Objective: The cross sectional study aimed to identify predominant co-occurrence patterns among six common mental health issues in college students, so as to provide empirical basis for designing targeted interventions. Methods: From October 2024, a total of 9 837 students from 4 universities in Xiangtan City, Hunan Province, participated in the current study by multistage random cluster sampling method. Participants completed self report measures, including the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7 item Scale (GAD-7), Young s Internet Addiction Diagnostic Questionnaire, the Adolescent Insomnia Symptom Self rating Scale, the Ottawa Self injury Inventory, and the Brief Community Assessment of Psychic Experiences Questionnaire. Demographic and co-occurrence characteristics were first compared using Chi square or trend Chi-square tests, followed by application of the Apriori algorithm to mine association rules for primary co-occurrence patterns. Results: The detection rate of co-occuring the common mental health issues was 46.44%. The detection rate was significantly higher in female than in male students (50.42%, 43.61%; χ 2=44.46) and in students from rural versus urban areas (47.22%, 44.60%; χ 2=5.67) (both P <0.05). Significant differences were observed among freshmen, sophomores, juniors, and seniors (46.63%, 48.35%, 45.05% , 43.66%, respectively; χ 2=9.22, P <0.05), although no statistically significant trend was detected ( χ 2 trend =3.75, P = 0.05 ). Association rule mining identified “anxiety + depression” “anxiety + psychotic experiences + depression” and “anxiety + sleep disorder + depression” as the combinations with the highest support. In addition, “anxiety+depression+Internet addiction+psychotic experiences =>sleep disorder (>= refered to the occurrence of the latter item under the condition that the former item occurs)” and “anxiety + depression+Internet addiction=>sleep disorder” were combinations with relatively high confidence. Conclusions Co-occurrence of these mental health issues among college students is high and exhibits diverse patterns. Strategies to address this burden should prioritize integrated interventions that target these specific combinations of factors.

Primary cilia—those solitary, microtubule-based projections extending from the surface of most eukaryotic cells—are increasingly recognized not merely as cellular appendages, but as sophisticated signaling hubs. By compartmentalizing specific receptors (e.g., GPCRs) and effectors within a microdomain guarded by the transition zone, these organelles function effectively as high-gain sensors capable of integrating mechanical stimuli with metabolic cues. In this review, we examine the pivotal role of primary cilia across the nervous, bone-vascular, and renal landscapes, arguing for a unified “mechano-metabolic coupling” framework. Here, conserved ciliary modules are not static; rather, they are differentially deployed to uphold systemic homeostasis. Within the central nervous system, we position primary cilia as upstream integrators. We highlight how hypothalamic neuronal cilia concentrate metabolic receptors, such as the melanocortin 4 receptor (MC4R), to interpret energy status. Moreover, the recent identification of serotonergic “axon-cilium synapses” points to a direct mode of neurotransmission, wherein 5-HT6 receptors drive nuclear signaling and chromatin accessibility to rapidly modulate gene expression. Through these mechanisms, central cilia modulate sympathetic tone and neuroendocrine output, effectively establishing the mechanical and metabolic “boundary conditions” under which peripheral organs operate. Dysfunction in these central hubs is linked to obesity and neurodevelopmental disorders, including Bardet-Biedl syndrome. In peripheral tissues, cilia serve as versatile mechanotransducers that convert physical forces into biochemical responses. Regarding the bone-vascular system, we discuss the translation of mechanical loads and fluid shear stress into structural remodeling. In osteoblasts, specifically, ciliary integrity is intrinsically linked to cholesterol and glucose metabolism, fine-tuning the balance between Hedgehog and Wnt/β-catenin signaling to govern osteogenesis and bone repair. A similar dynamic exists in the vasculature, where endothelial cilia sense shear stress to modulate KLF4 expression and endothelial-to-mesenchymal transition—processes critical for valvulogenesis and vascular remodeling. Meanwhile, in the kidney, tubular cilia act as terminal effectors within a “shear-cilia-metabolism” axis. Here, fluid shear stress engages ciliary signaling to trigger AMPK-mediated lipophagy and mitochondrial biogenesis, thereby securing the ATP supply required for solute transport. Notably, dysregulation of this axis leads to metabolic reprogramming and aberrant proliferation, acting as a hallmark driver of cystogenesis in polycystic kidney disease (PKD). Crucially, this review attempts to dissect the often-conflated logic of cross-system integration by distinguishing 3 non-equivalent pathways: direct communication via ciliary extracellular vesicles, though this remains largely hypothetical in long-range signaling; “physiology-mediated cascades”, where ciliary dysfunction in a single organ—such as the kidney—precipitates systemic pathology through hemodynamic and metabolic shifts (e.g., altered blood pressure, fluid volume, or uremic toxins); and “parallel molecular defects”, where shared genetic mutations in ubiquitous components like the IFT machinery cause simultaneous, independent failures across multiple organ systems. Building on these distinctions, we propose a nested-loop model that links central set-points with peripheral feedback via physiological variables. Furthermore, we construct a “causality-to-translation” roadmap that pinpoints structural repair (e.g., targeting IFT assembly) and metabolic rescue (e.g., AMPK activation or autophagy induction) as promising therapeutic avenues. Ultimately, this framework provides a theoretical basis for deciphering the shared pathological mechanisms of multisystem ciliopathies, offering a strategic guide for the development of targeted interventions that go beyond symptomatic treatment.

[摘 要] 目的：探究着丝粒蛋白M（CENPM）在脑胶质瘤中的表达特征、临床预后价值及其对肿瘤恶性生物学行为的调控机制，为脑胶质瘤的精准治疗提供潜在靶点。方法：基于中国胶质瘤基因组图谱（CGGA）和癌症基因组图谱（TCGA）数据库分析CENPM在胶质瘤中的表达及其与患者临床病理特征和预后的相关性。通过基因本体论（GO）分析、京都基因与基因组百科全书（KEGG）富集分析和单细胞转录组分析探索CENPM的生物学功能和作用机制。WB法检测CENPM在胶质瘤细胞（LN-18、LN-229、U-138MG、U-251MG）和正常胶质细胞（HEB）中的表达；构建CENPM敲低细胞后，通过CCK-8、集落形成、Transwell和划痕实验评估恶性表型改变。结果：CENPM在WHO高级别胶质瘤中呈高表达（P < 0.05），与肿瘤恶性程度正相关。高表达组患者总体生存期显著短于低表达组（P < 0.01），Cox回归证实CENPM是影响胶质瘤患者预后的独立危险因素（P < 0.05）。功能富集分析结果显示CENPM相关基因主要富集于细胞周期调控、PI3K-Akt通路和免疫相关过程。单细胞分析结果显示CENPM主要在CD8⁺ T细胞高表达，并通过PTN-PTPRZ1/NCL配受体调控细胞通信。体外实验证实CENPM在胶质瘤细胞表达高于正常胶质细胞（LN-18：P < 0.01，LN-299：P < 0.05）；敲低CENPM显著抑制迁移（P < 0.05），但增强集落形成，提示其在肿瘤进展中的双重调控作用。结论：CENPM作为胶质瘤独立预后危险因子，通过调控细胞周期、PTN通路和免疫微环境驱动肿瘤进展，其差异化调控机制（抑制迁移、促进增殖）具有潜在的临床转化价值，可作为分子分型和靶向治疗候选标志物。

Background: This study was performed to characterize the incidence, costs, and risk factors associated with renal and cardiovascular adverse outcomes following primary percutaneous coronary intervention (pPCI) in patients with ST-elevation myocardial infarction (STEMI). Methods: Patients with STEMI who underwent pPCI using iso-osmolar contrast were enrolled at 39 centers. The incidence of acute kidney injury (AKI) and major adverse renal and cardiovascular events (MARCE) was analyzed, as well as inpatient costs. Logistic regression analysis was performed to identify risk factors. Results: Among 2,293 patients, the incidence of AKI and MARCE within 72 hours post-pPCI was 4.14% (n = 95) and 4.40% (n = 101), respectively. AKI and/or MARCE were associated with systolic blood pressure (AKI: odds ratio [OR], 1.009; 95% confidence interval [CI], 1.000–1.018), hypertension (AKI: OR, 1.815; 95% CI, 1.133–2.906; MARCE: OR, 1.760;95% CI, 1.118–2.769), anterior wall infarction (AKI: OR, 1.895; 95% CI, 1.196–3.004; MARCE:OR, 1.939; 95% CI, 1.240–3.032), Killip class (AKI: OR, 1.465; 95% CI, 1.117–1.922; MARCE:OR, 1.467; 95% CI, 1.131–1.903), and serum creatinine (SCr; MARCE: OR, 1.006; 95% CI, 1.000–1.012). Hospitalization costs for patients with STEMI who developed AKI or MARCE were significantly higher than for those without AKI (9,595 ± 5,795 vs. 8,279 ± 3,872 USD, P = 0.003) or without MARCE (9,890 ± 5,616 vs. 8,255 ± 3,859 USD, P < 0.001). Conclusions In patients with STEMI undergoing pPCI with iso-osmolar contrast, the incidence of AKI and MARCE was associated with higher hospitalization costs. Systolic blood pressure, hypertension, anterior wall infarction, Killip class, and SCr were identified as risk factors for these outcomes.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Coronary heart disease （CHD） has a distinct circadian rhythmicity in its onset， which is closely related to the waxing and waning of qi and blood in the channel passages of the human body. Grounded in the circadian rhythm theory of "correspondence between heaven and humankind"， this paper explores the pathogenic evolution rules of qi-blood disharmony， channel obstruction， interacting as cause and effect with circadian rhythm disorders in CHD. Clinically， CHD can be observed to attack in the early morning， at night， in the afternoon， and at fixed or unfixed circadian intervals. The pathogenesis rhythms at different time nodes can correspondingly reflect the susceptibility tendencies and pathological stress responses of the human body to core pathogeneses such as yang deficiency with cold congelation， yin deficiency with yang floating， qi and yin deficiency， and liver constraint with qi stagnation under specific temporal states. Treatment should be adapted to the season and time， regulating yin and yang in accordance with biological rhythms. It is recommended to regulate qi during the daytime by promoting ascending and dispersing to unblock the channel passages， and select the appropriate timing for administration to harness natural timing. At the Mao （卯） hour， yang ascends while yin descends， and taking medication at this time conforms to the ascending of yang qi. At the Wu （午） hour， the heart meridian is active， and taking medication at this time warms the channel passages and assists heart yang， which can be treated with modified Buzhong Yiqi Decoction （补中益气汤） or Shengyang Yiwei Decoction （升阳益胃汤）. At night， blood should be harmonized， when the channel passages are unblocked through nourishing， restraining， and searching， with Lianmei Decoction （连梅汤） combined with Dahuang Zhechong Pill （大黄䗪虫丸）. Following the temporal rhythm， the focus is put on the transitional time of day and night and the four seasons to treat disease before it arises， resonating and corresponding with the natural rhythm.

Objective To explore the value of integrating CT image features and quantitative dual-energy computed tomography(DECT)parameters in predicting cervical lymph nodes metastasis from laryngeal and hypopharyngeal squamous cell carcinoma(LHSCC).Methods The clinical and imaging data of 99 patients with LHSCC confirmed by pathology were retrospectively analyzed.All patients were divided into metastatic group(41 cases)and non-metastatic group(58 cases).The CT image features,including location,size and depth,were analyzed,respectively.The quantitative DECT parameters in the arterial and venous phases including iodine concentration(IC)and normalized iodine concentration(NIC)were measured.The rank sum test or independent-samples t-test were used to compare the difference of CT image features and quantitative DECT parameters between the two groups.The multivariate logistic regression analysis was used to build the models based on CT image features(image feature model)and combination of CT image features and quantitative DECT parameters(combined model).The receiver operating characteristic(ROC)curve was performed to analyze and compare the difference of predictive efficiency between the two groups.Results There were significant differences in tumor location between the non-metastatic group and the metastatic group(χ2=21.736,P<0.001).Size(33.20 mm vs 24.95 mm,P<0.001),depth(21.10 mm vs 13.15 mm,P<0.001)and NIC in the arterial phase(0.18 vs 0.14,P<0.001)in the metastatic group were significantly higher than those in the non-metastatic group.The area under the curve(AUC),sensitivity,specificity,positive predictive value,negative predictive value,accuracy of the combined model were 0.851,75.6%,82.8%,58.5%,87.9%and 75.8%for predicting cervical lymph nodes metastasis.The AUC,sensitivity,specificity,positive predictive value,negative predictive value,accuracy of the image feature model were 0.792,95.1%,56.9%,53.7%,81.0%and 69.7%,respectively.The prediction performance of the combined model was better than that of the image feature model(Z=-2.028,P=0.043).Conclusion Integrating CT image features and quantitative DECT parameters has important value for predicting cervical lymph nodes metastasis from LHSCC.

Arrhythmia refers to abnormal heart rhythm or frequency,which causes the heart unable to pump blood effectively,thus affecting the overall heart function.Its occurrence can be attributed to a variety of factors.The pathological mechanisms of arrhythmia are closely related to the"Xuanfu-Qiye-Luomai"framework.From a clinical perspective,the direct cause of arrhythmia is the disorder of Xuanfu opening and closing,where the imbal-ance of Qi and fluids is the key factor.Furthermore,the underlying cause of the disease's progression lies in the defi-ciency and stagnation of the Luo vessels.Based on this"Xuanfu-Qiye-Luomai"pathogenesis model,traditional Chinese medicine(TCM)treatment should focus on the holistic principles of"opening the Xuanfu,regulating Qi and fluids,and nourishing the vessels".This approach involves the flexible use of herbal medicines to regulate the Xuan-fu,blood-activating and blood-stasis-eliminating herbs to balance Qi and fluids,along with tonifying agents to nourish and unblock the vessels.The goal is to transform tangible pathogenic factors,such as phlegm and dampness,into intangible Qi,thereby harmonizing the Qi and blood to restore heart function.This approach provides new in-sights into the TCM diagnosis and treatment of arrhythmia.

Objective:To analyze the factors associated with long-term survival after radical resection of hepatitis B virus(HBV)-associated hepatocellular carcinoma(HCC),and to construct random forest and Cox regression models,to evaluate the two models.Methods:A total of 368 patients with HBV-infected HCC who underwent radical resection were selected retrospectively.These patients were categorized as having a good prognosis(n=266)or a poor prognosis(n=102)based on their survival and mortality status.Univariate and Cox regression analysis were used to identify fac-tors that predict poor prognosis in HCC patients after surgery,and Cox regression and random forest prediction models were constructed and evaluated.Results:There were significant differences in smoking history,Child-Pugh classifica-tion,cirrhosis,microvascular invasion,TNM staging,tumor capsule integrity,platelet-to-lymphocyte ratio(PLR),regular antiviral therapy,HBV-DNA load,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR),systemic immune in-flammatory index(SII),and albumin-to-globulin ratio(AGR)between the two groups(P<0.05);Cox regression showed that cirrhosis,microvascular invasion,regular antiviral treatment,HBV-DNA load,NLR,PLR,SII,and AGR were related factors that negatively affected the prognosis of patients with HBV-infected HCC after surgery(P<0.05),with an AUC of 0.870 for predicting prognosis;the importance ranking obtained by the random forest model was HBV-DNA load,cirrho-sis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII,with an AUC of 0.926 for predicting prog-nosis;the AUC predicted by the random forest model was greater than that predicted by the Cox regression model(Z=2.411,P=0.016).Conclusion:HBV-DNA load,cirrhosis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII are factors that affect the poor prognosis of patients with HBV-related HCC after surgery.The random for-est prediction model constructed based on these factors has high predictive value and is superior to the Cox regression prediction model.