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Background: Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods: A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effectsmodel was preferred formeta-analysis using R4.2.0 because of the different variables and parameters. Results: A total of 44 studies were included in thismeta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion: ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. [ABSTRACT FROM AUTHOR]

Background: Perinatal depression (PND) is common in pregnant and postpartum women. Previous research indicates that there are clinical subtypes of PND, and the time of onset is an important factor to identify PND subtypes. However, differences of PND in different trimesters and in the postpartum period are not clear. And studies examining core symptoms that could be used to identify PND subtypes are lacking. Methods: Women during the second (N = 161) and third trimesters (N = 248) and within 6 weeks postpartum (N = 110) were recruited. All participants completed the assessment of the Edinburgh Postnatal Depression Scale. Network analysis was used to explore the relationships between depressive symptoms in all three periods. Results: Postpartum women showed the highest prevalence of PND and the most severe depressive symptoms. The symptom 'Sadness and misery' had the strongest centrality in both the second and third trimesters, but decreased in the postpartum period. The symptom 'Fear and panic' had the strongest centrality in the postpartum period. All three PND networks were relatively stable in strength and closeness. Conclusions: This study indicated the changes over time in central symptoms in pregnant and postpartum women, which shifted from being dominated by depression to being dominated by anxiety. Our findings suggested that more symptom-specific interventions should be used to improve the mental health of mothers, fetuses and infants. All of this provided clinical evidence to investigate the biological mechanisms of PND subtypes to help diagnose and treat PND with precision. [ABSTRACT FROM AUTHOR]

Investigates the role of afferent input from the lung and lower airways in magnitude estimation of inspiratory resistive loads. Comparison of sensations related to inspiratory R, reflected by subjects' percentage of handgrip responses between double-lung transplant recipients with normal lung function and healthy control subjects; Perceptual sensitivity to the external load; Ventilatory responses to external loads.