Search Results (2,885)

Background. The effect of statins on CIMT progression and C-reactive protein (CRP) in T2DM patients is widely reported. However, some studies demonstrated no effect of statins on CIMT and CRP in T2DM patients, while others reported otherwise. Thus, the current study comprehensively and quantitatively analyzes data from previous studies to evaluate the overall effect of statins on CIMT and CRP in T2DM to rule out any inconsistencies observed in previous clinical evidence. Therefore, the aim of this meta-oanalysis was to evaluate the effect of statins on CIMT progression and CRP in T2DM. Methods. A comprehensive search for studies was performed using PubMed, Scopus, Web of Sciences, and the Cochrane Library, for publications from their inception to 16 July 2024. The meta-analysis was conducted using Jamovi (version 4.2.8) and Review Manager (version 5.4), with the overall effect sizes reported as standardized mean differences (SMD) and 95% confidence intervals (CI). Results. Evidence from eleven studies (fifteen statin dosages) that met the inclusion criteria with a sample size of 983 T2DM patients on statin treatment was analyzed. The overall effect size from the random effect model meta-analysis showed a reduction in the CIMT status amongst T2DM patients post-statin treatment compared to at baseline [SMD = −0.47, 95%CI (−0.76, −0.18), p = 0.001]. Furthermore, there was a reduction in the level of CRP in T2DM patients post-treatment [SMD = −1.80, 95% CI (−2.76, −0.84), p < 0.001]. Conclusions. Evidence gathered in this study suggests that statin therapy effectively reduces CIMT and CRP levels among patients living with T2DM. Interestingly, this evidence suggests that 20 mg of atorvastatin is more effective in reducing CIMT and CRP. Therefore, we recommend conducting further trials with larger sample sizes and proper methodology for T2DM. Full article

Antimicrobial resistance is increasingly concerning, causing millions of deaths and a high cost burden. Given that carbapenemase-producing Enterobacterales are particularly concerning due to their ability to develop structural modifications and produce antibiotic-degrading enzymes, leading to high resistance levels, we sought to summarize the available data on the efficacy and safety regarding the combination of meropenem-vaborbactam (MV) versus the best available therapy (BAT). Articles related to our objective were searched in the PubMed and Scopus databases inception to July 2024. To assess the quality of the studies, we used the Cochrane risk-of-bias tool, RoB2. The outcomes were pooled as a risk ratio (RR) and a 95% confidence interval (95%CI). A total of four published studies were involved: one retrospective cohort study and three phase 3 trials, including 432 patients treated with MV and 426 patients treated with BAT (mono/combination therapy with polymyxins, carbapenems, aminoglycosides, colistin, and tigecycline; or ceftazidime-avibactam; or piperacillin-tazobactam). No significant difference in the clinical response rate was observed between MV and the comparators at the TOC (RR = 1.29, 95%CI [0.92, 1.80], p = 0.14) and EOT (RR = 1.66, 95%CI [0.58, 4.76], p = 0.34) visits. MV was associated with a similar microbiological response as the comparators at TOC (RR = 1.63, 95%CI [0.85, 3.11], p = 0.14) and EOT assessment (RR = 1.16, 95%CI [0.88, 1.54], p = 0.14). In the pooled analysis of the four studies, 28-day all-cause mortality was lower for MV than the control groups (RR = 0.47, 95%CI [0.24, 0.92], p = 0.03). MV was associated with a similar risk of adverse events (AEs) as comparators (RR = 0.79, 95%CI [0.53, 1.17], p = 0.23). Additionally, MV was associated with fewer renal-related AEs than the comparators (RR = 0.32, 95%CI [0.15, 0.66], p = 0.002). MV was associated with a similar risk of treatment discontinuation due to AEs (RR = 0.76, 95%CI [0.38, 1.49], p = 0.42) or drug-related AEs (RR = 0.56, 95%CI [0.28, 1.10], p = 0.09) as the comparators. In conclusion, MV presents a promising therapeutic option for treating CRE infections, demonstrating similar clinical and microbiological responses as other comparators, with potential advantages in mortality outcomes and renal-related AEs. Full article

Heart disease continues to be one of the most fatal conditions worldwide. This is in part due to the maladaptive remodeling process by which ischemic cardiac tissue is replaced with a fibrotic scar. Direct cardiac reprogramming presents a unique solution for restoring injured cardiac tissue through the direct conversion of fibroblasts into induced cardiomyocytes, bypassing the transition through a pluripotent state. Since its inception in 2010, direct cardiac reprogramming using the transcription factors Gata4, Mef2c, and Tbx5 has revolutionized the field of cardiac regenerative medicine. Just over a decade later, the field has rapidly evolved through the expansion of identified molecular and genetic factors that can be used to optimize reprogramming efficiency. The integration of computational tools into the study of direct cardiac reprogramming has been critical to this progress. Advancements in transcriptomics, epigenetics, proteomics, genome editing, and machine learning have not only enhanced our understanding of the underlying mechanisms driving this cell fate transition, but have also driven innovations that push direct cardiac reprogramming closer to clinical application. This review article explores how these computational advancements have impacted and continue to shape the field of direct cardiac reprogramming. Full article

Since its inception, the intricate mathematical formalism of quantum mechanics has empowered physicists to describe and predict specific physical events known as quantum processes. However, this success in probabilistic predictions has been accompanied by a profound challenge in the ontological interpretation of the theory. This interpretative complexity stems from two key aspects. Firstly, quantum mechanics is a fundamental theory that, so far, is not derivable from any more basic scientific theory. Secondly, it delves into a realm of invisible phenomena that often contradicts our intuitive and commonsensical notions of matter and causality. Despite its notorious difficulties of interpretation, the most widely accepted set of views of quantum phenomena has been known as the Copenhagen interpretation since the beginning of quantum mechanics. According to these views, the correct ontological interpretation of quantum mechanics is incompatible with ontological realism in general and with philosophical materialism in particular. Anti-realist and anti-materialist interpretations of quantum matter have survived until today. This paper discusses these perspectives, arguing that materialistic interpretations of quantum mechanics are compatible with its mathematical formalism, while anti-realist and anti-materialist views are based on wrong philosophical assumptions. However, although physicalism provides a better explanation for quantum phenomena than idealism, its downward reductionism prevents it from accounting for more complex forms of matter, such as biological or sociocultural systems. Thus, the paper argues that neither physicalism nor idealism can explain the universe. I propose then a non-reductionistic form of materialism called inclusive materialism. The conclusion is that the acknowledgment of the qualitative irreducibility of ontological emergent levels above the purely physical one does not deny philosophical materialism but enriches it. Full article

Borehole hydraulic fracturing in coal seams can prevent dynamic coal mine disasters and promote the sustainability of the mining industry, and microseismic signal recognition is a prerequisite and foundation for microseismic monitoring technology that evaluates the effectiveness of hydraulic fracturing. This study constructed ultra-lightweight CNN models specifically designed to identify microseismic waveforms induced by borehole hydraulic fracturing in coal seams, namely Ul-Inception28, Ul-ResNet12, Ul-MobileNet17, and Ul-TripleConv8. The three best-performing models were selected to create both a probability averaging ensemble CNN model and a voting ensemble CNN model. Additionally, an automatic threshold adjustment strategy for CNN identification was introduced. The relationships between feature map entropy, training data volume, and model performance were also analyzed. The results indicated that our in-house models surpassed the performance of the InceptionV3, ResNet50, and MobileNetV3 models from the TensorFlow Keras library. Notably, the voting ensemble CNN model achieved an improvement of at least 0.0452 in the F1 score compared to individual models. The automatic threshold adjustment strategy enhanced the identification threshold’s precision to 26 decimal places. However, a continuous zero-entropy value in the feature maps of various channels was found to detract from the model’s generalization performance. Moreover, the expanded training dataset, derived from thousands of waveforms, proved more compatible with CNN models comprising hundreds of thousands of parameters. The findings of this research significantly contribute to the prevention of dynamic coal mine disasters, potentially reducing casualties, economic losses, and promoting the sustainable progress of the coal mining industry. Full article

When uncrewed agricultural machinery performs autonomous operations in the field, it inevitably encounters obstacles such as persons, livestock, poles, and stones. Therefore, accurate recognition of obstacles in the field environment is an essential function. To ensure the safety and enhance the operational efficiency of autonomous farming equipment, this study proposes an improved YOLOv8-based field obstacle detection model, leveraging depth information obtained from binocular cameras for precise obstacle localization. The improved model incorporates the Large Separable Kernel Attention (LSKA) module to enhance the extraction of field obstacle features. Additionally, the use of a Poly Kernel Inception (PKI) Block reduces model size while improving obstacle detection across various scales. An auxiliary detection head is also added to improve accuracy. Combining the improved model with binocular cameras allows for the detection of obstacles and their three-dimensional coordinates. Experimental results demonstrate that the improved model achieves a mean average precision (mAP) of 91.8%, representing a 3.4% improvement over the original model, while reducing floating-point operations to 7.9 G (Giga). The improved model exhibits significant advantages compared to other algorithms. In localization accuracy tests, the maximum average error and relative error in the 2–10 m range for the distance between the camera and five types of obstacles were 0.16 m and 2.26%. These findings confirm that the designed model meets the requirements for obstacle detection and localization in field environments. Full article

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases. Full article

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice. Full article

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis. Full article

Rheumatoid arthritis (RA) is an autoimmune disease primarily affecting the joints and requires various treatments, including medication, injection, and physiotherapy. Wu tou decoction (WTD) is a traditional Chinese medicine prescribed for RA, with several articles documenting its effectiveness in RA treatment. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of WTD for RA. We searched for randomized controlled trials (RCTs) comparing WTD with conventional treatments (including medication, injection, and physiotherapy) from its inception to May 2024. Primary outcomes were disease activity scores, including effective rate, tender joint count, and morning stiffness. Secondary outcomes comprised blood test results (erythrocyte sedimentation rate, C-reactive protein, and rheumatoid factor) and adverse events. Nineteen RCTs involving 1794 patients were included. Statistically, WTD demonstrated better improvement than conventional treatments (18 medications and 1 injection) across the effective rate, joint scale, and blood tests, regardless of the treatment type (monotherapy or combination therapy). Adverse events were reported in 11 studies, with no statistical differences observed between them. The numerical results showed that WTD may offer potential benefits for managing RA. However, the significant discrepancy between clinical practice and the low quality of the RCTs remains a limitation. Therefore, further well-designed studies with larger patient cohorts are needed to draw definitive conclusions. Full article

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. Full article

Background/Objectives: A comprehensive and up-to-date review on cardiovascular disease (CVD) risk in patients with COPD is needed. Therefore, we aimed to systematically review the risk of a range of CVD in patients with COPD. Methods: We searched three databases (Pubmed, Web of Science, SCOPUS) from inception to September 2023 using terms related to COPD and CVD. Observational studies were included if they (1) were conducted in adults with a diagnosis of COPD based on the GOLD criteria, spirometry, physician diagnosis, or review of electronic health records; (2) reported the risk of CVD, namely of myocardial infarction (MI), ischaemic heart disease (IHD), atrial fibrillation (AF), heart failure, cerebrovascular disease, pulmonary hypertension, and peripheral vascular disease, compared with a control population using a measure of risk. A narrative synthesis was used. Results: Twenty-four studies from 2015 to 2023, mainly from Europe (n = 17), were included. A total of 3,485,392 patients with COPD (43.5–76.0% male; 63.9–73.5 yrs) and 31,480,333 (40.0–55.4% male, 49.3–70.0 yrs) controls were included. A higher risk of CVD in patients with COPD was evident regarding overall CVD, MI, IHD, heart failure, and angina. Higher risks of arrhythmia and AF, stroke, sudden cardiac death/arrest, pulmonary embolism, pulmonary hypertension, and peripheral vascular disease were also found, although based on a small amount of evidence. Conclusions: Patients with COPD have a higher risk of CVD than the general population or matched controls. This review underscores the need for vigilant and close monitoring of cardiovascular risk in individuals with COPD to inform more precise preventive strategies and targeted interventions to enhance their overall management. Full article

The COVID-19 pandemic brought into prominence several emergent medical and surgical entities, but, also, it served as trigger and contributor for numerous apparently unrelated ailments such as arterial and venous thromboembolic complications. Additional risk factors for these thrombotic traits may be concurrent (known or unknown) malignancies, including at hepatic level. Among these, cholangiocarcinoma (CCA), a rare cancer of intra- and extra-hepatic biliary ducts, represents a very aggressive condition that typically associates local and distant advanced stages on first presentation requiring a prompt diagnosis and a stratified management. This neoplasia has been reported to present a large spectrum of paraneoplastic syndromes in terms of dermatologic, renal, systemic, neurologic, endocrine, and cardiovascular settings, that, overall, are exceptional in their epidemiologic impact when compared to other cancers. Our aim was to introduce a most unusual case of CCA-associated distant thrombosis in a male adult who initially was considered to experience COVID-19-related thrombotic features while having a history of obesity and bariatric surgery. This is a hybrid type of paper: this clinical vignette is accompanied by two distinct sample-focused analyses as a basis for discussion; they each had different methods depending on their current level of statistical evidence. We only included English-published articles in PubMed, as follows: Firstly, we conducted a search of reports similar to the present case, regarding distant vein thrombosis in CCA, from inception until the present time. We performed a literature search using the keywords “cholangiocarcinoma”, “thrombosis”, and “Trousseau’s syndrome” and identified 20 cases across 19 original papers; hence, the current level of evidence remains very low Secondly, we searched for the highest level of statistical evidence concerning the diagnosis of venous thrombosis/thromboembolism in patients who underwent COVID-19 infection (key search terms were “COVID-19”, alternatively, “coronavirus”, and “SARS-CoV-2”, and “thrombosis”, alternatively, “thromboembolism”) and included the most recent systematic reviews and meta-analyses that were published in 2024 (from 1 January 2024 until 8 July 2024). After excluding data on vaccination against coronavirus or long COVID-19 syndrome, we identified six such articles. To conclude, we presented a probably unique case of malignancy with an initial manifestation consisting of recurrent superficial vein thrombosis under anticoagulation therapy, with no gastrointestinal manifestations, in a patient with a notable history for multiple episodes of SARS-CoV-2 infection and a prior endocrine (gastric) surgery. To our knowledge, this is the first identification of a CCA under these specific circumstances. Full article

Alzheimer’s Disease, a progressive brain disorder that impairs memory, thinking, and behavior, has started to benefit from advancements in deep learning. However, the application of deep learning in medicine faces the challenge of limited data resources for training models. Transfer learning offers a solution by leveraging pre-trained models from similar tasks, reducing the data and computational requirements to achieve high performance. Additionally, data augmentation techniques, such as rotation and scaling, help increase the dataset size. In this study, we worked with magnetic resonance imaging (MRI) datasets and applied various pre-processing and augmentation techniques including include intensity normalization, affine registration, skull stripping, entropy-based slicing, flipping, zooming, shifting, and rotating to clean and expand the dataset. We applied transfer learning to high-performing pre-trained models—ResNet-50, DenseNet-201, Xception, EfficientNetB0, and Inception V3, originally trained on ImageNet. We fine-tuned these models using the feature extraction technique on augmented data. Furthermore, we implemented ensemble learning techniques, such as stacking and boosting, to enhance the final prediction performance. The novel methodology we applied achieved high precision (95%), recall (94%), F1 score (95%), and accuracy (95%) for Alzheimer’s disease detection. Overall, this study establishes a robust framework for applying machine learning to diagnose Alzheimer’s using MRI scans. The combination of transfer learning, via pre-trained neural networks fine-tuned on a processed and augmented dataset, with ensemble learning, has proven highly effective, marking a significant advancement in medical diagnostics. Full article

This paper presents an innovative approach that utilizes infused images from vibration signals and visual inspections to enhance the efficiency and accuracy of structure health monitoring through GoogLeNet. Scrutiny of the structure of GoogLeNet identified four key parameters, and thus, the optimization of GoogLeNet was completed through manipulation of the four key parameters. First, the impact of the number of inception modules on the performance of GoogLeNet revealed that employing eight inception layers achieves remarkable 100% accuracy while requiring less computational time compared to nine layers. Second, the choice of activation function was studied, with the Rectified Linear Unit (ReLU) emerging as the most effective option. Types of optimizers were then researched, which identified Stochastic Gradient Descent with Momentum (SGDM) as the most efficient optimizer. Finally, the influence of learning rate was compared, which found that a rate of 0.001 produces the best outcomes. By amalgamating these findings, a comprehensive optimized GoogLeNet model was found to identify damage cases effectively and accurately through infused images from vibrations and visual inspections. Full article