Objective: To understand the epidemiological characteristics and disease burden of influenza like illness (ILI) among student populations, so as to provide data support for policy formulation and optimal allocation of health resources. Methods: From January 2024 to February 2025, a questionnaire survey was conducted among parents of kindergarten, primary school, junior and senior high school students in 9 districts of Shenzhen, including Longhua, Futian, Bao an, Longgang, Luohu, Nanshan, Guangming, Pingshan and Yantian. Parents were asked to complete the questionnaire based on whether their children had fever, cough, vomiting, diarrhea, rash and other common symptoms in 2024. A total of 3 537 parents were investigated, and 444 ILI cases were included as study subjects. The epidemiological burden, including incidence rate of influenza, visitation rate, years lived with disability (YLDs) and economic burden (including direct economic burden, indirect economic burden and intangible burden) were analyzed. Results: The incidence rate of influenza among students in Shenzhen in 2024 was 12.55%. The ILI incidence rates in kindergarten, primary school, junior and senior high school were 14.01%, 11.69% and 5.23%, respectively, with a statistically significant difference ( χ 2= 45.20, P <0.01). The ILI consultation rate among students was 85.36%, and the consultation rates in kindergarten (87.36%) and primary school students (84.62%) were higher than those in junior and senior high school students ( 56.52 %) ( χ 2=16.47, P <0.01). A total of 78.88% of cases did not receive etiological detection.The median total economic burden per ILI case was 2 354.62 yuan, including direct medical costs of 300.00 yuan, direct non medical costs of 212.50 yuan, indirect costs of 1 000.00 yuan, and intangible burden of 500.00 yuan. Conclusions Schools are high risk environment for influenza, and younger students are a high risk group for ILI. The disease burden caused by student ILI remains substantial.

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit. REGISTRATION No. CRD42023427480; https://www.crd.york.ac.uk/prospero/display_record.php?
Humans ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Dose-Response Relationship, Drug ; Randomized Controlled Trials as Topic

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

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.

Increasing evidence shows that the early lesions of Parkinson's disease (PD) originate from gut, and correction of microbiota dysbiosis is a promising therapy for PD. FLZ is a neuroprotective agent on PD, which has been validated capable of alleviating microbiota dysbiosis in PD mice. However, the detailed mechanisms still need elucidated. Through metabolomics and 16S rRNA analysis, we identified glycoursodeoxycholic acid (GUDCA) was the most affected differential microbial metabolite by FLZ treatment, which was specially and negatively regulated by Clostridium innocuum, a differential microbiota with the strongest correlation to GUDCA production, through inhibiting bile salt hydrolase (BSH) enzyme. The protection of GUDCA on colon and brain were also clarified in PD models, showing that it could activate Nrf2 pathway, further validating that FLZ protected dopaminergic neurons through promoting GUDCA production. Our study uncovered that FLZ improved PD through microbiota-gut-brain axis, and also gave insights into modulation of microbial metabolites may serve as an important strategy for treating PD.

Although enteric glial cell (EGC) abnormal activation is reported to be involved in the pathogenesis of Parkinson's disease (PD), and inhibition of EGC gliosis alleviated gut and dopaminergic neuronal dysfunction was verified in our previous study, the potential role of gut microbiota on EGC function in PD still need to be addressed. In the present study, fecal microbiota transplantation revealed that EGC function was regulated by gut microbiota. By employing 16S rRNA and metabolomic analysis, we identified that 3-indolepropionic acid (IPA) was the most affected differential microbial metabolite that regulated EGC gliosis. The protective effects of IPA on PD were validated in rotenone-stimulated EGCs and rotenone (30 mg/kg i.g. for 4 weeks)-induced PD mice, as indicated by decreased inflammation, improved intestinal and brain barrier as well as dopaminergic neuronal function. Mechanistic study showed that IPA targeted pregnane X receptor (PXR) in EGCs, and inhibition of IL-13Rα1 involved cytokine-cytokine receptor interaction pathway, leading to inactivation of downstream JAK1-STAT6 pathway. Our data not only provided evidence that EGC gliosis was critical in spreading intestinal damage to brain, but also highlighted the potential role of microbial metabolite IPA in alleviating PD pathological damages through gut-brain axis.

Hepatic stellate cells (HSCs) are the primary fibrogenic cells in the liver, and their activation plays a crucial role in the development and progression of hepatic fibrosis. Here, we report that retinoid X receptor-alpha (RXRα), a unique member of the nuclear receptor superfamily, is a key modulator of HSC activation and liver fibrosis. RXRα exerts its effects by modulating calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ)-mediated activation of AMP-activated protein kinase-alpha (AMPKα). In addition, we demonstrate that K-80003, which binds RXRα by a unique mechanism, effectively suppresses HSC activation, proliferation, and migration, thereby inhibiting liver fibrosis in the CCl₄ and amylin liver NASH (AMLN) diet animal models. The effect is mediated by AMPKα activation, promoting mitophagy in HSCs. Mechanistically, K-80003 activates AMPKα by inducing RXRα to form condensates with CaMKKβ and AMPKα via a two-phase process. The formation of RXRα condensates is driven by its N-terminal intrinsic disorder region and requires phosphorylation by CaMKKβ. Our results reveal a crucial role of RXRα in liver fibrosis regulation through modulating mitochondrial activities in HSCs. Furthermore, they suggest that K-80003 and related RXRα modulators hold promise as therapeutic agents for fibrosis-related diseases.

[This corrects the article DOI: 10.1016/j.apsb.2025.02.029.].

Lactic acid (LA) accumulation in tumor microenvironments (TME) has been implicated in immune suppression and tumor progress. Diverse roles of LA have been elucidated, including microenvironmental pH regulation, signal transduction, post-translational modification, and metabolic remodeling. This review summarizes LA functions within TME, focusing on the effects on tumor cells, immune cells, and stromal cells. Reducing LA levels is a potential strategy to attack cancer, which inevitably affects the physiological functions of normal tissues. Alternatively, transporting LA into the mitochondria as an energy source for immune cells is intriguing. We underscore the significance of LA in both tumor biology and immunology, proposing the burning of LA as a potential therapeutic approach to enhance antitumor immune responses.
Humans ; Tumor Microenvironment/immunology* ; Neoplasms/therapy* ; Lactic Acid/immunology* ; Mitochondria/metabolism* ; Animals ; Signal Transduction