Research library

B

grade

In patients with atrial fibrillation, clonal hematopoiesis is associated with a greater burden of silent brain lesions and accelerated cognitive decline over time.

CHIP carriers had higher odds of cerebral microbleeds (OR, 1.45 [95% CI, 1.09 to 1.93]) and WMLs (OR, 1.56 [95% CI, 1.20 to 2.04]) at baseline.

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B

grade

TIPR may provide valuable insights into the lived experience of individuals with type 1 diabetes, complementing traditional guideline-defined glucose targets.

Not reported in abstract.

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C

grade

RVG-engineered exosomes from young plasma show potential in reducing AD pathology and improving cognition in mice, but human relevance remains uncertain.

Not reported in abstract.

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C

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Jiajian Shuyu Pills may help reduce injury from cerebral ischemia-reperfusion in mice by affecting specific signaling pathways, but human applicability is not established.

Not reported in abstract.

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B

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The training program significantly improved clinician confidence in delivering cognitive processing therapy to support UK women veterans affected by military sexual trauma.

3-month post-training self-efficacy in delivering CPT increased significantly (p < .001, r = 0.915).

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C

grade

Differential readout in paired Rulkov neurons can improve signal retention under high shared-noise conditions, but its effectiveness diminishes with parameter mismatch or weak noise correlation.

Not reported in abstract.

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B

grade

Relative knee extension strength may be linked to cognitive function in older adults with T2DM, but more research is needed to confirm causality.

B=8.30, 95% CI -0.07 to 16.70, p=0.052 after adjusting for age.

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B

grade

The GTBL-AF model provides a promising approach for improving PKD recognition through EEG-fMRI fusion, achieving high classification accuracy.

94.2% classification accuracy in PKD recognition.

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B

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Current AI interpretation designs may mislead clinicians and require alignment with clinical reasoning for effective integration into practice.

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C

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The EDL10 model achieved a high accuracy of 97.0% in classifying potato leaf diseases, highlighting the potential of optimized CNNs and data augmentation in agricultural applications.

Accuracy score of 97.0% for EDL10 model.

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D

grade

GlucoDiaNet achieved a RMSE of 5.2435 mg/dL for blood glucose prediction at 30 minutes, indicating strong predictive performance, but further validation is needed before clinical application.

RMSE of 5.2435 mg/dL at 30-min prediction horizon.

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D

grade

The YOLOv10s-SeqOpt framework achieved 96.0% accuracy in detecting avocado foliar diseases, demonstrating potential for real-time monitoring in orchards.

96.0% accuracy on multispectral validation set.

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B

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The proposed deep learning model achieved a DSC of 75.41% for ischemic stroke lesion segmentation, indicating a notable improvement over traditional methods.

DSC of 75.41%

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B

grade

PKRNN-2CM shows a statistically significant improvement in predicting vancomycin concentrations compared to the simpler model, but the clinical relevance of this improvement remains to be established.

Root mean square error 5.55 vs 5.65 for real dataset, n=5483, P=.01.

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B

grade

The study presents a wearable ECG-based system that achieved 98.18% accuracy in monitoring anesthesia depth, but the clinical implications of this finding remain unclear.

98.18% test accuracy in distinguishing between awake and deep sleep anesthesia states.

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D

grade

GlucoDiaNet shows promise in predicting blood glucose levels with low error metrics, but further validation in real-world settings is necessary.

Root Mean Squared Error (RMSE) of 5.2435 mg/dL at 30-min prediction horizon.

summary