Open Fractures – Time from Trauma to Efficient Surgical Debridement Is the Key Factor for Post-Traumatic Infection by Olivera Lupescu, Mihail Nagea, Alexandru Lisias Dimitriu, Nicolae Marian Ciurea, Alina Grosu, Gheorghe Ion Popescu

“…That is why medical research is directed to establishing the most effi cient diagnostic and therapeutic algorithms able to decrease the incidence of septic complications and promote optimal fracture healing. …”
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Tumor mutational burden and survival on immune checkpoint inhibition in >8000 patients across 24 cancer types by Vivek Subbiah, Samuel J Klempner, Neeraj Agarwal, David P Carbone, Amit Mahipal, Shilpa Gupta, David Fabrizio, Jeffrey S Ross, Gerald Li, David R Gandara, Miles C Andrews, Jonathan W Riess, Ramez N Eskander, Ryon P Graf, Geoffrey R Oxnard, Sarah Sammons, Jeremy Snider, Lilia Bouzit, Cheryl Cho-Phan, Megan Price, Julia C F Quintanilha, Richard Sheng Poe Huang

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Using actigraphy to assess sleep characteristics by G. A. Trusov, A. V. Korobeinikova, L. V. Getmantseva, S. Yu. Bakoev, A. N. Lomov, A. A. Keskinov, V. S. Yudin

“…The importance of accurate methodology and validated algorithms for  actigraphy data analysis is  emphasized through a  review of various sleep assessment algorithms and their application in clinical and research settings. …”
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TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data by Qingrong Shen, Xiaoqian Fan, Yangyang Sun, Hao Gao, Xiaoquan Su

“…Conclusions Therefore, TaxaCal offers a cost-effective solution for generating high-resolution microbiome species profiles that closely align with WGS results, enhancing the utility of 16S-based profiling in microbiome research. As microbiome-based diagnostics continue to evolve, TaxaCal has the potential to be a crucial tool in advancing the utility of 16S sequencing in clinical and research settings.…”
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Management of chronic subdural hematoma in spontaneous intracranial hypotension by Manou Overstijns, Amir El Rahal, Katharina Wolf, Niklas Lützen, Urs Würtemberger, Lucas Becker, Horst Urbach, Daniel Casanova Martinez, Jürgen Beck, Florian Volz

“…Research question: This study analyses characteristics of cSDH in SIH to establish a practicable management algorithm. …”
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Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare by Keyi Tang, Shuyuan Ma, Xiaohui Sun, Dongfang Guo

“…This study explores the use of machine learning and deep learning algorithms, including Support Vector Classifier (SVC), RandomForest, XGBoost, and LinearSVC, for ECG classification, aiming to improve accuracy and diagnostic capabilities. …”
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Leveraging Advanced Technologies for Real-Time Plant Disease Monitoring by Mishra Archana, Afzal Md

“…The monitoring of the plant diseases has advanced dramatically and is taking us in a path towards real time accurate and scalable diagnostics due to the advances in the technology. To examine the integration of Machine Learning (ML), remote sensing and Mobile Applications for improved Real Time plant disease monitoring, this research paper has been written. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…The application of ML and DL in PD research offers significant potential for improving diagnostic accuracy. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Conclusions</b>: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.…”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Digital Pathology for Analyzing Endomyocardial Biopsy Specimens by A. A. Slavinskiy, A. A. Verevkin, L. M. Chuprinenko, V. L. Drushevskaya, M. V. Kristesiashvili

“…Detailed digital characterization of EMB specimens based on automated or semi-automated computer-assisted morphometry improves the diagnostic accuracy of EMB.   Objective: To develop a method of and algorithms for digital computer-assisted analysis of EMB specimens to quantify pathomorphological and immunophenotypic changes in the myocardium of patients with various types and grades of heart transplant rejection.   …”
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GASTROINTESTINAL FOOD ALLERGY IN CHILDREN by Svetlana G. Makarova, Leyla S. Namazova-Baranova, Elena A. Vishneva, Oksana A. Ereshko, Irina G. Gordeeva

“…The article describes clinical forms of gastrointestinal food allergy according to the existing classification. Diagnostic algorithms and modern approaches  to differential diagnosis of disease based on evidence-based  medicine and corresponding to international consensus papers are given.…”
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The potential crosstalk genes and molecular mechanisms between systemic lupus erythematosus and periodontitis by Kai Zhao, Xiaolong Li, Qingmiao Zhu, Mengyu Zhu, Jinge Huang, Ting Zhao

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AI illuminates paths in oral cancer by Devesh U. Kapoor, Pushpendra Kumar Saini, Narendra Sharma, Ankul Singh, Bhupendra G. Prajapati, Gehan M. Elossaily, Summya Rashid

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Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence by David B. Olawade, Kusal Weerasinghe, Mathugamage Don Dasun Eranga Mathugamage, Aderonke Odetayo, Nicholas Aderinto, Jennifer Teke, Stergios Boussios

“…The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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SEARCH FOR CLINICAL PREDICTORS OF PULMONARY HYPERTENSION IN PATIENTS WITH SYSTEMIC SCLEROSIS by N. A. Klyaus, M. A. Simakova, A. L. Maslyansky, O. M. Moiseeva

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Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia by Lin Li, Wang haijun, Chen Tianyu, Wang Wenjing, Wang Zi, Cheng yibing

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Elevation of transaminases. What if not the liver? by I. V. Sharkova

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Detection and location of EEG events using deep learning visual inspection. by Mohammad Amin Fraiwan

“…The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. …”
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