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Copper-supported thiol-functionalized cellulose as a paper-based catalyst for imine synthesis
CDX1 improves nicotine induced cardiac fibroblasts activation and cardiomyocyte hypertrophy by alleviating autophagic flux impairment through modulation of LAPTM4B
Body representation in dreams of congenital and early-life amputees
Abstract Phantom limb pain (PLP) is a common consequence of the amputation of a limb. Individuals with congenital limb absence (here: congenital amputees), however, seem to rarely experience PLP. Previous results suggest that the experience of PLP in the waking state affects the recalled body appearance in dreams of individuals with acquired limb amputation, with PLP being associated with the recall of an impaired rather than an intact body. However, it remains unclear how congenital amputees – who never experienced an intact body and rarely PLP – recall their body appearance in dreams. In the present cross-sectional study, we assessed body-related dream content in a sample of adult congenital amputees and compared their reports with those from adult persons with an acquired limb amputation early in life. We found that congenital amputees reported the least frequent dreams with an intact body, and after birth, the age at amputation positively predicted the recall of an intact body in dreams. The effects were not explained by time since amputation and the presence or absence of PLP. This suggests that life experiences of an intact body find expression in self-related dream content.
Physical activity and mental health in Chinese high school students: a cross-sectional study
MicroRNA-155 targets p65 to regulate PD-L1 expression in the early pregnancy endometrium
Impact of dietary habit changes on college students physical health insights from a comprehensive study
Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease
Abstract Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for training comprehensive Machine Learning (ML) modelsThis study aims to investigate the possible effects of segmentation of the utterance of vowel "a" on the performance of ML classifiers CatBoost (CB), Random Forest (RF), and Support Vector Machine (SVM). This research involves training individual ML models using three distinct dataset constructions: full-sequence, segment-wise, and group-wise, derived from the utterance of the vowel "a" which consists of 1058 recordings belonging to 48 participants. This approach comprehensively analyzes how each data categorization impacts the model's performance and results. A nested cross-validation (nCV) approach was implemented with grid search for hyperparameter optimization. This rigorous methodology was employed to minimize overfitting risks and maximize model performance. Compared to the full-sequence dataset, the findings indicate that the second segment yielded higher results within the four-segment category. Specifically, the CB model achieved superior accuracy, attaining 97.8% and 84.6% on the validation and test sets, respectively. The same category for the CB model also demonstrated the best balance regarding true positive rate (TPR) and true negative rate (TNR), making it the most clinically effective choice. These findings suggest that time-sensitive properties in vowel production are important for COPD classification and that segmentation can aid in capturing these properties. Despite these promising results, the dataset size and demographic homogeneity limit generalizability, highlighting areas for future research. Trial registration The study is registered on clinicaltrials.gov with ID: NCT06160674.
Serum levels of galanin-like peptide and alarin are highly correlated with polycystic ovary syndrome
The impact of air pollution on employment mobility of college graduates in the Yangtze River Delta
Blood pressure variability associated with in-hospital and 30-day mortality in heart failure patients: a multicenter cohort study
The optimization and impact of public sports service quality based on the supervised learning model and artificial intelligence
A cellular assay to determine the fusion capacity of MFN2 variants linked to Charcot–Marie-Tooth disease of type 2 A
Abstract Charcot–Marie-Tooth Disease (CMT) is an inherited peripheral neuropathy with two main forms: demyelinating CMT1 and axonal CMT2. The most frequent subtype of CMT2 (CMT2A) is linked to mutations of MFN2, encoding a ubiquitously expressed GTP-binding protein anchored to the mitochondrial outer membrane and essential for mitochondrial fusion. The use of Next-Generation Sequencing has led to the identification of increasing numbers of MFN2 variants, yet many of them remain of unknown significance, depriving patients of a clear diagnosis. In this work, we establish a cellular assay allowing to assess the impact of 12 known MFN2 variants linked to CMT2A on mitochondrial fusion. The functional analysis revealed that out of the 12 selected MFN2 mutations, only six exhibited reduced fusion activity. The classification of MFN2 variants according to the results of the functional assay revealed a correlation between the fusion capacity, the age at onset of CMT2A and computational variant effect predictions relying on the analysis of the protein sequence. The functional assay and the results obtained will assist and improve the classification of novel MFN2 variants identified in patients.