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Decoding depression: Event related potential dynamics and predictive neural signatures of depression severity.

Journal of affective disorders
Depression is a heterogeneous disorder marked by disruptions in cognitive and affective processing. While self-reported measures and clinical interviews remain the diagnostic standard, integrating objective neurophysiological markers could enhance as...

Response inhibition deficits in unmedicated youth with ADHD: An ERP/sLORETA study.

Journal of affective disorders
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by impairments in executive functioning, particularly response inhibition (RI). This study combines time-domain analysis and source analyses of event-related potentials (ERP...

Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Scientific reports
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical...

Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus.

Scientific reports
An underlying association between primary open-angle glaucoma (POAG) and COVID-19 has been hypothesized, but the causal link and shared mechanisms remain unclear. This study integrates epidemiological and bioinformatics approaches to investigate thei...

Development and validation of a machine learning-based nomogram for survival prediction of patients with hilar cholangiocarcinoma after curative-intent resection.

Scientific reports
Hilar cholangiocarcinoma (hCCA), a rare cancer of the biliary system, has a poor prognosis. This study aimed to investigate the risk factors affecting the survival of patients with hCCA after curative-intent resection and establish a survival predict...

Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model.

Scientific reports
Gastrointestinal bleeding (GIB) occurs more frequently in cardiovascular patients than in the general population, significantly affecting morbidity and mortality. However, existing predictive models often lack sufficient accuracy and interpretability...

Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.

Scientific reports
Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosi...

Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.

Scientific reports
Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acut...

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Translational psychiatry
Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Lan...

Integrated clinicopathological-radiomic-blood model for glioma survival prediction via machine learning: a multicenter cohort study.

Neurosurgical review
BACKGROUND: Glioma is characterized by a poor prognosis and limited possibilities for treatment. Previous studies have developed prediction models for glioma using genetic, clinical, pathological, imaging and other aspects; however, few studies have ...