Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is the primary cause of mortality in sepsis, with its core pathophysiological mechanism being severe ischemia and hypoxia in critical units—composed of microcirculation and the mitochondria of functional cells—resulting from disruptions in blood flow and oxygen flow following a dysregulated host response. Due to the systemically convergent yet clinically heterogeneous nature of the host response, current understanding and management strategies for hemodynamics remain inconsistent, often leading to inadequate resuscitation or overtreatment. To improve the quality of care, based on a systematic review of the "blood flow-oxygen flow" theory, an expert panel emphasizes reevaluating septic shock from an integrated perspective of blood flow and oxygen flow, and has formulated the Expert Consensus on Blood Flow and Oxygen Delivery Phenotyping and Clinical Management of Septic Shock (2025). The consensus proposes that clinical typing of blood flow-oxygen flow should comprehensively consider cardiac function, vascular tone, oxygen flow utilization status in critical units, and disease trajectory, while optimizing subtype identification by integrating host response phenotypes and artificial intelligence technologies. It advocates establishing a continuous assessment system through multi-site oxygen flow monitoring for organ perfusion, peripheral perfusion monitoring, and critical care ultrasound. Under the framework of the "critical care triangle", the consensus promotes the implementation of individualized, bundled management strategies, providing guidance for multiple management points to restore blood flow-oxygen flow matching, reduce the risk of organ failure, and decrease patient mortality.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

Objective:To present a set of clinically representative quality assurance (QA) test cases for stereotactic radiosurgery (SRT) plans of multiple intracranial metastases, in order to assess the plan quality and machine execution capabilities.Methods:Based on the clinical characteristics of multiple brain metastases, four groups of test cases with three target volumes (TVs), six TVs, nine TVs, and TVs near organs at risk (OARs) were designed. For these cases, SRT plans were developed, and plan quality was assessed using metrics including the Radiation Therapy Oncology Group conformality index (RTOG CI), gradient index (GI), homogeneity index (HI), and the volume of normal brain tissue receiving a dose of 24 Gy ( V24 Gy), which was defined as the volume enclosed by the 24 Gy isodose line around the Brain-PTV ( V24 Gy of Brain-PTV). Verification plans were generated for each test case, including the verification of point doses, planar doses (PD), and SRS MapCHECK (SMC) semiconductor matrix planar doses. Compared with the calculated result of the treatment planning system (TPS), the criteria for the γ analysis of planar doses were set at 1 mm/2% and 2 mm/2%. Results:For the four groups of test cases, the mean CI, GI, HI, and V24 Gy of Brain-PTV were 1.04±0.03, 3.79±0.40, 0.73±0.01 and (7.46±3.80) cm 3, respectively. The mean deviations of the point doses were 0.88%±0.98%, 1.47%±0.79%, 1.52%± 0.76%, and 1.17% ± 0.38%, respectively. The mean γ passing rates of the single fields for PDs were greater than 98% at 2 mm/2% and exceeding 96% at 1 mm/2%, and the mean γ pass rates of the SMC semiconductor matrix for PDs were 97.75% ± 2.31% and 99.33% ± 0.62%, at 1 mm/2% and 2 mm/2% respectively. Conclusions:The proposed QA test cases for SRT of multiple intracranial metastases allow for the effective assessments of the plan quality and machine execution capabilities and, thus, can assist various centers in clinical applications.

Objective:To present a set of clinically representative quality assurance (QA) test cases for stereotactic radiosurgery (SRT) plans of multiple intracranial metastases, in order to assess the plan quality and machine execution capabilities.Methods:Based on the clinical characteristics of multiple brain metastases, four groups of test cases with three target volumes (TVs), six TVs, nine TVs, and TVs near organs at risk (OARs) were designed. For these cases, SRT plans were developed, and plan quality was assessed using metrics including the Radiation Therapy Oncology Group conformality index (RTOG CI), gradient index (GI), homogeneity index (HI), and the volume of normal brain tissue receiving a dose of 24 Gy ( V24 Gy), which was defined as the volume enclosed by the 24 Gy isodose line around the Brain-PTV ( V24 Gy of Brain-PTV). Verification plans were generated for each test case, including the verification of point doses, planar doses (PD), and SRS MapCHECK (SMC) semiconductor matrix planar doses. Compared with the calculated result of the treatment planning system (TPS), the criteria for the γ analysis of planar doses were set at 1 mm/2% and 2 mm/2%. Results:For the four groups of test cases, the mean CI, GI, HI, and V24 Gy of Brain-PTV were 1.04±0.03, 3.79±0.40, 0.73±0.01 and (7.46±3.80) cm 3, respectively. The mean deviations of the point doses were 0.88%±0.98%, 1.47%±0.79%, 1.52%± 0.76%, and 1.17% ± 0.38%, respectively. The mean γ passing rates of the single fields for PDs were greater than 98% at 2 mm/2% and exceeding 96% at 1 mm/2%, and the mean γ pass rates of the SMC semiconductor matrix for PDs were 97.75% ± 2.31% and 99.33% ± 0.62%, at 1 mm/2% and 2 mm/2% respectively. Conclusions:The proposed QA test cases for SRT of multiple intracranial metastases allow for the effective assessments of the plan quality and machine execution capabilities and, thus, can assist various centers in clinical applications.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

Objective:The purpose of this study is to test and assess the model of modeling data of TaiChi accelerator in the Raystation Treatment Planning System(RayStation system)according to the test method and item of TG119 report of American Association Physicians Medicine(AAPM).Methods:The intensity-modulated radiation therapy(IMRT)and volumetric-modulated arc therapy(VMAT)plans of the test cases of different clinical situations,which included the simulated multi target region,prostate target region,head and neck target region,easy type C-shape target region plan and difficult type C-shape target region plan,were designed according to the AAPM TG119 report in the treatment planning system.The deviations of the doses of point and area of the two kinds of plans were measured,and the measured results were compared and analyzed with the recommended standards of AAPM TG119 report.The IBA CC13 ionization chamber and the ArcCHECK matrix ionization chamber were used respectively to verify the point dose and area dose,and the assessment standard was γ passing rate under 3%3mm.The confidence interval was adopted to judge the consistency between the measured dose and the calculated dose.Results:The accuracies of plan dose target,point dose deviation and area dose distribution of tested cases could meet the requirement of the TGl19 report.The deviations of mean doses for the high-dose points of IMRT plan and VMAT plan of tested cases were respectively(0.39±1.02)%and(1.27±0.64)%,and the confidence intervals of them were respectively 2.39%and 2.52%.The average dose deviations of low doses of organ at risk(OAR)of IMRT plan and VMAT plan were respectively(0.53±1.73)%and(0.88±1.11)%,and the confidence intervals were respectively 3.92%and 3.06%.The average γ passing rate under 3%/3mm of IMRT plan and VMAT plan were respectively(99.52±0.366)%and(99.86±0.136)%,and the confidence intervals of them were respectively 1.196%and 0.406%.Conclusion:The TaiChi accelerator performance and the accuracy of Raystation system 6MV FFF model fitting can meet the standard of TG119 report,and the subsequent standards of the quality control of equipment and patients were established according to these tested results,which would provide reference for the improvement of the performance of subsequent accelerator.

Ulcerative colitis is a chronic,non-specific inflammatory disease.The persistent damage to its intestinal epithelium is key to the development of the disease.In recent years,a new form of cell death has been identified by researchers-iron death-which is thought to be an important contributor to intestinal epithelial cell death.The occurrence of iron death is often associated with abnormal intracellular iron metabolism,reduced cystine/glutamate reverse transporter activity,abnormal lipid metabolism,voltage-dependent anion channel activation and overexpression of the Nrf2 gene.Iron death can lead to smaller mitochondria,increased membrane density and reduced number of cristae,unlike conventional cell death,which does not exhibit specific phenomena.Studies have found that TCM can alleviate iron death in intestinal epithelial cells by reducing intracellular iron content,inhibiting lipid reactive oxygen species production and regulating Nrf2 gene expression,thus acting as a treatment for ulcerative colitis.Therefore,Chinese medicine may become an important tool for the treatment of ulcerative colitis.This paper reviews the relationship between cellular iron death and ulcerative colitis and the research progress of Chinese medicine in treating ulcerative colitis through the iron death pathway.

Objective To explore the value of multimodal MRI in evaluating the efficacy of retinoblastoma(RB)after selective ophthalmic artery infusion(SOAI)in children.Methods The MRI and clinical data of 80 children with intraocular RB with monocu-lar disease after chemotherapy were collected.The changes of MRI parameters in children with successful and failed eye protection before and after chemotherapy was analyzed.Results After chemotherapy,the maximum tumor diameter and △ SI were significantly reduced,the distance between tumor and optic disc and apparent diffusion coefficient(ADC)were significantly increased,the calcifi-cation/tumor ratio was increased,and the change of eyeball size was not obvious in the children with successful eye protection com-pared with before treatment.After chemotherapy,the maximum tumor diameter and the range of retinal detachment were significantly increased,and the cross-sectional area of the eyeball was significantly reduced,and △ SI,ADC and calcification/tumor ratio were not significantly changed compared with before treatment in the children with failed eye protection.Conclusion Multimodal MRI can accu-rately evaluate the effectiveness of chemotherapy in children and provide conclusive evidence for the formulation of subsequent treat-ment plans.

In recent years, with the rapid development of Chinese domestic surgical robot technology and the expansion of the application market, the "industry-university-research-medicine" collaborative innovation transformation mode has gradually developed and formed. Medical institutions play an important role in multi-party cooperation with enterprises, universities, and research institutes, as well as in product planning, technology research and development, achievement transformation, and personnel training. On the basis of reviewing the current situation of the development of the "industry-university-research-medicine" collaborative innovation transformation mode of domestic surgical robots, this study explores the multiple roles played by medical institutions in this mode and challenges, further putting forward corresponding recommendations.
Humans ; Robotics ; Universities ; Medicine ; Industry ; Technology

Objective:To evaluate the clinical application of online adaptive radiotherapy based on iterative cone-beam computed tomography (iCBCT) for the pelvic malignancies.Methods:This was a prospective clinical trial of iCBCT guided online adaptive radiotherapy for pelvic malignancies in Department of Radiation Oncology, Peking Union Medical College Hospital. Clinical data of 13 patients with pelvic malignancies who received online adaptive radiotherapy from August to November, 2022 were preliminarily analyzed (2 cases of cervical cancer, 4 postoperative cervical cancer, 3 postoperative endometrial cancer, 3 bladder cancer and 1 prostate cancer). The feasibility of online adaptive radiotherapy, adaptive radiotherapy time, the frequency and magnitude of edits for organs at risk and target volume, target volume coverage and organs at risk doses were analyzed. Statistical analysis was performed by SPSS software. Data conforming to normal distribution were described by Mean±SD, and data with non-normal distribution were expressed by M ( Q1, Q3). Data with homogeneous variances were analyzed by t-test, and data with non-normal distribution or heterogeneous variances were analyzed by nonparametric test. Results:The average adaptive time was 15 min and 38 s (from acceptance of acquired CBCT scan to completion of the final plan selection). 85.4% (830/972 fractions) of influencer structures (system-defined organs adjacent to and with high impact on the generation of clinical target volume and planning target volume, primarily bladder, rectum and small intestine in pelvic neoplasms) automatically generated by artificial intelligence required no edits or minor editors, and 89.8% (491/547 fractions) of clinical target volume automatically generated by artificial intelligence required no edits or minor editors. The adapted plan was adopted in 98.5% (319/324 fractions) of radiotherapy fractions. Compared with the scheduled plan, the adapted plan showed better target volume coverage and reduced the dose of organs at risk.Conclusions:iCBCT guided online adaptive radiotherapy for the pelvic malignancies can be achieved within clinically acceptable timeslots. In addtion, better dose coverage of target volume shows the advantages of online adaptive radiotherapy.