Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

ObjectiveTo compare the performance of different automatic evaluation methods for detecting pattern hallucinations generated by large language models （LLMs） in traditional Chinese medicine （TCM） syndrome differentiation and to identify a strategy with higher overall discriminative performance against expert manual judgment. MethodsA standardized TCM syndrome-differentiation dataset containing 598 cases was constructed from case reports published in Chinese core journals indexed by Peking University Core Journals or Chinese Science Citation Database. Each of the 598 cases was submitted to five Chinese LLMs， with each model generating one syndrome-pattern output per case， yielding 2990 outputs in total. The 598 outputs generated by Qwen3-Next-80B-A3B-Thinking were independently annotated for syndrome-pattern hallucinations by two licensed TCM physicians with intermediate or higher professional titles. Disagreements were adjudicated by a third licensed TCM physician with a senior associate professional title and more than 10 years of clinical experience. The resulting consensus annotations served as the reference labels for training and validating the automated evaluation method. Based on these reference labels， three categories of automated evaluation methods were developed and compared following a progressive strategy from text-level semantic similarity， to syndrome-element structural consistency， and finally multi-feature fusion. Method A employed sentence-embedding models based on semantic similarity between pattern texts. Method B adopted a rule-based threshold method using weighted Jaccard coefficients of disease-location and disease-nature syndrome elements. Method C utilized a machine-learning classifier integrating syndrome-element matching scores， missing and redundant syndrome-element counts， and multiple semantic-similarity features. Five-fold cross-validation was used to evaluate the discriminative performance of each method against manual judgment. The optimal method was then applied to uniformly assess the pattern hallucination rates of five LLMs. ResultsIn method A， text2vec-large-chinese showed best overall performance， with an area under the receiver operating characteristic curve （AUROC）， area under the precision-recall curve （PR-AUC）， F1-score， sensitivity， and specificity of 0.849， 0.884， 0.794， 0.801， and 0.691， respectively. In method B， the corresponding metrics of the rule-based syndrome-element Jaccard threshold method were 0.848， 0.851， 0.817， 0.845， and 0.684， respectively； those of the LLM-based syndrome-element Jaccard method were 0.872， 0.906， 0.795， 0.764， and 0.780， respectively. In method C， the multilayer perceptron （MLP） classifier showed the best overall performance， with AUROC， PR-AUC， F1-score， sensitivity， and specificity of 0.933， 0.951， 0.871， 0.857， and 0.846， respectively. Under the unified evaluation framework combining DeepSeek-R1 syndrome-element extraction and MLP classifier， the estimated pattern hallucination rates of the five LLMs ranged from 56.0% to 77.4%. ConclusionAmong the three automatic evaluation methods， Method C integrating syndrome-element discrepancy features and text semantic features using an MLP classifier showed the highest overall discriminative performance against manual judgment， and is more suitable for evaluating syndrome hallucinations in LLM-based TCM syndrome differentiation tasks.

Objective:To investigate the prevalence and risk factors of overweight and obesity among Chinese children aged 3-18 years from 11 provinces, antonomous regions, or municipalities.Methods:This national cross-sectional community health survey utilized a multistage stratified cluster-random sampling method to recruit 193 997 nationally representative participants from 11 provinces, autonomous regions, or municipalities between January 2017 and December 2019. All participants underwent physical examinations, and their caregivers completed questionnaires assessing participants′ dietary, lifestyle, familial, and perinatal information. Multilevel multinomial logistic regression models were employed to identify the potential risk factors.Results:The cohort comprised 193 997 children (102 178 boys, 91 819 girls),aged (10±4) years. Overall prevalence rates were 30 574(15.8%)overweight children and 17 217(8.9%) obesity children. Boys exhibited higher overweight and obesity rates than girls (17.0% (17 368/102 178) vs. 14.4% (13 206/102 178), 11.3% (11 553/91 819) vs. 6.2% (5 664/91 819), χ2=249.12,1 578.69,both P<0.001). The detection rates of obesity in Tanner stage 2 and 3 were the highest in boys and girls, with 13.4%(2 231/16 665) and 8.6%(880/10 221) respectively. Risk factors for obesity included parental overweight (paternal OR=2.34 and maternal OR=2.29), annual household income of 100 000-200 000 yuan (compared with<100 000 yuan, OR=1.04), higher paternal education (compared with below high school,high school and a college education OR=1.09,1.14), birth weight >4.0 kg (≤5 and>5 years old OR=1.74, 1.44,respectively), and western food consumption≥1 time/month (compared with<1, 1-2, 3-4,>4 times/month OR=1.36, 1.30, 1.67(≤5 years), 1.19, 1.16, 1.15 (>5 years), respectively) (all P<0.05). Conversely, coarse grain intake≥1 times/week (compared with<1 times/week, every day, 3-4, 1-2 times/week OR=0.74, 0.80, 0.71 (≤5 years), 0.75, 0.87, 0.90(>5 years), respectively, all P<0.05) was associated with reduced obesity risk. Conclusions:Obesity epidemiology in children demonstrates significant heterogeneity across age, gender, geographic regions, and pubertal stages. It is necessary to establish a personalized prevention and control strategy.

Objective To analyze the effect of sodium cantharidinate and vitamin B6 injection(SCV)on four human hepatocellular carcinoma(HCC)cell lines(SMMC-7721,Bel-7402,Huh7,and HepG2)and explore its mechanism.Methods Normal hepatic cell line L02 was treated with SCV at concentrations of 0 μmol/L(control),0.5,1,2,4,8,16,and 32 μmol/L,and the cytotoxicity of SCV on L02 cells was detected using CCK-8 assay.Human HCC cell lines(SMMC-7721,Bel-7402,Huh7,and HepG2)were cultured.SCV-untreated control group(0 μmol/L)and 2,4,and 8 μmol/L SCV-treated groups were set up.CCK-8 assay,plate cloning formation assay,Transwell assay,wound healing assay,and flow cytometry were used to detect the effects of SCV on the growth and proliferation capacity,colony formation ability,invasion and migration capabilities,cell cycle,and apoptosis of the four hepatocellular carcinoma cell lines,respectively.Western blotting was performed to detect the expression levels of apoptosis-related proteins,including nuclear factor kappa-B subunit p65(p65),B-cell lymphoma 2(Bcl-2),and Caspase-3,and to preliminarily explore the underlying mechanism.Results The CCK-8 assay showed that SCV at 0.5,1,2,4,and 8 μmol/L had no significant cytotoxic effect on L02 cells compared with untreated control group,so 2,4,and 8 μmol/L SCV were selected for subsequent experiments.Compared with the untreated control group(0 μmol/L),SCV at different concentrations(2,4,and 8 μmol/L)significantly inhibited the proliferation of the four HCC cell lines(P<0.001).The plate cloning formation assay showed that SCV at different concentrations(2,4,and 8 μmol/L)significantly reduced the colony formation ability of the four HCC cell lines(P<0.05 or P<0.01 or P<0.001).In addition,Transwell and wound healing assays revealed that SCV at different concentrations(2,4,and 8 μmol/L)significantly inhibited the invasion and migration of HCC cells(P<0.05 or P<0.01 or P<0.001).In the above results,the inhibitory effect of SCV was concentration-dependent.Flow cytometry analysis indicated that SCV arrested cells in the G2/M phase(P<0.05 or P<0.01 or P<0.001)and significantly promoted cell apoptosis(P<0.05 or P<0.01 or P<0.001).Western blotting showed that SCV significantly down-regulated the expression of p65(P<0.05 or P<0.01)and Bcl-2(P<0.05),and up-regulated the expression of Caspase-3(P<0.05 or P<0.01).Conclusions SCV can significantly inhibit the proliferation,colony formation,invasion,and migration of multiple human HCC cell lines and arrest the cell cycle.SCV may inhibit the expression of p65 and Bcl-2,thereby lifting their inhibitory effect on the apoptotic pathway and activating Caspase-3 to promote apoptosis.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To investigate the impact of ginseng alcohol extract(GEE)on improving sleep quality in the aged Drosophila model by regulating the redox balance,and to elucidate its associated mechanism.Methods:Thirty-two male drosophila melanogaster(7-days-old)were randomly selected as young group,while 64 male Drosophila melanogaster flies(35-days-old)were randomly assigned to aged model group(n=32)and GEE group(n=32).The sleep parameters,including total sleep duration,daytime sleep duration,night sleep duration,0-4 h of sleep duration after lights off(ZT0-4 sleep duration),deep sleep duration,sleep episodetimes,sleep fragmentation,and the activity parameters such as the total number of locomotor activity daytime locomotor activity amount and nighttime locomotor activity amount were analyzed using the DAM2 Drosophila behavioral analysis system 7 d after administration.The grouping of the drosophila was as above,and there were 100 drosophila ineach group.The differentially expressed proteins in drosophila brain tissue were screened,identified,and functionally analyzed using two-dimensional fluorescence difference gel electrophoresis(2D-DIGE)and matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF/TOF-MS)proteomic methods.The grouping of the drosophila was as above,and there were 100 drosophila in each group.The activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)and the levels of lipid peroxidation product(MDA)in brain tissue of the drosophila were determined using assay kits.Results:Compared with young group,the total sleep duration daytime sleep duration and night sleep cluration of the drosophila in agaed group were decreased(P<0.05 or P<0.01);and the sleep rhythm amplitude was shortened.Compared with aged group,the total sleep duration and daytime and nighttime sleep durations of the drosphila in GEE group were lengthened(P<0.01).Compared with young group,the ZT0-4 sleep duration deep sleep duration and sleep fragment of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the sleep rhythm amplitude was shortened.Compared with young group,the ZT0-4 sleep duration,deep sleep duration,and single sleep fragment of the drosphila in GEE group were significantly prolonged(P<0.01),and the sleep amplitude was increased.Compared with young group,there was no significant difference in diurnal spontaneous activity or total spontaneous activity of the drosophila in aged group(P>0.05),while the nocturnal spontaneous activity was significantly increased(P<0.05).Compared with aged group,the diurnal spontaneous activity,nocturnal spontaneous activity,and total spontaneous activity of the drosophila in GEE group were significantly decreased(P<0.05 or P<0.01).A total of 47 differentially expressed proteins were selected in the 2D-DIGE electrophoretic mapping.Compared with young group,the expressions of 47 differentially expressed protein sites in aged group were down-regulated mainly including glutathione S-transferase,peroxiredoxin 1 and dihydrolipoic dehydrogenase,which were related to redox balance.Compared with young group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the level of MDA was increased(P<0.01);compared with aged group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosphila in GEE group were increased(P<0.05 or P<0.01),and the MDA level was decreased(P<0.05).Conclusion:GEE has improvement effect on the sleep quality of aged drosophila,and its possible mechanism may be related to upregulating the activities of antioxidant enzymes,inhibiting the accumulation of lipid peroxidation products,and maintaining redox balance.

In this work,with tris(4-aminophenyl)amine(TAPA)and 1,3,5-tris(4-formylphenyl)benzene(TFPB)as monomers,an imine-type porous organic polymer,TAPA-TFPB,was synthesized using a simple method under the catalysis of acetic acid.The material TAPA-TFPB was used as solid-phase extraction adsorbent and combined with ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry(UHPLC-QTOF-MS)to establish a detection method for four kinds of nitrofuran metabolites(NFMs)residues in meat samples.The parameters of the adsorbent dosage,the pH value and volume of sample,and the type and volume of washing and eluent solvents were optimized,respectively.Under the optimal extraction conditions,low detection limits(0.11-1.60 μg/kg)were achieved for four kinds of NFMs.At three different spiked levels,the intra-day and inter-day precisions(Relative standard deviations)were 2.8% -10.9% and 4.3% -16.2%,respectively,and the spiked recoveries were 72.0% -107.2%.The results showed that the method chould be used for efficient extraction and analysis of trace NFMs residues in meat samples,indicating that TAPA-TFPB was a kind of promising SPE adsorbent.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.