This article provides a systematic interpretation and review of the Society of Critical Care Medicine 2024 Guidelines on Adult ICU Design. Developed based on the Grading of Recommendations Assessment, Development and Evaluation methodology, the guideline address five core themes: ICU layout, room design, infection control, infrastructure, and staff spaces, and put forward 17 evidence-based recommendations, including 5 good practice statements. This article briefly introduces the guideline development methods and evidence characteristics, and focuses on summarizing key recommendations, including high-visibility layouts, single-bed ward settings, natural lighting and noise reduction strategies, integrated infection prevention and control design, remote monitoring, and flexible capacity expansion capabilities. Furthermore, it delves into the applicability and localized implementation pathways within China's healthcare environment, analyzing limitations in terms of evidence quality, specialty applicability, and cost-effectiveness. Furthermore, by integrating the Chinese Guidelines for the Construction and Development of Critical Care Medicine (2025 Edition) and current domestic practices, the article proposes tiered and phased implementation recommendations. This article aims to provide domestic healthcare institutions with a scientific reference that balances international cutting-edge concepts with China's real-world conditions for the construction and renovation of ICUs, thereby promoting the development of intensive care environments centered on patient safety, healthcare worker well-being, and infection control.

Objective To introduce the calculation of the DISO index in multi-dimensional data ranking for disease surveillance, and evaluate its applicability. Methods Using the data from the quality supervision and inspection of infectious disease reporting in Jingzhou City in a certain year as an example, the DISO index was used to rank 16 investigated medical institutions and compared with the TOPSIS method. The consistency between the two evaluation results was analyzed using Spearman correlation. Then, The DISO index from different medical institutions was used to evaluate the ranking of 8 counties (cities, districts). The evaluation indicators for the supervision and inspection included six items: report rate, timely report rate, completeness of report card filling, accuracy of report card filling, consistency rate with network information report, and completeness of valid ID number filling. Results Among the 16 medical institutions, 8 institutions had all six evaluation indicators at 100%. Using the two methods to rank the 16 medical institutions, the two methods showed that the 8 medical institutions with all six evaluation indicators at 100% were ranked first. There was a strong correlation between the rankings of the 16 medical institutions using the two methods (r_s=0.992, P=0.000). The three counties (cities, districts) with all six evaluation indicators at 100% were all ranked first, while country G was ranked seventh and country E was ranked eighth. Conclusion The results of the DISO index and the TOPSIS method were similar in one-dimensional ranking. DISO can be used for multi-dimensional ranking of disease surveillance data and deserves to be promoted and applied.

With the rapid advancement of medical technology and the increasing complexity of clinical environments, competence in clinical ethical decision-making has become an indispensable core competency for exemplary physicians. However, traditional medical ethics education has long been confronted with the persistent challenges of theory-practice dissociation and the disconnect between pedagogical settings and clinical realities. Thus, how to construct a "Clinical Ethics" curriculum that aligns with the objectives of training high-caliber medical professionals has emerged as a critical and challenging issue in current medical education reform. To effectively address this challenge, the Office of Education at Peking University Third Hospital, since 2021, has assembled a multidisciplinary "Clinical Ethics" teaching and management team comprising faculty from Peking University School of Humanities and key clinical specialists from the hospital. In 2022, the "Clinical Ethics" elective course was formally launched for fourth-year students in the eight-year MD program who had completed coursework in "Medical Ethics" and organ-system-based curricula, with the aim of exploring and operationalizing a pre-clinical ethics education model tailored for senior medical students. The course employs a modular instructional design encompassing "theoretical review-case-based deliberation-clinical workshop", which effectively facilitates the deep integration of medical humanities with clinical practice.Following four years of iterative refinement through the "plan-act-observe-reflect" cycle, the course has demonstrated positive outcomes in enhancing students' ethical awareness and decision-making capabilities, and has preliminarily established a sustainable collaborative mechanism among the multidisciplinary faculty team. This article systematically delineates the course development background, pedagogical model and methodological innovations, and the multidisciplinary collaborative framework, alongside reflections and summaries based on implementation outcomes, with the intent of providing references for the future development of "Clinical Ethics" curricula.

Objective:To construct a risk prediction model of hyperuricemia (HUA) for community-dwelling residents.Methods:This cross-sectional study was conducted from March to November 2020. A total of 1 967 residents in the Nanzhai community of Taiyuan city were selected by stratified sampling method as study subjects, among whom 1 555 (80%) subjects served as the training set and the remaining 412 (20%) as the validation set. Blood uric acid was measured in all subjects and level>420 mmol/L was defined as HUA. The risk factors of HUA were determined with multivariate logistic regression analysis, and a risk prediction model was constructed. The Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the model.Results:Among the 1 555 residents in the training set, HUA was detected in 285 cases (18.3%). The detection rate in men was significantly higher than that in women [29.8% (220/739) vs. 8.0% (65/816), χ 2=123.17, P<0.05]. Compared to non-HUA group, the waist circumference and BMI, and the proportion of smoking, drinking, staying up late, and prevalence of hypertension and dyslipidemia were significant higher in HUA group. There was significant difference in the frequency of drinking tea, coffee, milk, and eating fruits, as well as the amount of beverages consumed between the two groups ( P<0.05). Multivariate logistic regression analysis showed that high BMI ( OR=1.132, 95 %CI:1.070-1.197, P<0.001), coffee consumption ( OR=1.337, 95 %CI:1.027-1.742, P=0.032), and dyslipidemia ( OR=1.479, 95 %CI:1.049-2.086, P=0.025) were risk factors of HUA, while female sex ( OR=0.213, 95 %CI:0.146-0.390, P<0.001) was protective factor of HUA. When all factors were included in the logistic regression model, gender, BMI, coffee consumption, beverage intake, sodium salt intake, sleep quality, and dyslipidemia ( OR=0.213, 1.113, 1.353, 0.788, 1.320, 0.788, 1.651) were important components of the HUA prediction model. By substituting the constant, these factors, and the regression coefficients into the logistic regression equation, the Logit(P) formula was obtained: Logit(P)=-1.530-1.547×gender+0.107×BMI+0.303×coffee consumption frequency-0.238×sodium salt intake+0.278×beverage intake-0.238×sleep quality+0.502×dyslipidemia. The ROC curve showed an area under the curve ( AUC) of 0.750. The Hosmer-Lemeshow goodness-of-fit test: P=0.632, indicating a satisfactory fit. When the formula was applied to the validation set for internal validation, the AUC was 0.745. Conclusion:The occurrence of HUA is influenced by multiple factors. The prediction model constructed in this study has good prediction performance,which may be used to predict the risk of HUA in primary care settings for community-dwelling residents.

Objective To develop and validate a deep learning-based segmentation model for low-dose cervical spine X-ray,aiming to address the insufficient segmentation accuracy in low-dose protocols while balancing radiation protection and diagnostic accuracy.Methods A total of 1 363 patients cervical spine X-ray images data were collected.A dose-attenuation mathematical simulation sys-tem was constructed to generate 14 122 dynamic low-dose cervical spine images incorporating quantum noise,contrast degradation,and blur artifacts.A neural network model was developed for automated segmentation of low-dose cervical spine X-ray using this dataset.Results Within the dose range of 50%to 7.5%,the average reults of automatic segmentation by the neural network model and manual segmentation for each group were as follows:50%dose group,intersection over union(IoU)=0.98 vs 0.93(P=0.707)and Dice coefficient(Dice)=0.99 vs 0.96(P=0.749);10%dose group,IoU=0.97 vs 0.87(P=0.201)and Dice=0.99 vs 0.93(P=0.219);7.5%dose group,IoU=0.97 vs 0.67(P<0.01)and Dice=0.98 vs 0.80(P<0.01).Conclusion The developed deep learning model achieved robust cervical spine segmentation(IoU>0.96,Dice>0.98)below diagnostic dose thresholds[peak signal-to-noise ratio(PSNR)<38 dB,structural similarity index(SSIM)<0.65].Under ultra-low-dose conditions(PSNR=27.710 dB,SSIM=0.274),it demonstrated a 44.78%IoU improvement and 22.5%Dice improvement over manual segmentation.This model enables minimal radia-tion exposure while preserving diagnostic performance,confirming its theoretical feasibility for low-dose X-ray image analysis and clinical research potential.

Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the"therapeutic module"of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

Objective:To evaluate the anti-reflux effect of digestive tract reconstruction using esophageal-tubular gastric asymmetric anastomosis after radical resection of esophageal and esophagogastric junction cancer.Methods:The main steps were as follows:(1)oblique incision of the lower esophagus;(2)curved incision of the tubular anterior gastric wall;(3)the lower end of the esophagus was anastomosed to the tubular gastric incision with a 90-degree torsion; (4)The anterior wall of the anastomosis was reinforced with a transverse-inverted suture,the posterior wall with a folded suture,and the corners of the gastric stump were buried with sutures.The anastomosis operation time,postoperative complications and postoperative hospital stay were recorded;the reconstructed structure and anti-reflux effect of the anastomosis were observed by digestive tract radiography,gastroscopy and follow-up investigation.Results:The Department of Gastrointestinal and Thoracic Surgery of Huaian First People's Hospital, affiliated to Nanjing Medical University, treated 5 patients of esophagogastric junction cancer and 20 esophageal cancer cases between August 2022 and November 2024, including 19 men and 6 women, with a mean age of (66.7±7.4) years. The mean anastomosis time was (35.4±5.9) minutes, the intraoperative blood loss was (117.6±33.4) ml and the mean postoperative hospital stay was(16.6±5.2) days, with no complications such as anastomotic leakage and bleeding. Postoperative digestive tract radiography (Trendelenburg position)showed that all the patients had no contrast reflux,gastroscopy showed no signs of reflux esophagitis and bile reflux gastritis, the anastomosis showed an inverted whiskers valve-like structure. The median follow-up time was (16.8±6.3) months, and all patients had no reflux symptoms such as acid reflux and belching,and no acid suppressive medication was needed.Conclusion:The esophageal-tubular gastric asymmetric anastomosis is a safe and effective antireflux reconstruction technique.

As the brain's resident immune cells, microglia perform crucial functions such as phagocytosis, neuronal network maintenance, and injury restoration by adopting various phenotypes. Dynamic imaging of these phenotypes is essential for accessing brain diseases and therapeutic responses. Although numerous probes are available for imaging pro-inflammatory microglia, no PET tracers have been developed specifically to visualize anti-inflammatory microglia. In this study, we present an ¹⁸F-labeled PET tracer (QTFT) that targets the P2Y₁₂, a receptor highly expressed on anti-inflammatory microglia. [¹⁸F]QTFT exhibited high binding affinity to the P2Y₁₂ (14.43 nmol/L) and superior blood-brain barrier permeability compared to other candidates. Micro-PET imaging in IL-4-induced neuroinflammation models showed higher [¹⁸F]QTFT uptake in lesions compared to the contralateral normal brain tissues. Importantly, this specific uptake could be blocked by QTFT or a P2Y₁₂ antagonist. Furthermore, [¹⁸F]QTFT visualized brain lesions in mouse models of epilepsy, glioma, and aging by targeting the aberrantly expressed P2Y₁₂ in anti-inflammatory microglia. In a pilot clinical study, [¹⁸F]QTFT successfully located epileptic foci, showing enhanced radioactive signals in a patient with epilepsy. Collectively, these studies suggest that [¹⁸F]QTFT could serve as a valuable diagnostic tool for imaging various brain disorders by targeting P2Y₁₂ overexpressed in anti-inflammatory microglia.

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.