Background Per- and poly-fluoroalkyl substances (PFAS) can influence gestational diabetes mellitus (GDM); however, current studies on their association are limited and have yielded inconsistent findings. Objective To investigate the association between maternal exposure to PFAS, as measured by urinary concentrations in early pregnancy, and the risk of developing GDM. Methods Based on the Wuhu Birth Cohort in Anhui Province conducted between 2020 and 2023, this study included 1910 pregnant women. Urine samples were collected during the first trimester, and a 75 g oral glucose tolerance test (OGTT) was completed at 24–28 gestational weeks. The concentrations of six PFAS in urine were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Participants were categorized into low, medium, and high exposure groups according to tertiles of individual PFAS concentrations. Logistic regression analysis was employed to assess the potential impact of early-pregnancy PFAS exposure levels on the risk of GDM, with stratified analyses by maternal age and fetal sex. A generalized weighted quantile sum regression (gWQS) model was applied to evaluate the relative weight distribution of the six PFAS in their mixture effect. Results The logistic regression results revealed that both medium exposure to perfluorohexane sulfonate (PFHxS) (OR=1.475, 95%CI: 1.040, 2.093) and high exposure to perfluorononanoic acid (PFNA) (OR=1.430, 95%CI: 1.013, 2.018) were positively associated with diagnosed GDM. This association was more pronounced among women older than 28 years. Among pregnant women carrying male fetuses, high-level exposures to per fluorohexanoic acid (PFHxA) (OR=1.708, 95%CI: 1.006, 2.899) and PFHxS (OR=2.116, 95%CI: 1.247, 3.584) showed positive associations with diagnosed GDM. In contrast, among those carrying female fetuses, moderate perfluorooctanesulfonic acid (PFOS) exposure was associated with a lower risk of GDM (OR=0.542, 95%CI: 0.311, 0.946). The gWQS results demonstrated that PFOS (0.48) and perfluorobutane sulfonate (PFBS) (0.34) contributed the most to the mixture effect. Conclusion Early-pregnancy exposure to PFAS (particularly PFHxS and PFNA) may be an etiological factor for GDM, with maternal age and fetal sex potentially acting as effect modifiers in this association.

Objective: To investigate the antibody testing demand and its influencing factors among college students in Shaoxing City Zheijiang Province, so as to provide evidence for carrying out HIV antibody testing services for college students. Methods: From November to December 2022, stratified cluster random sampling method was used to survey college students from 13 colleges in Shaoxing City. Data on socio-demographic information, demand for HIV antibody testing, sexual behaviors, and HIV-related knowledge were collected through a self - designed questionnaire. Factors affecting HIV antibody demand among college students were analyzed using a multivariable logistic regression model. Results: A total of 43 716 college students were surveyed, including 19 471 males (44.54%) and 24 245 females (55.46%). There were 2 004 college students (4.58%) with HIV antibody testing demand. Multivariable logistic regression analysis showed that female students (OR=1.344, 95%CI: 1.217-1.485), those in undergraduate institutions (OR=1.296, 95%CI: 1.168-1.438), homosexuals (OR=1.742, 95%CI: 1.301-2.333) or bisexual/uncertain (OR=1.342, 95%CI: 1.171-1.537), those who had sexual experience (OR=1.416, 95%CI: 1.210-1.657), those who had sex with men in the past 6 months (OR=2.164, 95%CI: 1.405-3.333), those who received HIV testing promotion at school in the past year (OR=1.500, 95%CI: 1.327-1.695), and those with perceived risk of HIV infection (OR=5.875, 95%CI: 5.336-6.468) had greater HIV antibody testing demand. Conclusions The HIV testing demand among college students is associated with gender, school type, sexual orientation, sexual behaviors, HIV testing promotion, and perceived risk of HIV infection. It is recommended to strengthen HIV knowledge dissemination of AIDS knowledge and mobilization for HIV antibody testing in colleges to improve the accessibility of HIV antibody testing services.

ObjectiveTo establish an educational intervention framework of electronic picture books for children with autism spectrum disorder (ASD) based on Creative Problem Solving (CPS) model, and observe the effect on social function. MethodsElectronic picture books were designed using CPS model, including the program of reading, interactive animation design and associated artistic activities. From March to June, 2023, 24 children with mild to moderate ASD were recruited from Maanshan Institute of Special Education, and randomly assigned into experimental group 1 (CPS-based cognitive picture book interventions, n = 8), experimental group 2 (CPS-based social picture book interventions, n = 8) and control group (Bloom's Taxonomy-based picture book interventions, n = 8), and received the interventions for four weeks, with a total of twelve times. They were assessed with speech, social, perceptual and health behavior using Autism Treatment Evaluation Checklist (ATEC) before and after treatment, and the differences were calculated. ResultsThere was a significant difference among the groups in the social and perceptual scores differences (F > 4.344, P < 0.05) and a near-significant difference in the health behaviour score difference (F = 2.921, P = 0.076). Post Hoc test showed that the differences in social scores were higher in both experimental groups than in the control group (P < 0.05); the difference in perceptual scores was significantly higher in experimental group 1 than in experimental group 2 (P < 0.01), but there was no significant difference with the control group (P > 0.05); the difference in health behavior scores was higher in experimental group 2 than in the control (P < 0.05), and the difference in experimental group 1 was slightly higher than in the control group (P = 0.072). ConclusionElectronic picture book interventions based on the CPS model may be more effective in enhancing social, perceptual and health behavior in children with ASD, but there may be differences among models.

ObjectiveTo explore the possible mechanisms of Modified Shoutai Pill （寿胎丸加味方， MSP） in treating polycystic ovary syndrome （PCOS） with hyperandrogenism， insulin resistance， and miscarriage， focusing on inflammatory response and endometrial receptivity. MethodsThirty female SPF-grade SD rats with regular estrous cycles and in proestrus， and 15 male SPF-grade SD rats were housed together in a 2∶1 ratio at 18：00. At 8：00 next morning， rats showing abundant sperm and vaginal plugs were considered pregnant on the day 0.5. The 30 pregnant rats were randomly divided into three groups， normal group， model group， and MSP group， with 10 rats in each group. From day 0.5 to day 13.5 of pregnancy， the MSP group was given 26.6 g/（kg·d） of the MSP via gavage twice a day for 14 consecutive days. The normal group and the model group received 4 ml of normal saline daily. From day 7.5 to day 13.5 of pregnancy， the rats in the model group and MSP group were intraperitoneally injected with dihydrotestosterone （DHT） and insulin （INS） for 7 consecutive days to establish a PCOS model with hyperandrogenism， insulin resistance， and miscarriage. On day 13.5 of pregnancy， an oral glucose tolerance test （OGTT） was performed to measure blood glucose levels at 0， 30， 60， 90， and 120 minutes. On day 14.5， serum level of progesterone （P₄）， estradiol （E₂）， fasting insulin （FINS）， interleukin-6 （IL-6）， and tumor necrosis factor-alpha （TNF-α） were measured by ELISA. The insulin resistance index （HOMA-IR） was calculated. Embryo implantation， miscarriage rate， and average number of live fetuses were observed. Uterine tissue pathology was examined by HE staining， and mRNA expression of Il-6， Tnf-α， leukemia inhibitory factor （Lif）， homeobox gene 10 （Hoxa10）， prolactin family 8 subfamily A member 2 （Prl8a2）， and insulin-like growth factor-binding protein 1 （Igfbp1） in the uterine tissue was detected by qRT-PCR. ResultsCompared with the normal group， the model group had significantly higher blood glucose level at 0， 30， 60， 90， and 120 minutes， increased miscarriage rate， elevated HOMA-IR， decreased average number of live fetuses， lower level of P₄ and E₂， higher level of IL-6， TNF-α， and FINS， and higher mRNA expression of Il-6 and Tnf-α in the uterine tissue. The mRNA expression of Lif， Hoxa10， and Prl8a2 was reduced （P＜0.05 or P＜0.01）. The uterus had a dark red color， visible areas of bleeding， fewer embryos with developmental abnormalities， and increased placental necrosis. Pathological examination revealed thrombus in the decidual layer， unclear decidual cell morphology， loose arrangement， scattered distribution， edema degeneration in the cytoplasm， and nuclear shrinkage or disappearance， with extensive infiltration of inflammatory cells. In contrast， compared with the model group， the MSP group showed significantly lower blood glucose level at 0， 30， 60， 90， and 120 min， reduced miscarriage rate， lower HOMA-IR， increased number of live fetuses， higher level of P₄ and E₂， and lower level of IL-6， TNF-α， and FINS. The mRNA expression of Il-6 and Tnf-α in the uterine tissue was lower， while the expression of Lif， Hoxa10， and Prl8a2 mRNA was higher （P＜0.05 or P＜0.01）. There was significant improvement in uterine and embryo conditions， as well as in uterine tissue pathology. ConclusionThe MSP can reduce the miscarriage rate in a PCOS model with hyperandrogenism， insulin resistance， and miscarriage. Its mechanism may involve inhibiting inflammation， improving endometrial receptivity， and restoring the defects in endometrial decidualization.

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Alzheimer's disease (AD) is characterized by cognitive and functional deterioration, with pathological features such as amyloid-beta (Aβ) aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein. Despite a thorough investigation, current treatments targeting the reduction of Aβ production, promotion of its clearance, and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations, posing a substantial obstacle in the development of drugs for AD. Recently, artificial intelligence (AI), computational biology (CB), and systems biology (SB) have emerged as promising methodologies in AD research. Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns, thereby enriching our comprehension of AD pathology. This paper provides a comprehensive examination of the utilization of AI, CB, and SB in the diagnosis of AD, including the use of imaging omics for early detection, drug discovery methods such as lecanemab, and complementary therapies like phototherapy. This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.

Hypoxic-ischemic (HI) brain damage poses a high risk of death or lifelong disability, yet effective treatments remain elusive. Here, we demonstrated that miR-100-5p levels in the lesioned cortex increased after HI insult in neonatal mice. Knockdown of miR-100-5p expression in the brain attenuated brain injury and promoted functional recovery, through inhibiting the cleaved-caspase-3 level, microglia activation, and the release of proinflammation cytokines following HI injury. Engineered extracellular vesicles (EVs) containing neuron-targeting rabies virus glycoprotein (RVG) and miR-100-5p antagonists (RVG-EVs-Antagomir) selectively targeted brain lesions and reduced miR-100-5p levels after intranasal delivery. Both pre- and post-HI administration showed therapeutic benefits. Mechanistically, we identified protein phosphatase 3 catalytic subunit alpha (Ppp3ca) as a novel candidate target gene of miR-100-5p, inhibiting c-Fos expression and neuronal apoptosis following HI insult. In conclusion, our non-invasive method using engineered EVs to deliver miR-100-5p antagomirs to the brain significantly improves functional recovery after HI injury by targeting Ppp3ca to suppress neuronal apoptosis.
Animals ; MicroRNAs/metabolism* ; Extracellular Vesicles/metabolism* ; Mice ; Recovery of Function/physiology* ; Hypoxia-Ischemia, Brain/therapy* ; Mice, Inbred C57BL ; Antagomirs/administration & dosage* ; Male ; Animals, Newborn ; Apoptosis/drug effects* ; Brain Injuries/metabolism* ; Glycoproteins ; Peptide Fragments ; Viral Proteins

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.