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Shift from visceral to subcutaneous adipose tissue in Cyp17a1-knockout rats prevents the progression of metabolic syndrome
In this study, we investigated the effects of Cyp17a1 gene knockout (KO) on obesity and metabolic syndrome. Cyp17a1 KO in rats using CRISPR-Cas9 resulted in sex dimorphism and obesity, and interestingly, site-specific accumulation was found in subcutaneous adipose tissue (SAT). Surprisingly, an insulin tolerance test and oral glucose tolerance test did not show any issues with insulin sensitivity and secretion despite hyperglycemia. In addition, Cyp17a1 KO rats showed normal plasma insulin and free fatty acid levels compared to wild-type rats, and blood biochemistry analysis revealed normal triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels. Cyp17a1 KO adipose-tissue-derived stem cells from SAT showed increased expression of KLF5, an early adipogenesis marker, which implies enhanced adipogenic potential in SAT. When gene expression associated with lipid, glucose, and insulin metabolism as well as inflammation in adipose tissue was examined, a metabolic shift to SAT was discovered in the Cyp17a1 KO group. In conclusion, in the Cyp17a1 KO rat models we generated for the first time, the phenotype promoted by obesity reflected metabolically healthy obesity hypothesis, but this did not exhibit metabolic syndrome-like features due to enhanced metabolism in the SAT.
Socio-structural determinants of burn injuries in Africa: The role of social inequality, informal housing, and access to clean cooking energy technology
In many African countries, burn injuries often lead to chronic disability, psychological trauma and socio-economic hardship. These consequences have a long-term impact on people’s well-being and livelihoods. It is argued that the contributions of selected socio-structural determinants to burns are an important focus especially for population prevention measures. Therefore, this study examines the contribution of key socio-structural determinants to the non-fatal burden of burn injuries in Africa, with a focus on social inequality, informal housing and access to clean cooking energy technologies. The study applies the panel correlated standard errors regression, ordinary least squares regression with robust standard errors with adjusted predictions and marginal effects plots, and the least squares dummy variables analysis using panel data of all five regions of Africa for the period 2000–2022. The study finds that social inequality and informal housing exacerbate the burden of burn injuries in Africa. Conversely, access to clean cooking energy technology reduces this burden. The findings are confirmed by robustness checks. In this regard, the study recommends policies that lower structural inequities to reduce the risk of burns for vulnerable populations. This involves concretizing strategies to reduce poverty and providing more appropriately targeted social safety nets. There is also a need for region-specific strategies that tackle inequality, improve energy accessibility, and enhance housing conditions as a means of mitigating burn-related hazards across Africa.
AGPAT2 acts at the crossroads of lipid biosynthesis and DRP1-mediated ER morphogenesis
Transferrin-phosphatidylserine liposomes target TDP-43 and neuroinflammation in male mice with neuropathic pain
Preparation of silver-loaded metal-organic frameworks as a novel agent for arresting dental caries of primary teeth
Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning
Abstract Coulomb explosion imaging (CEI) can map the real-time coordinated motion of atoms in molecules during ultrafast photochemical reactions via correlations embedded in the resulting high-dimensional data. However, this rich information remains largely underexploited due to challenges in visualizing relationships between multiple observables in multidimensional parameter space. Here, we present a new approach to CEI of polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations. Our method yields high-dimensional, background-free momentum-space data and establishes an automated, scalable framework for extracting insightful structural information, enabling robust identification and differentiation of molecular structures. We demonstrate the method by imaging and distinguishing dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.