Objective To explore the pharmacokinetic(PK)characteristics of desloratadine tablets and reference drugs in healthy subjects,and evaluate their bioequivalence and safety.Methods The random,open,two-period,cross-over pharmacokinetic study method was adopted,each subject received a single oral dose of desloratadine tablets test drug(T)or reference drug(R)for 5 mg.The concentrations of desloratadine and 3-hydroxy desloratadine in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS);and the PK parameters were calculated by WinNonlin 8.1 software to evaluate the bioequivalence.Results The main PK parameters of T and R of desloratadine were as follows:the fasting condition Cmax were respectively(3 809.82±1 016.54)and(3 642.36±777.07)pg·mL-1;AUC0-120h were respectively(5.75 ×104±5.03 ×104)and(5.51 × 104±4.00 × 104)pg·h·mL-1;AUC0-∞ were respectively(6.85× 104±1.03× 104)and(6.37 × 104±7.92 × 104)pg·h·mL-1.The fed condition Cmax were respectively(4 398.98±1 191.22)and(4 744.4±1 511.97)pg·mL-1;AUC0-120h were respectively(5.25 × 104±1.82 × 104)and(5.55 × 104±1.98 × 104)pg·h·mL-1;AUC0-∞ were respectively(5.37 × 104±1.86 × 104)and(5.68 × 104±2.04 × 104)pg·h·mL-1.The 90％confidence interval of Cmax,AUC0-t and AUC0-∞ of desloratadine were all within 80.00％～125.00％.Conclusion There was no significant difference in the main PK parameters between T tablets and R under fasting or high-fat postprandial conditions,and desloratadine tablets were bioequivalent,safe and well tolerated.

Objective To compare the efficacy of artificial intelligence(AI)diagnostic group and artificial reading group in the diagnosis for osteoporotic vertebral compression fractures.Methods From January 2023 to December 2023,80 patients with osteoporotic vertebral compression fractures and 20 patients without fractures but with nonspecific low back pain were included in the study.According to the patient's computed tomography(CT)image,the AI software diagnosis and physicians of different seniority(one senior physician,one intermediate physician and one junior physician)diagnosis were performed.The diagnostic efficacy of different detection methods was compared.Results The sensitivity,specificity,positive predictive value,negative predictive value and area under the receiver operating characteristic(ROC)curve(AUC)and Kappa value of each group were as follows:AI image interpretation:0.975,0.900,0.975,0.900,0.938,0.875;senior physician:0.950,0.900,0.974,0.818,0.925,0.819;intermediate physician:0.825,0.850,0.957,0.548,0.837,0.560;and junior physician:0.750,0.750,0.923,0.429,0.751,0.390.Conclusion The diagnostic performance of AI was comparable to that of senior physician,and significantly higher than that of intermediate and primary physicians.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Objective To compare the efficacy of artificial intelligence(AI)diagnostic group and artificial reading group in the diagnosis for osteoporotic vertebral compression fractures.Methods From January 2023 to December 2023,80 patients with osteoporotic vertebral compression fractures and 20 patients without fractures but with nonspecific low back pain were included in the study.According to the patient's computed tomography(CT)image,the AI software diagnosis and physicians of different seniority(one senior physician,one intermediate physician and one junior physician)diagnosis were performed.The diagnostic efficacy of different detection methods was compared.Results The sensitivity,specificity,positive predictive value,negative predictive value and area under the receiver operating characteristic(ROC)curve(AUC)and Kappa value of each group were as follows:AI image interpretation:0.975,0.900,0.975,0.900,0.938,0.875;senior physician:0.950,0.900,0.974,0.818,0.925,0.819;intermediate physician:0.825,0.850,0.957,0.548,0.837,0.560;and junior physician:0.750,0.750,0.923,0.429,0.751,0.390.Conclusion The diagnostic performance of AI was comparable to that of senior physician,and significantly higher than that of intermediate and primary physicians.

Objective:This study aimed to analyze the prognostic risk factors for hepatoid adenocarcinoma of the stomach (HAS) and construct two nomogram-based clinical prediction models to predict overall survival (OS) and recurrence-free survival (RFS) in patients with HAS.Methods:Data were retrospectively collected from 82 patients (64 males, 18 females; mean age 60.3 ± 9.4 years) who underwent radical gastrectomy and were pathologically diagnosed with gastric hepatoid adenocarcinoma at the First Medical Center of the PLA General Hospital between February 2006 and September 2023. Statistical analyses were conducted using SPSS 25.0 and R 4.3.2. Survival analyses were performed using the Kaplan-Meier method, and univariate analyses were used to identify clinical and pathological factors associated with prognosis. Variables with P<0.05 in the univariate analysis were included in multivariate Cox regression models to identify independent risk factors for OS and RFS. These factors were incorporated into the prediction models to construct nomograms. The discriminatory power of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analyses, while calibration curves, decision curve analysis (DCA), and comparisons with the 8th edition of the TNM staging system of the American Joint Committee on Cancer (AJCC) were employed to evaluate model performance. Results:Among the 82 patients, 36 (43.9%) exhibited vascular infiltration, 61 (74.4%) had nerve infiltration, and lymph node metastasis was observed in 60 cases (73.2%). Pathological stages I, II, III, and IV were distributed as 11 (13.4%), 26 (31.7%), 44 (53.7%), and 1 (1.2%) cases, respectively. Inflammatory markers included neutrophil-to-lymphocyte ratio (NLR) ≥ 4.33 in 22 cases (26.8%), platelet-to-lymphocyte ratio (PLR) ≥ 142.2 in 50 cases (61.0%), monocyte-to-lymphocyte ratio (MLR) ≥ 0.411 in 22 cases (26.8%), α-fetoprotein (AFP) ≥ 2.48 μg/L in 64 cases (78.0%), and C-reactive protein (CRP) ≥ 7.506 mg/L in 12 cases (14.6%). Among the 82 patients, 3 cases (3.6%) were lost to follow-up. The median follow-up time was 52 (range: 8–147) months, with a median OS of 61(2–147) months. The 1-year and 3-year OS rates were 78.5% and 58.5%, respectively, while the 1-year and 3-year RFS rates were 77.3% and 60.3%, respectively. Multivariate analysis identified several independent risk factors influencing OS in patients with HAS: advanced pathological stage, MLR ≥ 0.411, AFP ≥ 2.545 μg/L, and CRP ≥ 7.51 mg/L. The hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: 5.218 (1.230–22.143), 2.610 (1.287–5.294), 2.950 (1.013–8.589), and 2.594 (1.145–5.877), respectively (all P < 0.05). For RFS, advanced pathological stage, PLR ≥ 152.0, and MLR ≥ 0.411 were independent risk factors, with HRs (95% CIs) of 4.735 (1.080–20.760), 3.759 (1.259–11.226), and 2.714 (1.218–6.048), respectively (all P < 0.05). The AUC values for OS prediction at 1 year, 3 years, and 5 years were 0.7765, 0.7525, and 0.7702, respectively. For RFS, the AUC values were 0.7304, 0.8137, and 0.8307 at 1 year, 3 years, and 5 years, respectively. The calibration curves demonstrated strong agreement between nomogram- predicted outcomes and observed survival data. DCA indicated that both TNM staging and the nomogram-based clinical prediction models provided a net positive benefit in predicting OS and RFS in HAS patients, with the nomogram model demonstrating superior performance. Conclusion:The nomogram-based clinical prediction models developed in this study demonstrated robust performance in predicting long-term OS and RFS in patients with HAS.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Image 1.

This study explores the basic characteristics and potential functions of the terpene synthase(TPS) gene family members in Lonicera japonica. The L. japonica TPS(LjTPS) gene family was identified and functionally analyzed using bioinformatics methods. The results showed that a total of 70 members of the LjTPS gene family were identified in L. japonica, with protein lengths ranging from 130 to 1 437 amino acids. Most of these proteins were hydrophilic, and they were unevenly distributed across nine chromosomes. Phylogenetic analysis showed that the LjTPS gene family members were divided into six subfamilies, mainly consisting of members from the TPS-a, TPS-b, and TPS-e subfamilies. Promoter cis-acting element analysis showed that LjTPS members contained a large number of stress-responsive cis-acting elements. Aphid inoculation experiments showed that key enzyme genes in the MVA pathway for terpenoid backbone synthesis in L. japonica, such as HMGS, HMGR, MK, MPD, and the key enzyme gene in the DXP pathway, DXS, exhibited an initial increase followed by a decrease under aphid stress. The qRT-PCR analysis showed that the expression levels of the α-farnesene synthase genes LjTPS34 and LjTPS39 were down-regulated, while the expression levels of(E)-β-caryophyllene synthase genes LjTPS15 and LjTPS17 were up-regulated 12 h before aphid feeding, then began to decline. Farnesyl pyrophosphate synthase(FPS), which interacted with these genes, also displayed a pattern of increasing followed by decreasing expression. The expression of linalool synthase genes LjTPS12 and LjTPS33 was significantly up-regulated after 72 h of aphid feeding(P<0.000 1), reaching 24.39 and 22.64 times the initial expression, respectively. This pattern was in close alignment with the trend of linalool content in L. japonica. This study provides a theoretical foundation for future research on the interaction between L. japonica and pests, as well as on the functional roles of the LjTPS gene family.
Animals ; Aphids/physiology* ; Alkyl and Aryl Transferases/chemistry* ; Lonicera/parasitology* ; Phylogeny ; Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Multigene Family ; Terpenes/metabolism*

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.