Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus.

Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.

Reliability and validity of the revised indebtedness scale in Chinese university students.

The purpose of this study was to evaluate the psychometric properties of the Chinese version of the Revised Indebtedness Scale (IS-R-C) in mainland China. A total of 1057 university students participated in this study using a two-wave whole-group sampling method. Sample 1, consisting of 537 participants, was used for item analysis and exploratory factor analysis (EFA) of the Revised Indebtedness Scale (IS-R).

Mobile Application-Based Care on Health Outcomes in Patients With Rheumatoid Arthritis: A Systematic Review and Meta-Analysis.

Background: Effective disease management is crucial for rheumatoid arthritis (RA) patients as it can significantly reduce disease-associated symptoms. Currently, the utilization of mobile applications for managing RA patients has gained widespread popularity in clinical settings. However, there is a notable absence of a comprehensive meta-analysis exploring their effectiveness specifically in the context of RA patients.

Prognostic implications of immune classification based on PD-L1 expression and tumor-infiltrating lymphocytes in endocervical adenocarcinoma.

Background: Endocervical adenocarcinoma (ECA) comprises a heterogeneous group of diseases whose incidence has increased significantly in recent decades. ECA can be histologically classified into human papillomavirus-associated (HPVA) and non-HPVA (NHPVA) types. Given the variability in pathological features and clinical behavior between the subtypes, evaluating their respective immune microenvironments is essential.

Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected.