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.

BACKGROUND:Due to the mismatch between the design of the proximal femoral nail antirotation Asian version(PFNA-Ⅱ)and Asian population,extrusion of the proximal femoral intertrochanteric nail may occur in the treatment of femoral intertrochanteric fractures.The influence of the protruding length on the curative effect of the operation needs to be further discussed. OBJECTIVE:To quantitatively measure the protruding length of the proximal trochanter of the femur with intramedullary nail after PFNA-Ⅱ,and to analyze the effect of protruding length on the efficacy of PFNA-Ⅱ in the treatment of femoral intertrochanteric fractures. METHODS:Totally 68 patients with femoral intertrochanteric fractures treated with PFNA-Ⅱ internal fixation in the First Affiliated Hospital of Anhui Medical University were selected.The extramedullary process of the proximal trochanter of the femur was quantitatively measured on the anterior and posterior X-ray films of the hip joint within 6 months after operation.According to the existence of extrusion of the proximal trochanter intramedullary nail,the patients were divided into protruding group and non-protruding group.The data of sex,height,fracture type,length and diameter of the intramedullary nail,the position of screw blade in the femoral neck and protruding length of proximal greater trochanter were collected.The postoperative curative effect was judged by visual analog scale pain score and hip joint Harris score at 6 months after operation.The influence of protruding proximal trochanter of the PFNA-Ⅱ intramedullary nail on the operative effect was observed. RESULTS AND CONCLUSION:(1)There were significant differences in sexual characteristics between the protruding group and the non-protruding group(P=0.001).(2)According to AO/OTA classification,there were no significant differences in fracture type between the protruding group and the non-protruding group(P=0.289).(3)There was no significant difference in the length and diameter of the intramedullary nail between the two groups(P=0.067,P=1.000).(4)There was no significant correlation between the height of all patients and the length of the intramedullary nail(P=0.510),but there was a significant correlation between height and protruding length(P=0.034).There was no significant correlation between screw blade position and protruding length(P=0.968).(5)Six months after operation,there was no significant difference in the hip Harris score(P=0.373),but the visual analog scale pain score was significantly higher in the protruding group than that in the non-protruding group(P=0.000).(6)The results suggest that nail extrusion often occurs in the proximal greater trochanter when PFNA-Ⅱ is used in the treatment of intertrochanteric fractures in Asians.When the nail extended into the proximal soft tissue of the greater trochanter,patients complained of proximal greater trochanteric pain and the visual analog scale score of proximal greater trochanter pain in the patient was significantly higher than that in the non-protruding group.To be more suitable for the Asian population,we suggest that the PFNA-Ⅱ should be improved to further shorten the proximal nail end to obtain better clinical results of femoral intertrochanteric fracture fixation.

Surgery is an important treatment for the anterior mediastinal disease. With the rapid development of minimally invasive techniques, complete resection of the lesion in most patients with thymic disease can be achieved through thoracoscopic surgery. Practice has proved that the three-port resection of anterior mediastinal thymus disease via the subxiphoid approach is an ideal surgical method for the treatment of anterior mediastinal thymic tumors at present, which has strong popularization and popularity and can benefit the patients. The procedure focuses primarily on the anterior and upper mediastinum and can thoroughly expose the anatomy of the mediastinum and both sides, with minimal intraoperative bleeding, high safety, minimal trauma and postoperative pain, and a short hospital stay. It has clear advantages over conventional thoracic open-heart surgery and transversal resection. However, the surgical approach and field of view, and intraoperative precautions of this procedure are completely different from those of previous thoracoscopic procedures, and from the subxiphoid single-port approach adopted by other centers. Based on 10 years of surgical experience at our center, a modular mode of surgical operation has been developed and its procedure has been standardized. This paper will share and discuss relevant operational points and experiences.

Background::Previous studies have reported associations of specific maternal and paternal lifestyle factors with offspring’s cognitive development during early childhood. This study aimed to investigate the prospective associations between overall parental lifestyle and offspring’s cognitive performance during adolescence and young adulthood in China.Methods::We included 2531 adolescents aged 10-15 years at baseline in 2010 from the China Family Panel Studies. A healthy parental lifestyle score (ranged 0-5) was constructed based on the following five modifiable lifestyle factors: Smoking, drinking, exercise, sleep, and diet. Generalized estimating equation models were used to examine the association between baseline parental healthy lifestyle scores and offspring’s fluid and crystallized intelligence in subsequent years (2012, 2014, 2016, and 2018).Results::Offspring in the top tertile of parental healthy lifestyle scores performed better in overall fluid intelligence (multivariable-adjusted β = 0.53, 95% confidence interval [CI]: 0.29-0.77) and overall crystallized intelligence (multivariable-adjusted β = 0.35, 95% CI: 0.16-0.54) than those in the bottom tertile of parental healthy lifestyle scores. The results were similar after further adjustment for the offspring’s healthy lifestyle scores and persisted across the subgroups of parental socioeconomic status. Additionally, maternal and paternal healthy lifestyle scores were independently associated with better offspring’s cognitive performance, with significant contribution observed for paternal never-smoking, weekly exercise, and diversified diet. When both parents and offspring adhered to a healthier lifestyle, we observed the highest level of the offspring’s overall crystallized intelligence. Conclusions::Our study indicates that parental adherence to a healthier lifestyle is associated with significantly better offspring’s cognitive performance during adolescence and early adulthood, regardless of socioeconomic status. These findings highlight the potential cognitive benefits of promoting healthy lifestyles among parents of adolescents.

Objective:To construct an evaluation index system for(magnetic resonance imaging)MRI equipment allocation management in hospitals,and to provide objective basis for MRI equipment allocation decision-making.Methods:Following the principles of practicality,objectivity and accessibility,the evaluation index system of hospital MRI equipment allocation management was designed and constructed,based on the actual work needs of the hospital,the index system and evaluation rules were determined by combining literature research method,expert seminars,and expert consultation method.The evaluation index system was used to evaluate and analyze the MRI equipment application allocation management of the two hospitals included in the study in 2023(Hospital A and Hospital B),By comprehensively considering hospital grading and index score,the allocation requirements of MRI were quantitatively calculated and evaluated,and comparable and quantifiable evaluation results were achieved.Results:The MRI equipment allocation management index system formed update scenarios,additional scenarios and ladder allocation scenarios,and established relevant evaluation matrices and multi-category indicators based on the allocation scenarios.The evaluation results showed that Hospital A's application for one mid-range MRI equipment was approved,and Hospital B's application was not approved,and the evaluation results were consistent with the actual decision-making.Conclusion:The evaluation index system for hospital MRI allocation management can realize the evaluation of MRI equipment allocation management,provide effective data support for MRI allocation decision-making,and have a guiding role in improving equipment service capabilities,refined management,and optimized resource allocation.

Objective To construct and validate a risk prediction model for enteral nutrition feeding intolerance (FI) in patients with severe cerebral hemorrhage based on machine learning algorithms. Methods The clinical data of 485 patients with cerebral hemorrhage admitted to the neurological intensive care unit of Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2020 to December 2022 were retrospectively analyzed. The patients were randomly divided into training set (n=339) and validation set (n=146) in a 7 to 3 ratio. Five machine learning algorithms were used to construct FI risk prediction models. The receiver operating characteristic (ROC) curve was plotted, and the model with the best predictive performance was selected based on the area under the curve (AUC). A nomogram model was constructed based on the optimal model. The calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical net benefit of the nomogram model. Results The incidence of enteral nutrition FI in patients with severe cerebral hemorrhage was 38.4%(186/485). Among the five machine learning algorithm models, the Logistic regression model had the best predictive performance(AUC=0.88). The analysis results of the Logistic regression model showed that the use of diuretics, mechanical ventilation, Glasgow Coma Scale score ≤5, vasoactive drugs, and albumin level<35 g/L were risk factors for enteral nutrition FI in patients with severe cerebral hemorrhage. A nomogram model was further constructed based on these five risk factors. The calibration curve analysis showed that the calibration curve fitted well with the ideal curve, indicating a high calibration degree of the nomogram model. The DCA results showed that when the threshold probability was 5% to 73%, the application of the nomogram model for screening could clinically benefit patients. Conclusion The construction of a nomogram model for predicting the risk of enteral nutrition FI in patients with severe cerebral hemorrhage based on machine learning methods can help to early screen high-risk patients for enteral nutrition FI and timely formulate preventive measures, thereby reducing the incidence of enteral nutrition FI in patients with severe cerebral hemorrhage.

Objective To retrieve relevant literature on risk prediction model for methicillin-resistant Staphylococcus aureus (MRSA) infection among hospitalized patients from databases and evaluate the predictive model. Methods The literature on risk prediction models for MRSA infection among hospitalized patients was retrieved from PubMed, Embase, Scopus, Cochrane library, China National Knowledge Infrastructure (CNKI), WanFang data, and VIP database, with a time range from the inception of the database to January 1, 2024. Two researchers independently screened the literature, extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was applied to evaluate the risk of bias and applicability of the prediction model in the literature, and descriptive analysis was conducted. Results A total of 12 articles (15 prediction models) were included in this study, with significant differences in the total sample size, the number of MRSA infection events, sample size of modeling, and sample size of validation among the studies. Common predictors in the prediction models were admission to the intensive care unit, antibiotic use, history of residence in nursing facilities, age, chronic kidney disease, and previous hospitalization history. Nine articles conducted internal validation, and three articles conducted both internal and external validation. Nine articles reported the area under the receiver operating characteristic curve, and only three articles reported the calibration of the model based on the Hosmer-Lemeshow test. PROBAST analysis showed that 10 articles were assessed as high risk bias, mainly stemming from statistical analysis. Conclusion Most of the MRSA infection risk prediction models in the current literature have good predictive efficacy for MRSA infection, but they all have higher overall risk of bias, and only a few models have undergone external validation. Researchers should follow PROBAST standards to construct and externally validate models in the future so as to develop models suitable for clinical practice.

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Tuberculosis (TB) is an ancient infectious disease. Before the availability of effective drug therapy, it had high morbidity and mortality. In the past 100 years, the discovery of revolutionary anti-TB drugs such as streptomycin, isoniazid, pyrazinamide, ethambutol and rifampicin, along with drug combination treatment, has greatly improved TB control globally. As anti-TB drugs were widely used, multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis emerged due to acquired genetic mutations, and this now presents a major problem for effective treatment. Genes associated with drug resistance have been identified, including katG mutations in isoniazid resistance, rpoB mutations in rifampin resistance, pncA mutations in pyrazinamide resistance, and gyrA mutations in quinolone resistance. The major mechanisms of drug resistance include loss of enzyme activity in prodrug activation, drug target alteration, overexpression of drug target, and overexpression of the efflux pump. During the disease process, Mycobacterium tuberculosis may reside in different microenvironments where it is expose to acidic pH, low oxygen, reactive oxygen species and anti-TB drugs, which can facilitate the development of non-replicating persisters and promote bacterial survival. The mechanisms of persister formation may include toxin-antitoxin (TA) modules, DNA protection and repair, protein degradation such as trans-translation, efflux, and altered metabolism. In recent years, the use of new anti-TB drugs, repurposed drugs, and their drug combinations has greatly improved treatment outcomes in patients with both drug-susceptible TB and MDR/XDR-TB. The importance of developing more effective drugs targeting persisters of Mycobacterium tuberculosis is emphasized. In addition, host-directed therapeutics using both conventional drugs and herbal medicines for more effective TB treatment should also be explored. In this article, we review historical aspects of the research on anti-TB drugs and discuss the current understanding and treatments of drug resistant and persistent tuberculosis to inform future therapeutic development.
Humans ; Pyrazinamide/therapeutic use* ; Isoniazid/therapeutic use* ; Antitubercular Agents/therapeutic use* ; Tuberculosis, Multidrug-Resistant/microbiology* ; Mycobacterium tuberculosis/genetics* ; Tuberculosis/drug therapy* ; Rifampin/therapeutic use* ; Mutation ; Drug Resistance, Multiple, Bacterial/genetics*

Objective:To investigate the application prospect of 3D virtual reconstruction and printing technology based on thin-slice CT images in network cloud+dual-track teaching.Methods:A total of 120 medical students who were on probation in The Second Affiliated Hospital of Air Force Medical University from April 2021 to April 2022 were selected as subjects and were randomly divided into experimental group and control group, with 60 students in each group. The students in the experimental group received 3D virtual reconstruction and printing technology combined with network cloud+dual-track teaching, and those in the control group received network cloud+dual-track teaching alone. After 6 months of learning, the teaching effect was compared by closed-book examination, skill operation, speech defense, and questionnaire survey. SPSS 23.0 was used for the t-test and the chi-square test. Results:Compared with the control group in terms of department examination, the experimental group had significantly better scores of theoretical knowledge [(84.25±5.53) vs. (79.43±6.69), P<0.001] and operational skills [(87.68±5.72) vs. (82.97±5.32), P<0.001]. The experimental group had significantly higher scores than the control group in speech [(44.90±2.56) vs. (41.88±2.71), P<0.001] and defense [(45.83±2.62) vs. (43.85±2.56), P<0.001]. The questionnaire survey showed that compared with the control group, the experimental group had significantly higher scores of practical ability, active learning ability, expression ability, practice enthusiasm, and information acquisition ability ( P<0.001). Conclusion:The network cloud+dual-track teaching model assisted by 3D virtual reconstruction and printing technology can significantly improve the objective learning effect and subjective learning initiative of students and has a relatively high value of teaching application and promotion.