ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To compare and analyze malaria epidemic trends and characteristics of initial confirmed cases before and after malaria eliminating in Qingjiangpu District, Huai′an City, and to provide a scientific basis for improving strategies to prevent imported malaria retransmission. Methods Malaria case data from Qingjiangpu District were obtained from the National Infectious Disease Information Reporting System for 2010-2023. Case investigation and epidemiological data were summarized and analyzed for the pre-elimination period (2010-2016)and post-elimination period(2017-2023). Results A total of 185 malaria cases were reported between 2010 and 2023, with one locally acquired case in both 2010 and 2011; the remainder were imported, primarily falciparum malaria. Post-elimination cases decreased from 140 to 45, showing a statistically significant difference in case distribution ratios(χ2=9.642, P<0.05). Seasonal peaks occurred in early January and mid-July before elimination but showed no distinct pattern afterward. Malaria cases were mainly imported from Africa countries, and most patients were young adult males. Post-elimination, the proportion of migrant workers decreased, while technical and business personnel increased significantly(χ2=17.855, P<0.05). Both time-to-diagnosis and time-to-certification changed markedly after elimination, with township health centers showing improved diagnostic accuracy at initial visits. All confirmed patients were treated at designated municipal medical institutions. Conclusions After malaria elimination in Qingjiangpu District, imported malaria remains a serious concern, with cases reported each year. Therefore, it is necessary to continue strengthening the malaria monitoring system, enhance screening and evaluation of key populations, improve health education, raise diagnostic and treatment capacity across all medical institutions, and consolidate the achievements of malaria elimination.

Objective The aim was to evaluate the effects of efforts directed toward controlling Oncomelania hupensis snails on small environmental modification projects in Anqing City to provide a reference for comprehensively controlling O. hupensis in hilly areas where schistosomiasis is endemic. Methods Field and retrospective surveys were conducted to collect data using questionnaires. The data from 35 questionnaires on small-scale environmental modification projects in Anqing City and the effects of snail obtained from monitoring forms were sorted. The data were descriptively analyzed using basic information on environmental modifications and investment costs. The Wilcoxon signed-rank test was used to compare snail status before and after environmental interventions. The Friedman test was applied to evaluate the changes in snail populations in the years after environmental intervention. Non-parametric independent sample tests were adopted to compare differences in the effects of different environmental interventions, environmental characteristics, and between project and control areas. Results A total of 35 environments altered from 2017 to 2023, including the creation of 29 ditches,1 dry land, and 5 pond-weirs. The main environmental interventions included hardening of ditches and pond-weirs as well as tilling with intercropping. The average investment costs of mortared rubble masonry hardening and tilling with intercropping were the highest(147.10 yuan/m2)and lowest(4.62 yuan/m2), respectively. The pre-intervention snail-infested area was 78 900 m2, which decreased to 18 100 m2, a decrease of 77.06%. After environmental intervention, the density and rate of living snails significantly decreased on frames with snails(P<0.001). In ditch environments, the smoothness of the ditch walls and the presence of snails in the upper reaches did not significantly decrease the living snail density; however, the presence of cracks on the inner side of the ditches significantly impacted the decline in the rate(P<0.001). No significant difference was found in the rates at which the living snail density or snail frame occurrence decreased among different environmental modification method. The effects of brick masonry, concrete, and mortared rubble masonry on snail control were significantly stronger than those of chemical snail control(P<0.01), whereas no significant difference was found in the decline in these two rates between project areas using other modification method and control areas using chemical snail control. Conclusions Schistosomiasis-prevention-oriented environmental interventions effectively eliminate snails from hilly snail-infested areas. Cost-effective modification method should be selected based on the environmental type, and post-modification monitoring and maintenance must be strengthened to further increase snail control effectiveness.

Objective To analyze the epidemiological characteristics of poisonous mushroom-related foodborne disease incidents in Chongqing from 2010 to 2024, and to provide a scientific basis for effective prevention and control of mushroom-related foodborne diseases. Methods Data of poisonous mushroom related foodborne disease incidents reported by the CDCs of 39 districts and counties in Chongqing from 2010 to 2024 were collected, and their epidemic characteristics were analyzed. Results A total of 264 poisonous mushroom poisoning outbreaks were reported in Chongqing from 2010 to 2024, resulting in 1 046 cases of illness, 543 hospitalizations, and 15 deaths, with a mortality rate of 1.43%. The peak of poisoning incidents was from June to October, accounting for 89.02% of the total number of incidents. The top three high-incidence areas were Pengshui Miao Tujia Autonomous County, Dianjiang County, and Nanchuan District. Poisonous mushroom poisoning incidents occurring in household settings, accidental picking and ingestion, and cases involving unidentified mushroom species accounted for 89.02%, 83.33%, and 90.15% of all foodborne disease outbreak incidents, respectively. Conclusion Foodborne disease incidents related to poisonous mushrooms in Chongqing City show an upward trend, and the effectiveness of prevention and control needs to be improved. Based on their seasonal, regional and household clustering characteristics, efforts should be made before the peak season of mushroom poisoning in high-risk areas, targeting key populations with health education, monitoring and early warning, food safety supervision, and medical treatment.

ObjectiveFlavonoids are clinically significant natural products, yet their oxygen-glycosylation in aqueous environments relies heavily on expensive nucleotide-activated sugar donors such as UDP-glucose. Liquid-liquid phase separation (LLPS) creates specialized, membraneless physicochemical microenvironments capable of modulating enzymatic functions and overcoming mass transfer limitations. This study aims to investigate whether the gut microbiota-derived DgpB/C complex—a multienzyme system traditionally recognized for cleaving stable C-glycosidic bonds and facilitating isomerization—can undergo functional remodeling within phase-separated condensates. Our core objective is to elucidate the role of phase separation in expanding enzymatic catalytic plasticity and to provide a non-canonical, highly cost-effective biocatalytic mechanism for the direct utilization of free sugars in the synthesis of O-glycosylated natural products. MethodsAn artificial phase-separation platform was constructed utilizing the multivalent arginine-glycine-glycine motif (RGG)-repeat domain derived from the Caenorhabditis elegans LAF-1 protein. To ensure precise spatial compartmentalization, the DgpB/C complex was specifically recruited into the RGG condensates via a high-affinity SZ1/SZ2 heterodimerization tag system. Condensate formation and substrate partitioning were visualized using light and confocal fluorescence microscopy. The chemical structures and regioselectivity of the reaction products were rigorously characterized using high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS)/MS systems. Furthermore, molecular docking and 20-ns molecular dynamics (MD) simulations were performed via the Hermite platform and Uni-GBSA pipeline to elucidate the structural and thermodynamic basis underlying the phase-transition-induced functional shift. ResultsWe observed that the condensates formed by RGG proteins selectively recruited and significantly enriched hydrophobic flavonoid substrates. Strikingly, within the LLPS microenvironment, the DgpB/C complex—which typically exhibits only degradative or isomerase activities—underwent a profound functional remodeling, transforming into an efficientO-glycosyltransferase. Diverging from canonical pathways that require high-energy donors, the sequestered DgpB/C complex directly utilized unactivated free glucose to catalyze the formation of new O-glycosidic bonds. This remodeled activity was found to be evolutionarily conserved across DgpB/C homologs from diverse gut microbiota strains, such as P581a and W974-1. LC-MS/MS analysis further revealed that the phase-separated environment reduced the regioselectivity constraints of the enzyme, enabling catalytic action on multiple hydroxyl positions of the flavonoid scaffold. MD simulations further indicated that the low-water-activity microenvironment of the condensates reconfigured the conformational dynamics of the catalytic pocket, favoring a spatial orientation highly conducive to dehydration condensation. ConclusionThis study demonstrates that LLPS drives the functional remodeling of the gut microbiota enzyme DgpB/C through the reconfiguration of the physicochemical microenvironment. These findings challenge traditional perceptions regarding the functional boundaries of metabolic enzymes and reveal a novel biocatalytic pathway that bypasses the requirement for nucleotide-activated sugars. Consequently, this provides a highly promising artificial compartmentalization strategy for the green manufacturing of complex, high-value-added natural products.

Objective To analyze the factors affecting the prognosis of patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) undergoing immunotherapy and construct an individualized prognostic nomogram prediction model. Methods A retrospective analysis was conducted on the clinical data of 385 patients with driver gene-negative, locally advanced or metastatic NSCLC who received first-line immune checkpoint inhibitors. Univariate and multivariate Cox regression analyses were used to identify prognostic risk factors, and a prognostic nomogram model was established. The predictive performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC), and calibration curves. The cutoff value of the nomogram was calculated to stratify patients by risk. Survival curves were calculated by Kaplan-Meier analysis. Results Age (HR=1.775, 95%CI: 1.265-2.490), degree of differentiation (HR=0.365, 95%CI: 0.257-0.519), low PD-L1 expression (HR=0.661, 95%CI: 0.455-0.960), high PD-L1 expression (HR=0.423, 95%CI: 0.297-0.603), SCC-Ag (HR=1.549, 95%CI: 1.109-2.163), and CONUT score (HR=2.527, 95%CI: 1.797-3.554) were independent risk factors affecting overall survival (OS) of patients with NSCLC undergoing immunotherapy. The nomogram prediction model constructed on the basis of these factors had a C-index of 0.767. Time-dependent ROC curves for survival showed that the AUCs for 1-, 2-, and 3-year OS were 0.830, 0.853, and 0.886, respectively. Calibration curves indicated that the nomogram-predicted survival rates were in good agreement with the actual outcomes. The cutoff value for the study’s nomogram prediction model was 136.60 points, and survival curves showed statistically significant differences between different risk groups (P<0.05). Conclusion The nomogram model established in this study can effectively predict the prognosis of patients with driver gene-negative locally advanced or metastatic NSCLC treated with first-line immunosuppressive therapy. It provides a new tool for assessing prognosis and aids clinicians in formulating individualized treatment plans.

ObjectiveTo investigate the effects of paeoniflorin （PF） on neuroinflammation and cognitive impairment in the rat model of Alzheimer's disease （AD） and to clarify whether its mechanism is associated with regulation of the Janus kinase 2 （JAK2）/signal transducer and activator of transcription 3 （STAT3） signaling pathway. MethodsGlycosides related to AD-associated neuroinflammation were screened through bibliometric analysis， and the potential targets of PF were predicted through network pharmacology and molecular docking. A rat model of AD was established by intraperitoneal injection of D-galactose （100 mg·kg^-1·d^-1）. Rats were assigned into a control group， a model group， a PF （200 mg·kg^-1） group， and a donepezil （0.5 mg·kg^-1·d^-1） group， and drugs were administered by gavage for 21 days. The learning and memory abilities were evaluated by the Morris water maze （MWM）. Hematoxylin-eosin （HE） staining and Nissl staining were performed to observe hippocampal neuronal morphology. The mRNA levels of inflammatory factors were determined by Real-time quantitative polymerase chain reaction （Real-time PCR）， and the protein levels of molecules in the JAK2/STAT3 signaling pathway were determined by Western blot. ResultsCompared with the control group， the model group showed prolonged escape latency and decreased platform crossings and time spent in the target quadrant （P<0.05， P<0.01）， accompanied by increased expression of interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， as well as elevated ratios of phosphorylated （p）-JAK2/JAK2 and p-STAT3/STAT3 （P<0.01）. Compared with the model group， the PF group and donepezil group exhibited shortened escape latency and increased platform crossings and time spent in the target quadrant （P<0.05， P<0.01）， decreased expression levels of IL-1β and TNF-α， upregulated mRNA levels of interleukin-4 （IL-4） and interleukin-10 （IL-10）， and reduced ratios of p-JAK2/JAK2 and p-STAT3/STAT3 （P<0.05， P<0.01）. ConclusionPaeoniflorin can alleviate cognitive impairment and neuroinflammation in the rat model of AD by inhibiting the JAK2/STAT3 signaling pathway.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.