The advancement of artificial intelligence and big data has accelerated the exploration of decentralized clinical trials （DCTs）， and their future complementary relationship with traditional clinical trial models represents an inevitable trend. Given the greater complexity of preliminary research and protocol design in oncology drug clinical trials， the implementation of “decentralization” requires all stakeholders to clarify the responsibilities and authorities， enhance communication and collaboration， conduct comprehensive training， and progressively promote the integration and sharing of medical resources， thereby facilitating the establishment of a new quality and risk management framework. Concurrently， institutional review board should focus on reviewing and evaluating the scientificity， feasibility， and research risk controllability of DCT project protocol designs. It should also prioritize the implementation of informed consent norms， the management of safety event reporting and continuing review， and the protection of participants’ personal information and privacy. By adopting corresponding ethical review procedures， it emphasizes improving the risk identification and addressing capabilities of institutional review board members regarding the review projects， guiding researchers to adhere to DCT regulations. This aims to genuinely implement the “value-oriented， patient-centered” philosophy， thereby promoting the standardized and efficient conduct of DCTs.

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

Objective:To explore risk factors for delayed onset of lactogenesis in prenatally overweight women and construct risk prediction models based on machine learning for early identification of high-risk individuals.Methods:Convenience sampling was adopted to select 338 prenatally overweight women who delivered in the Obstetrics Departments of four ClassⅢ Grade A hospitals in Hengyang City from October 2023 to June 2024 for the study. Delivery women were randomly divided into training set and test set in the ratio of 7∶3. The survey was conducted with the General Information Questionnaire, Breastfeeding Self-efficacy Scale Short Form, Edinburgh Postnatal Depression Scale, LATCH Scale and Pittsburgh Sleep Quality Index. One-way analysis and LASSO regression were used to screen predictors using delayed onset of lactogenesis as the outcome variable. Risk prediction models were constructed based on three machine learning algorithms of Logistic regression, support vector, and random forest, respectively. The models were tuned by ten-fold cross-validation to filter out the best models.Results:Delayed onset of lactogenesis occurred in 140 of 338 prenatally overweight women, an incidence of 41.4%. Among the three predictive model performances, the random forest model had the highest area under the receiver operating characteristic curve, accuracy, precision, recall, and F1 value. The importance of each predictor was ranked according to the fandom forest algorithm, and in descending order of importance, they were breastfeeding 1 h after the birth of the newborn, number of previous deliveries, age, feeding mode 3 d postpartum, pregnancy complications, mode of delivery, number of breastfeeding 24 h postpartum, and monthly household income.Conclusions:Risk prediction models for delayed onset of lactogenesis in prenatally overweight women are constructed based on three machine learning algorithms, aiming to help provide a scientific basis for clinical healthcare professionals to take relevant decisions.

Objective:To explore risk factors for delayed onset of lactogenesis in prenatally overweight women and construct risk prediction models based on machine learning for early identification of high-risk individuals.Methods:Convenience sampling was adopted to select 338 prenatally overweight women who delivered in the Obstetrics Departments of four ClassⅢ Grade A hospitals in Hengyang City from October 2023 to June 2024 for the study. Delivery women were randomly divided into training set and test set in the ratio of 7∶3. The survey was conducted with the General Information Questionnaire, Breastfeeding Self-efficacy Scale Short Form, Edinburgh Postnatal Depression Scale, LATCH Scale and Pittsburgh Sleep Quality Index. One-way analysis and LASSO regression were used to screen predictors using delayed onset of lactogenesis as the outcome variable. Risk prediction models were constructed based on three machine learning algorithms of Logistic regression, support vector, and random forest, respectively. The models were tuned by ten-fold cross-validation to filter out the best models.Results:Delayed onset of lactogenesis occurred in 140 of 338 prenatally overweight women, an incidence of 41.4%. Among the three predictive model performances, the random forest model had the highest area under the receiver operating characteristic curve, accuracy, precision, recall, and F1 value. The importance of each predictor was ranked according to the fandom forest algorithm, and in descending order of importance, they were breastfeeding 1 h after the birth of the newborn, number of previous deliveries, age, feeding mode 3 d postpartum, pregnancy complications, mode of delivery, number of breastfeeding 24 h postpartum, and monthly household income.Conclusions:Risk prediction models for delayed onset of lactogenesis in prenatally overweight women are constructed based on three machine learning algorithms, aiming to help provide a scientific basis for clinical healthcare professionals to take relevant decisions.

For multi-center clinical research, how to ensure the quality of ethical review and improve the efficiency of ethical review through cooperation among centers is an important direction for clinical research management departments and research parties to explore. By combing and analyzing the existing pattern of multi-center ethical review at home and abroad, combining the current situation of the ethical review and management development in China, taking cancer clinical research as the breakthrough point, it was advocated to establish a cooperative review led by professional institute in domestic, on the basis of extensive and in-depth training exchanges and effective communication on the same platform, collaborative review, ensure quality and efficiency, so as to promote and implement the "mutual recognition" of ethical review. Then, this paper further put forward the concept of "whole-process linkage" in the ethical management process of multi-center clinical research, and pointed out that all research parties should clarify their responsibilities, enhance their awareness and ability, and jointly and comprehensively implement the protection of subjects among clinical researchers.

Novel coronavirus Omicron variant infection can cause severe illness and even death in certain populations. Omicron variant infection may lead to systemic inflammatory response, coagulation disorder, multi-organ dysfunction and other pathophysiological changes, which are different from other Novel coronavirus variants to a certain extent, so therapeutic strategies should not be the same. The National Medical Center for Major Public Health Events invited experts in fields of infectious diseases, respiratory medicine, intensive care, pediatrics and fever clinic to develop this quick guideline based on the current best evidence and extensive clinical practices. This quick guideline aims to standardize the diagnosis and treatment of novel coronavirus Omicron infection, and to improve the disease management abilities of clinicians.

BACKGROUND: Despite the progress in perinatal-neonatal medicine, complications of extremely preterm infants continue to constitute the major adverse outcomes in neonatal intensive care unit. Human umbilical cord Wharton’s Jellyderived mesenchymal stem cells (HUMSCs) may offer new hope for the treatment of intractable neonatal disorders. This study will explore the functional differences of HUMSCs between extremely preterm and term infants. METHODS: UMSCs from 5 extremely preterm infants(weeks of gestation: 22+5 w,24+4 w,25+3 w,26 w,28 w) and 2 term infants(39 w,39+2 w) were isolated, and mesenchymal markers, pluripotent genes, proliferation rate were analyzed.HUVECs were injured by treated with LPS and repaired by co-cultured with HUMSCs of different gestational ages. RESULTS: All HUMSCs showed fibroblast-like adherence to plastic and positively expressed surface marker of CD105,CD73 and CD90, but did not expressed CD45,CD34,CD14,CD79a and HLA-DR; HUMSCs in extremely preterm exhibited significant increase in proliferation as evidenced by CCK8, pluripotency markers OCT-4 tested by RT-PCR also showed increase. Above all, in LPS induced co-cultured inflame systerm, HUMSCs in extremely preterm were more capable to promote wound healing and tube formation in HUVEC cultures, they promoted TGFb1 expression and inhibited IL6 expression. CONCLUSIONS Our results suggest that HUMSCs from extremely preterm infants may be more suitable as candidates in cell therapy for the preterm infants.

Ergothioneine is a multifunctional physiological cytoprotector, with broad application in foods, beverage, medicine, cosmetics and so on. Biosynthesis is an increasingly favored method in the production of ergothioneine. This paper summarizes the new progress in the identification of key pathways, the mining of key enzymes, and the development of natural edible mushroom species and high-yield engineering strains for ergothioneine biosynthesis in recent years. Through this review, we aim to reveal the molecular mechanism of ergothioneine biosynthesis and then employ the methods of fermentation engineering, metabolic engineering, and synthetic biology to greatly increase the yield of ergothioneine.
Antioxidants ; Ergothioneine/metabolism* ; Fermentation ; Metabolic Engineering

Since 2010, the incidence of severe fever with thrombocytopenia syndrome (SFTS) has been increased. Owing the progress in diagnosis and treatment, the overall mortality of SFTS in China has decreased, while the mortality in critical SFTS patients is still high. In order to provide guidance and working procedures for clinicians to diagnose and treat critical SFTS, the National Medical Center for Major Public Health Events invited experts to discuss and formulate this consensus based on their experience and up-to-date knowledge on SFTS.