Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis.

BACKGROUND: Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets. METHODS: Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms. RESULTS: The Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo-keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity. CONCLUSION: These three genes are pivotal mitochondrial genes implicated in NASH progression.

Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis.

BACKGROUND: Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis. METHODS: To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used. RESULTS: The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures. CONCLUSION: ZFP36 is a key FRG involved in NASH pathogenesis.

Spatial Lifecourse Epidemiology and Infectious Disease Research.

Spatial lifecourse epidemiology aims to utilize advanced spatial, location-aware, and artificial intelligence technologies to investigate long-term effects of measurable biological, environmental, behavioral, and psychosocial factors on individual risk for chronic diseases. It could also further the research on infectious disease dynamics, risks, and consequences across the life course.

Real-Time Forecasting of Hand-Foot-and-Mouth Disease Outbreaks using the Integrating Compartment Model and Assimilation Filtering.

Hand-foot-and-mouth disease (HFMD) is a highly contagious viral infection, and real-time predicting of HFMD outbreaks will facilitate the timely implementation of appropriate control measures. By integrating a susceptible-exposed-infectious-recovered (SEIR) model and an ensemble Kalman filter (EnKF) assimilation method, we developed an integrated compartment model and assimilation filtering forecast model for real-time forecasting of HFMD. When applied to HFMD outbreak data collected for 2008-11 in Beijing, China, our model successfully predicted the peak week of an outbreak three weeks before the actual arrival of the peak, with a predicted maximum infection rate of 85% or greater than the observed rate. Moreover, dominant virus types enterovirus 71 (EV-71) and coxsackievirus A16 (CV-A16) may account for the different patterns of HFMD transmission and recovery observed. The results of this study can be used to inform agencies responsible for public health management of tailored strategies for disease control efforts during HFMD outbreak seasons.

Accelerated biodegradation of PLA/PHB-blended nonwovens by a microbial community.

In this study, the accelerated biodegradation of PLA/PHB (polylactic acid/polyhydroxybutyrate)-blended nonwovens was investigated in the presence of a microbial community. The PLA/PHB-blended nonwovens were buried in natural soil for 56 days, with soil samples collected for subsequent bacterial community domestication. The tensile strength and elongation at break of the PLA/PHB-blended nonwovens as well as the CO2 generated by the Gen III and natural soil communities were determined to assess the degradation rates of the polymer samples. After incubation for 15 days with the Gen III soil bacterial suspension, the surfaces and fibrous structure of nonwovens and the fibers within the nonwovens exhibited distinct changes. In addition, the amount of EvCO2 reached 566.79 mg, the tensile strength decreased from 10.95 ± 0.7 to 2.57 ± 0.31 MPa, a loss of 77%, and the elongation at break changed from 5.32 ± 0.45 to 7.07 ± 1.04%. The 16S rRNA pyrosequencing results showed that Proteobacteria and Firmicutes were the 2 most important bacterial phyla in the Gen III community, accounting for 80.4 and 19.4% of the total classified sequences, respectively. The results of this study demonstrate that compared to a natural soil microbial community, the domesticated strains in the Gen III community, especially members of the phyla Proteobacteria and Firmicutes, are useful in accelerating the degradation of PLA/PHB-blended nonwovens.