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.

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.

OBJECTIVE To investigate the effects of galangin on rheumatoid arthritis （RA） in rats by regulating the Janus kinase 3 （JAK3）/signal transducer and activator of transcription 3 （STAT3） pathway. METHODS Fifty male SD rats were taken， and an emulsion composed of bovine type Ⅱ collagen and Freund’s complete adjuvant was injected subcutaneously to establish an induced arthritis model. The rats that were successfully modeled were randomly divided into model group， low， medium and high dose groups of galangin （1， 5， 15 mg/kg）， and methotrexate group （positive control， 2 mg/kg）， with 10 rats in each group. Another 10 normal rats were taken as the normal group. Starting from the 15th day of modeling， each group of rats was gavaged with the corresponding drug solution or normal saline containing 0.5% Tween 80 once a day for 28 consecutive days. The arthritis index （AI） scores and paw volume of rats were compared before and after gavage administration. Twenty-four hours after the last administration， the serum levels of interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， IL-4 and IL-10 were determined， the pathological changes in ankle joint synovial tissue were observed， and the protein expressions of UNC-51 like kinase 1 （ULK1）， Beclin-1， microtubule-associated protein 1 light chain 3 （LC3）， B cell lymphoma 2 （Bcl-2）， Bcl-2-associated X protein （Bax）， caspase-3， JAK3， phosphorylated JAK3 （p-JAK3）， STAT3 and phosphorylated STAT3 （p-STAT3） in the synovial tissue of the ankle joint were detected， as well as the fluorescence intensity of LC3-positive areas. RESULTS Compared with the model group， the pathological changes such as cellular proliferation of ankle joint synovial tissue and infiltration of inflammatory cells in rats of each administration group showed improvement. Moreover， their AI scores and paw pad volumes （on day 28 after gavage）， the levels of IL-6 and TNF-α， the protein expression of Bcl-2， and the phosphorylation levels of JAK3 and STAT3 were all significantly reduced （ P ＜0.05）. The levels of IL-4 and IL-10， the protein expressions of ULK1， Beclin-1， Bax， caspase-3 and LC3， as well as the fluorescence intensity of LC3-positive areas， were all significantly increased （ P ＜0.05）. Moreover， the effect of galangin was in a dose-dependent manner （ P ＜0.05）. CONCLUSIONS Galangin can induce sustained autophagy in synovial tissue cells of RA rats， promote cell apoptosis， inhibit synovial cell proliferation， and alleviate persistent inflammatory responses. The above anti-RA effects may be related to the inhibition of the JAK3/STAT3 pathway.

[摘 要] 目的：探讨异位表达血红蛋白亚基（HBA/HBB）对缺氧条件下嵌合抗原受体T细胞（CAR-T细胞）功能障碍的改善作用及其对肿瘤细胞的杀伤效应。方法：全基因合成技术合成靶向HER2的CAR序列，构建共表达HBA或HBB的CAR慢病毒载体，包装慢病毒后感染人原代T淋巴细胞，制备异位表达HBA/HBB的CAR-T细胞，命名为HBA CAR-T和HBB CAR-T。采用缺氧探针检测小鼠实体瘤缺氧状态。通过流式细胞术检测瘤内CAR-T细胞占比、异位表达血红蛋白亚基的CAR-T细胞阳性率及CAR-T细胞的活性氧、凋亡水平。WB法检测HBA CAR-T和HBB CAR-T内相关血红蛋白亚基表达情况，采用细胞计数板计数检测细胞增殖水平，通过萤光素酶报告基因法检测CAR-T细胞对肿瘤细胞的杀伤能力，qPCR检测CAR-T细胞中缺氧诱导因子-1α（HIF-1α）表达水平，利用MitoXpress Intra试剂盒检测CAR-T细胞内氧气含量。结果：不同细胞构建的实体瘤模型均存在明显缺氧情况，且CAR-T细胞浸润水平与缺氧程度呈显著负相关（P < 0.000 1）。HBA CAR-T与HBB CAR-T构建成功（阳性率 > 60%），相应血红蛋白亚基可稳定表达。缺氧环境下HBA CAR-T和HBB CAR-T的ROS水平、凋亡水平显著下降，增殖、对肿瘤细胞的体外杀伤能力显著强于传统CAR-T细胞（均P < 0.05）。HBA CAR-T与HBB CAR-T内HIF-1α表达降低（均P < 0.001），且缺氧程度显著降低（均P < 0.001）。结论：异位表达血红蛋白亚基可改善缺氧条件下CAR-T细胞功能障碍并增强其对肿瘤细胞的体外杀伤作用。

The core pathogenesis of attention deficit hyperactivity disorder （ADHD） lies in deficiency， stasis and stagnation. Deficiency arises from kidney essence depletion and spleen dysfunction in transportation and transformation， leading to inadequate nourishment of the marrow sea. Stasis caused by qi deficiency leads to obstruction in channels and collaterals， resulting in obstructed marrow transport. Stagnation is associated with the excess of the five minds transforming into fire， which scorches the brain orifices and leads to loss of control over marrow utilisation. Based on this， a "supplementation-unblocking-regulation" therapeutic approach is proposed. For deficiency， the focus is on supplementing kidney and fortifying spleen， and replenishing the marrow sea. For stasis， the priority is to unblock and open the orifices， and clear the marrow channels. For stagnation， the core is to clear fire and contain the mind， regulate and restore vital activity. In clinical practice， it is necessary to identify the primary and secondary pathogenic mechanisms and apply dynamic， combined treatment， integrating Chinese herbal medicine， acupuncture， and guiding exercises throughout the process， aiming to provide a reference for the diagnosis and treatment of ADHD with traditional Chinese medical.

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.

ObjectiveTo investigate the risk factors for pyogenic liver abscess （PLA） comorbid with sepsis by analyzing clinical features， and to construct a predictive model. MethodsA retrospective analysis was performed for 489 patients who were hospitalized and diagnosed with PLA in The Affiliated Hospital of Xuzhou Medical University from January 2019 to December 2023， and according to the presence or absence of sepsis， they were divided into sepsis group with 306 patients and non-sepsis group with 183 patients. Related data were collected， including general information， laboratory markers， and outcome measures. The patients were further divided into a training set of 342 patients and a validation set of 147 patients at a ratio of 7∶3， and the training set was used for screening of variables and construction of a predictive model， while the validation set was used to test the performance of the model. An LASSO regression analysis was used for the screening of variables， and a multivariate Logistic regression analysis was used to construct the predictive model and plot a nomogram. The calibration curve， the receiver operating characteristic （ROC） curve， and the decision curve analysis were used for the validation of the model， and internal validation was performed for assessment. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical variables between groups. ResultsThere were significant differences between the sepsis group and the non-sepsis group in pulse rate， mean arterial pressure， duration pf symptoms， comorbidity of liver cirrhosis or malignant tumor， leukocyte count， neutrophil count， lymphocyte count， platelet count （PLT）， activated partial thromboplastin time， fibrinogen， C-reactive protein， aspartate aminotransferase， alanine aminotransferase， albumin， total bilirubin （TBil）， creatinine， potassium， and prognostic nutritional index （PNI） （all P<0.05）. In the training set， the LASSO regression analysis identified four predictive factors of pulse rate， PLT， TBil and PNI， and the multivariate Logistic regression analysis showed that pulse rate （odds ratio ［OR］=1.033， 95% confidence interval ［CI］： 1.006‍ ‍—‍ ‍1.061， P=0.018）， PLT （OR=0.981， 95%CI： 0.975‍ ‍—‍ ‍0.987， P<0.001）， TBil （OR=1.086， 95%CI： 1.053‍ ‍—‍ ‍1.125， P<0.001）， and PNI （OR=0.935， 95%CI： 0.882‍ ‍—‍ ‍0.988， P=0.019） were independent influencing factors for the risk of sepsis in patients with PLA. The model constructed based on these factors showed a good predictive ability， with an area under the ROC curve of 0.948 （95%CI： 0.923‍ ‍—‍ ‍0.973） in the training set and 0.912 （95%CI： 0.848‍ ‍—‍ ‍0.976） in the validation set. The decision curve analysis showed that the model has a good net benefit within the range of 0.3‍ ‍—‍ ‍0.9 for threshold probability. ConclusionThe nomogram prediction model constructed based on pulse rate， PLT， TBil， and PNI has a certain clinical value and can well predict the risk of sepsis in patients with PLA.

Objective To evaluate the application of shear wave elastography(SWE)in the diagnosis of peripheral neuropathy in patients with diabetes.Methods Totally 85 patients with type 2 diabetes(T2DM)were selected from the Chengdu Office Hospital of People's Government of Tibetan Autonomous Region,including 46 patients with peripheral neuropathy(DPN)and 39 patients without peripheral neuropathy(NDPN).Compared for clinical data(gender,age,disease duration),cross-sectional area of the median nerve measured by high-frequency ultra-sound(CSA)and shear wave elastography(SWE)parameters(mean Young's modulus value,Emean)and shear wave velocity(SWV)between two groups of patients.Multifactor Logistic regression analysis was carried out on the indicators between the above groups to screen independent predictors in the diagnosis of peripheral neuropathy in diabetes patients,and a combined model was constructed.The area under the operating characteristic curve(AUC),sensitivity and specificity of the subjects were used to evaluate the diagnostic efficacy of the single model and com?bined model of the quantitative parameters(CSA,Emean,SWV)measured by clinical data,high?frequency ultra?sound and SWE in the diagnosis of peripheral neuropathy in diabetes patients.Results Age,course of disease,Emean,SWV and CSA were statistically significant in the diagnosis of peripheral neuropathy in diabetes patients(all P<0.05).AUC was 0.658,0.754,0.839,0.822 and 0.736,respectively.The combination model based on disease course,CSA and SWV showed the highest diagnostic efficiency,with AUC,sensitivity,and specificity of 0.887(0.800-0.946),80.43％,and 84.62％,respectively.Conclusions The combined model based on the course of disease,CSA and SWV have a high diagnostic efficiency in peripheral neuropathy of diabetes patients,and has good clinical application value.

Chikungunya fever(CHIKF)is a mosquito-borne infectious disease caused by Chikungunya virus(CHIKV)infection,which has become a significant public health threat of global concern in recent years.The acute phase of CHIKF is primarily characterized by sudden high fever,severe polyarthralgia,and rash.The disabling arthralgia may progress to chronic arthritis,with symptoms lasting for months or even years.This not only severely reduces patients'quality of life and impairs their labor capacity,but also imposes a heavy socioeconomic burden.In recent years,significant progress has been made in CHIKF-related research.This review elaborates on the latest research achievements in the fields of the disease's epidemiology,clinical manifestations,pathogenic mechanisms,immune response,diagnostic techniques,and prevention and treatment strategies,aiming to provide comprehensive and in-depth references for researchers and clinicians in relevant fields.