Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.

Several types of arthritis share the common feature that the generation of inflammatory mediators leads to joint cartilage degradation. However, the shared mechanism is largely unknown. H2BK120ub1 was reportedly involved in various inflammatory diseases but its role in the shared mechanism in inflammatory joint conditions remains elusive. The present study demonstrated that levels of cartilage degradation, H2BK120ub1, and its regulator WW domain-containing adapter protein with coiled-coil (WAC) were increased in cartilage in human rheumatoid arthritis (RA) and osteoarthritis (OA) patients as well as in experimental RA and OA mice. By regulating H2BK120ub1 and H3K27me3, WAC regulated the secretion of inflammatory and cartilage-degrading factors. WAC influenced the level of H3K27me3 by regulating nuclear entry of the H3K27 demethylase KDM6B, and acted as a key factor of the crosstalk between H2BK120ub1 and H3K27me3. The cartilage-specific knockout of WAC demonstrated the ability to alleviate cartilage degradation in collagen-induced arthritis (CIA) and collagenase-induced osteoarthritis (CIOA) mice. Through molecular docking and dynamic simulation, doxercalciferol was found to inhibit WAC and the development of cartilage degradation in the CIA and CIOA models. Our study demonstrated that WAC is a key factor of cartilage degradation in arthritis, and targeting WAC by doxercalciferol could be a viable therapeutic strategy for treating cartilage destruction in several types of arthritis.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.

Purpose To evaluate the classification criteria of triple negative breast cancer(TNBC)based on histomorphol-ogy and immunohistochemistry(IHC),and to provide theoreti-cal basis for the classification and treatment of TNBCs.Methods TNBC subtyping was performed according to the histomorphologi-cal characteristics and the expression of immune markers AR,CD8 and FOXC1,and the clinicopathological features and prog-nostic differences were compared.Results Among 93 cases of TNBC,there were 23 cases(24.7％)of luminal androgen re-ceptor subtypes,24 cases(25.8％)of immunomodulatory type,39 cases(42.0％)of basal immunosuppressive type,and 7 ca-ses(7.5％)of mesenchymal type.There were significant differ-ences in the clinicopathological features of subtypes,including pT stage(P=0.030),histological grade(P＜0.001),intersti-tial lymphocyte infiltration pattern(P＜0.001),expression of PD-L1(P＜0.001),and HER2-low(P=0.024).There was no significant difference in disease-free survival among the sub-types(P＞0.05).Univariate survival analysis showed there was significant difference in disease-free survival among the subtypes at pT1 stage(P=0.011),and other clinicopathological features were not independent prognostic factors.Conclusion The clini-copathological characteristics of TNBC subtypes are different,which are expected to be an alternative choice for complex gene expression profile analysis and to provide theoretical basis for subtypic therapy and targeted therapy.

Breast cancer is one of the most common cancers of females and is a leading cause of tumor-related death in women.Negative emotion frequently presents in patients like anxiety and depression and chronic stress which may lead to depression is a major risk factor of the cancer development.Stress leads to dysfunctions of cells,molecules and neural circuits,thus promoting depression.Chronic stress promotes the occurrence and development of breast cancer through related neurotransmitters and also affects the prognosis of breast cancer patients.

A review was undertaken of the operation process and development of transcutaneous electrical acupoint stimulation (TEAS) and related devices for TEAS, with the aim to offer a reference for developing an international standard for the basic safety and essential performance of the devices. The articles related to TEAS and instruction of devices for TEAS were searched using the EMBASE, MEDLINE, and Web of Science databases with the time period from inception to July 18, 2023. In the absence of a parameter description of the stimulators, a multimeter was used to measure the output voltage, resistance, and current. Thirty-two related devices for TEAS were obtained. The safety parameters of most devices were neither clearly defined, nor stand ardized, and in some cases were missing. There was a noticeable disparity in the upper safety limits of the output current among the devices. The sizes of the skin electrode pads as well as the lengths of the electrode connecting wires of most devices were not clearly indicated. Acupoints on different parts of the human body, including the upper limbs, head, auricle, chest, abdomen, trunk, and lower limbs, required different maximum tolerable current intensities and current densities. It is important to indicate comprehensive output/safety parameters and essential performance for devices for TEAS to meet the need of global distribution, achieve precise stimulation parameters at different acupoints across the human body, and allay any safety concern of national therapeutic device authorities, the regulators, manufacturers, and end users.

ObjectiveTo determine the situation and challenges of innovation platforms in China， and to explore the construction strategy of Shanghai Nutrition Innovation Platform， which is suitable for Shanghai and may achieve the research and transformation of nutrition innovation and population health， so as to coordinate， unite and gather the superior resources of all parties and promote nutrition innovation. MethodsConstruction scheme and operational mechanism of Shanghai Nutrition Innovation Platform were explored by literature review， expert consultation and questionnaire. ResultsThere were various forms of innovation platforms in China. However， challenges were identified， such as decentralizing force， resource rearrangement and insufficient sharing effect. Shanghai Nutrition Innovation Platform adopted a modular organizational structure， which was divided into central group， node group， and subject group. Shanghai Center for Disease Control and Prevention， as the central organization， is responsible for the platform operation management. The expert database as an academic committee selected key organizations from nutrition-related universities， research institutes， academic associations， centers for disease control and prevention， hospitals and the industry. Based on the opening of its own innovation resources， the platform made effective use of external innovation resources and formed a closely integrated nutrition innovation network of multiple disciplines. ConclusionThis study promotes the construction of innovation platform model of cooperation， co-construction and resource sharing， and provides reference for the construction of innovation platform in China.

Objective:To explore the airway pathogen characteristics and examine the correlation between donor-derived pathogens and post-transplant outcomes in patients after lung transplantation (LT).Methods:Between January 1, 2015 and December 31, 2019, retrospective review was conducted for clinical and microbiological data of 88 LT recipients.Airway pathogen percentage of different microorganisms and evolution of drug-resistance were examined.Drug-resistant pathogen positive group (n=71) and negative group (n=17) were assigned according to whether or not drug-resistant pathogens were detected.Survival analysis was conducted by Log-rank with 3-year follow-ups.Between April 11, 2020 and September 5, 2020, prospective study was conducted in 14LT recipients.The potential pathogenic bacteria from donor lungs were detected by metagenomic next generation sequencing and the impact of those bacteria was examined on 1-year post-transplantation outcome in 2020.Microbial diversity and richness were shown with Shannon index.The outcome variables included heart rate, neutrophil count, lymphocyte count, immunoglobulin level and pulmonary spirometry.ANOVA and Pearson's correlation analysis were performed for elucidating the relationship between airway microbiota and post-LT outcomes.Results:From 2015 to 2019, 88 recipients were recruited and 992 strains of airway pathogens were isolated, including bacteria 796 strains and fungi 196 strains.Gram-negative bacteria (704 strains) accounted for 88.4% of all bacteria.The detection rates of Gram-positive bacteria, Klebsiella pneumonia (Kp), Acinetobacter baumannii (Ab), Stenotrophomonas maltophilia and Candida increased in 2019 than that in 2015 (8.2% vs. 5.3%, 13.6% vs. 13.2%, 33.2% vs. 17.5%, 6.5% vs. 5.3%, 26.6% vs. 20.2%). Drug resistance rate of Kp to imipenem was 68.18% in 2019 and drug resistance rate of Ab to imipenem 98.44%.The 3-year survival rate was 46.3% and 35.3% in drug-resistance positive and negative groups and the difference was insignificant ( P=0.410). Fourteen recipients were enrolled in 2020.Potential pathogenic bacteria could be detected in all donor samples.Five recipients carried the same bacteria and two died during 1-year follow-up.Nine recipients did not carry the donor-derived pathogens and two died during 1-year follow-up.The diversity of donor/recipient-derived airway microbiota (Shannon index) showed no correlation with the outcomes of 1-year follow-up by Pearson's correlation test. Conclusions:Gram-negative bacteria predominated in airway pathogens of recipients post-LT.The drug resistance rate to imipenem remained high.The donor/recipient-derived pathogen isolates showed no correlation with immediate outcomes post-LT.