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.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Background/Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic liver disease characterized by hepatic steatosis. Ubiquitin-specific protease 29 (USP29) plays pivotal roles in hepatic ischemiareperfusion injury and hepatocellular carcinoma, but its role in MASLD remains unexplored. Therefore, the aim of this study was to reveal the effects and underlying mechanisms of USP29 in MASLD progression. Methods: USP29 expression was assessed in liver samples from MASLD patients and mice. The role and molecular mechanism of USP29 in MASLD were assessed in high-fat diet-fed and high-fat/high-cholesterol diet-fed mice and palmitic acid and oleic acid treated hepatocytes. Results: USP29 protein levels were significantly reduced in mice and humans with MASLD. Hepatic steatosis, inflammation and fibrosis were significantly exacerbated by USP29 deletion and relieved by USP29 overexpression. Mechanistically, USP29 significantly activated the expression of genes related to fatty acid β-oxidation (FAO) under metabolic stimulation, directly interacted with long-chain acyl-CoA synthase 5 (ACSL5) and repressed ACSL5 degradation by increasing ACSL5 K48-linked deubiquitination. Moreover, the effect of USP29 on hepatocyte lipid accumulation and MASLD was dependent on ACSL5. Conclusions USP29 functions as a novel negative regulator of MASLD by stabilizing ACSL5 to promote FAO. The activation of the USP29-ACSL5 axis may represent a potential therapeutic strategy for MASLD.

Background/Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic liver disease characterized by hepatic steatosis. Ubiquitin-specific protease 29 (USP29) plays pivotal roles in hepatic ischemiareperfusion injury and hepatocellular carcinoma, but its role in MASLD remains unexplored. Therefore, the aim of this study was to reveal the effects and underlying mechanisms of USP29 in MASLD progression. Methods: USP29 expression was assessed in liver samples from MASLD patients and mice. The role and molecular mechanism of USP29 in MASLD were assessed in high-fat diet-fed and high-fat/high-cholesterol diet-fed mice and palmitic acid and oleic acid treated hepatocytes. Results: USP29 protein levels were significantly reduced in mice and humans with MASLD. Hepatic steatosis, inflammation and fibrosis were significantly exacerbated by USP29 deletion and relieved by USP29 overexpression. Mechanistically, USP29 significantly activated the expression of genes related to fatty acid β-oxidation (FAO) under metabolic stimulation, directly interacted with long-chain acyl-CoA synthase 5 (ACSL5) and repressed ACSL5 degradation by increasing ACSL5 K48-linked deubiquitination. Moreover, the effect of USP29 on hepatocyte lipid accumulation and MASLD was dependent on ACSL5. Conclusions USP29 functions as a novel negative regulator of MASLD by stabilizing ACSL5 to promote FAO. The activation of the USP29-ACSL5 axis may represent a potential therapeutic strategy for MASLD.

Background/Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic liver disease characterized by hepatic steatosis. Ubiquitin-specific protease 29 (USP29) plays pivotal roles in hepatic ischemiareperfusion injury and hepatocellular carcinoma, but its role in MASLD remains unexplored. Therefore, the aim of this study was to reveal the effects and underlying mechanisms of USP29 in MASLD progression. Methods: USP29 expression was assessed in liver samples from MASLD patients and mice. The role and molecular mechanism of USP29 in MASLD were assessed in high-fat diet-fed and high-fat/high-cholesterol diet-fed mice and palmitic acid and oleic acid treated hepatocytes. Results: USP29 protein levels were significantly reduced in mice and humans with MASLD. Hepatic steatosis, inflammation and fibrosis were significantly exacerbated by USP29 deletion and relieved by USP29 overexpression. Mechanistically, USP29 significantly activated the expression of genes related to fatty acid β-oxidation (FAO) under metabolic stimulation, directly interacted with long-chain acyl-CoA synthase 5 (ACSL5) and repressed ACSL5 degradation by increasing ACSL5 K48-linked deubiquitination. Moreover, the effect of USP29 on hepatocyte lipid accumulation and MASLD was dependent on ACSL5. Conclusions USP29 functions as a novel negative regulator of MASLD by stabilizing ACSL5 to promote FAO. The activation of the USP29-ACSL5 axis may represent a potential therapeutic strategy for MASLD.

BackgroundModified electroconvulsive therapy （MECT） is a common front-line strategy widely used in psychiatric practice， and the optimal first stimulus dosage in MECT is usually estimated clinically based on the factors influencing the patient's initial seizure threshold （IST）. However， previous studies on the influencing factors of IST have mostly suffered from limitations such as small sample sizes and single-dimensional research perspectives. ObjectiveTo explore the factors influencing IST in MECT for patients with mental disorders， so as to provide references for stimulus dosing strategies in MECT for the patients. MethodsA retrospective study was used to include 1 446 inpatients fulfilling the diagnostic criteria for any specific mental disorder listed in the ICD-10 and receiving MECT at Shandong Daizhuang Hospital from January 1， 2021 to August 1， 2023. Their general and clinical data were collected， including IST， psychiatric diagnostic categories， gender， ethnicity， age， body weight， body mass index （BMI）， course of disease， family history of psychiatric disorders， first episode status， use of antiepileptic drugs the day before treatment， use of benzodiazepines the day before treatment， and previous MECT treatment history. Pearson correlation analysis was utilized to test the correlation of IST with age， height， body weight， BMI， and course of disease， and stepwise multivariate linear regression analysis was performed to identify the factors affecting IST. ResultsIST yielded statistical difference among patients in terms of gender， first episode status， use of antiepileptic drugs the day before treatment， and use of benzodiazepines the day before treatment （t=2.256， -3.059， -2.136， -3.006， P<0.05 or 0.01）. IST in patients of different ages and psychiatric diagnostic categories also demonstrated statistical difference （F=913.120， 6.212， P<0.01）. Within young population， IST varied significantly based on the psychiatric diagnostic categories （F=2.986， P<0.05）. Correlation analysis indicated that IST was positively correlated with age， body weight， BMI and course of disease （r=0.886， 0.055， 0.184， 0.456， P<0.05 or 0.01）， and negatively correlated with height （r=-0.183， P<0.01）. Stepwise multivariate linear regression analysis revealed that age， gender， and body weight were influencing factors of IST （β=0.888， -0.049， -0.035， P<0.01）. ConclusionsAge， gender and body weight may be factors influencing IST in MECT for patients with mental disorders. ［Funded by Key R&D Plan Projects of Jining City in 2024 (number, 2024YXNS202）］

Although indigenous malaria has been eliminated in Wuhan since 2013,imported malaria remains a potential threat as an infectious source of local malaria transmission.The epidemiological and clinical characteristics of imported malaria are particularly important in areas where local malaria has been eliminated.This study was aimed at analyzing the epidemiological and clinical characteristics of imported malaria in Wuhan from 2012 to 2019,to provide a basis for further improving the preven-tion and control of imported malaria.Patients in Wuhan diagnosed with imported malaria from January 1,2012,to December 31,2019,were included in this study.A case-control study was con-ducted to analyze the features of patients with severe malaria.Uni-variate and multivariate logistic regression was used to identify risk factors for prolonged hospital length of stay(LOS).Among 229 imported malaria cases,212(92.6％)were in Chinese citizens,and most cases were in men(96.5％).The gender ratio is 28:1,and the age of cases is mainly concertrated between 18 and 50 years old(89.1％).More than 80％of patients were mi-grant workers,and most cases were infections from African countries(92.6％).Plasmodium falciparum(80.8％)was the dominant species.Fifty-three severe malaria cases were identified during the study period.Compared with uncomplicated cases,severe cases tended to occur in patients with no history of malaria(P=0.008),patients infected with Plasmodium falciparum(P=0.009),and patients who were initially misdiagnosed(P＜0.001).The median LOS was 6 days,and the species of infec-tion(Plasmodium falciparum),the use of antimalarial drugs(group B),antipyretic time(longer than 3 days),and the turn-around time of blood smear microscopy(longer than 3 days)were significantly associated with longer LOS(all P＜0.05).Al-though malaria has been eradicated in Wuhan for many years,imported cases continue to pose a threat.Efforts should be made to strengthen malaria knowledge education for outbound personnel.Additionally,medical institutions must enhance diagnosis and treatment capabilities for malaria,and adhere to standardized treatment processes,and the development of drug resistance and occurrence of severe malaria must be prevented.

Objective To propose a human life signal separation and reconstruction method based on optimized variational mode decomposition(VMD)to improve the accuracy and timeliness of the life detection radar in extracting and separating human life signals such as heartbeat and respiration.Methods Firstly,the particle swarm optimization algorithm was used to optimize the parameters of VMD,and the human life signal was decomposed into a series of intrinsic mode functions(IMFs);secondly,the alignment entropy of each IMF was calculated,the noise was removed based on the alignment entropy threshold,and the remaining components were reconstructed to form human life signals;finally,the method proposed was compared with infinite impulse response(IIR)filtering,VMD and complete ensemble empirical mode decomposition with adaptive nosie(CEEMDAN)to verify its performance.Results Under different noise levels the proposed method outperformed IIR filtering,VMD and CEEMDAN in evaluation metrics of signal-to-noise ratio and root-mean-square error,and behaved better than CEEMDAN in terms of computational time-consumption.Conclusion The proposed method realizes rapid separation and reconstruction of vital signals such as heartbeat and respiration while effectively filtering out the noise,which has broad application prospects in the fields of non-contact vital signs detection of bum/scald patients,infectious disease patients and newborns and the search and rescue of buried casualties after a disaster.[Chinese Medical Equipment Journal,2024,45(3):9-15]

Abstract: Objective To compare the differences in the expression of interleukin-8 (IL-8) and signal transducer and activator of transcription 3 (STAT3) among three groups of individuals: patients with multi-drug resistant tuberculosis (MDR-TB), patients with drug-susceptible tuberculosis (DSP-TB), and healthy controls. By analyzing the differences between these groups, the study seeks to provide a reference basis for clinical auxiliary diagnosis of new type tuberculosis. Methods The differences in mRNA expression profiles of chip from peripheral blood mononuclear cells (PBMCs) among three groups were analyzed. MDR-TB and DSP-TB patients were selected as study subjects, with healthy individuals serving as a control group, and the serum IL-8 concentration of each group was measured using an ELISA assay to compare differences between groups. Additionally, PBMCs from all groups were isolated and total RNA was extracted for analysis of IL-8 and STAT3 mRNA expression using quantitative real-time PCR(Q-PCR). Differences in STAT3 protein expression among the groups were also compared by Western blot assay. Results The serum IL-8 concentration in the MDR-TB and DSP-TB groups was significantly higher than that in the healthy control group, with a statistically significant difference (P<0.05). The relative expression level of IL-8 mRNA in PBMCs of the MDR-TB group and DSP-TB group was also significantly higher than that of the healthy control group, with a statistical significance (P<0.05). Both Q-PCR and Western Blot results revealed that the expression of STAT3 in the healthy, DSP-TB and MDR-TB groups sequentially increased, presenting statistically significant differences between the groups (P<0.05), consistent with the mRNA chip results. Conclusions The expression of IL-8 in active tuberculosis patients (ATB) is significantly increased, which can be used as a marker to distinguish ATB from healthy populations. The changes of STAT3 concentration not only serve as a reference for the diagnosis of ATB but can also distinguish between DSP-TB and MDR-TB populations. The combined detection of IL-8 and STAT3 holds promise as a clinical auxiliary diagnostic basis for different tuberculosis patients.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.