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The reporting quality and transparency of orthopaedic studies using Bayesian analysis requires improvement: A systematic review.

BackgroundBayesian methods are being used more frequently in orthopaedics. To advance the use and transparent reporting of Bayesian studies, reporting guidelines have been recommended. There is currently little known about the use or applications of Bayesian analysis in orthopedics including adherence to recommended reporting guidelines. The objective is to investigate the reporting of Bayesian analysis in orthopedic surgery studies; specifically, to evaluate if these papers adhere to reporting guidelines.MethodsWe searched PUBMED to December 2nd, 2020. Two reviewers independently identified s

Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing.

Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed d

Artificial Intelligence: How Is it Relevant to Advanced Practitioners?

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Естественные и синтетические АМП из Латинской Америки по всему спектру технологий и приложений в области разнообразия.

Биоразнообразие в Латинской Америке примерно 60% в целом по миру, подпитывая научные исследования в области природных продуктов, весьма значимой области открытия наркотиков по сей день. Это сопровождается увеличением числа антимикробных пептидов (АМП), полученных из натуральных продуктов в регионе, которые за последнее десятилетие были переработаны путем модификации, усовершенствования и уточнения их физико-химических свойств и антимикробной эффективности с использованием подходов, которые варьируются от изучения рациональной конструкции и соотношения структуры-активности (САР) до методов иску

Immunodiagnosis - the promise of personalized immunotherapy.

Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. H

Непрозрачная онтология: нейроимAGING классификация групп диагнозов ICD-10 в UK Biobank.

Фон. Использование машинного обучения для классификации диагностических случаев по сравнению с контролем, определенным на основе диагностических онтологий, таких как десятая редакция Международной классификации болезней (ICD-10), на основе нейровизуальных признаков, сейчас является распространенной практикой в широком спектре диагностических областей. Однако сравнения, которые пересекают диагностику (transdiagnostic), таких классификаций отсутствуют. Такие трансдиагностические сравнения важны для определения специфичности классификационных моделей, установления бенчмарков и оценки ценности диа

Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.

Health related quality of life (HRQoL) is increasingly assessed in oncology research and routine care, which has led to the inclusion of HRQoL in prediction models. This review aims to describe the current state of oncological prediction models incorporating HRQoL. A systematic literature search for the inclusion of HRQoL in prediction models in oncology was conducted. Selection criteria were a longitudinal study design and inclusion of HRQoL data in prediction models as predictor, outcome, or both. Risk of bias was assessed using the PROBAST tool and quality of reporting was scored with an ad

Скринэтон 2.0:: Проведение совместного обследования с участием человека и АИ на основе данных о состоянии здоровья пациентов.

Систематические обзоры имеют важное значение для проведения исследований, основанных на фактических данных, однако традиционный процесс отбора требует много времени и его трудно масштабировать. Скрининг, проводимый исключительно для человека, может привести к непоследовательности, в то время как полностью автоматизированные подходы с использованием моделей Большого языка зачастую не имеют контекстуального суждения, необходимого для принятия сложных решений. Для решения этой проблемы мы внедряем методологию скрининга на основе толпы, которая сочетает человеческий опыт и адаптивное машинное обуч

Body Composition Predictors of Adverse Postoperative Events in Patients Undergoing Surgery for Long Bone Metastases.

IntroductionBody composition assessed using opportunistic CT has been recently identified as a predictor of outcome in patients with cancer. The purpose of this study was to determine whether the cross-sectional area (CSA) and the attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), and paraspinous and abdominal muscles are the predictors of length of hospital stay, 30-day postoperative complications, and revision surgery in patients treated for long bone metastases.MethodsA retrospective database of patients who underwent surgery for long bone metastases from 1

Машинное обучение алгоритмов вводит эвоидные потенциальные возможности в качестве альтернативных биомаркеров для расширения масштабов инвалидности, прогнозируя пациентов с множественным склерозом.

Заметно возросло значение машинного обучения (ML) в области медицины, главным образом благодаря расширению доступа к медицинским данным и прогрессивному совершенствованию алгоритмов ML. Таким образом, ML может использоваться для разработки прогнозных моделей, способствующих диагностике заболеваний, прогнозированию развития заболеваний, адаптации лечения к индивидуальным потребностям пациентов и повышению оперативной эффективности систем здравоохранения. Своевременное обнаружение того или иного заболевания способствует эффективному лечению симптомов и гарантирует надлежащее лечение. В случае мн

Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks.

Primary aldosteronism (PA) is the most common cause of secondary hypertension and is associated with increased morbidity and mortality when compared with blood pressure-matched cases of primary hypertension. Current limitations in patient care stem from delayed recognition of the condition, limited access to key diagnostic procedures, and lack of a definitive therapy option for nonsurgical candidates. However, several recent advances have the potential to address these barriers to optimal care. From a diagnostic perspective, machine-learning algorithms have shown promise in the prediction of P

Рэйман жидкая биопсия: новый подход к множественной склерозовой диагностике.

Несмотря на распространенность множественных склерозов, в настоящее время нет биомаркеров, с помощью которых можно было бы точно определить это заболевание. Существующие методы диагностики являются либо дорогостоящими, либо малоконкретными. Поэтому поиск метода диагностики с высокой специфичностью и чувствительностью и в то же время не требует сложной обработки проб или дорогостоящего оборудования является неотложным. МЕТОДЫ: В статье обсуждается вопрос об использовании сыворотки крови с улучшенной спектроскопией Raman в сочетании с анализом машинного обучения отдельных лиц с множественным скл

Идентификация и проверка генов, связанных со старением, у пациентов с множественной миеломой.

Это исследование было направлено на выявление и проверку связанных со старением генов (ARGs), связанных с множественной миеломой (MM), что способствовало более глубокому пониманию молекулярных механизмов, лежащих в основе этого заболевания. Для изучения функциональных ролей ARG в MM были проведены генной онтологии и Киото-Энциклопедии по обогащению генов и геномов. Потенциальные гены, выявленные с помощью наименьшего абсолютного сокращения и селекционного оператора (LASSO) логистической регрессии и рекурсивного устранения векторных функций (SVM-RFE), были сопоставлены с помощью диаграммы Венна

C-Reactive Protein Monitoring and Clinical Presentation of Fever as Predictive Factors of Prolonged Febrile Neutropenia and Blood Culture Positivity after Autologous Hematopoietic Stem Cell Transplantation-Single-Center Real-Life Experience.

BackgroundFebrile neutropenia (FN) is a medical emergency that requires urgent evaluation, timely administration of empiric broad-spectrum antibiotics and careful monitoring in order to optimize the patient's outcome, especially in the setting of both allogeneic and autologous hematopoietic stem cell transplant (ASCT).MethodsIn this real-life retrospective study, a total of 49 consecutive episodes of FN were evaluated in 40 adult patients affected by either multiple myeloma (thirty-eight) or lymphoma (eleven), following ASCT, with nine patients having fever in both of the tandem transplantatio

Orthopaedic Oncology in Contemporary Practice: A Comprehensive Review of Primary Bone Tumours, Metastatic Pathways, and Evolving Surgical Techniques.

Orthopaedic oncology encompasses the diagnosis and management of primary bone sarcomas and metastatic skeletal disease, requiring integration of oncologic principles with complex musculoskeletal reconstruction. Advances in molecular classification, cross-sectional imaging, systemic therapy, and surgical technology have substantially transformed contemporary clinical practice. This comprehensive review synthesises current evidence across epidemiology, tumour biology, diagnostic strategies, staging systems, multidisciplinary management, reconstructive techniques, complication profiles, and survi

A review on in silico virtual screening methods in COVID-19 using anticancer drugs and other natural/chemical inhibitors.

The present coronavirus disease 2019 (COVID-19) pandemic scenario has posed a difficulty for cancer treatment. Even under ideal conditions, malignancies like small cell lung cancer (SCLC) are challenging to treat because of their fast development and early metastases. The treatment of these patients must not be jeopardized, and they must be protected as much as possible from the continuous spread of the COVID-19 infection. Initially identified in December 2019 in Wuhan, China, the contagious coronavirus illness 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (S

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… In this case, supervised machine learning was used to identify compounds with therapeutic potential against SARS-CoV-2. MD were simulated using PlayMolecule. The MD simulation was done with SimpleRun after the ligand was set up and the binding domain for the S-protein receptor was built [ 61 ]. …

Development of a Serum Metabolome-Based Test for Early-Stage Detection of Multiple Cancers.

BackgroundDetection of cancer at the early stage currently offers the only viable strategy for reducing disease-related morbidity and mortality. Various approaches for multi-cancer early detection are being explored, which largely rely on capturing signals from circulating analytes shed by tumors into the blood. The fact that biomarker concentrations are limiting in the early stages of cancer, however, compromises the accuracy of these tests. We, therefore, adopted an alternate approach that involved interrogation of the serum metabolome with machine learning-based data analytics. Here, we mon

Co-research in the development of AI and digital health tools for cancer management and care: a systematic review.

BackgroundParticipatory approaches (including co-research, co-design, Patient and Public Involvement [PPI], and Participatory Action Research [PAR]) are increasingly being incorporated into digital health innovation. However, their application in cancer-related digital and Artificial Intelligence (AI) tool lifecycle remains insufficiently synthesised, limiting understanding of how participatory approaches influence ethical outcomes, trust, and the real-world implementation of these technologies. This review moves beyond documenting participatory approaches to critically examine how far partici

Diagnostic accuracy of deep learning vs. human raters for detecting osteoporotic vertebral compression fractures in routine CT scans.

OBJECTIVES: Vertebral fractures are severe complications of osteoporosis but are frequently missed on computed tomography (CT). Differentiating true fractures from non-osteoporotic vertebral height loss remains challenging; deep learning (DL) models may improve detection and grading. MATERIALS AND METHODS: In this retrospective study, we evaluated eight human raters with different expertise (three students, three residents, and two attendings), four DL models, and one DL-based commercial software using the public Vertebral Segmentation (VerSe) 19 & 20 datasets. Vertebral fractures were graded

Federated Computing in Orthopaedic Surgery: A Paradigm Shift in Collaborative Multicenter Research.

➢ Multicenter studies remain rare in orthopaedics (9.9% of recent publications) due to regulatory complexity, restrictive data-sharing policies, and administrative delays, despite their proven value in generating robust, generalizable evidence. ➢ Federated computing (FC) brings analytic code to each institution's data and returns only encrypted, aggregated results. This preserves patient privacy, maintains institutional control, and streamlines compliance by reducing or eliminating the need for complex data-use agreements. ➢ Extensive applications in other medical fields show that FC can match