The imbalance between osteoblasts and osteoclasts is the cause of osteoporosis. Milk-derived extracellular vesicles (mEVs), excellent drug delivery nanocarriers can promote bone formation and inhibit bone resorption. In this study, we conjugated bone-targeting peptide (AspSerSer, DSS)₆ to mEVs by click chemistry and then loaded with SRT2104, a SIRT1 (silent mating-type information regulation 2 homolog 1) agonist that was proofed to help reduce bone loss. The engineered (DSS)₆-mEV-SRT2104 had the intrinsic anti-osteoporosis function of mEVs and SRT2104 to reverse the imbalance in bone homeostasis by simultaneously regulating osteogenesis and osteoclastogenesis. Furthermore, we labelled mEVs with MnB nanoparticles that can be used for the in vivo magnetic resonance imaging (MRI) visualization. The obtained nanocomposites significantly prevented bone loss in osteoporosis mice and increased bone mineral density, exhibiting superior bone accumulation under MRI. We believe the proposed of (DSS)₆-mEV-SRT2104/MnB provides a novel paradigm for osteoporosis treatment and monitoring.

Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.

Cell-penetrating peptides (CPPs) are attractive non-viral gene delivery vectors due to their high transfection capacity and safety. Previously, we have shown that cell-penetrating peptide RALA can be a promising gene delivery vector for chronic wound regeneration application. In this study, we engineered a novel peptide called RALA-E by introducing elastin-derived VGVAPG fragment into RALA, in order to target the elastin-binding protein on the cell surface and thus improve delivery efficacy of RALA. The transfection efficiency of RALA-E was evaluated by transfecting the HEK-293T and HeLa cell lines cells with RALA-E/pDNA complexes and the flow-cytometry results showed that RALA-E significantly increased the transfection efficiency by nearly 20% in both cell lines compared to RALA. Inhibition of pDNA transfection on HEK-293T cells via chlorpromazine, genistein and mβCD showed that the inhibition extent in transfection efficiency was much less for RALA-E group compared to RALA group. In addition, RALA-E/miR-146a complexes showed up to 90% uptake efficiency in macrophages, and can escape from the endosome and enter the nucleus to inhibit the expression of inflammation genes. Therefore, the developed RALA-E peptide has high potential as a safe and efficient vector for gene therapy application.

Aims: This mediation analysis was aimed at examining whether ghrelin mediates the association between gut microbiota and glucose metabolic indices. Materials and Methods: Fifty‐five patients with newly diagnosed Type 2 diabetes mellitus (T2DM) and 40 healthy controls (HCs) were included. Serum ghrelin levels were measured by ELISA. Fecal 16S rRNA gene sequencing was performed, and gut microbiota differences were assessed using linear discriminant analysis effect size (LEfSe). The relative abundance values of differential bacteria were extracted to correlate with ghrelin and glucose metabolic indices. Mediation analysis was conducted to evaluate ghrelin’s effect on the relationship between bacterial abundance and glucose metabolic indices. Results: Acylated ghrelin (AG) levels were significantly elevated in the T2DM group and showed positive correlations with glycated hemoglobin (HbA1c), fasting blood glucose (FBG), C‐peptide (CP), and homeostasis model assessment of insulin resistance (HOMA‐IR). Microbial analysis revealed decreased abundances of Akkermansia , Lachnospira , and Phocaeicola , but increased abundances of Enterobacter , Escherichia , Megamonas , Segatella , Streptococcus , and Leyella in T2DM. AG levels were negatively correlated with Akkermansia abundance. HbA1c and FBG showed positive correlations with Enterobacter , Segatella , and Leyella abundances, but negative correlations with Akkermansia , Lachnospira , and Phocaeicola abundances. HOMA‐IR was positively correlated with Segatella , Leyella , and Streptococcus abundances, but negatively correlated with Akkermansia and Lachnospira abundances. Mediation analysis demonstrated that serum AG partially mediated the association between Akkermansia abundance and both HOMA‐IR (26.15%) and HbA1c (27.91%). Conclusions: AG mediated the relationship between Akkermansia abundance and HOMA‐IR as well as HbA1c.

Background: Thyroid cancer (TC) has shown a rising prevalence worldwide. While numerous studies have explored the relationship between vitamin D levels and TC risk, their conclusions remain inconsistent.Objective: This meta-analysis aims to evaluate the association between serum vitamin D levels, vitamin D deficiency, and TC based on existing evidence.Methods: We systematically searched the Embase, Web of Science, and PubMed databases for human studies investigating the relationship between vitamin D and TC including a control group. A random-effects model with forest plots was employed to calculate the mean difference (MD) in serum vitamin D levels, the odds ratio (OR) for vitamin D deficiency, and the risk difference (RD) between TC cases and controls. Meta-regressions and subgroup analyses were conducted based on the season of serum 25(OH)D sampling, source of controls, timing of measurement, study type, and testing methods of 25(OH)D. A p-value <0.05 was considered statistically significant.Results: A total of 23 studies were included. The meta-analysis revealed that TC patients had significantly lower serum vitamin D compared to the controls [SMD = −0.38 (95% CI: −0.62 to −0.14)].Additionally, vitamin D deficiency was significantly more prevalent among TC patients (OR = 1.33, 95% CI: 1.02 to 1.73, P < 0.05). The subgroup analyses demonstrated significant differences across most subgroups, except for post-operative measurements. Seasonal variation in 25(OH)D sampling was identified as a key source of heterogeneity.Conclusions: The meta-analysis suggests that lower serum vitamin D levels and vitamin D deficiency are significantly associated with an increased risk of TC. However, further studies with standardized protocols for seasonal sampling of vitamin D, source of control, measurement timing, study type, and testing methods of 25(OH)D are needed to clarify this relationship and its underlying mechanisms.

Cognitive impairment is a complication of type 2 diabetes with high prevalence and serious harm. This study aims to identify serum and imaging biomarkers of type 2 diabetes with mild cognitive impairment (MCI) and to study whether spontaneous brain activities evaluated by functional MRI mediate the relationships between these serum neuroprotein biomarkers and cognitive ability. According to Montreal Cognitive Assessment (MoCA) scores, 38 patients with MCI and 32 patients with normal cognition in type 2 diabetes were recruited. Serum neuroproteins were determined with Olink Proteomics. Brain activities were evaluated by functional MRI. Logistic regression were performed to determine the biomarkers of type 2 diabetes with MCI. Mediation analysis were performed to examine the potential causal chain between the neuroproteins and cognitive ability. The levels of Epiregulin (EREG), Abhydrolase domain containing 14B (ABHD14B) and Inositol monophosphatase 1 (IMPA1) decreased, while the level of Cysteine-rich intestinal protein 2 (CRIP2) increased in type 2 diabetic patients with MCI (FDR p < 0.05). Combination of EREG and CRIP2, a ROC curve was generated with an AUC of 0.85 and an accuracy of 70.0%, and a sensitivity of 86.7% for diagnosing type 2 diabetes with MCI. Brain activities in bilateral superior parietal gyrus, bilateral postcentral gyrus, left inferior frontal gyrus, right cerebellum 6, left cerebellum crus1 decreased, while increased in right hippocampus and left middle frontal gyrus in type 2 diabetic patients with MCI (FDR p < 0.05). The mALFF values in the left cerebellum crus-1 mediated 29.1% of the association between the EREG and MoCA scores (p < 0.001). The mALFF values in the right cerebellum-6, left cerebellum crus-1, right hippocampus, right superior parietal gyrus and right postcentral gyrus all significantly and partially mediated the association between ABHD14B levels and MoCA scores, with mediating effects of 21.7%, 27.1%, 25.8%, 20.7% and 19.2%, respectively (all p values < 0.001). Type 2 diabetic patients with MCI exhibit specific serum neuroprotein and functional MRI characteristics. The relationships between these neuroproteins and cognitive ability were mediated by spontaneous brain activities.