Effectiveness of Educational Interventions for Health Workers on Antibiotic Prescribing in Outpatient Settings in China: A Systematic Review and Meta-Analysis

Educational interventions are considered an important component of antibiotic stewardship, but their effect has not been systematically evaluated in outpatient settings in China. This research aims to evaluate the effectiveness of educational interventions for health workers on antibiotic prescribing rates in Chinese outpatient settings. Eight databases were searched for relevant randomized clinical trials, non-randomized trials, controlled before-after studies and interrupted time-series studies from January 2001 to July 2021. A total of 16 studies were included in the systematic review and 12 in the meta-analysis. The results showed that educational interventions overall reduced the antibiotic prescription rate significantly (relative risk, RR 0.72, 95% confidence interval, CI 0.61 to 0.84). Subgroup analysis demonstrated that certain features of education interventions had a significant effect on antibiotic prescription rate reduction: (1) combined with compulsory administrative regulations (RR With: 0.65 vs. Without: 0.78); (2) combined with financial incentives (RR With: 0.51 vs. Without: 0.77). Educational interventions can also significantly reduce antibiotic injection rates (RR 0.83, 95% CI 0.74 to 0.94) and the inappropriate use of antibiotics (RR 0.61, 95% CI 0.51 to 0.73). The limited number of high-quality studies limits the validity and reliability of the results. More high-quality educational interventions targeting the reduction of antibiotic prescribing rates are needed

Prognosis of HIV Patients Receiving Antiretroviral Therapy According to CD4 Counts: A Long-term Follow-up study in Yunnan, China

We aim to evaluate the overall survival and associated risk factors for HIV-infected Chinese patients on antiretroviral therapy (ART). 2517 patients receiving ART between 2006 and 2016 were prospectively enrolled in Yunnan province. Kaplan-Meier analyses and Cox proportional hazard regression analyses were performed. 216/2517 patients died during a median 17.5 (interquartile range [IQR] 6.8-33.2) months of follow-up. 82/216 occurred within 6 months of starting ART. Adjusted hazard ratios were10.69 (95%CI 2.38-48.02, p = 0.002) for old age, 1.94 (95%CI 1.40-2.69, p < 0.0001) for advanced WHO stage, and 0.42 (95%CI 0.27-0.63, p < 0.0001) for heterosexual transmission compared to injecting drug users. Surprisingly, adjusted hazard ratios comparing low CD4 counts group (<50 cells/μl) with high CD4 counts group (≥500 cells/μl) within six months after starting ART was 20.17 (95%CI 4.62-87.95, p < 0.0001) and it declined to 3.57 (95%CI 1.10-11.58, p = 0.034) afterwards. Age, WHO stage, transmission route are significantly independent risk factors for ART treated HIV patients. Importantly, baseline CD4 counts is strongly inversely associated with survival in the first six months; whereas it becomes a weak prognostic factor after six months of starting ART

Negative Elongation Factor Controls Energy Homeostasis in Cardiomyocytes

SummaryNegative elongation factor (NELF) is known to enforce promoter-proximal pausing of RNA polymerase II (Pol II), a pervasive phenomenon observed across multicellular genomes. However, the physiological impact of NELF on tissue homeostasis remains unclear. Here, we show that whole-body conditional deletion of the B subunit of NELF (NELF-B) in adult mice results in cardiomyopathy and impaired response to cardiac stress. Tissue-specific knockout of NELF-B confirms its cell-autonomous function in cardiomyocytes. NELF directly supports transcription of those genes encoding rate-limiting enzymes in fatty acid oxidation (FAO) and the tricarboxylic acid (TCA) cycle. NELF also shares extensively transcriptional target genes with peroxisome proliferator-activated receptor α (PPARα), a master regulator of energy metabolism in the myocardium. Mechanistically, NELF helps stabilize the transcription initiation complex at the metabolism-related genes. Our findings strongly indicate that NELF is part of the PPARα-mediated transcription regulatory network that maintains metabolic homeostasis in cardiomyocytes

A correlation analysis of HHV infection and its predictive factors in an HIV-seropositive population in Yunnan, China

Human herpesviruses (HHVs) have a particularly high prevalence in certain high-risk populations and cause increased morbidity and mortality in patients with acquired immunodeficiency syndrome (AIDS). Screening and treating subclinical HHV infections reduce human immunodeficiency virus (HIV) infection incidence, disease progression, and transmission. However, there are few studies on HHVs, HIV coinfection rates, and their related risk factors. We aimed to clarify the prevalence of all eight HHVs in peripheral blood samples collected from HIV-positive patients, and explore the association of HHV infection in HIV-positive patients in an HIV-seropositive population in Yunnan. We recruited 121 HIV-positive patients with highly active antiretroviral therapy (HAART) and 45 healthy individuals. All the eight HHVs were detected using polymerase chain reaction and their epidemiological information and clinical data were collected and statistically analyzed. A high prevalence of HHVs (89.3%) was observed in individuals with HIV infections and with herpes simplex virus (HSV)-2 (65.3%), and HSV-1 (59.5%) being the most common. Coinfection with more than two different HHVs was more common in patients with HIV infections receiving HAART (72.7%) than in healthy controls. Older age, being married, higher HIV-1 plasma viral loads, and use of antiviral protease inhibitors were independently correlated with an increased frequency of HHVs, but we found no association with CD4 count, WHO HIV clinical stage, and HIV infection duration. Our findings are of great significance for the prevention of HHV opportunistic infection in patients with AIDS and their clinical treatment

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT

Risk prediction model for postoperative cognitive dysfunction after total knee replacement based on Bayesian network algorithm

Objective To establish a prediction model of risk for postoperative cognitive dysfunction (POCD) after total knee replacement (TKR) by Bayesian network (BN) algorithm and investigate its predictive performance. Methods A case-control trial was conducted on 1 260 inpatients who underwent TKR from January 2017 to December 2021 in the Department of Joint Surgery of our hospital. Their main diagnosis of inclusion was severe osteoarthritis of left/right knee joint. They were 240 cases of male (19.0%) and 1 020 cases of female (81.0%), at an average age of 66.73±8.46 (23~79) years and a mean body mass index (BMI) of 25.08±5.08 kg/m2. The POCD patients (n=71) after surgery (from the end of surgery to discharge) were randomly divided into A1 group and B1 group at a ratio of 7∶3, and those without POCD (1 189 cases) were also randomly divided into A2 group and B2 group at a same ratio. The patients from A1 and A2 groups were combined together as training set, and those out of B1 and B2 groups as test set. Thirty-six indexes related to perioperative anesthesia decision, disease outcome and length of stay in TKR were selected as nodes, and the probability distribution model diagram of each node was established by using BN algorithm to predict the probability of risk for POCD, so as to minimize the length of stay and promote the maximum recovery of patients. Results The prediction model of risk for POCD after TKR was established based on BN algorithm. The area value under receiver operating characteristic curve (ROC-AUC) of the training set was 0.966 1 (95% CI: 0.954 1~0.978 4), and the ROC-AUC value of the test set was 0.897 4 (95% CI: 0.867 2~0.928 5), with an accuracy of 96.43% (95%CI: 0.951 1~0.976 4) and 93.44% (95% CI: 0.909 2~0.959 6), respectively. Conclusion Our risk prediction model for POCD after TKR based on BN algorithm has good performance and high accurac

Establishment of prediction model for risk of postoperative cognitive dysfunction after non-cardiac surgery based on different machine learning algorithms

Objective To establish a risk model for predicting postoperative cognitive dysfunction (POCD) after non-cardiac surgery using preoperative indicators based on machine learning algorithm. Methods A case-control study was designed, and conducted on the POCD patients after non-cardiac surgery in the medical big data platform of our hospital from January 2014 to January 2019. Finally, 92 patients were included in the POCD group. According to surgical type and age matched of the POCD group, another 276 patients who did not develop POCD after surgery and discharged from the hospital during the same time period from the same big data platform were assigned into the non-POCD group at a ratio of 1∶3. At the same time, these 368 patients were randomly divided into modeling group (n=259) and validation group (n=109) at a ratio of 7∶3. After data preprocessing and feature selection of preoperative clinical indicators (general data, relevant scoring scales, surgical-related data and results of preoperative laboratory tests), the risk prediction model of POCD for non-cardiac surgery was established based on 3 machine learning algorithms, that is, logistic regression (LR), support vector machine (SVM) and Decision Tree. The model efficacy was evaluated by sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). Results The SVM algorithm was the best model among the 3 machine learning algorithms to predict the risk of POCD after non-cardiac surgery. The AUC value of the model in the validation group was 0.957 (95%CI: 0.905~1.000), with a sensitivity and specificity of 92.6% and 98.8%, respectively. Conclusion A prediction model of POCD after non-cardiac surgery is successfully established based on machine learning algorithm. This model shows good predictive performance for POCD. [Key words] machine learning , prediction model , postoperative cognitive dysfunction

Hydro-Geochemistry of the River Water in the Jiulongjiang River Basin, Southeast China: Implications of Anthropogenic Inputs and Chemical Weathering

This study focuses on the chemical weathering process under the influence of human activities in the Jiulongjiang River basin, which is the most developed and heavily polluted area in southeast China. The average total dissolved solid (TDS) of the river water is 116.6 mg/L and total cation concentration ( TZ + ) is 1.5 meq/L. Calcium and HCO 3 &#8722; followed by Na + and SO 4 2 &#8722; constitute the main species in river waters. A mass balance based on cations calculation indicated that the silicate weathering (43.3%), carbonate weathering (30.7%), atmospheric (15.6%) and anthropogenic inputs (10.4%) are four reservoirs contributing to the dissolved load. Silicates (SCW) and carbonates (CCW) chemical weathering rates are calculated to be approximately 53.2 ton/km2/a and 15.0 ton/km2/a, respectively. When sulfuric and nitric acid from rainfall affected by human activities are involved in the weathering process, the actual atmospheric CO 2 consumption rates are estimated at 3.7 &#215; 105 mol/km2/a for silicate weathering and 2.2 &#215; 105 mol/km2/a for carbonate weathering. An overestimated carbon sink (17.4 Gg C / a ) is about 27.0% of the CO 2 consumption flux via silicate weathering in the Jiulongjiang River basin, this result shows the strong effects of anthropogenic factors on atmospheric CO 2 level and current and future climate change of earth

A Plasmonic Chip-Scale Refractive Index Sensor Design Based on Multiple Fano Resonances

In this paper, multiple Fano resonances preferred in the refractive index sensing area are achieved based on sub-wavelength metal-insulator-metal (MIM) waveguides. Two slot cavities, which are placed between or above the MIM waveguides, can support the bright modes or the dark modes, respectively. Owing to the mode interferences, dual Fano resonances with obvious asymmetrical spectral responses are achieved. High sensitivity and high figure of merit are investigated by using the finite-difference time-domain (FDTD) method. In view of the development of chip-scale integrated photonics, two extra slot cavities are successively added to the structure, and consequently, three and four ultra-sharp Fano peaks with considerable performances are obtained, respectively. It is believed that this proposed structure can find important applications in the on-chip optical sensing and optical communication areas

A self‐supervised causal feature reinforcement learning method for non‐invasive hemoglobin prediction

Abstract Anemia (hemoglobin (Hb) < 12.0 g/dL) is significantly correlated with many diseases. An invasive technique is the peripheral blood Hb detection method, which is used to examine red and white blood cells and platelets in clinical laboratory settings. However, non‐invasive methods for measuring Hb mainly include low‐precision prediction based on eye images and complex operation prediction based on fundus images. Moreover, these types of anemia testing techniques are time‐consuming, tedious, or prone to errors. Thus, developing a convenient and high‐precision method is vital for predicting Hb concentration. This study proposes self‐supervised causal features using actor‐critical reinforcement learning to improve the model prediction performance. Two networks are proposed: Actor Predictor and Hemoglobin Predictor to predict Hb concentration. Moreover, the model performance is evaluated using different techniques, namely, Mean Absolute Error (MAE) and Mean Square Error (MSE), via real eye image data and a smartphone. This model achieved 1.19(1.01,1.38) on the MAE and 2.25(1.59,2.90) on the MSE, which outperformed previous eye images' Hb prediction methods and was nearly similar to the fundus images' Hb prediction methods. The inference time was less than 0.05 s, making it efficient and accurate for predicting Hb. This model can be used for mobile deployment and health self‐screening