Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid β‑protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

Objective To analyze the medication rules of Professor HUANG Feng for the treatment of low back pain using data mining methods.Methods The information of prescriptions for the effective cases of outpatients with low back pain treated by Professor HUANG Feng were collected and screened.Microsoft Excel 2019 was used to analyze the frequency of medication and the distribution of properties,flavors and meridian tropism of the drugs in the included prescription.IBM SPSS Modeler 18.0 was used for association rule analysis,and IBM Statistics 26.0 was used for cluster analysis.Results A total of 239 prescriptions and 75 Chinese medicines were included.There were 23 high-frequency Chinese medicines with the medication frequency being or over 20 times,and the top 10 Chinese medicines were Glycyrrhizae Radix et Rhizoma,vinegar-processed Corydalis Rhizoma,Cibotii Rhizoma,Atractylodis Macrocephalae Rhizoma,Zanthoxyli Radix,salt-processed Achyranthis Bidentatae Radix,Rehmanniae Radix,Dipsaci Radix,Coicis Semen,and Salviae Miltiorrhizae Radix et Rhizoma.The medicines were mainly warm in nature,and were sweet,bitter and pungent in flavor.Most of the drugs had the meridian tropism of liver,stomach and spleen meridians.Among the drug combinations obtained from association rule analysis with the top 20 highest support,vinegar-processed Corydalis Rhizoma,Cibotii Rhizoma,Atractylodis Macrocephalae Rhizoma and Zanthoxyli Radix were the core drugs.Cluster analysis yielded 6 clustering combinations.Conclusion For the treatment of low back pain,Professor HUANG Feng follows the principle of"treatment adapting to the climate,individuality,and environment"and"treating the root cause of the disease",usually adopts the drugs for activating blood,moving qi and relieving pain,nourishing the liver and kidney,and also uses the medicines for replenishing qi and strengthening the spleen.The ideas of HUANG Feng for the treatment of low back pain can be used as a reference for the clinical treatment.

Objective To explore the efficacy and safety of ticagrelor de-escalation and nicorandil therapy in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 300 elderly patients with ACS were selected from the Sixth and Seventh Medical Center of Chinese PLA General Hospital and Beijing Chaoyang Integrative Medicine Emergency Rescue and First Aid Hospital from November 2016 to June 2019,including 153 males and 147 females,aged>65 years old.All the patients received PCI,and all had double antiplatelet therapy(DAPT)scores≥2 and a new DAPT(PRECISE-DAPT)score of≥25.All patients were divided into two groups by random number table method before operation:ticagrelor group(n=146,ticagrelor 180 mg load dose followed by PCI,and ticagrelor 90 mg bid after surgery)and ticagrelor de-escalation + nicorandil group(n=154,ticagrelor 180 mg load dose followed by PCI,ticagrelor 90 mg bid+nicorandil 5 mg tid after surgery,changed to ticagrelor 60 mg bid+ nicorandil 5 mg tid 6 months later).Follow-up was 12 months.The composite end points of cardiovascular death,myocardial infarction and stroke,the composite end points of mild hemorrhage,minor hemorrhage,other major hemorrhage and major fatal/life-threatening hemorrhage as defined by the PLATO study,and the composite end points of cardiovascular death,myocardial infarction,stroke and bleeding within 12 months in the two groups were observed.Results The comparison of general baseline data between the two groups showed no statistically significant difference(P>0.05).There was also no significant difference in the composite end points of cardiovascular death,myocardial infarction and stroke between the two groups(P>0.05).The cumulative incidence of bleeding events in ticagrelor de-escalation + nicorandil group was significantly lower than that in ticagrelor group(P<0.05),while the composite end points of cardiovascular death,myocardial infarction,stroke and bleeding were also significantly lower than those in tecagrelor group(P<0.05).Conclusion In elderly patients with ACS,the treatment of ticagrelor de-escalation + nicorandil after PCI may not increase the incidence of ischemic events such as cardiovascular death,myocardial infarction or stroke,and it may reduce the incidence of hemorrhagic events.

Objective:To explore therapeutic effect of sacubitril valsartan sodium combined with Wenxin granule in the treatment of hypertension complicated with paroxysmal atrial fibrillation(AF)and its effect on cardiac electro-physiological structure.Methods:A total of 116 patients with hypertension and paroxysmal atrial fibrillation treated in our hospital from Oct 2021 to Nov 2022 were consecutively selected.According to random number table,they were divided into Wenxin granule group(received Wenxin granule treatment based on routine antihypertensive ther-apy)and combined treatment group(received sacubitril valsartan sodium combined Wenxin granule therapy based on routine antihypertensive therapy)with 58 cases in each group,and both groups were consecutively treated for six months.Clinical symptom score,AF burden,P wave duration,P wave dispersion,left atrial diameter(LAD),left ventricular end-diastolic diameter(LVEDd)and left ventricular ejection fraction(LVEF)were compared between two groups before and after treatment.Results:After treatment,compared with Wenxin granule group,there were significant reductions in clinical symptom score[(1.66±0.69)scores vs.(1.40±0.53)scores],AF burden[4.43(1.65)％vs.1.62(3.50)％],P wave duration[(112.17±6.46)ms vs.(109.29±8.59)ms],P wave dispersion[(32.47±8.11)ms vs.(29.02±7.49)ms]and LAD[(34.83±3.41)mm vs.(33.40±3.74)mm]in combined treatment group(P＜0.05 or＜0.01).There were no significant difference in LVEDd and LVEF between two groups,P＞0.05 both.Conclusion:Sacubitril valsartan sodium combined with Wenxin granule can significantly im-prove clinical symptoms and atrial fibrillation burden,reduce the susceptibility to atrial fibrillation,and inhibit atrial electrical remodeling and structural remodeling in patients with hypertension complicated with paroxysmal atrial fi-brillation.

Objective:To explore the application of the semi-flipped classroom combined with knowledge point auction learning method in the theoretical teaching among stomatological students and its effectiveness.Methods:Undergraduates from four classes at Guanghua School of Stomatology, Sun Yat-Sen University were taken as research subjects. Students from two classes ( n=198) were taught using the semi-flipped classroom combined with the knowledge point auction learning method, while students from the other two classes ( n=172) were taught with the regular classroom teaching method as controls. Classroom quizzes were used to compare the two classes' mastery of important and difficult points, and a questionnaire was used to investigate students' feedback on the class outcomes. SPSS 22.0 was used for the independent samples t-test. Results:The mean test score of the experimental class was higher than that of the control class (88.51±11.02 vs. 80.40±8.53). The results of the questionnaire showed that students' interest in the course, comprehension and absorption of knowledge points, degree of satisfaction with the class, and mastery of the contents in sections in the experimental class were significantly higher than those in the control class (7.28±1.51 vs. 9.10±0.73, 6.92±1.44 vs. 8.69±1.62, 6.09±1.87 vs. 7.61±1.46, 6.40±1.04 vs. 7.70±0.86 ).Conclusions:The semi-flipped classroom combined with the knowledge point auction learning method is innovative and achieves good teaching outcomes. It holds promise for wider use in the theoretical teaching of various clinical subjects. To explore the application of the semi-flipped classroom combined with knowledge point auction learning method in the theoretical teaching among stomatological students and its effectiveness.

To promote the construction of a wound repair and regeneration system with Chinese characteristics, it is necessary to follow the principle of combining traditional Chinese and Western medicine, and integrate theory, clinical practice, and teaching. Traditional Chinese medicine emphasizes a holistic concept and the principle of dialectical treatment, while Western medicine focuses on etiological analysis and local treatment. The combination of Chinese and Western medicine can complement each other's advantages and improve treatment effectiveness. The key technological innovations in repairing and regenerating systems cover areas such as drug therapy, physical therapy, and the application of biomaterials. This article discusses the development potential and challenges of combining traditional Chinese and Western medicine in the field of wound repair and regeneration, providing new ideas and methods for the development of wound repair and regeneration. It is expected to bring better medical services and treatment effects to patients undergoing repair and regeneration.

Objective:To investigate the association of exposure to PM 2.5 and its constituents during pregnancy and fetal growth and to further identify critical windows of exposure for fetal growth. Methods:We included 4 089 mother-child pairs from the Jiangsu Birth Cohort Study between January 2016 and October 2019. Data of general characteristics, clinical information, daily average PM 2.5 exposure, and its constituents during pregnancy were collected. Fetal growth parameters, including head circumference (HC), abdominal circumference (AC), and femur length (FL), were measured by ultrasound after 20 weeks of gestation, and then estimated fetal weight (EFW) was calculated. Generalized linear mixed models were adopted to examine the associations of prenatal exposure to PM 2.5 and its constituents with fetal growth. Distributed lag nonlinear models were used to identify critical exposure windows for each outcome. Results:A 10 μg/m 3 increase in PM 2.5 exposure during pregnancy was associated with a decrease of 0.025 ( β=-0.025, 95% CI: -0.048- -0.001) in HC Z-score, 0.026 ( β=-0.026, 95% CI: -0.049- -0.003) in AC Z-score, and 0.028 ( β=-0.028, 95% CI:-0.052--0.004) in EFW Z-score, along with an increased risk of 8.5% ( RR=1.085, 95% CI: 1.010-1.165) and 13.5% ( RR=1.135, 95% CI: 1.016-1.268) for undergrowth of HC and EFW, respectively. Regarding PM 2.5 constituents, prenatal exposure to black carbon, organic matter, nitrate, sulfate (SO 42-) and ammonium consistently correlated with decreased HC Z-score. SO 42- exposure was also associated with decreased FL Z-scores. In addition, we found that gestational weeks 2-5 were critical windows for HC, weeks 4-13 and 19-40 for AC, weeks 4-13 and 23-37 for FL, and weeks 4-12 and 20-40 for EFW. Conclusions:Our findings demonstrated that exposure to PM 2.5 and its constituents during pregnancy could adversely affect fetal growth and the critical windows for different fetal growth parameters are not completely consistent.

Objective To investigate the satisfaction of clinical trial service quality of hospitals in Shanghai for clinical research associate(CRA),so as to provide reference for improving the quality of clinical trial service.Methods From Oct to Nov 2023,CRAs were surveyed using the revised SERVQUAL(service quality)scale.The questionnaire included 29 items in 5 dimensions of assurance,reliability,tangibility,responsiveness and empathy.Based on the modified importance-performance analysis(IPA)analysis,it was figured out which item was in the improvement area.Results A total of 163 CRAs from 10 companies were surveyed,and 160 valid questionnaires were collected.According to the revised IPA,6,2,1,2 and 2 items of the dimensions of assurance,reliability,tangibility,responsiveness and empathy fell into the improvement area,respectively.Conclusion CRAs surveyed may not be satisfied with the clinical trial service of Shanghai hospitals.We need to propose optimization strategies to improve the quality of clinical trial service from the aspects of standardization awareness,review mode,process acquisition,response and management system flexibility.

Objective To explore the effect of a R language-based autoregressive integrated moving average(ARIMA)model for predicting the consumption of medical consumables.Methods The monthly consumption data of a certain type of pre-filled flush syringe from July 2018 to June 2023 was selected as the sample data,which underwent smoothness test and difference operation with R language.An ARIMA model was established and the optimal model was determined according to the Akaike and Bayesian information criteria.The corresponding data of the third quarter of 2023 was used as the validation set to predict the consumption,and the prediction result was compared with the actual values to evaluate the prediction effect of the ARIMA model.Results The ARIMA model with the best fitting was ARIMA(0,1,1)(1,0,0)12,all the predicted data were within 95％confidence interval,and its mean absolute percentage error MAPE was 9.92％.P-value proved to be higher than 0.05 when the residual series were tested using the Ljung-Box statistics,which meant the prediction result was satisfactory.Conclusion The R language-based ARIMA model behaves well in predicting the consumption of medical consumables,and provides references for demand planning,budgeting,purchasing and management of medical consumables.[Chinese Medical Equipment Journal,2024,45(10):84-87]