Objective: To investigate the serological identification and blood group gene sequencing analysis of two rare cases of K phenotype producing high-frequency antigen antibodies (anti-Ku), and to study the serological interrelationship between K cells and the high-frequency antigen antibody anti-KL. Methods: Serological methods were used to identify the antigen phenotypes of the ABO, Rh, and Kell blood group systems and to screen for and identify unexpected antibodies in the two patients. The characteristics of the unexpected antibodies were verified by the indirect antiglobulin test (IAT) using papain or dithiothreitol (DTT) -treated screening cells. The titer of anti-Ku was determined via the tube method using DTT-treated plasma. The Kell blood group genotype was determined by gene sequencing. The distinctive antigenicity of K cells was validated through their reactivity with anti-KL in IAT, and absorption-elution techniques were employed to corroborate the type of anti-KL. Results: Serological findings: Case 1 was blood group O, CCDee; Case 2 was blood group A, CCDee. Both cases exhibited the Kell phenotype: K-k-, Kp (a-b-). High-frequency antigen antibodies were detected in the plasma of both patients. The reactivity of these antibodies was slightly enhanced with papain-treated screening cells but became negative with DTT-treated cells. The anti-Ku (IgG) titer for Case 1 was 64. For Case 2, the anti-Ku (IgM) titer was<1, and the anti-Ku (IgG) titer was 32. Gene sequencing revealed that both cases harbored a homozygous c.715G>T mutation in the KEL gene, corresponding to the genotype KEL02N.24, consistent with the rare K phenotype. The unique high expression of the Kx antigen on K cells was confirmed through the antibody characteristics of anti-KL. Absorption-elution techniques demonstrated that K cells could separate anti-Km and anti-Kx, thereby supporting the classification of anti-KL. Conclusion: Serological and molecular biological assays identified both patients as having the rare Kell-null (K ) phenotype. If such rare blood types go undetected in transfusion medicine, the administration of standard blood products can readily induce the production of high-frequency antigen antibodies such as anti-Ku, potentially leading to a transfusion crisis due to the subsequent difficulty in finding compatible blood. The serological relationship between K cells and anti-KL clarified the characteristic high expression of the Kx antigen on K phenotype erythrocytes and concurrently supported the typological features of the rare high-frequency antibody anti-KL. This represents the first such verified report in China.

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.

Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

Objective To explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

Objective: To explore the longitudinal association between weight trajectories and physical fitness test composite scores among primary school students, so as to provide empirical evidence for school based weight management and physical health promotion. Methods: A total of 2 112 students from a primary school in Beijing who participated in physical fitness assessments annually from 2008 to 2023 were included and classified into different body weight trajectory groups based on body mass index (BMI) threshold: "normal/overweight-obese". Group based trajectory model was used to identify distinct weight trajectories. Generalized estimating equation and Cox regression were used to analyze the associations between weight trajectories and changes in physical fitness test scores, as well as event risks. Robustness checks were conducted. Results: The prevalence of overweight and obesity among primary school students was 26.50%. BMI showed a significant negative correlation with physical fitness composite scores ( r=-0.19, P <0.01). Four types of weight trajectories were identified: persistent normal group (64.5%, 1 362), persistent overweight- obese group (18.2%, 385), normal to overweight-obese group (14.0%, 296), and overweight-obese to normal group (3.3%, 69). Compared to the persistent normal group, both the normal to overweight-obese and persistent overweight-obese groups showed significantly higher risks of failing the physical fitness test ( HR =4.23, 4.60), and the speed of achieving excellent was slower ( HR = 0.52, 0.40) (all P <0.05). Robustness tests confirmed the stability of the findings. Conclusions Body weight trajectories have a long term impact on physical fitness performance among primary school students. Students with persistent or progressive overweight-obese trajectories have limited score improvement and a higher risk of failure; primary school students with persistent or progressive overweight and obesity should be a key focus.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To investigate the association between brain and muscle Arnt like protein 1( BMAL 1) gene and ambulatory blood pressure (ABP) parameters in college students, and to explore the influence of the interaction between the gene and weight status on the ABP levels, so as to provide reference for early prevention of chronic cardiovascular diseases related to hypertension. Methods: During September 2022 to June 2024, a total of 510 college students were recruited from a university in Changsha, China for on site questionnaire investigation, physical examination, ABP monitoring and genotyping testing. Multiple linear regression method was used to explore the association between the BMAL 1 gene and ABP indicators. An interaction term was included in the general linear model of multiple linear regression to analyze the interactive effects of BMAL 1 gene polymorphisms and weight status on ABP levels. Results: The prevalence of abnormal 24 hour blood pressure, abnormal daytime blood pressure, and abnormal nighttime blood pressure were 3.3%, 3.1%, and 3.9%, respectively. Multiple linear regression analysis showed that the BMAL 1/rs7950226 polymorphism was positively correlated with the 24 hour diastolic blood pressure(DBP) and the daytime DBP level after adjusting for gender, age, and body mass index( β =0.87,0.99, both P <0.05). Also, interaction between BMAL 1/ rs 11022775 and weight status on the nighttime DBP level ( P interaction =0.03) was found. The CC genotype carriers had significantly higher nighttime DBP level ( β =3.52, P <0.05) among overweight/obesity college students, but no differences in nocturnal DBP levels were observed between CC genotype carriers and TT+CT genotype carriers among college students with normal body weight( P > 0.05). Conclusions The rs 7950226 polymorphism is associated with 24 hours DBP and daytime DBP levels. Significant interaction between rs 11022775 with weight status on the nighttime DBP level is found among college students.

Chemical constituents of n-butanol fraction of ethanol extract from the leaves of Cyclocarya paliurus (Batalin) Iljinskaja were studied. Ten compounds were purified by silica gel, MCI, ODS, Sephadex LH-20 column chromatography and semi-preparative high-performance liquid chromatography. Based on the physicochemical properties and spectroscopic data, these compounds were identified as (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(3′′,5′′-dimethoxy-(E)-p-coumaroyl)]-β-D-glucopyranoside (1), (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(E)-feruloyl]-β-D-glucopyranoside (2), (1S,3R,4S,5R)-4,5-di-O-caffeoyl quinic acid methyl ester (3), (1R,3R,4S,5R)-3,4-di-O-caffeoyl quinic acid methyl ester (4), (3R,5R)-3,4,5-tri-O-caffeoylquinic acid methyl ester (5), (1R,3R,4S,5R)-3-O-caffeoyl-5-O-p-coumaroyl quinic acid methyl ester (6), (1R,3R,4S,5R)-3-O-p-coumaroy-4-O-caffeoyl quinic acid methyl ester (7), (1R,3R,4S,5R)-3-O-caffeoyl-4-O-p-coumaroy quinic acid methyl ester (8), (3R,5R)-3,5-di-O-caffeoyl quinic acid methyl ester (9) and (1S,3R,4R,5R)-4-O-caffeoyl-5-O-feruloyl quinic acid methyl ester (10). Among them, compound 1 was a new compound, and compounds 3–10 were isolated from the genus Cyclocarya for the first time.

Objective To explore the clinical value of albumin-globulin ratio(AGR)combined with prostate-specific antigen(PSA)and Gleason score in predicting biochemical recurrence(BCR)after radical resection in patients with localized prostate cancer, and to provide reference for early identification of BCR high-risk patients and individualized treatment. Methods The clinical data of 114 patients with localized prostate cancer treated during Sep.2019 and Sep.2023 were retrospectively analyzed.According to postoperative BCR status, patients were divided into the BCR group(n=43)and non-BCR group(n=71).Preoperative clinical and pathological data were compared between the two groups.Univariate and multivariate Cox regression analyses were used to assess the correlation between these indicators and BCR.The predictive performance of the combined model was evaluated using receiver operating characteristic(ROC)curve.The Kaplan-Meier method was used to compare biochemical recurrence-free survival(BFS)among patients with different AGR, PSA levels and Gleason scores.Results Compared with the non-BCR group, the BCR group had significantly higher PSA level and ISUP grading, but significantly lower AGR level(P＜0.001).Multivariate Cox regression analyses showed that AGR was negatively correlated with BCR(HR=0.031, 95%CI:0.008-0.119, P＜0.001), PSA was positively correlated with BCR(HR=1.009, 95%CI:1.002-1.016, P=0.017), and Gleason score was positively correlated with BCR(HR=1.551, 95%CI:1.199-2.005, P＜0.001).The area under the ROC curve(AUC)for the combined prediction model(AGR + PSA + Gleason score)was 0.886, with a sensitivity of 90.7% and a specificity of 78.9%.Kaplan-Meier analyses showed that patients in the low AGR group, high PSA group, and high Gleason score group had significantly lower BFS than those in the high AGR group, low PSA group and low Gleason score group, respectively(all P＜0.0001).Conclusion The combination of AGR, PSA and Gleason score is closely associated with postoperative BCR in patients with localized prostate cancer.The combined prediction model demonstrates high predictive efficacy and can provide an important reference for the early identification of high-risk BCR patients and for guiding individualized postoperative treatment.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.