ObjectiveTo introduce the three main techniques for tuberculosis screening currently used in China, to systematically evaluate their accuracy in diagnosing latent tuberculosis infection (LTBI), so as to provide scientific basis and recommendations for the formulation of China’s tuberculosis screening strategy. MethodsLiterature on the diagnosis of tuberculosis by tuberculin skin test (TST), interferon-γ release assay (IGRA), and recombinant Mycobacterium tuberculosis fusion protein (EC) skin test from January 1, 2010 to August 22, 2024 was comprehensively retrieved from PubMed, China National Knowledge Infrastructure (CNKI), and Wanfang Database through computerized search. Besides, all the literature was screened in accordance to the inclusion criteria for diagnostic tests, and characteristic information of the literature selected was extracted simultaneously. Meta-analysis was performed using Stata 17.0 software, with a random-effects model used for weighted quantitative synthesis of included literature, calculating pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and their 95% confidence intervals (CI). ResultsA total of 543 relevant articles were retrieved, with 105 ultimately included. Among them, 33 articles reported diagnostic data for TST, with a pooled sensitivity of 0.68 (95%CI: 0.62‒0.73), specificity of 0.67 (95%CI: 0.60‒0.73), positive likelihood ratio of 2.0 (95%CI: 1.7‒2.5), and negative likelihood ratio of 0.48 (95%CI: 0.40‒0.58). Ninety-four articles reported the diagnostic value of IGRAs test, with a pooled sensitivity of 0.88 (95%CI: 0.87‒0.89), specificity of 0.82 (95%CI: 0.79‒0.84), positive likelihood ratio of 4.8 (95%CI: 4.2‒5.6), and negative likelihood ratio of 0.15 (95%CI: 0.13‒0.17). Data on EC skin test was limited, but preliminary analysis showed that it had high sensitivity and specificity. ConclusionIGRA has a significant advantage in diagnosing LTBI, and EC skin test also shows good diagnostic performance, although relevant data is limited. TST remains suitable for large-scale screening due to its cost-effectiveness.

OBJECTIVES: To synthesize a temperature-responsive multimodal motion microrobot (MMMR) using temperature and magnetic field-assisted microfluidic droplet technology to achieve targeted drug delivery and controlled drug release. METHODS: Microfluidic droplet technology was utilized to synthesize the MMMR by mixing gelatin with magnetic microparticles. The microrobot possessed a magnetic anisotropy structure to allow its navigation and targeted drug release by controlling the temperature field and magnetic field. In the experiment, the MMMR was controlled to move in a wide range along a preset path by rotating a uniform magnetic field, and the local circular motion was driven by a planar rotating gradient magnetic field of different frequencies. The MMMR was loaded with simulated drugs, which were released in response to laser heating. RESULTS: Driven by a rotating magnetic field, the MMMR achieved linear motion following a predefined path. The planar gradient rotating magnetic field controlled circular motion of the MMMR with an adjustable radius, utilizing the centrifugal force generated by rotation. The drug-loaded MMMR successfully reached the target location under magnetic guidance, where the gelatin matrix was melted using laser heating for accurate drug release, after which the remaining magnetic particles were removed using magnetic field. CONCLUSIONS The MMMR possesses multimodal motion capabilities to enable precise navigation along a predefined path and dynamic regulation of drug release within the target area, thus having great potential for a wide range of biomedical applications.
Drug Delivery Systems/methods* ; Temperature ; Drug Liberation ; Magnetic Fields ; Robotics ; Gelatin/chemistry* ; Delayed-Action Preparations ; Microfluidics ; Motion

Objective To develop a risk prediction model for moderate-to-severe catheter-related bladder discomfort(CRBD)after non-transurethral surgery based on various machine-learning algorithms and to compare the performance of the models,so as to provide a reference for accurately identification and prevention of the postoperative moderate-to-severe CRBD.Methods A convenience sampling method was employed to recruit 719 patients as study subjects.The patients received non-transurethral surgery and intraoperative urinary catheterisation in a Tier-ⅢA hospital in Shanxi Province between January and May 2024.The clinical data were collected,with 70％of the randomly selected data was assigned to a training dataset(n=503)for the model building and the rest of 30％of data was used as the testing dataset(n=216)for internal model validation.Predictors were determined using least absolute shrinkage and selection operators(LASSO).Seven machine learning methods of logistic regression,K-nearest neighbours,random forest,artificial neural network,decision tree,light gradient boosting machine(LightGBM)and elastic net were employed to establish the risk prediction models.Performance of the models was evaluated based on the area under receiver operating characteristic curve(AUR-ROC),accuracy,precision,recall and F1 score.Results A total of 719 patients who underwent non-transurethral surgery were included in the study.It was found that 154(21.4％)patients presented with moderate to severe CRBD and 565(78.6％)patients were without or only with a mild CRBD.The predictors were deduced to six variables:gender,abdominal surgery,type of surgery,administration of dexmedetomidine before surgery,intraoperative administration of flurbiprofenate,and use of tramadol by the completion of surgery.It was found that the LightGBM model demonstrated a high stability,with 0.793 in AUC-ROC,0.763 in accuracy,0.879 in precision,0.747 in recall and 0.808 in F1.Conclusion The risk prediction model established through LightGBM for moderate-to-severe CRBD after a non-transurethral surgery exhibits a high stability.It offers a reference for medical practitioners to identify the patients with high-risk of moderate-to-severe CRBD and prepares for relevant interventions.

GBA1 gene mutation is an important genetic risk factor for Parkinson’s disease (PD). This paper reports a case of a 43-year-old male PD patient carrying a rare heterozygous Thr408Met mutation in the GBA1 gene identified through whole-exome sequencing, leading to a diagnosis of GBA1-associated PD. The patient’s motor symptoms were primarily characterized by bradykinesia and rigidity, without significant cognitive decline. Treatment with low-dose levodopa combined with a dopamine agonist resulted in significant symptomatic improvement.

Objective To develop a risk prediction model for moderate-to-severe catheter-related bladder discomfort(CRBD)after non-transurethral surgery based on various machine-learning algorithms and to compare the performance of the models,so as to provide a reference for accurately identification and prevention of the postoperative moderate-to-severe CRBD.Methods A convenience sampling method was employed to recruit 719 patients as study subjects.The patients received non-transurethral surgery and intraoperative urinary catheterisation in a Tier-ⅢA hospital in Shanxi Province between January and May 2024.The clinical data were collected,with 70％of the randomly selected data was assigned to a training dataset(n=503)for the model building and the rest of 30％of data was used as the testing dataset(n=216)for internal model validation.Predictors were determined using least absolute shrinkage and selection operators(LASSO).Seven machine learning methods of logistic regression,K-nearest neighbours,random forest,artificial neural network,decision tree,light gradient boosting machine(LightGBM)and elastic net were employed to establish the risk prediction models.Performance of the models was evaluated based on the area under receiver operating characteristic curve(AUR-ROC),accuracy,precision,recall and F1 score.Results A total of 719 patients who underwent non-transurethral surgery were included in the study.It was found that 154(21.4％)patients presented with moderate to severe CRBD and 565(78.6％)patients were without or only with a mild CRBD.The predictors were deduced to six variables:gender,abdominal surgery,type of surgery,administration of dexmedetomidine before surgery,intraoperative administration of flurbiprofenate,and use of tramadol by the completion of surgery.It was found that the LightGBM model demonstrated a high stability,with 0.793 in AUC-ROC,0.763 in accuracy,0.879 in precision,0.747 in recall and 0.808 in F1.Conclusion The risk prediction model established through LightGBM for moderate-to-severe CRBD after a non-transurethral surgery exhibits a high stability.It offers a reference for medical practitioners to identify the patients with high-risk of moderate-to-severe CRBD and prepares for relevant interventions.

Objective:To investigate the iodine nutritional status of pregnant women in Hangzhou City and analyze its influencing factors, in order to provide a basis for guiding pregnant women in Hangzhou City to supplement iodine scientifically.Methods:A stratified random sampling method was used to conduct a questionnaire survey on 1 400 pregnant women in Hangzhou City from March to October 2018. Random urine samples and household salt samples from pregnant women were collected, and the levels of urine iodine and salt iodine were measured using arsenic cerium catalytic spectrophotometry and direct titration, respectively. The iodine nutritional status of pregnant women with different basic characteristics and dietary levels were analyzed and compared (Kruskal-Wallis test), and the main influencing factors affecting the urinary iodine concentration level of pregnant women were identified (the ordinal multiple classification logistic regression analysis).Results:The median urinary iodine of pregnant women was 135.00 μg/L, and the qualified iodized salt consumption rate of pregnant women was 83.36% (1 167/1 400). In terms of basic characteristics, there were statistically significant differences in the distribution level of urine iodine among pregnant women with different pregnancy frequency, delivery frequency, and natural abortion frequency ( P < 0.05). In terms of diet, the frequency of consuming milk, yogurt, meat, and whether pregnant women consumed cabbage and cauliflower showed statistically significant differences in urinary iodine distribution levels ( P < 0.05). Ordinal multiple classification logistic regression analysis showed that the urine iodine levels of pregnant women who were pregnant twice were higher than those who were pregnant ≥3 times ( OR = 1.64, P = 0.003). Pregnant women who never or occasionally consumed yogurt had lower urine iodine levels than or equal to those who consumed 2 bottles of yogurt per day ( OR = 0.53, P = 0.044). Pregnant women who never or occasionally consumed meat and those who consumed meat once a week had higher urinary iodine levels than that who consumed meat ≥2 times per week ( OR = 1.40, 1.47, P < 0.05). Conclusions:The overall iodine nutrition of pregnant women in Hangzhou City is at an deficiency level. Pregnancy experience and dietary level are influencing factors on iodine nutrition of pregnant women. It is necessary to carry out in-depth health education for pregnant women, improve the dietary structure during pregnancy, and improve the abnormal iodine nutrition of pregnant women.

Objective:To optimize the preventive maintenance path of electric medical equipment by adopting multi-criteria decision analysis(MCDA),and to verify its optimization effect.Methods:The preventive maintenance paths for electric medical equipment were determined through literature research and management group discussions.An expert meeting was organized to discuss,determine,and demonstrate the preventive maintenance path evaluation criteria,and the weight of the criteria was set.Each preventive maintenance path was scored by experts based on criteria,the score of each maintenance path was calculated using the mean and sorted to form the optimal path.767 electric medical equipment of 7 categories in clinical use in the 960th Hospital of the PLA Joint Logistics Support Force from 2021 to 2022 were selected,conventional preventive maintenance management(referred to as conventional management mode)and preventive maintenance path management optimized by MCDA method(referred to as MCDA management mode)were adopted respectively according to different management modes.The changes in indicators such as failure rate,maintenance time,quality inspection pass rate and maintenance cost of electric medical equipment were compared between the two management models.Results:The failure occurrence rate of electric medical equipment of the MCDA management mode was 8.71%(67/767),which was lower than that of the conventional management mode,the difference was statistically significant(x2=3.960,P<0.05).The equipment maintenance time of the MCDA management mode was(2.24±1.17)days,which was lower than that of the conventional management mode,the difference was statistically significant(t=2.360,P<0.05).The quality inspection qualification rate of the MCDA management model was(96.57±2.74)%,which was higher than that of the conventional management mode,the difference was statistically significant(t=4.342,P<0.05).The average maintenance cost of equipment of the MCDA management model accounted for 2.37%of its assets,which was lower than that of the conventional management mode,the difference was statistically significant(x2=4.261,P<0.05).Conclusion:The MCDA method can provide a quantitative structural model for the optimization of preventive maintenance paths for electric medical equipment,and the optimized preventive maintenance paths can achieve efficient management of electric medical equipment,and focusing on the training of maintenance personnel's technical level can increase the self-repair rate and reduce the failure rate of medical equipment.

ObjectiveTo investigate the high-risk detection rate and aggregation of cardiovascular diseases（CVD） in 8 districts of Shanghai and influencing factors， and to provide scientific references for prevention and control of CVD. MethodsBased on the Cardiovascular Disease Screening and Management Program in Shanghai from 2016 to 2021， 104 685 participants aged 35 to 75 in 8 districts of Shanghai were selected for analysis. χ² test and multivariate logistic regression were used for statistical analysis of the influencing factors of CVD and aggregation of CVD. ResultsThe proportion of high-risk CVD individuals in the population was 19.17%， including the high-risk individuals with hypertension （8.65%）， dyslipidemia （6.33%）， CVD history （5.58%）， and WHO assessed risk ≥20% types （2.69%）， respectively. Old age， overweight and obesity， central obesity， smoking， drinking， farmers， unmarried， and low family income were the risk factors of CVD， while high education level was the protective factor. In the participants， 16 323 people （81.34%） were classified as CVD high-risk groups； The number of aggregation of 1， 2 and ≥3 high risk types of CVD were 16 323（81.34%）， 3 236（16.13%）， 509（2.54%）， respectively. Old age， low education level， low annual family income， farmers， unmarried， smoking， drinking， overweight， obesity and central obesity were associated with the risk of aggregation of high risk types of CVD， and the correlation strength increased with the increase of aggregation types. ConclusionThe prevention and control of CVD in Shanghai should focus on the hypertension， elderly， overweight， obesity， central obesity， smoking， drinking， low educated， low family income， farmers and unmarried people， and targeted intervention measures should be taken to reduce the risk of CVD among residents.

[Objective]To explore the diagnostic value of dual-energy computed tomography(DECT)for left atrial appendage thrombosis/spontaneous echocardiographic contrast(LAAT/SEC).[Methods]A retrospective analysis was done on 64 non-valvular atrial fibrillation patients who planned to undergo radiofrequency ablation between November 2020 and November 2021.All patients underwent DECT and transesophageal echocardiography(TEE),and then were divided into LAAT/SEC group and non-LAAT/SEC group based on the TEE results.The clinical data and imaging parameters of the two groups were compared and analyzed.Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for LAAT/SEC,and a prediction model was constructed.[Results]A total of 64 patients with atrial fibrillation were enrolled in this study,including 42 non-LAAT/SEC patients and 22 LAAT/SEC patients.There was no statistically significant difference in gender and body mass index(BMI)between the two groups(P>0.05).CHA2DS2-VASc score and left atrial diameter were higher in the LAAT/SEC group than in the non-LAAT/SEC group(P=0.047;P<0.001).Significant differences(P<0.001)were observed in the ratio of Hounsfield unit of LAA and ascending aorta(LAA/AA HU ratio)at the first phase of DECT and quantitative parameters such as Zeff,Rho,λHu,and nIC.Multivariate logistic regression showed that Zeff and Rho were the independent factors for the diagnosis of LAAT/SEC.A Zeff-Rho based model predicting LAAT/SEC was subsequently developed,with Area Under the Curve(AUC)of 0.96(95%CI:0.92,1.00)and the accuracy of 0.92(95%CI:0.83,0.97).[Conclusions]The two quantitative parameters Zeff and Rho are independent risk factors for the diagnosis of LAAT/SEC.The DECT derived Zeff and Rho may have better diagnostic performance than single parameter and conventional CT in detecting LAAT/SEC.

[This corrects the article DOI: 10.1016/j.apsb.2021.12.005.].