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Remyelination is a regenerative process that restores the lost neurological function and partially depends on oligodendrocyte differentiation. Differentiation of oligodendrocytes spontaneously occurs after demyelination, depending on the cell intrinsic mechanisms. By combining a loss-of-function genomic screen with a web-resource-based candidate gene identification approach, we identified that dimethylarginine dimethylaminohydrolase 1 (DDAH1) is a novel regulator of oligodendrocyte differentiation. Silencing DDAH1 in oligodendrocytes prevented the expression of myelin basic protein in mouse oligodendrocyte culture with the change in expression of genes annotated with oligodendrocyte development. DDAH1 inhibition attenuated spontaneous remyelination in a cuprizone-induced demyelinated mouse model. Conversely, increased DDAH1 expression enhanced remyelination capacity in experimental autoimmune encephalomyelitis. These results provide a novel therapeutic option for demyelinating diseases by modulating DDAH1 activity.The sperm quality of some males is in a critical state, making it hard for clinicians to choose the suitable fertilisation methods. This study aimed to develop an intelligent nomogram for predicting fertilisation rate of infertile males with borderline semen. 160 males underwent in vitro fertilisation (IVF), 58 of whom received rescue ICSI (R-ICSI) due to fertilisation failure (fertilisation rate of IVF ≤30%). A least absolute shrinkage and selection operator (LASSO) regression analysis identified sperm concentration, progressively motile spermatozoa (PMS), seminal plasma anti-Müllerian hormone (spAMH), seminal plasma inhibin (spINHB), serum AMH (serAMH) and serum INHB (serINHB) as significant predictors. The nomogram was plotted by multivariable logistic regression. This nomogram-illustrated model showed good discrimination, calibration and clinical value. The area under the receiver operating characteristic curve (AUC) of the nomogram was 0.762 (p less then .001). Calibration curve and Hosmer-Lemeshow test (p = .5261) showed good consistency between the predictions of the nomogram and the actual observations, and decision curve analysis showed that the nomogram was clinically useful. This nomogram may be useful in predicting fertilisation rate, mainly focused on new biomarkers, INHB and AMH. It could assist clinicians and laboratory technicians select appropriate fertilisation methods (IVF or ICSI) for male patients with borderline semen.Surgical skills are learned through deliberate practice, and veterinary educators are increasingly turning to models for teaching and assessing surgical skills. This review article sought to compile and review the literature specific to veterinary surgical skills models, and to discuss the themes of fidelity, educational outcomes, and validity evidence. Several literature searches using broad terms such as "veterinary surgery model," "veterinary surgical model," and "veterinary surgical simulator" were performed using PubMed, CAB abstracts, and Google scholar. All articles describing a model created and utilized for veterinary surgical training were included. Other review articles were used as a source for additional models. selleck kinase inhibitor Commercially available models were found using review articles, internet browser searches, and communication with veterinary clinical skills educators. There has been an explosion of growth in the variety of small animal surgical task trainers published in the last several decades. These models teach orthopedic surgery, ligation and suturing, open celiotomy and abdominal surgery, sterilization surgeries, and minimally invasive surgeries. Some models were published with accompanying rubrics for learner assessment; these rubrics have been noted where present. Research in veterinary surgical models is expanding and becoming an area of focus for academic institutions. However, there is room for growth in the collection of validity evidence and in development of models for teaching large animal surgery, training surgical residents, and providing continuing education to practitioners. This review can assist with evaluation of current surgical models and trends, and provide a platform for additional studies and development of best practices.

We aimed to ascertain risk indicators of in-hospital mortality and severity as well as to provide a comprehensive systematic review and meta-analysis to investigate the prognostic significance of the prognostic nutrition index (PNI) as a predictor of adverse outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients.

In this cross-sectional study, we studied patients with COVID-19 who were referred to our hospital from February 16 to November 1, 2020. Patients with either a real-time reverse-transcriptase polymerase chain reaction test that was positive for COVID-19 or high clinical suspicion based on the World Health Organization (WHO) interim guidance were enrolled. A parallel systematic review/meta-analysis (in PubMed, Embase, and Web of Science) was performed.

A total of 504 hospitalized COVID-19 patients were included in this study, among which 101 (20.04%) patients died during hospitalization, and 372 (73.81%) patients were categorized as severe cases. At a multivariable level, lower PNI, higher lactate dehydrogenase (LDH), and higher D-dimer levels were independent risk indicators of in-hospital mortality. Additionally, patients with a history of diabetes, lower PNI, and higher LDH levels had a higher tendency to develop severe disease. The meta-analysis indicated the PNI as an independent predictor of in-hospital mortality (odds ratio [OR] = 0.80; P < .001) and disease severity (OR = 0.78; P = .009).

Our results emphasized the predictive value of the PNI in the prognosis of patients with COVID-19, necessitating the implementation of a risk stratification index based on PNI values in hospitalized patients with COVID-19.

Our results emphasized the predictive value of the PNI in the prognosis of patients with COVID-19, necessitating the implementation of a risk stratification index based on PNI values in hospitalized patients with COVID-19.