xMEN: a modular toolkit for cross-lingual medical entity normalization.

Objective: To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.

Materials And Methods: We propose xMEN, a modular system for cross-lingual (x) medical entity normalization (MEN), accommodating both low- and high-resource scenarios. To account for the scarcity of aliases for many target languages and terminologies, we leverage multilingual aliases via cross-lingual candidate generation.

Dengue virus non-structural protein 1 binding to thrombin as a dengue severity marker: Comprehensive patient analysis in south Taiwan.

Background: Previously we identified a complex of non-structural protein (NS) 1 - Thrombin (NST) in dengue infected patients. Here, we investigated how the concentration of NS1 and NST differ in various dengue severity levels as well as their demographic and clinical features. Several comorbid (hypertension, diabetes, and chronic renal failure) often found in dengue patients were also measured and analyzed.

Enhancing Perioperative Outcomes of Pancreatic Surgery with Wearable Augmented Reality Assistance System: A Matched-Pair Analysis.

Objective: The present study aimed to evaluate the safety of the first wearable augmented reality assistance system (ARAS) specifically designed for pancreatic surgery and its impact on perioperative outcomes.

Background: Pancreatic surgery remains highly complex and is associated with a high rate of perioperative complications. ARAS, as an intraoperative assistance system, has the potential to reduce these complications.

Genetic overlap between breast cancer and sarcopenia: exploring the prognostic implications of SLC38A1 gene expression.

Background: Sarcopenia, an age-related syndrome characterized by a decline in muscle mass, not only affects patients' quality of life but may also increase the risk of breast cancer recurrence and reduce survival rates. Therefore, investigating the genetic mechanisms shared between breast cancer and sarcopenia is significant for the prevention, diagnosis, and treatment of breast cancer.

Methods: This study downloaded gene expression datasets and clinical data related to breast cancer and skeletal muscle aging from the GEO database.

Causal effects of thyroid volume change on thyroid disease: a Mendelian randomization study.

Background: Observational studies have suggested an association between thyroid volume changes and thyroid disease, but the causal relationship and direction of these effects remain unclear. This study employs a two-sample Mendelian randomization (MR) approach to assess the effect of thyroid volume on clinically common benign and malignant thyroid diseases.

Methods: Summary data from genome-wide association studies (GWAS) were utilized for secondary data analysis to investigate the link between thyroid volume and disease.

Socially interactive agents for robotic neurorehabilitation training: conceptualization and proof-of-concept study.

Introduction: Individuals with diverse motor abilities often benefit from intensive and specialized rehabilitation therapies aimed at enhancing their functional recovery. Nevertheless, the challenge lies in the restricted availability of neurorehabilitation professionals, hindering the effective delivery of the necessary level of care. Robotic devices hold great potential in reducing the dependence on medical personnel during therapy but, at the same time, they generally lack the crucial human interaction and motivation that traditional in-person sessions provide.

Self-reported Dizziness, Postural Stability, and Sensory Integration After Mild Traumatic Brain Injury: A Naturalistic Follow-up Study.

Objective: The aim of the study is to evaluate changes in dizziness, postural stability, and sensory integration after mild traumatic brain injury over a 12-wk period.

Methods: One hundred adults with mild traumatic brain injury were analyzed. The Dizziness Handicap Inventory questionnaire was used for subjective evaluations.

MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification.

Introduction: Requirements classification is an essential task for development of a successful software by incorporating all relevant aspects of users' needs. Additionally, it aids in the identification of project failure risks and facilitates to achieve project milestones in more comprehensive way. Several machine learning predictors are developed for binary or multi-class requirements classification.

FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system.

Many important information in medical research and clinical diagnosis are obtained from medical images. Among them, digital pathology images can provide detailed tissue structure and cellular information, which has become the gold standard for clinical tumor diagnosis. With the development of neural networks, computer-aided diagnosis presents the identification results of various cell nuclei to doctors, which facilitates the identification of cancerous regions.

Predicting executive functioning from walking features in Parkinson's disease using machine learning.

Parkinson's disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence is scarce or inconclusive. Therefore, we examined the relationship between walking features and executive functioning in patients with Parkinson's disease using state-of-the-art machine learning approaches.

R-RDSP: Reliable and Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management over DDS.

After natural disasters such as earthquakes, floods, or wars occur, cellular communication networks often sustain significant damage or become impaired. In these critical situations, first responders must coordinate with other rescue teams to communicate essential information to central command and survivors. To address this challenge, we have developed a reliable and rapidly deployable wireless ad hoc system for post-disaster management using Data Distribution Service (DDS) middleware, specifically RTI-DDS, named R-RDSP.

Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques.

Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis-SWNIR) spectroscopy (400-1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features.

Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection.

Purpose: To explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection.

Methods: Total 165 HCC patients (ER, = 96 vs. non-early recurrence (NER), = 69) were retrospectively collected and divided into a training cohort ( = 132) and a validation cohort ( = 33).

Self-supervised multi-modal feature fusion for predicting early recurrence of hepatocellular carcinoma.

Surgical resection stands as the primary treatment option for early-stage hepatocellular carcinoma (HCC) patients. Postoperative early recurrence (ER) is a significant factor contributing to the mortality of HCC patients. Therefore, accurately predicting the risk of ER after curative resection is crucial for clinical decision-making and improving patient prognosis.

Forecasting fMRI images from video sequences: linear model analysis.

Over the past few decades, a variety of significant scientific breakthroughs have been achieved in the fields of brain encoding and decoding using the functional magnetic resonance imaging (fMRI). Many studies have been conducted on the topic of human brain reaction to visual stimuli. However, the relationship between fMRI images and video sequences viewed by humans remains complex and is often studied using large transformer models.

Targeted elimination of tetravalent-Sn-induced defects for enhanced efficiency and stability in lead-free NIR-II perovskite LEDs.

Eco-friendly Sn-based perovskites show significant potential for high-performance second near-infrared window light-emitting diodes (900 nm - 1700 nm). Nevertheless, achieving efficient and stable Sn-based perovskite second near-infrared window light-emitting diodes remains challenging due to the propensity of Sn to oxidize, resulting in detrimental Sn-induced defects and compromised device performance. Here, we present a targeted strategy to eliminate Sn-induced defects through moisture-triggered hydrolysis of tin tetrahalide, without degrading Sn in the CsSnI film.

Laser therapy for Bell's palsy: a systematic review and meta-analysis of randomized trials.

This meta-analysis investigated therapeautic effects of laser therapies in patients with Bell's palsy (BP). The authors performed the literature search in the PubMed, Embase, and Cochrane Library databases using the following search terms: (facial paralysis OR Bell's palsy OR facial palsy OR idiopathic facial paralysis) AND (laser OR low-level laser OR photobiomodulation OR phototherapy). Relevant studies published before October 29th 2024 were identified.

Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task.

This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and young healthy subjects. Synthetic data for the three classes is generated based on class conditions and random noise by leveraging a combination of conditional adversarial generative neural networks and reconstruction networks.

Signal-Guided Multitask Learning for Myocardial Infarction Classification Using Images of Electrocardiogram.

Introduction: The diagnosis of myocardial infarction (MI) needs to be swift and accurate, but definitively diagnosing it based on the first test encountered in clinical practice, the electrocardiogram (ECG), is not an easy task. The purpose of the study was to develop a deep learning (DL) algorithm using multitask learning method to differentiate patients experiencing MI from those without coronary artery disease using image-based ECG data.

Methods: A DL model was developed based on 11,227 ECG images.

Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT.

Objective: Clinical narratives provide comprehensive patient information. Achieving interoperability involves mapping relevant details to standardized medical vocabularies. Typically, natural language processing divides this task into named entity recognition (NER) and medical concept normalization (MCN).

Corrigendum: Effectiveness of perindopril/amlodipine fixed-dose combination in the treatment of hypertension: a systematic review.

[This corrects the article DOI: 10.3389/fphar.2023.