ObjectiveTo understand the infection characteristics of multidrug-resistant organisms (MDROs) in hospitalized patients in a tertiary sentinel hospital in Shanghai, so as to provide an evidence for the development of targeted prevention and control measures. MethodsData of MDROs strains and corresponding medical records of some hospitalized patients in a hospital in Shanghai from 2021 to 2023 were collected, together with an analysis of the basic information, clinical treatment, underlying diseases and sources of sample collection. ResultsA total of 134 strains of MDROs isolated from hospitalized patients in this hospital were collected from 2021 to 2023 , including 63 strains of methicillin-resistant Staphylococcus aureus (MRSA), 57 strains of carbapenem-resistant Acinetobacter baumannii (CRAB), and 14 strains of carbapenem-resistant Klebsiella pneumoniae (CRKP). Of the 134 strains, 30 strains were found in 2021, 47 strains in 2022 and 57 strains in 2023. The male-to-female ratio of patients was 2.05∶1, with the highest percentage (70.90%) in the age group of 60‒<90 years. The primary diagnosis was mainly respiratory disease, with lung and respiratory tract as the cheif infection sites. There was no statistically significant difference in the distribution of strains between different genders and infection sites (P>0.05). However, the differences in the distribution of strains between different ages and primary diagnosis were statistically significant (P<0.05). Patients who were admitted to the intensive care unit (ICU), had urinary tract intubation, were not artery or vein intubated, were not on a ventilator, were not using immunosuppresants or hormones, and were not applying radiotherapy or chemotherapy were in the majority. There was no statistically significant difference in the distribution of strains for whether received radiotherapy or chemotherapy or not (P>0.05), while the differences in the distribution of strains with ICU admission history, urinary tract intubation, artery or vein intubation, ventilator use, and immunosuppresants or hormones use or not were statistically significant (all P<0.05). The type of specimen was mainly sputum, the hospitalized ward was mainly comprehensive ICU, the sampling time was mainly in the first quarter throughout the year, the number of underlying diseases was mainly between 1 to 2 kinds, the application of antibiotics ≥4 kinds, and those who didn’t receive any surgery recently accounted for the most. There were statistically significant differences in the distribution of strains between different specimen types, wards occupied and history of ICU stay (P<0.05), but no statistically significant difference in the distribution of strains between different sampling times, number of underlying diseases and types of antibiotics applied (P>0.05). ConclusionThe situation of prevention and control on MDROs in this hospital is still serious. Focus should be placed on high-risk factors’ and infection monitoring and preventive measures should be strengthened to reduce the incidence rate of MDROs infection.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Piper nigrum L. is an evergreen climbing vine, which belongs to the genus Piperia in the Piperaceae family. Piper nigrum L., which known as the “king of spices”, is used as both food and medicine. The main active substances in Piper nigrum L. are alkaloids mainly composed of amides, and essential oil, as well as phenolic compounds. In this paper, the chemical compositions, especially amide alkaloids, and their biological activities of Piper nigrum L. were summarized. These studies showed that Piper nigrum L., as a medicinal and food plant, had a wide range of biological activities and was deserved further research and in-depth utilization.

Animal experiments are an essential component of life sciences and medical research. However, the external validity and reliability of individual animal studies are frequently challenged by inherent limitations such as small sample sizes, high design heterogeneity, and poor reproducibility, which impede the effective translation of research findings into clinical practice. Systematic reviews and meta-analysis represent a key methodology for integrating existing evidence and enhancing the robustness of conclusions. Currently, however, the application of systematic reviews and meta-analysis in the field of animal experiments lacks standardized guidelines for their conduct and reporting, resulting in inconsistent quality and, to some extent, diminishing their evidence value. To address this issue, this paper aims to systematically delineate the reporting process for systematic reviews and meta-analysis of animal experiments and to propose a set of standardized recommendations that are both scientific and practical. The article's scope encompasses the entire process, from the preliminary preparatory phase [including formulating the population， intervention， comparison and outcome （PICO） question, assessing feasibility, and protocol pre-registration] to the key writing points for each section of the main report. In the core methods section, the paper elaborates on how to implement literature searches, establish eligibility criteria, perform data extraction, and assess the risk of bias, based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis （PRISMA) statement, in conjunction with relevant guidelines and tools such as Animal Research： Reporting of in Vivo Experiments （ARRIVE) and a risk of bias assessment tool developed by the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE). For the presentation of results, strategies are proposed for clear and transparent display using flow diagrams and tables of characteristics. The discussion section places particular emphasis on how to scientifically interpret pooled effects, thoroughly analyze sources of heterogeneity, evaluate the impact of publication bias, and cautiously discuss the validity and limitations of extrapolating findings from animal studies to clinical settings. Furthermore, this paper recommends adopting the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to comprehensively grade the quality of evidence. Through a modular analysis of the entire reporting process, this paper aims to provide researchers in the field with a clear and practical guide, thereby promoting the standardized development of systematic reviews and meta-analysis of animal experiments and enhancing their application value in scientific decision-making and translational medicine.

In 2022, the National Cancer Center (NCC) of China reported the nationwide statistics of 2016 using population-based cancer registry data from all available cancer registries in China, which was mainly about the cancer incidence and mortality. Cancer remains a major health problem currently in our country and requires long term cooperation to deal with. This article provided a key point interpretation and analysis of cancer prevalence data in China, and provided an analysis of several main risk factors for cancer, which was conducive to the development of cancer prevention and control programs in different regions.

ObjectiveTo explore the efficacy and predictive indicators of stellate ganglion block （SGB） as an adjunctive intervention for chronic subjective tinnitus and accumulate experience for the application of SGB in the clinical treatment of tinnitus. MethodsA retrospective review was conducted on the data of chronic subjective tinnitus patients who received SGB intervention， with unsatisfactory outcomes otherwise. Pure tone audiometry （PTA）， tinnitus loudness evaluation and Pittsburgh sleep quality index （PSQI） were used. The tinnitus handicap inventory （THI） scores were compared before and after SGB intervention. Correlation analysis and linear regression equations were employed to identify the potential indicators predicting the effectiveness of SGB intervention. Statistical analysis was performed by SPSS 24.0 software. ResultsBy April 2023， a total of 107 patients with chronic subjective tinnitus had undergone SGB intervention， including 67 male and 40 female， with a mean age of （45.32±11.40） years old and an average tinnitus history of （20.32±24.64） months ［16 （12~20）］. Only 7 patients （6.54%） quitted the intervention for personal reasons， which demonstrated good compliance with the intervention. No patients experienced adverse reactions such as infection at the injection site， hematoma， nerve injury， local anesthetic intoxication and so on， which revealed good safety. After SGB intervention， THI scores decreased to below 36 points in 77 patients and decrease by 10 points or more in 12 of the remaining patients， with a total effective rate of 89%. A paired sample t-test showed a significant difference in THI scores before and after SGB intervention （t=15.575， P<0.001）， indicating good improvement. Pearson correlation analysis suggested that pre-intervention THI scores and subjective tinnitus loudness were significantly positively correlated with the improvement level of THI scores （P<0.05）. Further stepwise linear regression analysis found that "pre-intervention THI scores" had statistical significance （P<0.001）， with a regression coefficient of 0.308， predicting a 17.4% improvement level in THI scores. ConclusionsDue to its good and safe short-term effects， SGB intervention can be used as a supplementary option for chronic subjective tinnitus when other interventions are not ideal， especially for patients with higher THI scores. However， further research is needed to clarify the long-term efficacy and underlying mechanisms， in order to establish a more solid theoretical basis for SGB intervention in the treatment of subjective tinnitus.