ObjectiveTo investigate the association of liver fibrosis scores and inflammation markers with gallstones in patients with metabolic dysfunction-associated fatty liver disease （MAFLD）， as well as the mediating role of liver fibrosis scores in the relationship between inflammation markers and gallstones. MethodsA total of 14 567 patients who received physical examination and were diagnosed with MAFLD in Subei People’s Hospital from January 2014 to June 2023 were enrolled in this study， and according to the results of abdominal color Doppler ultrasound， they were divided into gallstone group with 1 724 patients and non-gallstone group with 12 843 patients. Related clinical data were collected from all patients， including demographic data， medical history， family history， physical examination， Color Doppler ultrasound， and biochemical parameters. The biomarkers associated with metabolic disorders and insulin resistance included triglyceride-glucose index （TyG）， TyG-body mass index （BMI） index， atherogenic index of plasma （AIP）， and non-high-density lipoprotein cholesterol-to-high-density lipoprotein cholesterol ratio （NHHR）； the biomarkers associated with inflammation and nutritional status included neutrophil-to-lymphocyte ratio （NLR）， neutrophil percentage-to-albumin ratio （NPAR）， and monocyte-to-lymphocyte ratio （MLR）； the biomarkers for assessing liver fibrosis degree and liver function included albumin-bilirubin （ALBI） score， NAFLD fibrosis score （NFS）， fibrosis-4 （FIB-4） index， and aspartate aminotransferase-to-platelet ratio index （APRI）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， while the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. Multivariate Logistic regression analysis， restricted cubic spline analysis， and mediating effect analysis were used to assess the association of liver fibrosis markers and inflammation markers with the risk of gallstones. ResultsThe prevalence rate of gallstones was 11.8% among the MAFLD patients. There were significant differences between the gallstone group and the non-gallstone group in sex， age， smoking history， diabetes， hypertension， lymphocytes， platelets， glucose， albumin， serum uric acid， alanine aminotransferase， aspartate aminotransferase， red blood cell， NLR， NPAR， MLR， NFS， FIB-4 index， and ALBI score （all P<0.05）. The multivariate Logistic regression analysis showed that NLR （odds ratio ［OR］=1.091， 95% confidence interval ［CI］： 1.028　—　1.160， P<0.05）， NPAR （OR=1.073， 95%CI： 1.042　—　1.105， P<0.05）， MLR （OR=1.142， 95%CI： 1.057　—　1.232， P<0.05）， NFS （OR=1.239， 95%CI： 1.190　—　1.291， P<0.05）， and FIB-4 index （OR=1.326， 95%CI： 1.241　—　1.417， P<0.05） were influencing factors for the prevalence rate of gallstones. The restricted cubic spline analysis showed a significant non-linear association between NFS/FIB-4 index and the risk of gallstone （non-linear P<0.05）. The mediating effect analysis further showed that the association of NLR， MLR， and NPAR with gallstones was partially mediated by NFS or FIB-4 index， with a mediating effect accounting for 36.79%、28.09%、29.67% and 18.31%、17.70、11.57%， respectively. ConclusionNFS and FIB-4 index have a non-linear association with the prevalence rate of gallstones in MAFLD patients， and they also mediate the association of NLR， NPAR， and MLR with the risk of gallstone.

ObjectiveTo investigate the association between noninvasive liver fibrosis markers and carotid plaque （CP） in patients with lean metabolic dysfunction-associated fatty liver disease （MAFLD）， and to provide a basis for screening high-risk populations. MethodsA total of 957 patients with lean MAFLD who underwent physical examination in Subei People’s Hospital from January 2021 to June 2023 was enrolled as the observation cohort， with the presence or absence of CP as the outcome， and fibrosis-4 （FIB-4） index and nonalcoholic fatty liver disease fibrosis score （NFS） were used to assess liver fibrosis degree. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The multivariate logistic regression analysis， the restricted cubic spline analysis， the receiver operating characteristic curve， and the mediation effect analysis were used to investigate the association between liver fibrosis degree and CP. ResultsThe prevalence rate of CP was 36.6% in the lean MAFLD population. Compared with the non-CP group（n=607）， the CP group （n=350） had a significantly higher proportion of male patients， a significantly higher proportion of patients with smoking/diabetes/hypertension， and significantly higher levels of age， creatinine， blood urea nitrogen， triglycerides， fasting blood glucose， aspartate aminotransferase， aspartate aminotransferase/alanine aminotransferase ratio， NFS， and FIB-4 index， as well as significantly lower levels of platelet count and albumin （all P<0.05）. The multivariate logistic regression analysis showed that after adjustment for confounding factors， FIB-4 index （odds ratio［OR］=2.979， 95% confidence interval［CI］：2.141 — 4.219， P<0.001） and NFS （OR=1.747， 95%CI： 1.499 — 2.046， P<0.001） were positively correlated with CP. Both FIB-4 index and NFS had a good value in predicting CP. Hypertension had a significant indirect effect on the prevalence rate of CP through its impact on liver fibrosis markers， and its mediating effect accounted for 39.5% — 40.8% of the total effect （P<0.001）. ConclusionIn patients with lean MAFLD， NFS and FIB-4 index are significantly positively correlated with the prevalence rate of CP， and they can be used as potential epidemiological predictive indicators. Liver fibrosis markers may play a mediating role in the association between hypertension and CP. Interventions targeting hypertension and liver fibrosis markers may help to prevent and delay the progression of CP.

An effective method for simultaneously detecting four semivolatile earthy-musty odors in freshwater fish by gas chromatography-mass spectrometry(GC-MS)was developed.The concurrent extraction of geosmin(GSM),2-methylisoborneol(MIB),2-isopropyl-3-methoxypyrazine(IPMP),and 2-isobutyl-3-methoxypyrazine(IBMP)in fish tissue was conducted with n-hexane.The optimized QuEChERS material was implemented,and it was found that C18,primary secondary amine(PSA)and MgSO4 could adsorb the target analytes in n-hexane.So only the graphitized carbon black(GCB)could be used to purify the extraction.The adsorption rates of different materials for the four kinds of odors materials were explored in n-hexane and ethyl acetate.The experimental results revealed that the adsorption rates of silica for the four targets were 99.5%-100%in n-hexane and 0.7%-5.0%in ethyl acetate respectively.Then the silica solid phase extraction(SPE)method was utilized to eluent the compounds using 1.0 mL n-hexane/ethyl acetate in different proportions.The results of the comparative analysis demonstrated that n-hexane/ethyl acetate(4∶1,V/V)was the optimized eluent.Based on the obtained results,n-hexane extraction and GCB purification combined with silica SPE were used to isolate GSM,MIB,IPMP and IBMP from fish and the method was validated.It was found that the method showed good linearity in the range of 0.5-200 ng/mL,and with detection limits of 0.6 μg/kg for GSM and MIB,0.2 μg/kg for IPMP and IBMP.The limits of quantitation(LOQ)were 1.0 μg/kg for GSM and MIB,0.6 μg/kg for IPMP and IBMP.Good recoveries(77.5%-112.0%)and relative standard deviations(1.56%-9.42%)were also obtained.The use of silica SPE greatly mitigated the issue that the off-flavor compounds were easily lost in the gas blowing concentration process.There was no cross contamination in this method because the sample pretreatments were conducted separately,which was different with the most commonly used HS-SPME method for detecting semi-volatile substances.The sensitivity of this method was high enough to produce good quantitative results below the odor thresholds of the examined off-flavor compounds.

ObjectiveBased on functional near-infrared spectroscopy (fNIRS), we investigated the brain activity characteristics of gross motor tasks in children with autism spectrum disorder (ASD) and motor dysfunctions (MDs) to provide a theoretical basis for further understanding the mechanism of MDs in children with ASD and designing targeted intervention programs from a central perspective. MethodsAccording to the inclusion and exclusion criteria, 48 children with ASD accompanied by MDs were recruited into the ASD group and 40 children with typically developing (TD) into the TD group. The fNIRS device was used to collect the information of blood oxygen changes in the cortical motor-related brain regions during single-handed bag throwing and tiptoe walking, and the differences in brain activation and functional connectivity between the two groups of children were analyzed from the perspective of brain activation and functional connectivity. ResultsCompared to the TD group, in the object manipulative motor task (one-handed bag throwing), the ASD group showed significantly reduced activation in both left sensorimotor cortex (SMC) and right secondary visual cortex (V2) (P<0.05), whereas the right pre-motor and supplementary motor cortex (PMC&SMA) had significantly higher activation (P<0.01) and showed bilateral brain region activity; in terms of brain functional integration, there was a significant decrease in the strength of brain functional connectivity (P<0.05) and was mainly associated with dorsolateral prefrontal cortex (DLPFC) and V2. In the body stability motor task (tiptoe walking), the ASD group had significantly higher activation in motor-related brain regions such as the DLPFC, SMC, and PMC&SMA (P<0.05) and showed bilateral brain region activity; in terms of brain functional integration, the ASD group had lower strength of brain functional connectivity (P<0.05) and was mainly associated with PMC&SMA and V2. ConclusionChildren with ASD exhibit abnormal brain functional activity characteristics specific to different gross motor tasks in object manipulative and body stability, reflecting insufficient or excessive compensatory activation of local brain regions and impaired cross-regions integration, which may be a potential reason for the poorer gross motor performance of children with ASD, and meanwhile provides data support for further unraveling the mechanisms underlying the occurrence of MDs in the context of ASD and designing targeted intervention programs from a central perspective.

Objective:To construct a decision tree model to predict the risk of breast cancer in patients with pathological nipple discharge.Methods:A total of 157 patients with pathological nipple discharge,who were diagnosed and treated at Weifang Municipal Hospital of Traditional Chinese Medicine from January 2019 to April 2024 and met the inclusion criteria,were selected.A risk prediction model for concurrent breast cancer in patients with pathological nipple discharge was developed using Logistic regression analysis.A decision tree was then constructed,and the predictive performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC).Re-sults:The incidence of concurrent breast cancer among patients with pathological nipple discharge was 24.2%.Accord-ing to the results of binary Logistic regression analysis,elevated CEA and CA 153 levels in nipple discharge,as well as bloody discharge,emerged as independent risk factors for the development of breast cancer in such patients(P<0.05).Based on these findings,a decision tree model was constructed to predict the risk of concurrent breast cancer in patients with pathological nipple discharge.The validation results showed that the Logistic regression model had an AUC value of 0.800,while the decision tree model achieved an AUC value of 0.889.Conclusions:The decision tree model,built upon the identified influencing factors,exhibits strong predictive power for the risk of developing concurrent breast can-cer in patients with pathological nipple discharge,thus facilitating more precise preoperative diagnoses by clinicians for these patients.

Objective:To observe the role of impulsivity in cognitive impairment in manic episodes of bipolar Ⅰ disorder(BPDTI)patients.Methods:180 cases of patients with BPDTI manic episodes admitted to our hospital from February 2024 to December 2024 were selected as the manic group,and another 180 cases of volunteers who were psychologically tested as normal during the same period were selected as the healthy group.The impulse Barratt-11 scale(BIS-11)and the MATRICS Consensus Cognitive Test(MCCB)were used to investigate and evaluate impulsivity and cognitive function,and correlation analysis and risk factor analysis were conducted.Results:In the BIS-11 scale,there was no statistically significant difference between the two groups in the unplanned factor(P＞0.05),but there were significant differences compared between the two groups in cognitive,motor factors,and total score(P＜0.05).The MCCB scores of the manic group in information processing speed,working memory,word learning,visual learning,social cognition,reasoning and problem solving,and attention vigilance were significantly lower than those of the healthy group(P＜0.05).The relationship between BIS-11 total score and cognitive impulsivity factor and MCCB total score and word learning showed there were negative correlation(P＜0.05);The exercise factor in the BIS-11 scale were negatively correlated with the MCCB total score and working memory factor(P＜0.05);The unplanned factor in the BIS-11 scale showed there were negative correlation with the MCCB total score,word learning(P＜0.05),and information processing speed.BIS-11,the number of years of education was an influencing factor for BPDTI mania(P＜0.05).Conclusion:BPDTI mania patients had high impulsivity and cognitive impairment,high impulsivity and number of years of education were influencing factors for mania,high impulsivity affected cognitive function through corresponding brain regions,aggravating the condition.

Three typical failures of large-aperture 16-slice spiral Siemens SOMATOM Sensation Open CT were introduced in terms of phenomenon,cause and treatment method.References were provided for medical engineers to treat similar failures.

Objective:To construct a prediction model for the risk of sarcopenia in middle-aged and elderly patients with knee osteoarthritis (KOA) based on machine learning, and to provide a basis for carrying out the prevention of sarcopenia in patients with KOA.Methods:Clinical data of KOA patients from three tertiary hospitals in Guangdong Province were collected between December 2023 and September 2024 using a convenience sampling method. The data were randomly split into training and test sets at an 8:2 ratio, with the occurrence of sarcopenia as the outcome variable. Risk prediction models for sarcopenia were constructed using eight machine learning algorithms: logistic regression, K-nearest neighbors, support vector machine, decision tree, neural network, random forest, gradient boosting machine (GBM), and eXtreme gradient boosting. Model performance was evaluated based on metrics including the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. The optimal model was selected, and feature importance was visualized using the Shapley Additive exPlanations (SHAP) method.Results:Data from 640 KOA patients were analyzed, 143 males and 497 females, (67.51± 7.72) years, with 136 cases (21.25%) developing sarcopenia. All eight prediction models showed high AUC values, with the GBM model demonstrating the best performance. Its metrics included an AUC of 0.926 (95% CI 0.874 - 0.965), accuracy of 0.852, precision of 0.611, sensitivity of 0.815, specificity of 0.861, and F1 score of 0.698. SHAP analysis identified body mass index, calf circumference, body fat percentage, WOMAC score, and age as the most important predictive features. Conclusions:The GBM-based risk prediction model for sarcopenia in middle- aged and elderly KOA patients demonstrated optimal performance, enabling healthcare professionals to accurately and promptly identify high-risk groups among these patients and to develop effective, evidence-based intervention strategies.

Objective To develop a core outcome set(COS)for clinical effectiveness trials of heart failure with preserved ejection fraction(HFpEF).Methods Outcome measures were collected through database literatures search,clinical experts questionnaire survey and semi-structured patients interview.Then,the outcome measures pool was constructed and domains were divided.Candidate outcome measures of COS were screened through two rounds of Delphi survey.Finally,a consensus meeting was held to determine COS and reach a consensus.Results A total of 317 outcome measures which could be divided into 6 domains were collected through literature research,questionnaire survey and semi-structured interview.15 candidate outcome measures of COS were screened through two rounds of Delphi survey.Finally,the consensus meeting reached consensus on a COS with 6 entries.Conclusion In this study,a COS for clinical effectiveness trials of HFpEF was developed,which is conducive to the standardization of efficacy evaluation.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.