Material and methods: Human skeletal muscle myoblasts were cultured until senescence. Young and senescent cells were treated with TRF for 24 h before and after differentiation induction, followed by evaluation of cellular morphology and efficiency of differentiation. Expression of cell proliferation marker Ki67 protein and myogenic regulatory factors MyoD and myogenin were determined.
Results: Our findings showed that treatment with TRF significantly improved the morphology of senescent myoblasts. Promotion of differentiation was observed in young and senescent myoblasts with TRF treatment as shown by the increased fusion index and larger size of myotubes. Increased Ki67 and myogenin expression with TRF treatment was also observed in senescent myoblasts, suggesting amelioration of the myogenic program by TRF during replicative senescence.
Conclusions: TRF modulates the expression of regulatory factors related to proliferation and differentiation in senescent human myoblasts and could be beneficial for ameliorating the regenerative defects during aging.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.
Materials and Methods: Rat trapping was carried out within the Kota Bharu vicinity near a local wet market. A total of 38 rats were captured and subjected to peripheral blood smearing using Giemsa stain. Positive rats were sent for histopathological analysis for the evaluation of the organ samples.
Results: The presence of trypanosomes was found in one sample from a blood smear. This was connected to a histological lesion on kidney tissues, which revealed a high concentration of trypanosomes. Additionally, the positive sample was confirmed as T. lewisi based on molecular diagnosis via polymerase chain reaction and subsequent sequencing and phylogenetic analysis.
Conclusions: This finding serves as a baseline for further surveillance on T. lewisi population among urban rats in Kelantan and possible zoonotic transmission to humans.