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A population approach to cortical GABAergic interneuron function - PubMed
a month ago
- The IN-CODE consortium aims to study cortical GABAergic interneuron diversity.
- Large-scale recordings of interneuron types will be combined with machine-learning tools.
- The study focuses on physiological features, connectivity motifs, and cooperativity in cognitive functions.
- All authors declare no competing interests.
A Machine Learning-Derived Risk Score Based on Dietary Nutrient Intake for Early Detection and Prognostic Prediction of Preserved Ratio Impaired Spirometry - PubMed
a month ago
- PRISm (Preserved Ratio Impaired Spirometry) is linked to higher risks of COPD, cardiovascular disease, and mortality.
- A machine learning model was developed using NHANES data (2007-2012) to predict PRISm risk based on dietary and demographic factors.
- The model achieved an AUC of 0.818, showing strong predictive ability for PRISm.
- High-risk individuals had significantly higher mortality rates and associations with chronic conditions like hypertension, diabetes, and COPD.
- Healthy lifestyle adherence reduced adverse outcomes in low-risk individuals but not in high-risk groups.
- The study proposes a non-invasive tool for early PRISm detection and personalized prevention strategies.
Identification of potential biomarkers and therapeutic targets for liver cirrhosis based on Mendelian randomization and machine learning - PubMed
a month ago
- Identified five hub genes (ENPP2, FAM134B, PPARGC1A, SLFN11, TRIM22) associated with liver cirrhosis (LC).
- Constructed a nomogram risk prediction model with strong predictive performance (AUC = 0.944 in training set, 0.909 in validation set).
- Gene set enrichment analysis linked hub genes to antigen processing, cell adhesion, and immune regulation.
- Abnormal immune cell levels (NK cells, M2 macrophages, dendritic cells, neutrophils) observed in LC.
- Predicted potential therapeutic compounds, including valproic acid and tamoxifen, for LC treatment.
A 13-gene prognostic model developed using machine learning to predict the response to neoadjuvant chemoradiotherapy in rectal carcinoma - PubMed
a month ago
- A 13-gene prognostic model was developed using machine learning to predict response to neoadjuvant chemoradiotherapy in rectal carcinoma.
- The study declares no competing interests among the authors.
- Key references include recent studies on cancer statistics, neoadjuvant therapy for rectal cancer, and ASCO guidelines.
- Funding sources include the National Science and Technology Major Project, National Natural Science Foundation of China, and various Tianjin-based grants.
Functional Ultrasound Imaging-Based Mapping of Cocaine Induced Neural Changes in Awake Mice - PubMed
a month ago
- Cocaine is a commonly used addictive drug with significant medical consequences.
- The study used functional ultrasound imaging (fUS) to map neural changes in awake mice after cocaine exposure.
- Cerebral blood volume (CBV) was measured as an indicator of neuronal activity.
- Acute cocaine exposure increased cortical CBV, indicating heightened neuronal activation.
- Functional connectivity (FC) between the cortex, hippocampus, and thalamus was significantly reduced.
- A Support Vector Machine (SVM) classifier successfully distinguished between baseline and cocaine-exposed states using FC patterns.
- The study highlights the potential of combining neuroimaging and AI for understanding substance use disorders.
- Future research should explore chronic exposure models and include both sexes for better translational relevance.
The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study - PubMed
a month ago
- Sepsis prediction models in intensive care units (ICUs) use different evaluation strategies: fixed horizon, peak score, and continuous evaluation.
- The study compares these strategies using a model trained on the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset and tested on BerlinICU, a German ICU dataset.
- Performance metrics vary significantly by evaluation strategy, with continuous evaluation showing an AUROC of 0.67, peak score similar, and fixed horizon reduced to 0.61.
- Onset matching minimally affects continuous and fixed horizon evaluations but increases peak score estimates.
- Shorter prediction horizons improve performance across all strategies.
- Continuous evaluation aligns best with real-world clinical monitoring, while fixed-horizon and peak score may skew results without proper length of stay matching.

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#machine-learning

A population approach to cortical GABAergic interneuron function - PubMed

A Machine Learning-Derived Risk Score Based on Dietary Nutrient Intake for Early Detection and Prognostic Prediction of Preserved Ratio Impaired Spirometry - PubMed

Identification of potential biomarkers and therapeutic targets for liver cirrhosis based on Mendelian randomization and machine learning - PubMed

A 13-gene prognostic model developed using machine learning to predict the response to neoadjuvant chemoradiotherapy in rectal carcinoma - PubMed

Functional Ultrasound Imaging-Based Mapping of Cocaine Induced Neural Changes in Awake Mice - PubMed

The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study - PubMed