Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time-consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time-consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time-consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration.Myogenesis includes sequential stages of progenitor cell proliferation, myogenic commitment and differentiation, myocyte fusion, and myotube maturation. Different stages of myogenesis are orchestrated and regulated by myogenic regulatory factors and various downstream cellular signaling. Here we identify phosphatase orphan 1 (Phospho1) as a new player in myogenesis. During activation, proliferation, and differentiation of quiescent satellite cells, the expression of Phospho1 gradually increases. Overexpression of Phospho1 inhibits myoblast proliferation but promotes their differentiation and fusion. Conversely, knockdown of Phospho1 accelerates myoblast proliferation but impairs myotube formation. Moreover, knockdown of Phospho1 decreases the OXPHO protein levels and mitochondria density, whereas overexpression of Phospho1 upregulates OXPHO protein levels and promotes mitochondrial oxygen consumption. Finally, we show that Phospho1 expression is controlled by myogenin, which binds to the promoter of Phospho1 to regulate its transcription. These results indicate a key role of Phospho1 in regulating myogenic differentiation and mitochondrial function.Monoclonal antibodies are explored for their therapeutic potential in Psoriasis. To evaluate Risankizumab in the moderate to severe psoriasis with regard to efficacy, tolerability, and safety PubMed, Cochrane Central Register of Controlled Trials (CENTRAL) and clinicaltrials.gov, databases were searched for relevant RCTs. The reference lists of relevant publications were also scanned manually to identify any further studies not indexed in the searched databases. Only RCT aiming to evaluate the role of Risankizumab in the treatment of moderate to severe psoriasis were considered eligible for this systematic review. Intervention group was patients taking Risankizumab and placebo or other monoclonal antibody was considered as control group. Cochrane review manager 5 (RevMan) version 5.3 was used for data synthesis and meta-analysis. Quality assessment of included randomized controlled trials was done with Cochrane Collaboration risk of bias assessment tool, version 2.0 (ROB-2). Overall Grading of evidence for study objectives was performed with GRADE Pro GDT software. A total of seven studies were included in analysis with total of 1533 and 710 patients in Risankizumab and standard care groups, respectively. Statistically significant increase in percentage of individual achieving PASI90 (OR = 11.01 (95% CI = 8.67-13.99), DLQI-01 (OR = 6.95 (95% CI = 5.53-8.75), sPGA-01 (OR = 14.22 (95% CI = 11.10-18.22); sPGA-0 (OR = 6.39 (95% CI = 4.79-8.54) in risankizumab group as compared with control, with high quality of evidence. Increased risk of infections with risankizumab as compared with placebo (OR = 1.44 [95% CI = 1.13-1.83], high quality evidence), while no difference in SAE among two groups. Analysis of all outcome data from RCTs.  https://www.selleckchem.com/TGF-beta.html  In the light of evidence from systematic review on effectiveness of Risankizumab, we propose treatment with risankizumab for psoriasis patients not responding to available treatment.
  To personalize OSA management, several studies have attempted to better capture disease heterogeneity by clustering methods. The aim of this study was to conduct a cluster analysis of 23 000 OSA patients at diagnosis using the multinational ESADA.
 
  Data from 34 centres contributing to ESADA were used. An LCA was applied to identify OSA phenotypes in this European population representing broad geographical variations. Many variables, including symptoms, comorbidities and polysomnographic data, were included. Prescribed medications were classified according to the ATC classification and this information was used for comorbidity confirmation.
 
  Eight clusters were identified. Four clusters were gender-based corresponding to 54% of patients, with two clusters consisting only of men and two clusters only of women. The remaining four clusters were mainly men with various combinations of age range, BMI, AHI and comorbidities. The preferred type of OSA treatment (PAP or mandibular advancement) varied between clusters.
 
  Eight distinct clinical OSA phenotypes were identified in a large pan-European database highlighting the importance of gender-based phenotypes and the impact of these subtypes on treatment prescription. The impact of cluster on long-term treatment adherence and prognosis remains to be studied using the ESADA follow-up data set.
 Eight distinct clinical OSA phenotypes were identified in a large pan-European database highlighting the importance of gender-based phenotypes and the impact of these subtypes on treatment prescription. The impact of cluster on long-term treatment adherence and prognosis remains to be studied using the ESADA follow-up data set.