Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.

Objective:To investigate the rate-limiting steps of hand-assisted laparoscopic donor nephrectomy, analyze the relevant factors affecting surgical difficulty, and subsequently construct a mathematical model to predict the difficulty of the procedure preoperatively.Methods:A retrospective study was conducted on 100 kidney donors who underwent hand-assisted laparoscopic donor nephrectomy performed by the same surgeon at Beijing Friendship Hospital, Capital Medical University from January 2021 to January 2024. Preoperative demographic data, imaging findings, general condition, donor kidney size, and postoperative complications were collected and analyzed. The surgeon′s subjective rating (1-3 points) was used as a quantitative measure of surgical difficulty. ANOVA and Chi-square tests were employed to explore the differences in postoperative complications, recovery, operative time, and intraoperative blood loss among groups with varying levels of difficulty. The main procedure was divided into four steps (excluding abdominal closure): Trocar placement, renal hilar dissection, perinephric dissection, and kidney retrieval. The time for each step and the total operative time were recorded. Pearson correlation test was used to analyze the relationship between each step and the total operative time, and ANOVA test was used to assess the time differences between steps and to determine if the time for the same step varied across different difficulty subgroups, thereby identifying the rate-limiting step of hand-assisted laparoscopic donor nephrectomy. In terms of the risk factors influencing the difficulty of surgery, Pearson and Spearman correlation tests were used to investigate the relationship between preoperative donor data and surgical difficulty scores, and a predictive model was constructed using multiple linear regression. Finally, the model was internally and externally validated to confirm its accuracy and effectiveness.Results:As the surgical difficulty increased (groups 1, 2, and 3), the postoperative drainage tube duration was correspondingly prolonged [(5.92±1.48) d, (8.00±1.75) d, and (11.88±4.45) d, respectively, P<0.05], and the severity of postoperative complications also significantly increased (the incidence of Clavien-Dindo grade ≥2 was 5.66%, 31.82% and 64.00%, respectively, P<0.01). In the analysis of rate-limiting steps, the time taken for all steps, except for Trocar placement, showed significant differences among the difficulty subgroups ( P<0.001). However, the average time for renal hilar dissection was (19.82±5.65) min, which was significantly longer than the other steps ( P<0.001). Therefore, renal hilar dissection was identified as the rate-limiting step of hand-assisted laparoscopic donor nephrectomy. In terms of the influencing factors of surgical difficulty, donor obesity, kidney width, abdominal anteroposterior sagittal diameter, number of renal arteries, distance from renal artery bifurcation to the abdominal aorta, degree of renal artery calcification, and mayo adhesive probability (MAP) score were all correlated with the surgical difficulty score ( P<0.05). However, multiple linear regression analysis revealed that only the number of renal arteries and the MAP score were the independent risk factors for higher surgical difficulty of hand-assisted laparoscopic donor nephrectomy. The predictive equation was: surgical difficulty=0.649×number of renal arteries+ 0.770×MAP score. Both internal and external validation confirmed the model's good accuracy. Conclusions:This study established a reliable and objective predictive model for the difficulty of hand-assisted laparoscopic donor nephrectomy based on the number of renal arteries and the MAP score. Renal hilar dissection was identified as the rate-limiting step of the procedure. This provides a reference for selecting an appropriate surgeon based on the predicted surgical difficulty.

The method of decocting and taking medicine can directly influence the efficacy of the Chinese herbal medicine,and is the key to enhancing efficacy and reducing toxicity.As the originator of classic books for traditional Chinese medicine(TCM)prescriptions,Treatise on Exogenous Febrile Diseases has recorded various specific methods of decocting and taking medicine in details.This paper summarized and sorted out various methods of decocting the same Chinese herbal medicine,re-decocting method with the removal of dregs(for concentrating medicinal solution),method of decocting pills,dosage of medicine,time for taking medicine,and notices and healthcare after medication recorded in Treatise on Exogenous Febrile Diseases.Moreover,the dialectical view in the method of decocting medicine recorded in Treatise on Exogenous Febrile Diseases.was explored.The special method of decocting and taking medicine in Treatise on Exogenous Febrile Diseases included the TCM dialectical view of using the same Chinese herbal medicine for the treatment of different diseases,consideration of both Chinese herbal medicine and syndromes,drastic purgatives for chasing long-term efficacy.The method of taking medicine contained the TCM dialectical view of modification of the medicine dosage according to syndrome differentiation,adapting to the general trend,and suspension after medicine starting an effect.It is believed that the method of decocting and taking medicine for the prescriptions in Treatise on Exogenous Febrile Diseases is established according to syndrome differentiation,and the utilization of various methods of decocting and taking medicine as well as notices and healthcare after medication in accordance with the characteristics of diseases and syndromes ensures the prescriptions meeting the pathogenesis,and then enhance the clinical efficacy.

Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.

Colorectal polyps are important early markers of colorectal cancer, and their early detection is crucial for cancer prevention. Although existing polyp segmentation models have achieved certain results, they still face challenges such as diverse polyp morphology, blurred boundaries, and insufficient feature extraction. To address these issues, this study proposes a parallel coordinate fusion network (PCFNet), aiming to improve the accuracy and robustness of polyp segmentation. PCFNet integrates parallel convolutional modules and a coordinate attention mechanism, enabling the preservation of global feature information while precisely capturing detailed features, thereby effectively segmenting polyps with complex boundaries. Experimental results on Kvasir-SEG and CVC-ClinicDB demonstrate the outstanding performance of PCFNet across multiple metrics. Specifically, on the Kvasir-SEG dataset, PCFNet achieved an F1-score of 0.897 4 and a mean intersection over union (mIoU) of 0.835 8; on the CVC-ClinicDB dataset, it attained an F1-score of 0.939 8 and an mIoU of 0.892 3. Compared with other methods, PCFNet shows significant improvements across all performance metrics, particularly in multi-scale feature fusion and spatial information capture, demonstrating its innovativeness. The proposed method provides a more reliable AI-assisted diagnostic tool for early colorectal cancer screening.
Humans ; Colonic Polyps/diagnostic imaging* ; Colorectal Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Early Detection of Cancer

Objective:To evaluate the effects and underlying mechanisms of preoperative intestinal microbiota modulation on the recovery of prolonged postoperative ileus(PPOI)in gastric cancer patients with sarcopenia Methods:Skeletal muscle mass(SMM)was assessed using bioelectrical impedance analysis(BIA),and the skeletal muscle index(SMI)was calculated.A total of 156 gastric cancer patients with sarcopenia who underwent surgery at the Department of Gastrointestinal Surgery,Liyang People's Hospital,from January 2022 to December 2024 were randomly assigned to either the control group or the experimental group.The control group received conventional prehabilitation interventions,including nutritional support,respiratory function training,and physical exercise.The experimental group received probiotic-based intestinal microbiota modulation in addition to conventional prehabilitation interventions.Postoperative recovery indicators,including time to first flatus,incidence of postoperative complications,length of hospital stay,and hospitalization costs,were compared between the two groups.Results:Among the 156 sarcopenic gastric cancer patients,121 were male,and 35 were female.Statistically significant differences were observed between the two groups in terms of time to first flatus,incidence of postoperative complications,and length of hospital stay(P<0.05).Postoperative complications occurred in 38 patients(24.3%),with 10 cases(6.4%)in the experimental group and 28 cases(17.9%)in the control group.The time to first flatus in the experimental group was significantly shorter than in the control group(48.0 hours vs 72.0 hours,P<0.001).Conclusion:Preoperative intestinal microbiota modulation significantly reduces the incidence of PPOI and postoperative complications in gastric cancer patients with low SMI,thereby promoting postoperative gastrointestinal function recovery.

With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

Since its establishment in 2016, the National Rare Diseases Registry System of China (NRDRS) has accumulated valuable case data and bio-specimen for basic and clinical research on rare diseases in China. However, the emerging challenges in clinical diagnosis and treatment of rare diseases make it unable for data and resource platform to fully meet the diversified needs. Under this backdrop, we have developed a protocol to optimize and upgrade the system based on the core functions of the NRDRS platform. The goal is to leverage intelligent digital technologies to transform NRDRS into a new platform integrating multimodal data and auxiliary diagnostic and treatment functions. It is specified as the development and construction of "one platform and four intelligent tools." Currently, we have upgraded and developed NRDRS platform, intelligent tool for genotype-phenotype analysis of rare diseases, AI-assisted diagnostic tool for rare diseases, remote multidisciplinary diagnosis and teaching tool for rare diseases, drug screening and validation tool for rare diseases. The next step will focus on the promotion of the application of these tools in clinical settings in order to address the issue of severe imbalance in the allocation of resources for the diagnosis and treatment of rare diseases. This article provides an overview of the digital and intelligent upgrades of the NRDRS, the trials in applications in clinical settings, and direction in the future.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.