Objective To prepare a thermosensitive hydrogel scaffold loaded with bone marrow mesenchymal stem cells（BMSCs） and resveratrol liposomes （RSV-LIP） to form a therapeutic unit and evaluate its treatment efficacy for traumatic brain injury （TBI）. Methods BMSCs were extracted from rats, and RSV-LIP was prepared and characterized. Cell models were constructed to investigate the pharmacological effects of BMSCs combined with RSV-LIP. BMSCs and RSV-LIP were then loaded into the hydrogel, and a TBI mouse model was established to evaluate the therapeutic effects of the hydrogel. Results The RSV-LIP had a particle size of 127.8 nm, a Zeta potential of −4.9 mV, an encapsulation efficiency of 78.50%, and a drug loading content of 2.37%. Live-dead staining indicated good biocompatibility of the hydrogel. The combination of BMSCs and RSV-LIP significantly inhibited TNF-α and reduced ROS levels, promoting cell migration in scratch assays. Compared to the control group, the hydrogel group showed significantly lower mNSS scores （P<0.01）, higher hanging scores （P<0.001）, and reduced stepping errors （P<0.001）. Conclusion The combination of BMSCs and RSV-LIP exhibited antioxidative stress, anti-inflammatory, and neurogenic cell migration-promoting effects. When loaded into a hydrogel scaffold and locally implanted, it could improve the motor and sensory functions in TBI mice.

Based on the regulation of mitochondrial fatty acid β-oxidation through the PGC1α/PPARα/CPT1A pathway, this study investigated the effect of Jianpi Qinghua Formula on the mitochondrial fatty acid β-oxidation pathway in the livers of mice with metabolic-associated fatty liver disease(MAFLD) induced by a high-fat diet. MAFLD mice were fed a high-fat diet to establish the model, and after successful modeling, the mice were divided into the model group, the Jianpi Qinghua Formula group, and the metformin group, with an additional control group. Each group was treated with the corresponding drug or an equivalent volume of saline via gavage. Body mass and food intake were measured regularly during the experiment. At the end of the experiment, blood lipid levels and liver function-related indices were measured, liver pathological changes were observed, and protein expression levels of PGC1α, PPARα, PPARγ, and CPT1A were detected by Western blot. The results showed that, with no difference in food intake, compared to the model group, the body mass of the Jianpi Qinghua Formula group and the metformin group was reduced, liver weight and liver index decreased, and levels of cholesterol, triglycerides, and low-density lipoprotein cholesterol(LDL-C) were lowered. Additionally, a decrease in alanine aminotransferase(ALT) and aspartate aminotransferase(AST) was observed. Hematoxylin and eosin(HE) staining revealed reduced pathological damage to hepatocytes, while oil red O staining showed improvement in fatty infiltration. The liver disease activity score decreased, and transmission electron microscopy revealed improvement in mitochondrial swelling and restoration of internal cristae. Western blot analysis indicated that Jianpi Qinghua Formula significantly increased the expression of PGC1α, PPARα, and CPT1A proteins in the liver and reduced the expression of PPARγ. These results suggest that the Jianpi Qinghua Formula improves mitochondrial function, promotes fatty acid oxidation, and alleviates the pathological changes of MAFLD. In conclusion, Jianpi Qinghua Formula can improve MAFLD by mediating mitochondrial fatty acid β-oxidation through the PGC1α/PPARα/CPT1A pathway.
Animals ; PPAR alpha/genetics* ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Carnitine O-Palmitoyltransferase/genetics* ; Male ; Liver/metabolism* ; Fatty Liver/genetics* ; Humans ; Mice, Inbred C57BL ; Diet, High-Fat/adverse effects*

Traumatic brain injury (TBI) is intricately linked to the most severe clinical manifestations of brain damage. It encompasses dynamic pathological mechanisms, including hemodynamic disorders, excitotoxic injury, oxidative stress, mitochondrial dysfunction, inflammation, and neuronal death. This review provides a comprehensive analysis and summary of biomaterial-based tissue engineering scaffolds and nano-drug delivery systems. As an example of functionalized biomaterials, nano-drug delivery systems alter the pharmacokinetic properties of drugs. They provide multiple targeting strategies relying on factors such as morphology and scale, magnetic fields, pH, photosensitivity, and enzymes to facilitate the transport of therapeutics across the blood-brain barrier and to promote selective accumulation at the injury site. Furthermore, therapeutic agents can be incorporated into bioscaffolds to interact with the biochemical and biophysical environment of the brain. Bioscaffolds can mimic the extracellular matrix environment, regulate cellular interactions, and increase the effectiveness of local treatments following surgical interventions. Additionally, stem cell-based and exosome-dominated extracellular vesicle carriers exhibit high bioreactivity and low immunogenicity and can be used to design therapeutic agents with high bioactivity. This review also examines the utilization of endogenous bioactive materials in the treatment of TBI.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.