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.

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.

Objective To investigate the characteristics of intestinal flora and its correlation with peripheral blood microinflammatory factors in patients with pulmonary tuberculosis complicated with diabetes mellitus(PTB-DM).Methods A total of 162 patients with PTB-DM admitted to the hospital from September 2022 to September 2024 were selected as the PTB-DM group,and another 150 healthy subjects who underwent physi-cal examinations during the same period in the hospital were selected as the control group.The clinical data of all research subjects was collected.The composition of intestinal flora of the research subjects was analyzed by using 16S rRNA gene sequencing technology.The levels of peripheral blood microinflammatory factors[inter-feron-γ(IFN-γ),interleukin-6(IL-6),and tumor necrosis factor-α(TNF-α)]in all research subjects were detected.The clinical data,composition of intestinal flora and levels of microinflammatory factors of the PTB-DM group were compared with those of the control group.Spearman correlation analysis was used to analyze the correlation between the relative abundance of intestinal flora and the levels of peripheral blood microin-flammatory factors in patients with PTB-DM.Results The relative abundances of Bacteroidales and Clostridi-ales in the PTB-DM group were significantly lower than those in the control group,while the relative abun-dances of Enterobacterales and Actinobacteriales were significantly higher than those in the control group,and the differences were statistically significant(P<0.05).The levels of IFN-γ,IL-6 and TNF-α in the PTB-DM group were significantly higher than those in the control group,and the differences were statistically signifi-cant(P<0.05).The relative abundances of Bacteroidales and Clostridiales in the PTB-DM group were nega-tively correlated with the levels of IFN-γ,IL-6 and TNF-α.The relative abundances of Enterobacterales and Actinobacteriales were positively correlated with the levels of IFN-γ,IL-6 and TNF-α(all P<0.05).Conclu-sion There is a significant imbalance of intestinal flora in patients with PTB-DM,and the levels of microin-flammatory factors IFN-γ,IL-6 and TNF-α in peripheral blood are significantly increased,and are closely re-lated to the relative abundance of specific flora.

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.

BACKGROUND:Wogonin is a flavonoid extracted from the root of Scutellaria baicalensis.Previous studies have shown that baicalein has protective effects against cerebral ischemia-reperfusion injury,and can also reduce blood sugar and complications in diabetic mice,but its role and mechanism in diabetic cerebral infarction remain unclear. OBJECTIVE:To explore the effect of wogonin on nerve injury in rats with diabetic cerebral infarction and its mechanism. METHODS:Sprague-Dawley rats were randomly divided into six groups:control group,model group,low-dose wogonin group,medium-dose wogonin group,high-dose wogonin group,and high-dose wogonin+Ras homolog gene family member A(RhoA)activator group.Except for the control group,the other rats were established with diabetes and cerebral ischemia models using intraperitoneal injection of streptozotocin and middle cerebral artery occlusion.Low,medium-and high-dose wogonin groups were intragastrically given 10,20,40 mg/kg wogonin,respectively;high-dose wogonin+RhoA activator group was intragastrically given 40 mg/kg wogonin and intraperitoneally injected 10 mg/kg lysophosphatidic acid;control group and model group were given the same amount of normal saline once a day for 7 consecutive days.Rats in each group were evaluated for neurological deficits and their blood glucose levels were measured after the last dose.TTC staining was applied to detect the volume of cerebral infarction.Hematoxylin-eosin staining was applied to observe pathological changes in brain tissue.ELISA kit was applied to detect tumor necrosis factor-α,interleukin-6,malondialdehyde,and superoxide dismutase levels in brain tissue.Western blot was applied to detect the protein expression of RhoA and Rho-associated protein kinase(ROCK)2 in brain tissue. RESULTS AND CONCLUSION:Compared with the control group,the neuronal structure of rats in the model group was severely damaged,with cell necrosis and degeneration,the neurological deficit score,blood glucose level,and infarct volume were significantly elevated(P<0.05),the levels of tumor necrosis factor-α,interleukin-6,and malondialdehyde,and the protein expression of RhoA and ROCK2 in brain tissue were significantly increased(P<0.05),and the superoxide dismutase level was decreased(P<0.05).Compared with the model group,the low-,medium-,and high-dose wogonin groups showed improved neuronal damage,reduced cell degeneration and necrosis,a significant reduction in neurological deficit score,blood glucose level,infarct volume,and the levels of tumor necrosis factor-α,interleukin-6,and malondialdehyde,and the protein expression of RhoA and ROCK2 in brain tissue,and an increase in the superoxide dismutase level(P<0.05).Compared with the high-dose wogonin group,the high-dose wogonin+RhoA activator group significantly weakened the improvement in the above indexes of rats with diabetic cerebral infarction(P<0.05).To conclude,wogonin can improve the blood glucose level in rats with diabetic cerebral infarction,reduce cerebral infarction and nerve injury,and its mechanism may be related to the inhibition of RhoA/ROCK signaling pathway.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

The holistic view is the key in the study of traditional Chinese medicine(TCM). The component structure theory is based on the holistic view to investigate the correlation between material basis and efficiency, which enriches the holistic "component-effect" research of TCM. Gintonin is a newly isolated non-saponin component of ginseng. Compared to ginsenosides, gintonin has many different pharmacological activities, and it provides new knowledge for the holistic research of ginseng. Thus, taking the discovery of gintonin from ginseng as an example, this paper explored the linkage between ginsenosides and gintonin from the perspective of "component-effect" correlations and systematically sorted out the similarities and differences between them in terms of structural characteristics, modes of action, and pharmacological activities. Starting from the collaborative interaction of TCM compounds, the study discussed the application and value of the holistic view in TCM "component-effect" research in the light of the component structure theory to provide new thoughts for the development of modern TCM research.
Panax/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional ; Humans ; Ginsenosides/pharmacology* ; Animals

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