Objective To explore the application value of dual-phase dual-energy CT (DECT) perfusion imaging in preoperative lung function assessment of lung cancer patients. Methods Data were collected from patients with stageⅠA non-small cell lung cancer who underwent surgical treatment in the Department of Thoracic Surgery, the First Affiliated Hospital of Nanjing Medical University, from November 2022 to June 2024. All patients underwent DECT perfusion imaging and pulmonary function testing (PFT) before surgery. PFT observation indicators included ventilation function indicators such as forced expiratory volume in one second (FEV1), forced vital capacity (FVC), 1-second rate (FEV1/FVC), maximal voluntary ventilation (MVV), and diffusion function indicators such as diffusing capacity for carbon monoxide (DLCO) and DLCO per liter of alveolar volume (DLCO/VA). The software eXamine was used to obtain quantitative parameters of DECT perfusion imaging, including volume parameters and perfusion parameters of both lungs and each lung lobe. The correlation between the volume parameters and perfusion parameters of both lungs and the ventilation and diffusion function indicators of the patients, as well as the differences in quantitative parameters of each lung lobe, was analyzed. Results The end-inspiration lung volume and biphasic volume difference were strongly positively correlated with FEV1 and FVC (r=0.636, r=0.682, r=0.614, r=0.624, P<0.001) and moderately positively correlated with MVV and DLCO (r=0.499, r=0.514, r=0.549, r=0.447, P<0.001); the end-expiration lung volume was weakly negatively correlated with DLCO/VA (r=−0.295, P=0.026); the volume ratio was positively correlated with FEV1, FVC, MVV, and MVV% (r=0.424, r=0.399, r=0.415, r=0.310, P<0.05); the end-inspiration iodine content was weakly positively correlated with DLCO/VA% (rs=0.292, P=0.030); the end-expiration iodine content was weakly positively correlated with FEV1, FVC, MVV, DLCO%, and DLCO/VA (r=0.307, r=0.299, r=0.295, r=0.366, r=0.320, P<0.05) and moderately positively correlated with DLCO (r=0.439, P<0.001); the end-inspiration iodine concentration was negatively correlated with FEV1, FVC, MVV, and MVV% (rs=−0.407, rs=−0.426, rs=−0.352, rs=−0.277, P＜0.05); the end-expiratory phase iodine concentration was moderately positively correlated with DLCO/VA (r=0.403, P=0.002); both the iodine concentration difference and the iodine concentration ratio were moderately positively correlated with FEV1, FEV1%, FVC, MVV, MVV% (P<0.05). The lung volume and iodine concentration ratio values were both highest in the left upper lung lobe and lowest in the right middle lung lobe; the differences in lung volume, lung volume ratio, intrapulmonary iodine content, and intrapulmonary iodine concentration were all highest in the lower lobes of both lungs and lowest in the middle lobe of the right lung. Conclusion Dual-phase DECT perfusion imaging can accurately assess overall lung function and quantify regional lung function.

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.

Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.

Objective:To compare the efficacy of anrikefon and tegileridine for analgesia in patients with moderate-to-severe pain after abdominal surgery with general anesthesia.Methods:In this multicenter, randomized, double-blind, active-controlled clinical trial, 101 patients with moderate to severe pain (numeric pain rating scale [NRS] score ≥4 within 4 h after operation) after abdominal surgery with general anesthesia between February 24 and April 1, 2025, aged 18-70 yr, with a body mass index of 18-40 kg/m 2, were assigned to anrikefon group ( n=50) and tegileridine group ( n=51) in a 1∶1 ratio using stratified blocked randomization. Double-dummy design was employed to maintain blinding. Each group received an initial intravenous injection of anrikefon 1 μg/kg or tegileridine 1 mg, followed by connection to a patient-controlled intravenous analgesia (PCIA) pump (the PCIA solution contained normal saline in anrikefon group; the PCIA solution contained tegileridine 5 mg in tegileridine pump) within 10 min. If the patient′s NRS score ≥4 at 8 and 16 h after the initial injection, anrikefon 1 μg/kg was intravenously injected in anrikefon group, and tegileridine group received the equal volume of normal saline. The primary efficacy endpoint was the sum of pain intensity difference (SPID) over the first 24 h after the initial dose (SPID 0-24h). The secondary efficacy endpoints included the incidence and severity of vomiting and nausea, incidence of postoperative nausea and vomiting(PONV), the proportion of patients who received antiemetic treatment, and total consumption of antiemetics within 0-24 h after the initial dose, NRS score at rest ≤ 1 at 24 h after the initial dose, and NRS score at rest ≤ 3 over the first 24 h after the initial dose. Safety indicators included adverse events, vital signs, physical examination findings, 12-lead ECG and laboratory test indicators, and adverse events of special interest. Results:Compared with tegileridine group, no significant change was found in the SPID 0-24h ( P>0.05), and the incidence of vomiting, PONV, proportion of patients requiring antiemetic medication, and total consumption of antiemetics were significantly decreased within the first 24 h after the initial dose in tegileridine group ( P<0.05). One treatment-emergent adverse event of Common Terminology Criteria for Adverse Events grade 3 or higher occurred in tegileridine group, while no treatment-emergent adverse events of Common Terminology Criteria for Adverse Events grade 3 or higher were found in anrikefon group. Among the adverse events of special interest, one case of respiratory depression and one case of cough occurred in tegileridine group, while one case of cough occurred in anrikefon group, with no respiratory depression. Conclusions:Anrikefon and tegileridine provide comparable analgesic efficacy for moderate-to-severe pain after abdominal surgery with general anesthesia. However, anrikefon exhibits an advantage in reducing the risk of PONV, with a superior safety profile.

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.

Objective:To identify the components of Prunus mume f. viridicalyx based on ultra performance liquid chromatography-QE-Orbitrap mass spectrometry (UPLC-QE-Orbitrap-MS); To predict and analyze its substances and mechanisms to exert anti-depression effects combined with network pharmacology.Methods:UPLC-QE Orbitrap MS technology was used to analyze the chemical components of Prunus mume f. viridicalyx. Based on ChemSpider, mzCloud online platform, orbitrap TCM library and existing literature research, the secondary mass spectra of target compounds were compared and confirmed to identify the chemical composition of Prunus mume f. viridicalyx. The active components of the Prunus mume f. viridicalyx were screened. The Swiss Target Prediction database was used to predict targets with high correlation to active components in Prunus mume f. viridicalyx, and obtaining depression related disease targets from GeneCards and DisGeNET databases. The intersection targets of constituents and diseases were obtained using Venny platform. Protein-protein interaction network (PPI) was constructed by using String database, and the core targets were screened. Gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis of potential core targets were performed by using David database, and "active component-core target-signal pathway" network was constructed. PyMOL software was used to perform molecular docking between active components and key targets.Results:A total of 54 components, including organic acids, flavonoids and their glycosides, alkaloid, amino acids and other compounds were identified from Prunus mume f. viridicalyx. A total of 22 active components were screened and 92 active components and disease intersection targets were identified. A total of 13 core targets were screened through PPI network, including tumor necrosis factor, albumin, amyloid beta-protein precursor, AKT serine/threonine kinase 1 and so on. Enrichment analysis showed that Prunus mume f. viridicalyx mainly participated in transcription from RNA polymerase Ⅱ promoter, gene expression, protein binding and other functions, and presented the effects of anti-depression through MAPK, Toll-like receptor signaling pathway and other pathways. 12 key targets and 7 key active components were further obtained through the analysis of the "active component-core target-signal pathway" network, three of them were confirmed as kaempferol, quercetin, and isorhamnetin by reference substance. Molecular docking showed that 3 compounds could bind to the target proteins of depression well.Conclusion:Prunus mume f. viridicalyx exerts antidepressant effects through multiple components, targets, and pathways, mainly through the MAPK signaling pathway.

Objective To explore the relationship between dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and clinical pathological features of invasive breast cancer and lymphovascular invasion(LVI).Methods Imaging and clinical pathological data were retrospectively collected from 508 patients with invasive breast cancer who underwent breast DCE-MRI at the First Medical Center of Chinese PLA General Hospital from January 2019 to August 2021.Patients were divided into the LVI-positive(LVI+)group(n=79)and LVI-negative(LVI-)group(n=429)based on postoperative pathological results.Univariate and multivariate logistic regression analyses were used to identify risk factors for LVI.Results Compared with LVI-group,LVI+group had a higher proportion of patients aged<45 years(44.3%vs.27.0%,P=0.002),non-mass-like enhancement(NME)(31.7%vs.17.7%,P=0.004),Ki-67 expression rate(40.0%vs.30.0%,P<0.001),high Ki-67 expression(94.9%vs.78.1%,P=0.001),Luminal B subtype(76.0%vs.60.1%,P=0.008),and positive axillary lymph nodes rate(72.2%vs.31.5%,P<0.001),while the proportion of Luminal A subtype was lower(2.5%vs.21.5%,P<0.001).Univariate and multivariate logistic regression analyses showed that age≥45 years(OR=0.468,95%CI 0.280-0.783,P=0.004)was an independent protective factor for LVI,while NME(OR=1.987,95%CI 1.126-3.444,P=0.016)was an independent risk factor.Compared with Luminal A subtype,patients with Luminal B subtype(OR=10.482,95%CI 3.164-64.923,P=0.001),HER-2 overexpression subtype(OR=11.571,95%CI 2.755-79.341,P=0.003)and triple-negative subtypes(OR=8.433,95%CI 1.985-57.908,P=0.009)had a higher risk of LVI.Conclusions Age≥45 years is an independent protective factor for LVI,while NME is an independent risk factor.Among molecular subtypes,patients with Luminal B,HER-2 overexpression and triple-negative subtypes have a higher risk of LVI compared with the Luminal A subtype.

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.

BACKGROUND: Hypertension is associated with an increased risk of calcific aortic valve stenosis (CAVS). However, the directionality of causation between blood pressure traits and aortic stenosis is unclear, as is the benefit of antihypertensive drugs for CAVS. METHODS: Using genome-wide association studies (GWAS) summary statistics, we performed bidirectional two-sample univariable mendelian randomization (UVMR) to assess the causal associations of systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) with CAVS. Multivariable mendelian randomization (MVMR) was conducted to evaluate the direct effect of hypertension on CAVS, adjusting for confounders. Drug target mendelian randomization (MR) and summary-level MR (SMR) were used to estimate the effects of 12 classes of antihypertensive drugs and their target genes on CAVS risk. Inverse variance weighting was the primary MR method, with sensitivity analyses to validate results. RESULTS: UVMR showed SBP, DBP, and PP have causal effects on CAVS, with no significant reverse causality. MVMR confirmed the causality between hypertension and CAVS after adjusting for confounders. Drug-target MR analyses indicated that calcium channel blockers (CCBs), loop diuretics, and thiazide diuretics via SBP lowering exerted protective effects on CAVS risk. SMR analysis showed that the CCBs target gene CACNA2D2 and ARBs target gene AGTR1 were positively associated with CAVS risk, while diuretics target genes SLC12A5 and SLC12A1 were negatively associated with aortic stenosis risk. CONCLUSIONS Hypertension has a causal relationship with CAVS. Managing SBP in hypertensive patients with CCBs may prevent CAVS. ARBs might exert protective effects on CAVS independent of blood pressure reduction. The relationship between diuretics and CAVS is complex, with opposite effects through different mechanisms.