FDA-approved CAR T-cell Therapy: A Decade of Progress and Challenges.

CAR T-cell therapy is a promising approach for cancer treatment, utilizing a patient's own T-cells (autologous cell) or T-cells from a healthy donor (allogeneic cell) to target and destroy cancer cells. Over the last decade, significant advancements have been made in this field, including the development of novel CAR constructs, improved understanding of biology and mechanisms of action, and expanded clinical applications for treating a wider range of cancers. In this review, we provide an overview of the steps involved in the production of CAR T-cells and their mechanism of action. We also introduce different CAR T-cell therapies available, including their implementation, dosage, administration, treatment cost, efficacy, and resistance. Common side effects of CAR T-cell therapy are also discussed. The CAR T-cell products highlighted in this review are FDA-approved products, which include Kymriah® (tisagenlecleucel), Tecartus® (brexucabtagene autoleucel), Abecma® (Idecabtagene vicleucel), Breyanzi® (lisocabtagene maraleucel), and Yescarta® (axicabtagene ciloleucel). In conclusion, CAR T-cell therapy has made tremendous progress over the past decade and has the potential to revolutionize cancer treatment. This review paper provides insights into the progress, challenges, and future directions of CAR T-cell therapy, offering valuable information for researchers, clinicians, and patients.

ZNF143 inhibits hepatocyte mitophagy and promotes non-alcoholic fatty liver disease by targeting increased lncRNA NEAT1 expression to activate ROCK2 pathway.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common hepatic disorders worldwide. The mitophagy is suggested to be repressed in NAFLD, but the mechanism remains to be elucidated. METHODS: NAFLD cell and mouse models were established by treating with free fatty acid (FFA) and feeding a high fat diet (HFD), respectively. QRT-PCR, Western blotting, or IHC measured the expression of ZNF143, lncRNA NEAT1, ROCK2, and lipid formation/mitophagy-related proteins. Cell viability and mitophagy were evaluated by MTT and immunofluorescence. The chloroform-methanol extraction method measured triglyceride and total cholesterol levels. ELISA detected ALT and AST levels. The interactions among ZNF143, lncRNA NEAT1 and SND1 were analysed by ChIP, dual-luciferase reporter, pull-down, and RIP. The lipid droplets were determined by Oil-red O and HE staining. RESULTS: ZNF143 and lncRNA NEAT1 were upregulated in hepatic cells treated with FFA (p < 0.01 and p < 0.001). Knockdown of ZNF143 or lncRNA NEAT1 inhibited lipid droplets formation, while promoting mitophagy (p < 0.01 and p < 0.001). ZNF143 promoted lncRNA NEAT1 transcriptional expression through binding to its promoter. LncRNA NEAT1 increased ROCK2 mRNA stability by targeting SND1. LncRNA NEAT1 or ROCK2 overexpression reversed the effect of ZNF143 or lncRNA NEAT1 knockdown on hepatic steatosis and mitophagy (p < 0.01 and p < 0.001). ZNF143 or lncRNA NEAT1 knockdown inhibited HFD-induced steatosis and promoted mitophagy in vivo (p < 0.01 and p < 0.001). CONCLUSION: The upregulation of lncRNA NEAT1 caused by ZNF143 promoted NAFLD through inhibiting mitophagy via activating ROCK2 pathway by targeting SND1, providing potential targets for NAFLD therapy.

Integrating Multiple Data Sources and Learning Models to Predict Infectious Diseases in China.

The outbreaks of infectious diseases do not only endanger people's lives and property, but can also result in negative social impact and economic loss. Therefore, establishing early warning technologies for infectious diseases is of great value. This paper was built on the historical morbidity and mortality incidence data of infectious diseases, including typhoid fever, Hemorrhagic Fever with Renal Syndrome (HFRS), mumps, scarlatina, malaria, dysentery, pertussis, conjunctivitis, pulmonary tuberculosis, diarrhea from 2012 to 2016 in China. We also integrated search engine query data and seasonal information into the prediction models. Multiple models for prediction, including linear model, time series analysis model, boosting tree model and deep learning model (recurrent neural network, RNN) were constructed in order to predict the morbidity incidence of 10 infectious diseases. The RNN model has better predictive capability for these diseases. The improvement of techniques for infectious disease prediction can facilitate constructive and positive change towards disease prevention.