BACKGROUND: This study aimed to investigate programmed death-ligand 1 tumor proportion score in predicting the safety and efficacy of PD-1/PD-L1 antibody-based therapy in treating patients with advanced non-small cell lung cancer (NSCLC) in a real-world setting. METHODS: This retrospective, multicenter, observational study enrolled adult patients who received PD-1/PD-L1 antibody-based therapy in China and met the following criteria: (1) had pathologically confirmed, unresectable stage III-IV NSCLC; (2) had a baseline PD-L1 tumor proportion score (TPS); and (3) had confirmed efficacy evaluation results after PD-1/PD-L1 treatment. Logistic regression, Kaplan-Meier analysis, and Cox regression were used to assess the progression-free survival (PFS), overall survival (OS), and immune-related adverse events (irAEs) as appropriate. RESULTS: A total of 409 patients, 65.0% ( n = 266) with a positive PD-L1 TPS (≥1%) and 32.8% ( n = 134) with PD-L1 TPS ≥50%, were included in this study. Cox regression confirmed that patients with a PD-L1 TPS ≥1% had significantly improved PFS (hazard ratio [HR] 0.747, 95% confidence interval [CI] 0.573-0.975, P = 0.032). A total of 160 (39.1%) patients experienced 206 irAEs, and 27 (6.6%) patients experienced 31 grade 3-5 irAEs. The organs most frequently associated with irAEs were the skin (52/409, 12.7%), thyroid (40/409, 9.8%), and lung (34/409, 8.3%). Multivariate logistic regression revealed that a PD-L1 TPS ≥1% (odds ratio [OR] 1.713, 95% CI 1.054-2.784, P = 0.030) was an independent risk factor for irAEs. Other risk factors for irAEs included pretreatment absolute lymphocyte count >2.5 × 10 9 /L (OR 3.772, 95% CI 1.377-10.329, P = 0.010) and pretreatment absolute eosinophil count >0.2 × 10 9 /L (OR 2.006, 95% CI 1.219-3.302, P = 0.006). Moreover, patients who developed irAEs demonstrated improved PFS (13.7 months vs. 8.4 months, P <0.001) and OS (28.0 months vs. 18.0 months, P = 0.007) compared with patients without irAEs. CONCLUSIONS A positive PD-L1 TPS (≥1%) was associated with improved PFS and an increased risk of irAEs in a real-world setting. The onset of irAEs was associated with improved PFS and OS in patients with advanced NSCLC receiving PD-1/PD-L1-based therapy.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Lung Neoplasms/metabolism* ; Aged ; B7-H1 Antigen/metabolism* ; Programmed Cell Death 1 Receptor/metabolism* ; Adult ; Aged, 80 and over ; Immune Checkpoint Inhibitors/therapeutic use*

The Baihe Dihuang Decoction(BDD) is a representative traditional Chinese medicine formula that has been used to treat depression. This study employed metabolomics and network pharmacology to investigate the mechanism of BDD in the treatment of depression. Fifty male Sprague-Dawley(SD) rats were randomly assigned to the normal control group, model group, fluoxetine group, and high-and low-dose BDD groups. A rat model of depression was established through chronic unpredictable mild stress(CUMS), and the behavioral changes were detected by forced swimming test and open field test. Metabolomics technology was used to analyze the metabolic profiles of serum and hippocampal tissue to screen differential metabolites and related metabolic pathways. Additionally, network pharmacology and molecular docking techniques were used to investigate the key targets and core active ingredients of BDD in improving metabolic abnormalities of depression. A "component-target-metabolite-pathway" regulatory network was constructed. BDD could significantly improve depressive-like behavior in CUMS rats and regulate 12 differential metabolites in serum and 27 differential metabolites in the hippocampus, involving tryptophan metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, alanine, aspartate, and glutamate metabolism, tyrosine metabolism, and purine metabolism. Verbascoside, isorbascoside, and regaloside B were the key active ingredients for improving metabolic abnormalities in depression. Epidermal growth factor receptor(EGFR), protooncogene tyrosine-protein kinase(SRC), glycogen synthase kinase 3β(GSK3β), and androgen receptor(AR) were the key core targets for improving metabolic abnormalities of depression. This study offered a preliminary insight into the mechanism of BDD in alleviating metabolic abnormalities of depression through network regulation, providing valuable guidance for its clinical use and subsequent research.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Depression/genetics* ; Antidepressive Agents/chemistry* ; Network Pharmacology ; Hippocampus/drug effects* ; Humans ; Molecular Docking Simulation ; Behavior, Animal/drug effects* ; Disease Models, Animal

OBJECTIVE: Emerging evidence suggests that exposure to ultrafine particulate matter (UPM, aerodynamic diameter < 0.1 µm) is associated with adverse cardiovascular events. Previous studies have found that Shenlian (SL) extract possesses anti-inflammatory and antiapoptotic properties and has a promising protective effect at all stages of the atherosclerotic disease process. In this study, we aimed to investigated whether SL improves UPM-aggravated myocardial ischemic injury by inhibiting inflammation and cell apoptosis. METHODS: We established a mouse model of MI+UPM. Echocardiographic measurement, measurement of myocardialinfarct size, biochemical analysis, enzyme-linked immunosorbent assay (ELISA), histopathological analysis, Transferase dUTP Nick End Labeling (TUNEL), Western blotting (WB), Polymerase Chain Reaction (PCR) and so on were used to explore the anti-inflammatory and anti-apoptotic effects of SL in vivo and in vitro. RESULTS: SL treatment can attenuate UPM-induced cardiac dysfunction by improving left ventricular ejection fraction, fractional shortening, and decreasing cardiac infarction area. SL significantly reduced the levels of myocardial enzymes and attenuated UPM-induced morphological alterations. Moreover, SL significantly reduced expression levels of the inflammatory cytokines IL-6, TNF-α, and MCP-1. UPM further increased the infiltration of macrophages in myocardial tissue, whereas SL intervention reversed this phenomenon. UPM also triggered myocardial apoptosis, which was markedly attenuated by SL treatment. The results of in vitro experiments revealed that SL prevented cell damage caused by exposure to UPM combined with hypoxia by reducing the expression of the inflammatory factor NF-κB and inhibiting apoptosis in H9c2 cells. CONCLUSION Overall, both in vivo and in vitro experiments demonstrated that SL attenuated UPM-aggravated myocardial ischemic injury by inhibiting inflammation and cell apoptosis. The mechanisms were related to the downregulation of macrophages infiltrating heart tissues.
Animals ; Apoptosis/drug effects* ; Particulate Matter/adverse effects* ; Mice ; Male ; Inflammation/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Mice, Inbred C57BL ; Myocardial Ischemia/drug therapy* ; Cell Line

Objective To plan microwave antenna puncture direction effectively and realize personalized preoperative simulation by exploring microwave ablation(MWV)outcomes of lung cancer based on real anatomical model.Methods Firstly,a personalized MWA simulation model consisting of the lung tissue,tumor and vascular system was constructed based on the lung CT data of real patients.Secondly,the MWA effect was simulated by coupling 2 physical fields including an electromagnetic field and a biological heat transfer field,so as to determine the volume of the ablation thermocoagulation zone and the temperature distribution of the lung tissue.Finally,the effects of the vascularized network on the ablation results were quantitatively evaluated in terms of conductivity and blood perfusion,and the ablation results were analyzed with different distances between the large vessels and the antennae(5 and 10 mm from the antennae tips)and puncture angles(large vessels parallelling or intersecting with the antennae tips).Results The vascularized network reduced the volume of the thermocoagulation zone by 0.5％to 3.7％,and blood perfusion made the ablation temperature and the volume of the thermocoagulation zone decreased significantly.The cooling effect gradually diminished with the increase of the distance between the large vessels and the antenna.With the same treatment parameters,the thermocoagulation zone formed when the large vessels paralleled with the antenna was obviously larger than that when the vessles intersected with the antenna.Conclusion Personalized MWA simulation analysis based on real CT data contributes to clarifying the temperature distribution and damage estimation close to the actual situation,which provides guidance and reference for precise treatment of the lung tumor and determination of microwave antenna puncture direction.[Chinese Medical Equipment Journal,2024,45(9):27-34]

Near infrared spectroscopy(NIRS)has emerged as an indispensable analytical technology for quality monitoring in industrial and agricultural production.It is widely used in quantitative analysis in areas such as food,agriculture and medicine.To meet the requirements of industrial and agricultural production,it is particularly important to develop a NIRS quantitative analysis model that can predict the multicomponent of different samples.In this study,the multicomponent quantitative analysis model of NIRS based on convolution neural network(MulCoSpecNet)was proposed.MulCoSpecNet was composed of an encoding and decoding module,an expert module,a gate module,a multicomponent quantitative prediction module,and a hyperparameter optimizer.The spectral noise and random errors were mitigated,and the signal-to-noise ratio was enhanced through up-sampling and down-sampling in the encoding and decoding module.Diverse weightings were employed by the expert module and gate module to construct distinct sub-spectra.The model prediction accuracy and generalization ability were enhanced by the multicomponent quantitative prediction module,which employed convolutional and pooling operations.The hyperparameters in the hyperparameter space were synchronously optimized by the hyperparameter optimizer.By taking public NIRS datasets of grain and corn as examples,the prediction results of MulCoSpecNet were compared with partial least squares(PLS),extreme learning machine(ELM),support vector regression(SVM)and back propagation neural network(BP).The results showed that compared to PLS,the prediction accuracy of MulCoSpecNet to grain and corn were increased by 25.5%?45.2%and 10.0%?35.7%,respectively.Compared to ELM,the prediction accuracy of MulCoSpecNet were increased by 17.8%?38.6%and 18.2%?37.2%,respectively.Compared to SVM,the prediction accuracy of MulCoSpecNet were increased by 33.6%?47.0%and 31.3%?50.7%,respectively.Compared to BP,the prediction accuracy of MulCoSpecNet were increased by 2.0%?58.5%and 29.6%?48.6%,respectively.The issues of low prediction accuracy and poor generalization ability were effectively solved by the MulCoSpecNet,which was a NIRS multicomponent prediction model based on convolutional neural network.It provided a theoretical foundation for establishing non-destructive and high-precision NIRS multicomponent quantitative analysis model.

Objective To study the correlation between traditional Chinese medicine(TCM)constitution and pathogenic factors in patients with ankylosing spondylitis(AS).Methods One hundred patients of AS and their family members who had medical consultation in the Fifth Hospital of Xi'an(i.e.,Shaanxi Hospital of Integrated Traditional Chinese and Western Medicine)in August 2019 and September 2020 were selected as the study subjects.The guidelines of Classification and Determination of Traditional Chinese Medicine Constitution issued by the China Association of Chinese Medicine were adopted to determine the traditional Chinese medicine(TCM)constitution types of the study subjects.The sociodemographic information,living habits,clinical symptoms,and TCM constitution types of the AS patients and their family members were collected by means of questionnaires and clinical investigations,and then the pathogenic factors of the patients with AS were investigated.The binomial Logistic regression model was used to analyze the correlation between TCM constitution types and pathogenic factors in patients with AS.Results(1)Among the 100 AS patients,the majority of them had the biased constitutions,and the biased constitutions with the occurrence frequency in descending order were yang deficiency constitution,qi deficiency constitution,and damp-heat constitution,which accounted for 33.00%,14.00%,and 18.00%,respectively.(2)The prevalence rates of AS in the first-,second-,and third-degree relatives of AS patients were 56.25%,40.00%and 25.00%,respectively.For the positive rates of human leukocyte antigen B27(HLA-B27)in AS patients and their family members,HLA-B27 in AS patients was all positive,while the positive rates of HLA-B27 in the first-,second-,and third-degree relatives of AS patients were 44.31%,30.67%and 15.63%,respectively.(3)The results of regression analysis showed that the disease duration of AS patients was significantly correlated with qi deficiency constitution,the grading of sacroiliac arthritis was correlated with qi stagnation constitution,and age was correlated with blood stasis constitution(P＜0.05 or P＜0.01).The results indicated that disease duration and age were the important factors affecting the constitution types of AS patients,and disease duration was closely related to qi deficiency while age was closely related to blood stasis.Conclusion AS is a highly hereditary autoimmune disease,and its onset is associated with HLA-B27.Yang deficiency is the basic constitution type of AS,and damp-heat constitution is the main constitution type in the progression of AS(especially in the active stage of the disease).The prolongation of the disease will exacerbate the illness condition of AS and then the manifestations of qi deficiency will be more obvious.

Objective To explore the prediction of hospital indoor microbial concentration in air based on real-time indoor air environment monitoring data and machine learning algorithms.Methods Four locations in a hospital were selected as monitoring sampling points from May 23 to June 5,2022.The"internet of things"sensor was used to monitor a variety of real-time air environment data.Air microbial concentration data collected at each point were matched,and the gradient boosting decision tree(GBDT)was used to predict real-time indoor microbial concentra-tion in air.Five other common machine learning models were selected for comparison,including random forest(RF),decision tree(DT),k-nearest neighbor(KNN),linear regression(LR)and artificial neural network(ANN).The validity of the model was verified by the mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE).Results The MAPE value of GBDT model in the outpa-tient elevator room(point A),bronchoscopy room(point B),CT waiting area(point C),and nurses'station in the supply room(point D)were 22.49％,36.28％,29.34％,and 26.43％,respectively.The mean performance of the GBDT model was higher than that of other machine learning models at three sampling points and slightly lower than that of the ANN model at only one sampling point.The mean MAPE value of GBDT model at four sampling points was 28.64％,that is,the predicted value deviated from the actual value by 28.64％,indicating that GBDT model has good prediction results and the predicted value was within the available range.Conclusion The GBDT machine learning model based on real-time indoor air environment monitoring data can improve the prediction accuracy of in-door air microbial concentration in hospitals.

Hydrogen sulfide,as a third gas signal molecule and neurotransmitter,can play a neuroprotective role by anti-oxidative stress,anti-inflammatory response,metabolic inhibition and other mechanisms.It is of great significance for the occurrence and development of neurodegenerative diseases including Alzheimer's disease(AD)and Parkinson's disease(PD)mediated by neuroinflammation.This article reviews the research progress of hydrogen sulfide and neuroinflammation and its mediated neurodegenerative diseases,so as to provide new ideas for the treatment of neurodegenerative diseases.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Objective: To evaluate the efficacy and safety of hybutimibe monotherapy or in combination with atorvastatin in the treatment of primary hypercholesterolemia. Methods: This was a multicenter, randomized, double-blind, double-dummy, parallel-controlled phase Ⅲ clinical trial of patients with untreated primary hypercholesterolemia from 41 centers in China between August 2015 and April 2019. Patients were randomly assigned, at a ratio of 1∶1∶1∶1∶1∶1, to the atorvastatin 10 mg group (group A), hybutimibe 20 mg group (group B), hybutimibe 20 mg plus atorvastatin 10 mg group (group C), hybutimibe 10 mg group (group D), hybutimibe 10 mg plus atorvastatin 10 mg group (group E), and placebo group (group F). After a dietary run-in period for at least 4 weeks, all patients were administered orally once a day according to their groups. The treatment period was 12 weeks after the first dose of the study drug, and efficacy and safety were evaluated at weeks 2, 4, 8, and 12. After the treatment period, patients voluntarily entered the long-term safety evaluation period and continued the assigned treatment (those in group F were randomly assigned to group B or D), with 40 weeks' observation. The primary endpoint was the percent change in low density lipoprotein cholesterol (LDL-C) from baseline at week 12. Secondary endpoints included the percent changes in high density lipoprotein cholesterol (HDL-C), triglyceride (TG), apolipoprotein B (Apo B) at week 12 and changes of the four above-mentioned lipid indicators at weeks 18, 24, 38, and 52. Safety was evaluated during the whole treatment period. Results: Totally, 727 patients were included in the treatment period with a mean age of (55.0±9.3) years old, including 253 males. No statistical differences were observed among the groups in demographics, comorbidities, and baseline blood lipid levels. At week 12, the percent changes in LDL-C were significantly different among groups A to F (all P<0.01). Compared to atorvastatin alone, hybutimibe combined with atorvastatin could further improve LDL-C, TG, and Apo B (all P<0.05). Furthermore, there was no significant difference in percent changes in LDL-C at week 12 between group C and group E (P=0.991 7). During the long-term evaluation period, there were intergroup statistical differences in changes of LDL-C, TG and Apo B at 18, 24, 38, and 52 weeks from baseline among the statins group (group A), hybutimibe group (groups B, D, and F), and combination group (groups C and E) (all P<0.01), with the best effect observed in the combination group. The incidence of adverse events was 64.2% in the statins group, 61.7% in the hybutimibe group, and 71.0% in the combination group during the long-term evaluation period. No treatment-related serious adverse events or adverse events leading to death occurred during the 52-week study period. Conclusions: Hybutimibe combined with atorvastatin showed confirmatory efficacy in patients with untreated primary hypercholesterolemia, which could further enhance the efficacy on the basis of atorvastatin monotherapy, with a good overall safety profile.
Male ; Humans ; Middle Aged ; Atorvastatin/therapeutic use* ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use* ; Hypercholesterolemia/drug therapy* ; Cholesterol, LDL/therapeutic use* ; Anticholesteremic Agents/therapeutic use* ; Treatment Outcome ; Triglycerides ; Apolipoproteins B/therapeutic use* ; Double-Blind Method ; Pyrroles/therapeutic use*