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Soldiers rely heavily on their vision, which is crucial not only for daily activities but also for the effective operation of defense systems, weaponry, and other military applications. However, various eye disorders, such as those related to increased intra-ocular pressure, can lead to irreversible vision loss, severely impacting a soldier's operational capabilities. While extensive research has been conducted on detecting such ocular conditions, there remains a critical need for more accurate diagnostic methods to ensure early detection and treatment. In this study, we propose a novel approach combining Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) for enhanced detection of eye disorders from retinal fundus images. The proposed method includes a comprehensive preprocessing phase, followed by decomposition using EMD and VMD techniques. The decomposed images undergo feature extraction through feature combination, with subsequent normalization and selection using z-score and the Relief method, respectively. Classification is performed using Support Vector Machines (SVM) with various kernels, including cubic, Gaussian, linear, and quadratic. The results demonstrate that the proposed method achieves high accuracy, with SVM kernel functions yielding accuracies of 98.30 %, 96.59 %, 96.59 %, and 97.87 % for 10-fold cross-validation, respectively. Additionally, the evaluation metrics, including sensitivity and specificity, indicate superior performance compared to state-of-the-art methods for similar datasets. This advanced diagnostic approach offers significant improvements in detecting eye disorders, which could be crucial in defense applications. Early and accurate diagnosis by military ophthalmologists can lead to better decision-making and timely interventions, ultimately preserving the vision and effectiveness of soldiers in the war. [ABSTRACT FROM AUTHOR]

Motivation Thousands of genomes are publicly available, however, most genes in those genomes have poorly defined functions. This is partly due to a gap between previously published, experimentally characterized protein activities and activities deposited in databases. This activity deposition is bottlenecked by the time-consuming biocuration process. The emergence of large language models presents an opportunity to speed up the text-mining of protein activities for biocuration. Results We developed FuncFetch—a workflow that integrates NCBI E-Utilities, OpenAI's GPT-4, and Zotero—to screen thousands of manuscripts and extract enzyme activities. Extensive validation revealed high precision and recall of GPT-4 in determining whether the abstract of a given paper indicates the presence of a characterized enzyme activity in that paper. Provided the manuscript, FuncFetch extracted data such as species information, enzyme names, sequence identifiers, substrates, and products, which were subjected to extensive quality analyses. Comparison of this workflow against a manually curated dataset of BAHD acyltransferase activities demonstrated a precision/recall of 0.86/0.64 in extracting substrates. We further deployed FuncFetch on nine large plant enzyme families. Screening 26 543 papers, FuncFetch retrieved 32 605 entries from 5459 selected papers. We also identified multiple extraction errors including incorrect associations, nontarget enzymes, and hallucinations, which highlight the need for further manual curation. The BAHD activities were verified, resulting in a comprehensive functional fingerprint of this family and revealing that ∼70% of the experimentally characterized enzymes are uncurated in the public domain. FuncFetch represents an advance in biocuration and lays the groundwork for predicting the functions of uncharacterized enzymes. Availability and implementation Code and minimally curated activities are available at: https://github.com/moghelab/funcfetch and https://tools.moghelab.org/funczymedb. [ABSTRACT FROM AUTHOR]

Cardiovascular disease causes more deaths than any other cause in the globe. The present method of illness identification involves electrocardiogram (ECG) analysis, a medical monitoring gadget that captures heart activity. Regrettably, a great deal of medical resources is required to locate specialists in ECG data. Consequently, ML feature detection in ECG is rapidly gaining popularity. Human intervention is required for "feature recognition, complex models, and lengthy training timeframes"--limitations that are inherent to these traditional approaches. Using the "MIT-BIH Arrhythmia" database, this study presents five distinct categories of heartbeats and the efficient and effective deep-learning (DL) classification algorithms that go along with them. The five types of pulse features are classified experimentally using the wavelet self-adaptive threshold denoising method. Models such as AlexNet and CNN are employed in this dataset. For model evaluation use some performance metrics, like recall, accuracy, precision, and f1-score. The suggested Alex Net model achieves an overall classification accuracy of 99.68%, while the recommended CNN model achieves an accuracy of 99.89%. The end findings demonstrate that the suggested models outperform the current model on several performance criteria and are more efficient overall. With its accurate categorization, important medical resources are better preserved, which has a positive effect on the practice of medicine. [ABSTRACT FROM AUTHOR]

Porphyrins and porphyrin precursors are normally detected in small amounts in healthy individuals but are found in large quantities in the urine, feces, blood, plasma, bone marrow, and liver in patients with various types of porphyrias. These are intermediates, or are derived from intermediates, in the pathway for heme biosynthesis. Heme is synthesized in all body tissues but in the largest amounts in the bone marrow and liver. Accurately measuring these compounds is important for diagnosis and monitoring of porphyrias. In addition, measurement of enzyme activities and mutation analyses by DNA sequencing enables confirmation of a porphyria diagnosis and genetic counseling. Biochemical approaches described here include measurements of porphyrin precursors and porphyrins in the urine, feces, plasma, erythrocytes, and liver, and determination of specific enzyme activities in erythrocytes and other cells. © 2025 Wiley Periodicals LLC., (© 2025 Wiley Periodicals LLC.)