Background: This study aimed to construct a risk prediction model to estimate the odds of osteoporosis (OP) in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate its prediction efficiency. Methods: This study included 21,070 elderly patients with T2DM who were hospitalized at six tertiary hospitals in Southwest China between 2012 and 2022. Univariate logistic regression analysis was used to screen for potential influencing factors of OP and least absolute shrinkage. Further, selection operator regression (LASSO) and multivariate logistic regression analyses were performed to select variables for developing a novel predictive model. The area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance and clinical utility of the model. Results: The incidence of OP in elderly patients with T2DM was 7.01% (1,476/21,070). Age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and surgical history were the influencing factors. The seven-variable model displayed an AUROC of 0.713 (95% confidence interval [CI]:0.697–0.730) in the training set, 0.716 (95% CI: 0.691–0.740) in the internal validation set, and 0.694 (95% CI: 0.653–0.735) in the external validation set. The optimal decision probability cut-off value was 0.075. The calibration curve (bootstrap = 1,000) showed good calibration. In addition, the DCA and CIC demonstrated good clinical practicality. An operating interface on a webpage (https://juntaotan.shinyapps.io/osteoporosis/) was developed to provide convenient access for users. Conclusions: This study constructed a highly accurate model to predict OP in elderly patients with T2DM. This model incorporates demographic characteristics and clinical risk factors and may be easily used to facilitate individualized prediction.

Background: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. Methods: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. Results: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799–0.808), 0.794 (95% CI 0.788–0.800), 0.754 (95% CI 0.739–0.770), and 0.743 (95% CI 0.722–0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface (https://cytjt007.shinyapps.io/prolonged_los/) was developed to provide convenient access for users. Conclusion: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.

Background: Short peptide hydrogel was reported as a possible adjuvant for vaccines. In order to evaluate whether the Tetra-Peptide Hydrogel can be a promising adjuvant for an H7N9 vaccine against the highly pathogenic H7N9 virus, we conducted this study. Methods: Tetra-Peptide Hydrogels (D and L conformations) were prepared by a self-assembly system using a Naproxen acid modified tetra peptide of GFFY (Npx-GFFY). Mice received two immunizations with the D-Tetra-Peptide Hydrogel adjuvant vaccine, the L-Tetra-Peptide Hydrogel adjuvant vaccine, or the split vaccine. Fourteen days following the second dose, the mice were challenged with the highly pathogenic A/Guangdong/GZ8H002/2017(H7N9) virus. The mice were observed for signs of illness, weight loss, pathological alterations of the lung tissues and immune responses in the following 2 weeks. Results: The D/L-Tetra-Peptide Hydrogels resembled long bars with hinges on each other, with a diameter of ~10 nm. The H7N9 vaccine was observed to adhere to the hydrogel. All the unvaccinated mice were dead by 8 days post infection with H7N9. The mice immunized by the split H7N9 vaccine were protected against infection with H7N9. Mice immunized by D/L-Tetra-Peptide Hydrogel adjuvant vaccines experienced shorter symptomatic periods and their micro-neutralization titers were higher than in the split H7N9 vaccine at 2 weeks post infection. The hemagglutinating inhibition (HI) titer in the L-Tetra-Peptide Hydrogel adjuvant vaccine group was higher than that in the split H7N9 vaccine 1 week and 2 weeks post infection. The HI titer in the D-Tetra-Peptide Hydrogel adjuvant vaccine group was higher than that in the split H7N9 vaccine at 2 weeks post infection. Conclusion: The D/L Tetra-Peptide Hydrogels increased the protection of the H7N9 vaccine and could be promising adjuvants for H7N9 vaccines against highly pathogenic H7N9 virus.

Alopecia is a prevalent condition that significantly impacts appearance, mental health, and overall quality of life. Current clinical treatments for alopecia include medication, hair transplantation, low-level light therapy, and stem cell therapy, among others. However, the effectiveness of these treatments remains far from ideal. Extracellular vesicles (EVs) have emerged as a promising therapeutic strategy, yet challenges, such as mass production and clinical application persist. This review aims to explore recent advancements in the use of EVs for the treatment of alopecia. A comprehensive literature search was conducted in the PubMed database using a range of relevant keywords: extracellular vesicles, alopecia, hair loss, preparation, exosomes, microvesicles, apoptotic bodies, nanoscale particles, preclinical studies, clinical trial, clinical transformation, regenerative medicine, tissue engineering, stem cell, bioactive molecules, and extraction methods. The papers mostly focus on the biological characteristics and preparation of EVs, the basic and clinical research of EVs in the treatment of hair loss, and the challenges of clinical transformation of EVs were retracted. EVs are nanoscale, spherical particles enclosed by a lipid bilayer. They exert their effects by activating signaling pathways through ligand binding or by entering cells via membrane fusion or endocytosis. Compared to traditional medications or stem cell therapies, EVs offer several advantages, including high biological safety, sustained efficacy, low risk of immune rejection, and ease of storage and transportation. These properties make them a highly promising therapeutic option. Significant progress has been made in understanding the preparation, mechanisms, and early clinical applications of EVs. However, further research is needed to optimize key factors, such as dosing, cell sources, and administration frequency to develop more effective and safer therapies. With continued innovation in manufacturing technologies and collaborative efforts between academia and industry, EVs hold great potential for advancing clinical treatments for alopecia in the future.

A large number of papers have been published since the discovery of the monkeypox virus (MPXV). In 2022, human MPXV broke out in the USA and many European countries, and on July 23 of that year, the World Health Organization declared the MPXV outbreak a "Public Health Emergency of International Concern." In this study, we performed bibliometric analysis of MPXV literature published in core journals of the Web of Science core Collection database, which was conducted on 956 articles from 2000 to 2022. We analyzed the publication country, subject category, main authors and institutions, research hotspots of important publications and research frontiers. Through the analysis of keywords co-occurrence, multi-cited literature and co-cited literature, the research hotspots and trends were summarized. This report revealed that MPXV research has focused on hot areas such as infection and vaccination. Since the 2022 human MPXV outbreak, researchers have increasingly focused on infection control, vaccines, and treatment of monkeypox. This article summarizes the research directions and hotspots of MPXV related literature since the 2000, and we hope to provide support and clear research ideas for the future research and prevention of MPXV in humans.