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.

In this work,four salicylhydrazone compounds(L1?L4)were designed and synthesized by using vanillin derivatives as raw material.The structures were confirmed by nuclear magnetic resonance(NMR)and high-resolution mass spectrometry(HRMS).The optical experiments showed that probe L1 and probe L3 could be used as aluminium ion(Al3+)fluorescence probes.The fluorescence color of probe L1 solution changed from colorless to blue after adding Al3+,and the limit of detection was 25.1 nmol/L.Compared with probe L1,the fluorescence color of probe L3 solution changed from colorless to green after complexing with Al3+,and the limit of detection was 17.3 nmol/L.Probe L1 and probe L3 showed the advantages of fast response speed,high selectivity and good anti-interference.The mechanism of Al3+recognition was further demonstrated by HRMS and 1H NMR.Cell imaging experiments showed that probe L1 and L3 had low cytotoxicity and had great application potential in detection of Al3+in vivo.

A second-order calibration method combined with excitation-emission matrix(EEM)fluorescence spectroscopy was presented for simultaneous quantitative analysis of two anti-hypertensive drugs,metolazone(MET)and valsartan(VAL),in human serum and urine,and the quantitative results were compared with the results obtained by zero-and first-order calibration methods.The results indicated that the methods based on zero-and first-order calibration were inadequate for accurately quantifying the components of interest in cases where severe spectral overlap and unknown interferences coexisted.However,it was possible to obtain satisfactory results with the second-order calibration method based on alternating normalization-weighted error(ANWE)algorithm because of its strong"mathematical separation",even when the fluorescence spectra of the target analytes and unknown interferents considerably overlapped.Correlation coefficients for both analytes were greater than 0.99,with mean recoveries of 104.9%±5.7%and 107.8%±9.2%for MET and VAL in human serum,and 103.7%±8.9%and 94.7%±3.8%in human urine,respectively.In addition,the sensitivity,selectivity,limit of detection,limit of quantification,repeatability,and reproducibility of the proposed second-order calibration method were thoroughly examined.All results indicated that the established method was capable of achieving simultaneous and accurate quantification of MET and VAL in human body fluids,which was expected to be applied to analysis of both drugs in clinical settings.

Malonylation is an important post-translational modification of proteins.In this work,a comprehensive malonylation proteomics study on hepatocellular carcinoma(HCC)tumorous and non-tumorous tissues using antibody enrichment combined with high performance liquid chromatography-mass spectrometry for discovery of early diagnostic biomarkers or potential new drug targets of HCC was performed.A total of 1299 malonylated peptides containing 1064 malonylated sites were identified from HCC tissues,corresponding to 511 malonylated proteins.Quantitative results showed that 56 and 80 malonylated proteins were up-regulated and down-regulated in HCC tissues,including 60 and 101 malonylated sites,respectively.Kyoto encyclopedia of genes and genomes(KEGG)pathway analysis showed that these differentially modified proteins were involved in various important pathways such as metabolic pathways,fatty acid degradation,and glycolysis/gluconeogenesis.As a key enzyme in glycolysis/gluconeogenesis,phosphoenolpyruvate carboxykinase 1(PCK1)was malonylated at lysine 244(K244)and the malonylation was only detected in HCC tumorous tissues.More importantly,the K244 site served as a binding site for Mn2+and highly conserved across different species.Therefore,it could speculate that the malonylation of K244 would affect its activity and played a role in liver cancer by affecting its binding with Mn2+,which requied further verification through site mutation experiments.Western blot analysis by malonylation pan antibody showed that the malonylation level reduced markedly in HCC tumorous tissues compared with adjacent non-tumorous tissues,which was consistent with mass spectrometry data.In addition,the proliferation and invasion of PLC/PRF/5 cell was significantly inhibited and protein malonylation level was increased obviously when treated with sodium malonate.All the evidence indicated that protein malonylation played an important role in HCC pathogenesis,and its molecular mechanism deserved further investigation.Furthermore,the 136 differentially malonylated proteins provided rich source of candidate targets for further research on HCC pathogenesis.

Objective To addresses the challenges arising from the rapid expansion of pharmaceutical clinical trials and the growing demands for quality management,this paper investigates the application of low-code technology in project management.Its goals are to enhance the operational efficiency and execution capabilities of clinical trial institutions,ensure trial quality and safety,and accelerate the translation of pharmaceutical scientific achievements.Methods A brainstorming session was conducted to analyze the technical and functional requirements for managing pharmaceutical clinical trial projects.Utilizing the ＂template design＂ and ＂decision analysis＂ functionalities of low-code technology,the study adopted a modular and visually driven data management approach to develop a system compliant with Good Clinical Practice(GCP)standards.This system integrates key functionalities,including project progress management,funding management,drug inventory management,and quality control.Its effectiveness was evaluated through real-world operation and performance validation.Results The system had demonstrated stable operation with substantial improvements in practical application.Compared with conventional management approaches,it significantly enhanced project management efficiency:the time required for project schedule management was reduced by 80%,the efficiency of financial processing increased by 95%,drug inventory management efficiency improved by 75%,and the time spent on quality control was shortened by 60%.Conclusion The pharmaceutical clinical trial project management system developed using low-code technology offers substantial advantages and promising application potential.It represents a critical practice in applying digital and intelligent tools to advance pharmaceutical productivity in the medical and healthcare sectors.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Extensive penile ulcer secondary to granulomatosis with polyangiitis（GPA）is clinically rare. This study reports a 20-year-old male patient with pathologically confirmed GPA-induced penile ulcers. Following control of GPA progression and wound infection management，reconstruction using a scrotal pedicled flap was performed to address the penile skin defect. The follow-up of 1 year results demonstrated favorable flap survival and significant improvement in penile appearance.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Objective:To explore the influence of initial high-risk cytogenetic abnormalities on the outcomes of children with acute myeloid leukemia (AML) after post-transplant Cyclophosphamide (PTCy)-based allogeneic hematopoietic stem cell transplantation (allo-HSCT).Methods:A retrospective cohort study.AML children who underwent PTCy-based allo-HSCT after the first complete remission at Wuhan Children′s Hospital, Tongji Medical College, Huazhong University of Science and Technology between April 2017 and April 2024 were enrolled.Patients were divided into intermediate-risk and high-risk groups based on their initial cytogenetic features.These patients were further divided into complex karyotype, 11q23 rearrangement, and other karyotype groups.Clinical characteristics and survival outcomes were compared among these groups.Measurement and count data were analyzed using Wilcoxon rank-sum/Kruskal-Wallis and χ2 tests, respectively.Survival and risk factor analyses were performed using Kaplan-Meier and Cox proportional hazards methods, respectively. Results:A total of 51 AML children who underwent allo-HSCT were included in this study.The median age at transplantation was 3.2 years and the median follow-up time was 4.6 years.There were 26 cases in the intermediate-risk group and 25 cases in the high-risk group; 8 cases in the complex karyotype group, 14 cases in the 11q23 rearrangement group, and 29 cases in the other karyotype groups.By the end of the follow-up on November 30, 2024, 11 patients relapsed, 8 patients died, and 13 patients developed grades Ⅱ-Ⅳ acute graft-versus-host disease (GVHD).The 3-year overall survival (OS), relapse-free survival (RFS), and grades Ⅱ-Ⅳ acute GVHD-free and relapse-free survival (GRFS) were 84.0% (95% CI: 74.4%-94.8%), 74.5% (95% CI: 63.4%-87.5%), and 58.8% (95% CI: 46.7%-74.0%), respectively.The 3-year OS of the high-risk group was significantly lower than that of the intermediate-risk group (71.8% vs.96.2%, P=0.022), while differences in 3-year RFS and GRFS between the 2 groups were not statistically significant (68.0% vs.80.8%, P=0.400; 52.0% vs.65.4%, P=0.420).The 3-year OS, RFS and GRFS of the complex karyotype group were significantly lower than those of 11q23 rearrangement and other karyotype groups (50.0% vs.85.7%, 93.1%, P=0.009; 37.5% vs.85.7%, 79.3%, P=0.022; 25.0% vs.64.3%, 65.5%, P=0.049).Multivariate analysis showed that a complex karyotype was an independent prognostic factor affecting 3-year OS and GRFS [OS: HR=6.79 (95% CI: 1.13-43.80), P=0.044; GRFS: HR=3.72(95% CI: 1.13-12.20), P=0.030]. Conclusions:High-risk cytogenetic features are significant predictors of survival outcomes in pediatric AML patients undergoing PTCy-based allo-HSCT.