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.

Malignant tumors remain one of the most critical global public threats to human health. The early diagnosis and precise therapeutic interventions are pivotal for improving patient survival rates and prognosis. Gold nanoclusters (Au NCs), distinguished by their ultra-small size (<3 nm), tunable optical properties, and exceptional biocompatibility, have emerged as transformative agents in precision oncology. This comprehensive review systematically summarizes the multifaceted applications of Au NCs in malignant tumor treatment. We discuss their roles as follows. (1) Intelligent delivery vehicles for targeted chemotherapy and controlled release through surface functionalization. (2) Therapeutic agents for chemodynamic therapy (CDT). This capability stems from their intrinsic enzyme-like catalytic activity or potent thioredoxin reductase (TrxR) inhibitory function, which disrupts the intracellular redox homeostasis and effectively activates downstream apoptotic pathways.(3) Direct therapeutic agents are characterized by their energy conversion capabilities: they can either convert absorbed light into heat to directly kill cancer cells, or transfer that photon energy to surrounding oxygen molecules to generate cytotoxic reactive oxygen species (ROS), leading to cell apoptosis or necrosis. (4) Potent radiosensitizers that enhance radiotherapy efficacy by enhancing localized radiation dose and promoting ROS generation. This review systematically summarizes the recent advances in Au NCs as intelligent delivery systems, direct chemotherapeutic agents, phototherapeutic agents, and efficient radiosensitizers in tumor treatment, elucidating how Au NCs overcome traditional therapeutic limitations through synergistic strategy. It establishes a robust theoretical foundation for next-generation nanotheranostic platforms. However, the translation of laboratory findings into functional clinical technologies confronts three significant challenges. First, although researchers can synthesize atomically precise Au NCs, achieving large-scale production of batches with completely consistent structure, size, and surface chemistry remains extremely challenging. To effectively control the final synthetic product, a deep understanding of the characteristics and formation mechanisms of Au NCs is essential. The traditional “trial-and-error” experimental approach faces inherent limitations when dealing with vast combinations of variables, which is time-consuming, labor-intensive, and struggles with systematic exploration and reproducibility. Machine learning has emerged as a powerful tool to bridge fundamental research and clinical application, which can guide experiments in reverse by predicting synthesis success through data mining and multi-variable analysis. In the future, we anticipate to achieve precise prediction and on-demand design of Au NCs’ structure and properties. Secondly, a systematic framework for evaluating the in vivo pharmacokinetics and long-term toxicity of Au NCs is absent. To address this gap, it is crucial to develop advanced imaging methodologies and integrated theranostic platforms. Au NCs, serving as both a therapeutic core and a highly promising photoluminescent material, are key to constructing such platforms through integration with other agents. These multifunctional systems are designed to achieve optimal synergistic therapy by combining multiple treatment modalities. Finally, the investigation of Au NCs is still largely confined to preclinical cellular and animal studies. Progress necessitates comprehensive clinical research to rigorously assess their safety and efficacy across a range of human cancer models, thereby ensuring broad clinical applicability. In summary, Au NCs-based platforms hold immense promise for translation into clinical anticancer therapy.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

A method for determination and targeted screening of diflorasone components in health products using ultra performance liquid chromatography-quadrupole time of flight mass spectrometry(UPLC-Q-TOF/MS)was established.Four representative diflorasone and esters(diflorasone,diflorasone diacetate,diflorasone-17-propionate,and diflorasone-21-propionate)were selected to optimize the pretreatment conditions,and 10 mL of extraction solvent dosage,15 min of extraction time and 5 g of salting-out agent as the optimal conditions were selected by response surface methodology.The results showed that the four analytes exhibited good linearity within the concentration range of 2.0?100 μg/L with the chromatographic peak area,and the correlation coefficients(R2)were all greater than 0.9990,while the results of recovery and relative standard deviation could satisfy the requirements of determination.The common characteristic ions of diflorasone and esters werem/z121 andm/z335,and their specific structures were obtained by analyzing the cleavage pathway based on the optimized determination conditions.A targeted screening method for other esters of diflorasone based on characteristic ions guidance strategy was established.This method had many advantages such as high efficiency,high sensitivity and good reproducibility,and could be used for targeted screening of diflorasone and esters in health products.The developed characteristic ion guided strategy could be employed to construct mass spectral databases for various glucocorticoids,enabling comprehensive targeted screening across a broad range of compounds.

Objective:To investigate the dynamic changes of plasma high-mobility group box 1 (HMGB1) and its correlation with functional outcome and symptomatic intracranial hemorrhage (sICH) after endovascular treatment (EVT) in patients with acute large vessel occlusion stroke (ALVOS).Methods:Patients with ALVOS admitted to the Department of Neurology, Zengcheng District, Nanfang Hospital, Southern Medical University from June 2021 to April 2023 were included retrospectively. Plasma HMGB1 before EVT and at 6, 24, and 48 hours after procedure was detected, and the dynamic changes of plasma HMGB1 were compared and analyzed. The primary endpoint was the functional outcome evaluated using the modified Rankin Scale at 90 days of onset. A score of 0-2 was defined as good outcome and >2 was defined as poor outcome. The secondary endpoint was sICH, which was defined as the occurrence of hemorrhagic infarction after EVT and an increase of ≥4 in the National Institutes of Health Stroke Scale (NIHSS) score from baseline. Multivariate logistic regression analysis was used to evaluate the predictive value of HMGB1 for poor outcome and sICH. Results:A total of 73 patients with ALVOS received EVT were included. There were 54 males (74.0%), aged 62±12 years. The median time from onset to door was 90 minutes (interquartile range, 40-180 minutes), and the median time from onset to femoral artery puncture was 181 minutes (interquartile range, 140-280 minutes). Twenty-nine patients (39.7%) underwent bridging intravenous thrombolysis (IVT). At 90 days after onset, 37 patients (50.7%) had poor outcome, and 12 (16.4%) died during follow-up. Eleven patients (15.1%) developed sICH. After EVT, plasma HMGB1 showed a temporal increase, reaching its peak at 48 hours (median, 102.57 μg/L). Subgroup analysis showed that HMGB1 in the bridging IVT group at 6 hours ( P<0.05) and 24 hours ( P<0.05) after procedure were significantly higher than that at baseline. The non-bridging IVT group showed a significant increase at 6 hours after procedure ( P<0.05). There was no statistically significant difference in HMGB1 between the bridging IVT group and the non-bridging IVT group at the same time point. Multivariate logistic regression analysis showed that after adjusting for age, ischemic heart disease, triglycerides, uric acid, baseline NIHSS score, and sICH, the third quartile (adjusted odds ratio 7.087, 95% confidence interval 1.243-40.419; P=0.027) and fourth quartile (adjusted odds ratio 7.544, 95% confidence interval 1.260-45.172; P=0.027) of plasma HMGB1 were independent risk factors for poor outcome at 6 hours after procedure. The postoperative plasma HMGB1 in the sICH group was significantly higher than that in the non-sICH group ( P<0.05), but multivariate analysis showed no independent correlation between plasma HMGB1 and sICH. Conclusion:The elevation of plasma HMGB1 in patients with ALVOS at 6 hours after EVT is independently associated with poor outcome at 90 days after onset, but not with sICH.

Objective:The components of Rubus parvifolius L. were analyzed based on UPLC-MS/MS technology and combined with network pharmacology analysis to explore the mechanism of action of Rubi Parvifolii Radix in treating inflammation, cough, fever, influenza and sore throat. Method:The chemical constituents of Rubi Parvifolii Radix were identified according to the information of mass spectrometry. The network pharmacology was used to analyze the corresponding targets and related pathways of its chemical components, and the "component-target-pathway" interaction diagram was drawn. PyMOL 2.5.7 software wasused to perform molecular docking between active components and key targets.Results:Twenty chemical components were identified by UPLC-MS/MS, and 15 components were screened out by network pharmacology, which can be used as quality markers of Rubi Parvifolii Radix, namely Azelaic acid, Procyanidol B3, Caprolactam, Bis (2-ethylhexyl) adipate, Cryptochlorogenic acid, 3-O-Feruloylquinic, Ellagic acid, Aurantiamide acetate, 2 α,3 β,19 α,23-Tetrahydroxyurs-12-en-28-oic acid, L-Epicatechin, (E)-3-Indoleacrylic acid, Euscaphic acid, Suberic acid, Diisononyl phthalate and Prodelphinidin T4. Molecular docking showed that 5 compounds compared with the reference substance could bind to the target proteins of disease well. Conclusions:The 15 active ingredients in Rubi Parvifolii Radix, including Caprolactam and (E)-3-Indoleacrylic acid, may play a therapeutic role in treating colds, high fever, sore throat, and inflammation by acting on targets such as AKT1 and TNF. This provides a certain reference for the clinical application of Rubi Parvifolii Radix.

Gastric cancer is one of the most common malignant tumors in the digestive tract, which has the characteristics of high morbidity and mortality. However, gastric cancer is not achieved overnight but is gradually developing through the interaction of many factors. Therefore, actively delaying or blocking the "inflammation-cancer" transformation in gastric mucosa is the key to treatment. Nod-like receptor protein 3(NLRP3) inflammasome is a multi-protein signal complex and one of the important innate immune signal receptors. Inflammation plays an important role in the occurrence and development of gastric cancer, and continuous inflammation mediation will trigger the transformation from inflammation to cancer. Therefore, the significance of NLRP3 inflammasome to gastric mucosa lies in the transformation between inflammation and cancer. Traditional Chinese medicine(TCM) has the functions of multi-components, multi-targets, and few adverse reactions. A large number of studies show that TCM and related monomers have significant effects in treating liver, kidney, and immune diseases through mediating NLRP3 inflammasome, but there is less research on the "inflammation-cancer" transformation in gastric mucosa. By combing the NLRP3-related nuclear factor-κB transcription factor(NF-κB), hypoxia inducible factor-1α(HIF-1α), phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt), and other signal pathways, this paper clarified their mechanisms in the "inflammation-cancer" transformation in gastric mucosa, delayed the process of "inflammation-cancer" transformation in gastric mucosa through four aspects: energy metabolism, pyroptosis, immune response, and vascular endothelial growth factor, and prevented and treated "inflammation-cancer" transformation in gastric mucosa from three aspects: TCM monomer, TCM compound prescription, and other therapies, so as to provide ideas for the subsequent treatment of "inflammation-cancer" transformation in gastric mucosa with TCM.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/metabolism* ; Gastric Mucosa/metabolism* ; Stomach Neoplasms/pathology* ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional ; Inflammation/drug therapy* ; Signal Transduction/drug effects*

Guided by the concept of quality by design(QbD), this study optimizes the calcination and quenching process of calcined Magnetitum and establishes the XRD characteristic spectra of calcined Magnetitum, providing a scientific basis for the formulation of quality standards. Based on the processing methods and quality requirements of Magnetitum in the Chinese Pharmacopoeia, the critical process parameters(CPPs) identified were calcination temperature, calcination time, particle size, laying thickness, and the number of vinegar quenching cycles. The critical quality attributes(CQAs) included Fe mass fraction, Fe~(2+) dissolution, and surface color. The weight coefficients were determined by combining Analytic Hierarchy Process(AHP) and the criteria importance though intercrieria correlation(CRITIC) method, and the calcination process was optimized using orthogonal experimentation. Surface color was selected as a CQA, and based on the principle of color value, the surface color of calcined Magnetitum was objectively quantified. The vinegar quenching process was then optimized to determine the best processing conditions. X-ray diffraction(XRD) was used to establish the characteristic spectra of calcined Magnetitum, and methods such as similarity evaluation, cluster analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to evaluate the quality of the spectra. The optimized calcined Magnetitum preparation process was found to be calcination at 750 ℃ for 1 h, with a laying thickness of 4 cm, a particle size of 0.4-0.8 cm, and one vinegar quenching cycle(Magnetitum-vinegar ratio 10∶3), which was stable and feasible. The XRD characteristic spectra analysis method, featuring 9 common peaks as fingerprint information, was established. The average correlation coefficient ranged from 0.839 5-0.988 1, and the average angle cosine ranged from 0.914 4 to 0.995 6, indicating good similarity. Cluster analysis results showed that Magnetitum and calcined Magnetitum could be grouped together, with similar compositions. OPLS-DA discriminant analysis identified three key characteristic peaks, with Fe_2O_3 being the distinguishing component between the two. The final optimized processing method is stable and feasible, and the XRD characteristic spectra of calcined Magnetitum was initially established, providing a reference for subsequent quality control and the formulation of quality standards for calcined Magnetitum.
X-Ray Diffraction/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Particle Size

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.