already widely deployed. This layering technique makes the combination of algorithms significantly more resistant to traditional cryptanalysis attacks, while also addressing increased concerns over future quantum computing attacks against these algorithms. For example, concerns over published attacks against 256-bit AES and potential future attacks emanating from Grover’s algorithm and other unknowns in the quantum computing era are explored. Several approaches for evolving AES are considered. MAACS uses a concept termed "aperiodic modulation", whereby of a set of cryptographic algorithms are modified by an- other, independent software algorithm in an unpredictable, pseudo-random manner. In the MAACS reference architecture, a 256-bit ChaCha20 cryptographically secure pseudorandom number generator (CSPRNG) drives an aperiodic modulator that orchestrates encryption/decryption processes multiplexed across eight parallel 128-bit AES instances, yielding a cryptosystem with a composite strength of 1,376 bits or more in larger configurations.

The chicken poultry business is becoming a crucial sector for our nation's sustainable food supply. To increase the health of chickens in coops and maybe increase the output of chicken meat and eggs, several innovations and inventions have been developed. Therefore, the creation of an automated chicken feeding device might be highly beneficial for the expansion of the poultry sector. This equipment may take the role of a worker in the chicken feeding process, solving the industry's labor shortage and introducing a semi-automatic procedure. The main processor of the automated chicken feeding system is a Raspberry Pi 3 Model B+. The machine consists of five main components: a Raspberry Pi with an amplifier that makes noise to draw the attention of free-range chickens, a Raspberry Pi with a pi camera that counts and detects the chickens, a Raspberry Pi with a servomotor that moves the food from storage to a container, a Raspberry Pi with a load cell that controls the number of feeds to avoid waste on foods, and a Raspberry Pi with a temperature sensor that monitors the food's freshness. This enhances the coop's atmosphere while lowering labor costs, conserving food, and ensuring that the chickens are fed on time. It also regulates the temperature sensor to ensure that the chicken food is fresh and prevents food from becoming polluted by insects and chicken excrement.

We present a new method of estimating fitness of countries and complexity of products by exploiting a non-linear non-homogeneous map applied to the publicly available information on the goods exported by a country. The non homogeneous terms guarantee both convergence and stability. After a suitable rescaling of the relevant quantities, the non homogeneous terms are eventually set to zero so that this new method is parameter free. This new map reproduces the findings of the method proposed by Tacchella et al. [1], and allows for an approximate analytic solution in case of actual binarized matrices based on the Revealed Comparative Advantage (RCA) indicator. This solution is connected with a new quantity describing the neighborhood of nodes in bipartite graphs, representing in this work the relations between countries and exported products. Moreover, we define the new indicator of country net-efficiency quantifying how a country efficiently invests in capabilities able to generate innovative complex high quality products. Eventually, we demonstrate analytically the local convergence of the algorithm.

Goals: To use visual logistics for interpreting COVID-19 molecular and rapid antigen test (RAgT) performance, determine prevalence boundaries where risk exceeds expectations, and evaluate benefits of recursive testing along home, community, and emergency spatial care paths. Methods: Mathematica/open access software helped graph relationships, compare performance patterns, and perform recursive computations. Results: Tiered sensitivity/specificity comprise: T1) 90%/95%; T2) 95%/97.5%; and T3) 100%/≥99%, respectively. In emergency medicine, median RAgT performance peaks at 13.2% prevalence, then falls below T1, generating risky prevalence boundaries. RAgTs in pediatric ERs/EDs parallel this pattern with asymptomatic worse than symptomatic performance. In communities, RAgTs display large uncertainty with median prevalence boundary of 14.8% for 1/20 missed diagnoses, and at prevalence >33.3-36.9% risk 10% false omissions for symptomatic subjects. Recursive testing improves home RAgT performance. Home molecular tests elevate performance above T1, but lack adequate validation. Conclusions: Widespread RAgT availability encourages self-testing. Asymptomatic RAgT and PCR-based saliva testing present the highest chance of missed diagnoses. Home testing twice, once just before mingling, and molecular-based self-testing help avoid false omissions. Community and ER/ED RAgTs can identify contagiousness in low prevalence (<22%). Real-world trials of performance, cost-effectiveness, and public health impact could identify home molecular diagnostics as the optimal diagnostic portal.

Background: Left ventricular hypertrophy (LVH) may be due to different causes, ranging from benign secondary forms to severe cardiomyopathies. Transthoracic Echocardiography (TTE) and ECG are the first level examination for LVH diagnosis. Cardiac magnetic resonance (CMR) defines accurately LVH type, extent and severity. Objectives: to evaluate the diagnostic and prognostic role of CMR in patients with TTE and/or ECG evidence of LVH. Methods: We performed CMR in 300 consecutive patients with echocardiographic and/or ECG signs of LVH. Results: Overall, 275 patients had TTE evidence of LVH with initial suspicion of hypertrophic cardiomyopathy (HCM) in 132 (44%), cardiac amyloidosis in 41 (14%), hypertensive LVH in 48 (16%), aortic stenosis in 4 (1%), undetermined LVH in 50(16%). The initial echocardiographic diagnostic suspicion of LVH was confirmed in 172 patients (57.3%) and changed in 128 patients (42.7%, p<0.0001): the diagnosis of HCM increased from 44% to 71% of patients; hypertensive and undetermined LVH decreased significantly (respectively to 4% and 5%). CMR allowed a diagnosis in 41 out of 50 (82%) with undetermined LVH at TTE. CMR also identified HCM in 17 out of 25 patients with apparently normal echo but with ECG criteria for LVH. Finally, the reclassification of the diagnosis by CMR was associated with a change of survival risk of patients: after CMR reclassification no events occurred in patients with undetermined or hypertensive LVH. Conclusions: CMR changed echocardiographic suspicion in almost half of patients with LVH. In the subgroup of patient with abnormal ECG, CMR identified LVH (particularly HCM) in 80% of patients. This study highlights the indication of CMR to better characterize the type, extent and severity of LVH detected at echocardiography and suspected with ECG.

Background: Cervical myelopathy (CM) is a common cause of morbidity and disability in patients with mucopolysaccharidosis (MPS) and, therefore, early detection is crucial for best surgical intervention and follow-up. Transcranial Magnetic Stimulation (TMS) non-invasively evaluates the conductivity along the cortico-spinal tract, also allowing preclinical diagnosis and monitoring. Methods: motor evoked potentials (MEPs) to TMS were recorded in a group of 8 patients with MPS-related CM. Responses were obtained during mild tonic contraction through a circular coil applied over the “hot spot” of the first dorsal interosseous and tibialis anterior muscles, bilaterally. Central motor conduction time was estimated as the difference between MEP cortical latency and the peripheral motor conduction time by cervical or lumbar magnetic stimulation. Peak-to-peak MEP amplitude to cortical stimulation and right-to-left difference of each parameter were also measured. Results: TMS revealed abnormal findings from both upper and lower limbs compatible with axonal damage and demyelination in 6 of them. Notably, a subclinical cervical spinal disease was detected before the occurrence of an overt CM in two patients, whereas TMS signs compatible with a CM of variable degree persisted despite surgery in all treated subjects. Conclusions: TMS screening should be performed in MPS patients, before and after surgery.

The high prevalence of thyroid nodules and increased availability of neck ultrasound have led to an increased incidence of diagnostic thyroid fine needle aspirations, with approximately 20% yielding indeterminate results. The recent availability of molecular tests has helped guide the clinical management of these cases. This paper aims to review and compare three main commercially available molecular cytology platforms in the U.S. – Afirma GSC, Thyroseq GC, and ThyGeNEXT+ThyraMIR. Sequential improvements of the Afirma GSC and Thyroseq GC tests have led to higher positive and negative predictive values, sensitivity, and specificity. Comparative studies revealed similar diagnostic performance between these tests, with considerations for factors such as cost and processing time. Thyroseq GC provides detailed genomic information and specific management recommendations. ThyGeNEXT+ThyraMIR, though less studied, presents promising results, particularly in miRNA analysis for weak driver mutations. Challenges in interpreting results include variations in reporting and the evolving nature of testing platforms. Questions persist regarding cost-effectiveness and the utility of ultrasound characteristics in selecting candidates for molecular testing. While molecular testing has primarily served diagnostic purposes, advancements in understanding genetic alterations now offer therapeutic implications. FDA-approved options target specific genetic alterations, signaling a promising future for tailored treatments.