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Discovery of a DNA methylation episignature for Weiss-Kruszka syndrome.

Weiss-Kruszka syndrome (WSKA; OMIM 618619) is a rare autosomal dominant neurodevelopmental disorder caused by haploinsufficiency of ZNF462, a zinc-finger transcription factor involved in chromatin regulation and early embryonic development. WSKA is characterized by developmental delay, hypotonia, craniofacial dysmorphic features (around 8) and variable congenital anomalies. Genome-wide DNAm profiling was performed on peripheral blood DNA from 9 WSKA cases with (likely) pathogenic ZNF462 variants and matched controls to look for differential methylation. Analysis using the EpiSign™ pipeline ide

Automatic Classification and Acoustic Auscultation of Heart, Lung, and Bowel Sounds Using Artificial Intelligence.

Auscultation of heart, lung, and bowel sounds remains a fundamental diagnostic technique in clinical practice despite significant technologi … eed for objective, reproducible diagnostic support tools. Our approach leverages recent advances in supervised machine learning and signal processing to extract distinctive acoustic signatures from publicly available, digitized heart, lung, and bowel sounds. By analyzing spectral, temporal, and morphological features across diverse asymptomatic populations, the algorithm ach …

An Artificial Intelligence Tool for the Diagnosis of Facial Pain.

BACKGROUND AND OBJECTIVES: Differentiation between temporomandibular disorders (TMDs) and trigeminal neuralgia (TN) as causes of orofacial p … s is not made initially. METHODS: We completed a study of 101 patients with either TMD or TN using supervised machine learning. A predictive model was developed using the 2 inputs of a questionnaire and directed physical examination. RESULTS: The network was trained to achieve the corresponding correct output, which was based on orofacial physical examination and expert di …

Неконтролируемое обнаружение аномалий в медицинской съемке с использованием агрегированной нормативной диффузии.

Раннее обнаружение аномалий в таких медицинских снимках, как магнитный резонанс мозга (MRI), имеет большое значение для диагностики и лечения многих медицинских заболеваний. Методы машинного обучения под наблюдением ограничиваются небольшим числом патологий, в которых имеются хорошие данные с маркировкой. В отличие от этого неконтролируемое обнаружение аномалий (УАД) способно выявить более широкий спектр аномалий путем выявления отклонений от обычных моделей. Результаты наших исследований свидетельствуют о том, что предыдущие новейшие подходы, основанные на ОБД, не позволяют хорошо обобщать ра

Совпадение в аннотации оригинала
Early detection of anomalies in medical images such as brain magnetic resonance imaging (MRI) is highly relevant for diagnosis and treatment of many medical conditions. Supervised machine learning methods are limited to a small number of pathologies where there is good availability of labeled data. In contrast, unsupervised anomaly detection (UAD) has the po…

Application of Machine Learning in the Development of Fourth Degree Quantitative Structure-Activity Relationship Model for Triclosan Analogs Tested against Plasmodium falciparum 3D7.

Despite new therapies against malaria, this disease remains one of the main causes of death affecting humanity. The phenomenon of resistance has caused concern as the drugs no longer have the same efficacy, forcing scientific research to develop new methods that prospect new molecules. With the advancement of artificial intelligence, it becomes possible to apply machine learning (ML) techniques in the discovery and evaluation of new molecules, by employing the quantitative structure-activity relationship (QSAR), a classic method that uses regressions to create a model that allows identifying a

Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

OBJECTIVES: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT … stic risk factors for PT outcomes in CLBP treatment using a sub-type of AI. Bootstrap random forest supervised machine learning analysis was employed to identify the outcomes-associated variables. RESULTS: The top variables identified as predictive were: FOTO™ predicted functional status (FS) change score; FOTO™ predicted number of visits; initial FS score, age; history of …

Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records.

Amyloidosis refers to a range of medical conditions in which misshapen proteins accumulate in various organs and tissues, forming insoluble fibrils. Cardiac amyloidosis is frequently linked to the buildup of misfolded transthyretin (TTR) or immunoglobulin light chains (AL). Delayed diagnosis, due to lack of disease awareness, results in a poor prognosis, especially in patients with AL amyloidosis. Early identification is therefore a key factor to improve patient outcomes. This study investigates the use of supervised machine-learning algorithms to support clinicians in classifying amyloidosis

A Screening Platform for Hearing Loss and Cognitive Decline: WHISPER (Widespread Hearing Impairment Screening and PrEvention of Risk).

The WHISPER (Widespread Hearing Impairment Screening and PrEvention of Risk) platform was recently developed for screening for hearing loss … bjects tested, number of features analyzed, and the range of algorithms (including supervised and unsupervised machine learning) used to identify novel measures able to predict the individual hearing and cognitive abilities, also including components related to the individual risk.

The Bayesian brain in imbalance: Medial, lateral and descending pathways in tinnitus and pain: A perspective.

Tinnitus and pain share similarities in their anatomy, pathophysiology, clinical picture and treatments. Based on what is known in the pain … non-surgically. The model can also be used to develop objective measures for tinnitus and pain via supervised machine learning.

Decoding letter position in word reading.

A fundamental computation underlying visual word recognition is the ability to transform a set of letters into a visual word form. Neuropsychological data suggest that letter position within a word may be independently affected by brain damage, resulting in a dissociable subtype of peripheral dyslexia. Here we used functional magnetic resonance imaging and supervised machine learning techniques to classify letter position based on activation patterns evoked during reading Hebrew words. Across the entire brain, activity patterns in the left intraparietal sulcus provided the best classification …

Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms.

Rice (Oryza sativa L.) is Bangladesh's staple crop, cultivated across three distinct seasons: Rabi (Boro), Kharif-1 (Aus) and Kharif-2 (Aman). This study develops an integrated, cloud-independent framework for mapping Aus and Aman rice areas using freely available Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery; to accomplish this, supervised machine-learning (ML) algorithms are used in the Google Earth Engine (GEE) platform. Time-series dual-polarization (VV and VH) backscatter data were analyzed for June-November 2021 (Aman) and March-August 2022 (Aus). Four ML models such as Classi

Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learning.

This study was conducted to investigate the potential use of Fourier-transform near-infrared (FT-NIR) spectroscopy combined with machine learning (ML) algorithms to accurately identify three foodborne pathogens, including Salmonella spp., Listeria monocytogenes, and Escherichia coli O157:H7. Each bacterial strain (two per pathogen) was individually cultured and purified through sequential washing using ethanol-deionized (DI) water solutions. Each purified culture was transferred into each well of a 96-well cell plate covered with custom-cut filter paper and then vacuum dried at 50 °C under 20 

Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling.

BACKGROUND: Immune dysregulation, including autoimmunity, autoinflammation, allergy, and malignancy predisposition, adds significant disease … ders with ≥10 patients each; range, 1-196 per IEI), including 177 scores from 141 treated patients. Supervised machine learning models (k-nearest neighbors, support vector machine, logistic regression, random forest) classified patients into disease groups and ranked corresponding predictive features, while unsupervised uniform manifold approximation and projection (UMAP) vi …

A predictive model developed to classify Leishmania promastigotes at two distinct life stages using MALDI-TOF mass spectrometry.

Investigating the molecular differences between procyclic (non-infective) and metacyclic (infective) promastigotes is essential for understanding the Leishmania life cycle in the sandfly vector and may aid in identifying molecular markers specific to these parasite stages. MALDI-TOF MS, a robust mass spectrometry technique, identifies protein profiles by measuring their mass-to-charge (m/z) ratios. Machine learning (ML) aids in analysing, interpreting, and classifying the complex spectral dataset obtained. This research aims to develop a predictive model to classify procyclic and metacyclic st

Dysfunctional resting state network connectivity predicts postoperative delirium after major surgery.

BACKGROUND: Postoperative delirium is associated with increased morbidity, mortality, future cognitive decline, or dementia. Understanding t … s across the sub-networks of the default mode which overlap with higher-order cognitive processing. Supervised machine learning identified that the visual and salience-ventral attentional networks predicted postoperative delirium incidence with an accuracy of 68%. CONCLUSIONS: Resting state functional connectivity is a neural correlate of vulnerability to postoperative deli …

Innovative molecular diagnostic strategy for salivary gland lesions based on ATR-FTIR spectroscopy coupled with machine learning.

OBJECTIVE: This study aimed to identify the biochemical signatures that distinguish pleomorphic adenoma (PA) and mucoepidermoid carcinoma (MEC) from normal parotid tissue (NP), while also exploring molecular differences relevant to benign-malignant tumor differentiation using Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with machine-learning (ML) approaches. STUDY DESIGN: A total of 48 formalin-fixed, paraffin-embedded (FFPE) salivary gland tissue sections (5 µm thickness) were analyzed, including 8 NP, 24 PA, and 16 MEC specimens. ATR-FTIR spectra

Serum neurofilament levels reflect outer retinal layer changes in multiple sclerosis.

BACKGROUND: Serum neurofilament light chain (sNfL) and distinct intra-retinal layers are both promising biomarkers of neuro-axonal injury in … (B = 5.974, SE = 2.420, p = 0.019). sNfL levels were 75.7% accurate at predicting OPL volume in the supervised machine learning. CONCLUSIONS: In summary, sNfL levels were a good predictor of future outer retinal thinning in MS. Changes within the neurofilament-rich OPL could be considered as an additional retinal marker linked to MS neurodegeneration.

Human-in-the-loop machine learning with community engagement reduces title, abstract and full-text screening workload in knowledge synthesis.

OBJECTIVE: Manual screening of titles, abstracts, and full texts for large-volume knowledge synthesis requires significant time investment. Machine-assisted tools offer solutions for facilitating screening. Most applications to date have focused on actively reordering citations or suggesting screening decisions for reviewers to prioritize relevant studies, or on automating exclusions based on narrowly defined eligibility criteria. However, few tools extend to full-text screening, and among those that report, a majority have largely been developed with little engagement of knowledge users. Know

Machine learning-enabled behavioural and psychological symptoms of dementia management intervention for dementia caregivers: protocol for a hybrid factorial SMART-MRT trial.

BACKGROUND: Behavioural and psychological symptoms of dementia (BPSD) affect over 90% of people living with dementia and are a major contributor to stress and adverse health outcomes among family caregivers. Caregiver‑led behavioural management interventions based on the Antecedents-Behaviour-Consequences (ABC) framework are effective, yet their overall impact is modest. This may be due to the highly individualised and fluctuating nature of BPSD and insufficient adaptive support to sustain engagement. Mobile health interventions offer scalable support; however, engagement and adherence remain

Radiomics-assisted unsupervised clustering and predictive modeling to identify optimal candidates for virtual navigation bronchoscopy localization and CT-guided localization in thoracoscopic lung resection.

Accurate localization of small pulmonary nodules during thoracoscopic lung resection remains challenging, and the selection of preoperative localization methods largely relies on surgeon experience. This study aimed to integrate radiomic features and clinical characteristics to develop a data-driven patient clustering approach and to explore the suitability of different preoperative localization methods across patient subgroups. Patients who underwent thoracoscopic wedge resection or segmentectomy with virtual navigation bronchoscopy (VNB) localization, computed tompgraphy (CT)-guided localiza