Objective: To explore the relationship between snack consumption and depressive symptoms in first year junior high school students with different left behind experiences in Yunnan Province, so as to provide a basis for improving depressive symptoms among first year junior high school students with different left behind experiences. Methods: From October to December 2022,a cluster random sampling method was used to select 8 500 first year junior high school students from 11 ethnic minority areas (Fugong County, Longling County, Longyang District, Luchun County, Mojiang County, Nanjian County, Qiaojia County, Shuangjiang County, Tengchong City, Yuanmou County, Zhenyuan County) in Yunnan Province for a questionnaire survey. The Chinese version of Depression Anxiety Stress Scale-21 was applied to assess depressive symptoms in first year junior high school students, and snack consumption was collected by employing food frequency questionnaire. The generalized linear model was used to analyze the association between first year junior high school students snack consumption and depressive symptoms, and the analysis was stratified according to left behind experience. Results: The detection rates of depressive symptoms among firstyear junior high school students with and without left behind experience were 36.25% and 26.91%, respectively. After controlling for confounding variables, the generalized linear model analysis showed that sweet snacks ( β=0.16, 95%CI =0.07-0.25), fast food ( β=0.14, 95%CI =0.04-0.23) and carbonated drinks ( β=0.09, 95%CI =0.01-0.17) of first year junior high school students with left behind experience (all P <0.05). Compared with those without such behavior, the risk of depressive symptoms was higher in consumption of fast food ( β=0.13, 95%CI =0.07-0.18) and carbonated drinks ( β=0.10, 95%CI =0.06-0.15)among first year junior high school students without left behind experience (both P <0.05). Conclusion Snack consumption among first year junior high school students in Yunnan may increase the risk of developing depressive symptoms, while first year junior high school students with left behind experience may have a greater risk of developing depressive symptoms.

Objective: To translate the common metrics for donation attitude, subjective norm, perceived behavioral control, and intention for the blood donation context (BD-ASPI) into Chinese, and to test its reliability and validity among college students. Methods: A research team was established. Following Beaton's cross-cultural adaptation guidelines, the BD-ASPI was translated, culturally adapted, and pre-tested to develop the Chinese version. Using convenience sampling, 620 students from four universities in Wuhan were surveyed form August to November 2024 to test the scale's reliability and validity. Results: The Chinese version of the scale consisted of 21 items across four dimensions: attitude towards blood donation, subjective norm, perceived behavioral control, and intention. The item-level content validity index ranged from 0.89 to 1.00, and the average scale-level content validity index was 0.984. Confirmatory factor analysis indicated a good fit for the second-order factor model. The Criterion validity was 0.509 (P<0.001). The overall Cronbach's α coefficient was 0.965, with the coefficients for each dimension ranging from 0.891 to 0.974. The test-retest reliability was 0.894. Conclusion: The Chinese version of the BD-ASPI demonstrates good reliability and validity, and can serve as an effective tool for assessing the behavioral intention of voluntary blood donation among college students in China.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

In modern society， the comorbidity of insomnia with amnesia has shown a significantly increasing incidence and become increasingly severe， emerging as a public health issue that endangers people's physical and mental health and quality of life. Anmei Dan （AMD）， originally recorded in the Shishi Milu by CHEN Shiduo in the Qing Dynasty， possesses profound theoretical foundations and distinct clinical advantages in ameliorating insomnia with amnesia. Basic research has unequivocally confirmed its definite therapeutic effects on insomnia with amnesia and revealed partial mechanisms. AMD is therefore a veritable classic prescription embodying the principle of treating different diseases with the same method for insomnia and amnesia. However， few researchers have reviewed the related research progress. This article systematically summarized the theoretical connotations of traditional Chinese medicine in treating insomnia with amnesia， delineated the multi-component material basis of AMD in ameliorating insomnia with amnesia （including various chemical components such as ginsenosides， jujubosides， and salvianolic acids）， and evaluate its multi-dimensional clinical efficacy （e.g.， prolonging total sleep time， improving sleep efficiency， and enhancing memory）. Moreover， it systematically summarizes the multi-pathway mechanisms of action. The results indicate that AMD has a clear material basis， definite clinical efficacy， and a favorable safety profile in ameliorating insomnia and amnesia. AMD may exert the therapeutic effect by regulating sleep-wake rhythms， enhancing synaptic plasticity， inhibiting neuroimmune inflammation， and modulating autophagy， oxidative stress， and energy metabolism. In conclusion， AMD exerts synergistic effects through multi-component， multi-dimensional， and multi-pathway approaches， serving as an important prescription for enriching and developing the theory of treating different diseases with the same method for insomnia with amnesia.

Objective To establish a rapid on-site detection method for detecting formaldehyde in workplace air using the length detector tube method. Methods The length detector tube was applied to detect the level of formaldehyde in workplace air. Performance indicators, including accuracy, precision, stability, and interference susceptibility, of the method were validated. Results The formaldehyde length detector tube can determine formaldehyde mass concentrations ranging from 0.05 to 4.00 mg/m³ at 25 ℃, covering 0.5 to 2.0 times the occupational exposure limit for formaldehyde (0.50 mg/m³). Under conditions of 10 to 35 ℃ and relative humidity <80.00%, the average recovery was 93.33%-101.67%, and all the within-run method ranges from -21.67% to 20.00%, and all the relative standard deviations were <10.00%. The average decrease rate of formaldehyde in gas bags within 24 hours ranged from -8.33% to 8.33%. Common coexisting substances in workplaces, such as methanol, benzene, toluene, hydrogen chloride, and nitrogen dioxide, did not interfere with the measurements. However, aldehydes and ketones might cause overestimation. Conclusion The formaldehyde length detector tube method demonstrates high accuracy and specificity for detecting formaldehyde in air, making it suitable for rapid monitoring of formaldehyde in workplace air.

Objective:To investigate the clinical manifestation, therapy, and prognosis of Susac syndrome and enhance the understanding of this disease.Methods:A case summary was made.The clinical data of two siblings with Susac syndrome treated at Children′s Medical Center, Peking University First Hospital in January 2024 were summarized.Reported cases of pediatric Susac syndrome were reviewed.Results:The onset of the disease in the two siblings was at the age of 3.00 and 6.75 years, with recurrent headaches, tinnitus, hearing loss and encephalopathy symptoms.Cranial magnetic resonance imaging showed multiple cerebral microbleeding and microinfarction lesions, " snowball like" in the corpus callosum and diffuse white matter edema in the brain.Audiometry revealed sensorineural hearing loss.In one case, ophthalmic fluorescein angiography revealed ischemic changes due to branch retinal artery occlusions.No pathogenic variants were detected in gene testing.This child was diagnosed with Susac syndrome, and the symptoms were improved after treatment with Corticosteroids and Rituximab.No relapse was observed during the 9-month follow-up.A total of 20 pediatric cases of Susac syndrome were retrieved, including 18 reported previously and 2 cases from this study.There were 2 boys and 18 girls, with the age of onset ranging from 2.5 to 17.0 years.The common initial symptoms included headache (19 cases), vertigo and tinnitus or hearing loss (9 cases), and vision impairment or visual field defect (4 cases). The symptoms were improved after immunotherapy.Conclusions:With a low incidence, Susac syndrome is rare in children and difficult to diagnose.There may be a genetic predisposition in such disease.Early diagnosis and immunotherapy can low the relapse and improve the prognosis.

Objective:To investigate the impact of plasmapheresis therapy on the clinical efficacy in predicted severe hypertriglyceridemia-associated acute pancreatitis (HTG-AP) patients.Methods:The clinical data of 500 HTG-AP patients admitted to 36 medical centers across China in the Chinese Acute Pancreatitis Clinical Trials Group-PERFORM database from November 2020 to June 2023 were retrospectively analyzed. Besides the inclusion and exclusion criteria from PERFORM study, patients who had acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score ≥8 or CRP>150 mg/L on admission were included in the final analyses ( n=189). Patients were categorized into the plasmapheresis group ( n=51) and the routine treatment group ( n=138) according to the triglyceride-lowering therapies they received. General data, laboratory findings, AP severity, and clinical outcomes were recorded. Results:Patients undergoing plasmapheresis had higher initial triglyceride levels, APACHEⅡ score, SOFA score, and more organ failure than those receiving routine medical treatment. Results of multivariable logistic regression models showed that the plasmapheresis group, as compared to the routine treatment group, was neither associated with decreased risk of persistent organ failure within 14 days [54.9% (28/51) vs 37.7% (52/138), OR=0.89, 95% CI 0.36-2.21, P=0.810], nor with reduced incidence of organ failure on day 7 [17.7% (9/51) vs 15.9% (22/138), OR=0.60, 95% CI 0.19-1.88, P=0.378]. There was no significant difference on the dynamic changes of serum triglyceride within the first three days of admission ( P=0.108). Conclusions:Early plasmapheresis is not associated with reduced incidence of persistent organ failure in predicted severe HTG-AP patients.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Autism spectrum disorder(ASD),characterized by unknown etiology and high heterogeneity,ne-cessitates precise diagnostic and intervention strategies.Neuroimaging techniques have shown great promise in un-covering the neural mechanisms of ASD,providing a foundation for aided diagnosis and transcranial magnetic stim-ulation(TMS)interventions.This review highlights that integrating multimodal neuroimaging and developing indi-vidualized indices with developmental specificity can significantly improve the accuracy of ASD diagnosis and clas-sification.Furthermore,TMS interventions guided by functional connectivity derived from functional magnetic reso-nance imaging(fMRI)offer a personalized approach to ASD treatment.