DNA origami is a powerful technique for generating nanostructures with dynamic properties and intelligent controllability. The precise geometric shapes, high programmability, and excellent biocompatibility make DNA origami nanostructures an emerging drug delivery vehicle. The shape, size of the carrier material, as well as the loading and release of drugs are important factors affecting the bioavailability of drugs. This paper focuses on the controllable design of DNA origami nanostructures, efficient drug loading, and intelligent drug release. It summarizes the cutting-edge applications of DNA origami technology in biomedicine, and discusses areas where researchers can contribute to further advancing the clinical application of DNA origami carriers.

Objective: To evaluate the clinical value of the MeltPro MTB assays in the diagnosis of drug-resistant tuberculosis. Methods: A cross-sectional study design was used to retrospectively collect all 4 551 patients with confirmed tuberculosis between January 2018 and December 2019 at Beijing Chest Hospital, Capital Medical University. Phenotypic drug sensitivity test and GeneXpert MTB/RIF (hereafter referred to as "Xpert") assay were used as gold standards to analyze the accuracy of the probe melting curve method. The clinical value of this technique was also evaluated as a complementary method to conventional assays of drug resistance to increase the detective rate of drug-resistant tuberculosis. Results: By taking the phenotypic drug susceptibility test as the gold standard, the sensitivity of the MeltPro MTB assays to detect resistance to rifampicin, isoniazid, ethambutol and fluoroquinolone was 14/15, 95.7%(22/23), 2/4 and 8/9,respectively; and the specificity was 92.0%(115/125), 93.2%(109/117), 90.4%(123/136) and 93.9%(123/131),respectively; the overall concordance rate was 92.1%(95%CI:89.6%-94.1%),and the Kappa value of the consistency test was 0.63(95%CI:0.55-0.72).By taking the Xpert test results as the reference, the sensitivity of this technology to the detection of rifampicin resistance was 93.6%(44/47), the specificity was100%(310/310), the concordance rate was 99.2%(95%CI:97.6%-99.7%), and the Kappa value of the consistency test was 0.96(95%CI:0.93-0.99). The MeltPro MTB assays had been used in 4 551 confirmed patients; the proportion of patients who obtained effective drug resistance results increased from 83.3% to 87.8%(P<0.01); and detection rate of rifampicin, isoniazid, ethambutol, fluoroquinolone resistance, multidrug and pre-extensive drug resistance cases were increased by 3.2%, 14.7%, 22.2%, 13.7%, 11.2% and 12.5%, respectively. Conclusion: The MeltPro MTB assays show satisfactory accuracy in the diagnosis of drug-resistant tuberculosis. This molecular pathological test is an effective complementary method in improving test positivity of drug-resistant tuberculosis.
Humans ; Rifampin/therapeutic use* ; Antibiotics, Antitubercular/therapeutic use* ; Mycobacterium tuberculosis ; Ethambutol/pharmacology* ; Isoniazid/pharmacology* ; Paraffin Embedding ; Retrospective Studies ; Cross-Sectional Studies ; Drug Resistance, Bacterial ; Sensitivity and Specificity ; Tuberculosis, Multidrug-Resistant/drug therapy*

Objective:To analyze and compare the association between different obesity-related indices and vitamin D deficiency in middle-aged and elderly population dwelled in Lanzhou city.Methods:From May, 2011 to September, 2012, middle-aged and elderly individuals with complete baseline data were included via randomly cluster sampling from 3 communities in Lanzhou. The subjects were divided into 4 subgroups by vitamin D levels and various obesity-related indices were compared across subgroups with the same gender. The relationship between the obesity-related indices and the severity of vitamin D deficiency was analyzed using Spearman correlation analysis, and the effects of different obesity-related indices on the severity of vitamin D deficiency was analyzed using multivariate logistic regression analysis.Results:A total of 9 437 residents were included. The overall prevalence of vitamin D deficiency was 97.7%. Compared with the group with lower vitamin D level, participants in the group with higher vitamin D level showed evidently lower body mass index (BMI), waist circumference (WC), lipid accumulation product (LAP), visceral adiposity index (VAI) and triglyceride/ high density lipoprotein cholesterol (TG/HDL-C) ratio in the total population and females, while only WC, LAP, VAI and TG/HDL-C in the males (all P<0.05). Spearman correlation analysis showed that BMI, WC, LAP, VAI and TG/HDL-C were positively correlated with the severity of vitamin D deficiency in the total population and the females, while only LAP, VAI and TG/HDL-C in the males (all P<0.05) . Multivariate logistic regression analysis showed that higher levels of these obesity related indices were correlated with more severe vitamin D deficiency in the total population and the females, while only higher LAP, VAI and TG/HDL-C in the males (all P<0.05). The effects of higher LAP was the most prominant in the total population ,the females and the males. Conclusion:Various obesity phenotypes are closely related to vitamin D deficiency in middle-aged and elderly women, while only visceral obesity and abnormal lipid metabolism are related to vitamin D deficiency in middle-aged and elderly men, with LAP being the most important influencing factor.

Objective:A machine learning algorithm was used to develop a predictive model of self-injury among college students and to explore the high-risk factors for self-injury among college students.Methods:From November to December 2022, a convenience sample of 791 college students from a university in Hebei Province was selected.Whether the self-injurious behavior occurred or not was regarded as an outcome variable.The basic demographics data were collected for statistical analysis.The adolescent self-harm questionnaire, the acquired helplessness scale, the Chinese version of the interpersonal needs questionnaire, the adolescent life events scale, and the childhood traumatic experiences questionnaire were used for assessment.The predictor variables were statistically analyzed by SPSS 26.0 software, and the performance of the model was evaluated by random forest, support vector machine and logistic regression so as to predict the self-injury behavior of college students.The model performance was evaluated by the accuracy, F1 score, sensitivity, specificity, and AUC value of the model, and the optimal model was selected.Finally, the optimal model was used to analyze the high-risk factors of college students' self-injury behaviors.Results:(1) The results of one-way ANOVA showed that the detection rate of self-injury behavior among college students was 42.4%(335/791), and the detection rate of male students was significantly higher than that of female students ( χ2=14.139, P<0.05). Individuals with lower-middle monthly household income(RMB 3 000-5 999) had a significantly higher detection rate of self-injury behavior than those with other monthly household income( P<0.05). (2) The accuracy of random forest, support vector machine, and logistic regression models were 85.53%, 85.96%, and 68.86%, F1 scores were 0.853, 0.864, and 0.676, and sensitivities were 83.91%, 89.04%, and 64.91%, respectively.The AUCs of support vector machine, logistic regression models and random forest were 0.89, 0.73 and 0.92.(3) The top ten characteristic variables of high risk factors for college students' self-injury behaviors based on the random forest algorithm with better predictive efficacy were emotional abuse, frustration of belonging, helplessness, interpersonal relationship factor, despair, emotional neglect, academic stress factor, monthly family income, perception of tiredness, and health adaptation factor, in that order. Conclusions:Random forest is optimal for predicting self-injury behavior among college students compared to support vector machine and logistic regression.Factors influencing self-injury behavior among college students originate from environmental factors, individual factors and interpersonal factors.

Objective:To investigate the characteristics of nasal flora and the pathogenic role of differential microbiome in patients with allergic rhinitis (AR) and non-allergic rhinitis (nAR).Methods:Thirty-five patients with AR who attended the rhinology outpatient clinic of the Second Hospital of Harbin Medical University from February to July 2022 were selected. A total of 35 nAR patients were selected as the test group, and 20 cases of healthy people with physical examination at the same period were selected as the control group, including 39 males and 51 females, aged 8 to 55 years. 16SrDNA High-throughput sequencing was used to analyze the relative abundance from nasal flora in the three groups of subjects. Alpha diversity index analysis was conducted with R software, and differences between groups were analyzed with LEfSe, Metastats, and t tests. At the same time, the role of microbiome and its relationship with environmental factors were analyzed with R software. Results:There was a significant difference in the bacterial composition of the samples from the three groups, with the relative abundance of Staphylococcus aureus ( P=0.032) and Corynebacterium proinquum ( P=0.032) within the AR group being significantly higher than that of the nAR group, and that of Lactobacillus murinus, Lactobacillus kunkeei, and Alcaligenes faecalis ( P value was 0.016, 0.005, and 0.001, respectively) being significantly lower than that of the nAR group. The relative abundance of Ackermannia muciniphila within the nAR group was higher than that of the control group ( P=0.009). Correlation analysis of environmental factors showed a negative correlation between Lactobacillus kunkeei and IgE ( P=0.044), and a positive correlation between Lactobacillus murinus and age ( P=0.019). AR and nAR random forest prediction models were constructed for the five genera, respectively, and the area under the curve (AUC) of the models of Streptococcus-SP-FF10, Pseudoalteromonas luteoviolacea, Pseudomonas parafulva, Acinetobacter ursingii, and Azotobacter chroococcum in the AR group was 100% (95%CI: 100% to 100%). The AUC for the Pseudomonas parafulva, Azotobacter chroococcum, Closoridium baratii, Turicibacter-SP-H121, and Streptococcus lutetiensis models in the nAR group was 98.4% (95%CI: 94.9% to 100%). Conclusions:The distribution of nasal flora in AR patients, nAR patients and healthy subjects is significantly different, and the changes of bacterial flora abundance are significantly related to the occurrence of AR and nAR. Combined detection of microbiota has the potential to diagnose AR and nAR patients.

Objective: The impact of serotonergic system on obsessive-compulsive disorder (OCD) is well studied. However, the correlation between OC presentations and autonomic nervous system (ANS) is still unclear. Furthermore, whether the correlation might be modulated by serotonin is also uncertain. Methods: We recruited eighty-nine healthy subjects. Serotonin transporter (SERT) availability by [ 123 I]ADAM and heart rate variability (HRV) tests were measured. Symptoms checklist-90 was measured for the OC presentations. The interaction between HRV and SERT availability were calculated and the correlation between HRV and OC symptoms were analyzed after stratified SERT level into two groups, split at medium. Results: The interactions were significant in the factors of low frequency (LF), high frequency (HF), and root mean square of successive differences (RMSSD). Furthermore, the significantly negative correlations between OC symptoms and the above HRV indexes existed only in subjects with higher SERT availability. Conclusion OC symptoms might be correlated with ANS regulations in subjects with higher SERT availability.

Aspergillus fumigatus ; Cytokines ; Humans ; Interleukin-1beta ; Keratitis ; Receptors, IgE ; Tumor Necrosis Factor-alpha ; Tumor Necrosis Factors

Immune thrombocytopenia (ITP) is an autoimmune disease mediated by acquired immunity, with platelets decreased and hemorrhage being the primary clinical manifestations. The responses and platelet count levels after early treatment are important factors affecting long-term prognosis, however, current diagnostic methods and disease evaluation approaches are limited, and lack of specific biomarkers. In recent years, various biomolecules have been proposed as potential biomarkers for ITP. This paper reviews the value and advances in ITP-related biomarkers.

Background::Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology.Methods::A retrospective study of patients’ data was conducted using the Emergency Rescue Database of Chinese People’s Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost, and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity, and the areas under receiver operator characteristic curves (ROC-AUC).Results::The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the logistic regression were cardiac troponin T (CTnT) (coefficient = 0.346, OR = 1.413), temperature (T) (coefficient = 0.235, OR = 1.265), respiratory rate (RR) (coefficient= –0.206, OR = 0.814), serum kalium (K) (coefficient = 0.137, OR = 1.146), pulse oxygen saturation (SPO 2) (coefficient = –0.101, OR = 0.904), and albumin (ALB) (coefficient = –0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heart rate, and systolic blood pressure. Conclusions::The main clinical indicators of concern included CTnT, RR, SPO 2, T, ALB, and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.