The precise and rapid monitoring of multiple organ dysfunction is crucial in drug discovery. Traditional methods, such as pathological analysis, are often time-consuming and inefficient. Here, we developed a multiplexed near-infrared window two (NIR-II) fluorescent bioimaging method that allows for real-time, rapid, and quantitative assessment of multiple organ dysfunctions. Given that existing probes did not fully meet requirements, we synthesized a range of NIR-II hemicyanine dyes (HDs) with varying absorption and emission wavelengths. By modifying these dyes, we achieved high spatial and temporal resolution imaging of the liver, kidneys, stomach, and intestines. This method was further applied to investigate disorders induced by cisplatin, a drug known to cause gastric emptying issues along with liver and kidney injuries. By monitoring the metabolic rate of the dyes in these organs, we accurately quantified multi-organ dysfunction, which was also confirmed by gold-standard pathological analysis. Additionally, we evaluated the effects of five aristolochic acids (AAs) on multiple organ dysfunction. For the first time, we identified that AA-I and AA-II could cause gastric emptying disorders, which was further validated through transcriptomics analysis. Our study introduces a novel approach for the simultaneous monitoring of multi-organ dysfunction, which may significantly enhance the evaluation of drug side effects.

To investigate the characteristics and differences of immune cell infiltration between different SDHx gene expression and pheochromocytoma/paraganglioma(PPGL).

Objective To systematically evaluate the risk factors for postoperative delirium after surgery for Stanford type A aortic dissection. Methods We searched the CNKI, SinoMed, Wanfang data, VIP, PubMed, Web of Science, EMbase, The Cochrane Library database from inception to September 2022. Case-control studies, and cohort studies on risk factors for postoperative delirium after surgery for Stanford type A aortic dissection were collected to identify studies about the risk factors for postoperative delirium after surgery for Stanford type A aortic dissection. Quality of the included studies was evaluated by the Newcastle-Ottawa scale (NOS). The meta-analysis was performed by RevMan 5.3 software and Stata 15.0 software. Results A total of 21 studies were included involving 3385 patients. The NOS score was 7-8 points. The results of meta-analysis showed that age (MD=2.58, 95%CI 1.44 to 3.72, P＜0.000 01), male (OR=1.33, 95%CI 1.12 to 1.59, P=0.001), drinking history (OR=1.45, 95%CI 1.04 to 2.04, P=0.03), diabetes history (OR=1.44, 95%CI 1.12 to 1.85, P=0.005), preoperative leukocytes (MD=1.17, 95%CI 0.57 to 1.77), P=0.000 1), operation time (MD=21.82, 95%CI 5.84 to 37.80, P=0.007), deep hypothermic circulatory arrest (DHCA) time (MD=3.02, 95%CI 1.04 to 5.01, P=0.003), aortic occlusion time (MD=8.94, 95%CI 2.91 to 14.97, P=0.004), cardiopulmonary bypass time (MD=13.92, 95%CI 5.92 to 21.91, P=0.0006), ICU stay (MD=2.77, 95%CI 1.55 to 3.99, P＜0.000 01), hospital stay (MD=3.46, 95%CI 2.03 to 4.89, P＜0.0001), APACHEⅡ score (MD=2.76, 95%CI 1.59 to 3.93, P＜0.000 01), ventilation support time (MD=6.10, 95%CI 3.48 to 8.72, P＜0.000 01), hypoxemia (OR=2.32, 95%CI 1.40 to 3.82, P=0.001), the minimum postoperative oxygenation index (MD=−79.52, 95%CI −125.80 to −33.24, P=0.000 8), blood oxygen saturation (MD=−3.50, 95%CI −4.49 to −2.51, P＜0.000 01), postoperative hemoglobin (MD=−6.35, 95%CI −9.21 to −3.50, P＜0.000 1), postoperative blood lactate (MD=0.45, 95%CI 0.15 to 0.75, P=0.004), postoperative electrolyte abnormalities (OR=5.94, 95%CI 3.50 to 10.09, P＜0.000 01), acute kidney injury (OR=1.92, 95%CI 1.34 to 2.75, P=0.000 4) and postoperative body temperature (MD=0.79, 95%CI 0.69 to 0.88, P＜0.000 01) were associated with postoperative delirium after surgery for Stanford type A aortic dissection. Conclusion The current evidence shows that age, male, drinking history, diabetes history, operation time, DHCA time, aortic occlusion time, cardiopulmonary bypass time, ICU stay, hospital stay, APACHEⅡ score, ventilation support time, hypoxemia and postoperative body temperature are risk factors for the postoperative delirium after surgery for Stanford type A aortic dissection. Oxygenation index, oxygen saturation, and hemoglobin number are protective factors for delirium after Stanford type A aortic dissection.

Objective To observe the effects of wheat grain moxibustion for"Huantiao"on sciatic nerve function,pathological morphology of sciatic nerve stem and expressions of TLR4/MyD88/NF-κB signaling pathway expression in spinal cord tissue of rats with sciatic nerve injury(SNI);To explore the possible mechanism of wheat grain moxibustion for the treatment of SNI.Methods Totally 24 SD male rats were randomly divided into blank group,sham-operation group,model group and wheat grain moxibustion group,with 6 rats in each group.The model group and the wheat grain moxibustion group used a rat model with sciatic nerve clamping injury.From the 7th day after modeling,the rats were treated with moxibustion on the affected side of"Huantiao"for 6 strokes each time,once a day,for consecutive 10 days.The sciatic nerve function index(SFI)of rats on the 7th day after modeling and after intervention were observed,mechanical withdraw threshold(MWT)in rats were measured using a fiber optic pain gauge,ELISA was used to detect NO and iNOS content in spinal cord tissue,HE staining was used to observe the morphology of sciatic nerve stem,the expression of TLR4,NF-κBp65,p-NF-κBp65,MyD88,IκBα and p-IκBα in spinal cord tissue were detected by Western blot.Results Compared with the sham-operation group,the SFI and MWT of the rats in the model group significantly decreased(P<0.01),the arrangement of nerve fibers in sciatic nerve stem was disordered,with a significant increase in the number of Schwann cells and a large number of vacuolar degeneration,the content of NO,iNOS and the expression of TLR4,p-NF-κBp65,MyD88,p-IκBα protein in spinal cord tissue significantly increased(P<0.01).Compared with the model group,the SFI and MWT of the rats in the wheat grain moxibustion group increased significantly(P<0.01),the damage of sciatic nerve stem was reduced,with orderly cell arrangement,a decrease in the number of Schwann cells,and a decrease in axonal demyelination and cellular vacuolar degeneration,the content of NO,iNOS and the expression of TLR4,p-NF-κBp65,MyD88,p-IκBα in spinal cord tissue significantly decreased(P<0.05).Conclusion Wheat grain moxibustion for"Huantiao"can down-regulate TLR4,p-NF-κBp65,MyD88 and p-IκBα protein expressions in spinal cord tissue of SNI rats,reduce the secretion of NO and iNOS,thereby relieve pain and damaged nerve tissue inflammation response.

Objective To systematically review the risk prediction models for postoperative pancreatic fistula(POPF)after pancreaticoduodenectomy(PD),and to provide a reference for the clinical screening and application of POPF-related risk models.Methods This study was conducted according to the PRISMA guidelines,with a PROSPERO registration number of CRD42023437672.PubMed,Scopus,Embase,Web of Science,the Cochrane Library,CNKI,VIP,Wanfang Data,China Medical Journal Full-text Database,and CBM were searched for studies on establishing risk prediction models for POPF after PD published up to April 26,2024.The PROBAST tool was used to assess the quality of articles,and RevMan 5.4 and MedCalc were used to perform the Meta-analysis.Results A total of 36 studies were included,involving 20 119 in total,and the incidence rate of POPF after PD was 7.4%—47.8%.A total of 55 risk prediction models were established in the 36 articles,with an area under the receiver operating characteristic curve(AUC)of 0.690-0.952,among which 52 models had an AUC of>0.7.The quality assessment of the articles showed high risk of bias and good applicability.MedCalc was used to perform a statistical analysis of AUC values,and the results showed a pooled AUC of 0.833(95%confidence interval:0.808-0.857).The Meta-analysis showed that body mass index,amylase in drainage fluid on the first day after surgery,preoperative serum albumin,pancreatic duct diameter,pancreatic texture,fat score,tumor location,blood loss,sex,time of operation,main pancreatic duct index,and pancreatic CT value were predictive factors for POPF(all P<0.05).Conclusion The risk prediction models for POPF after PD is still in the exploratory stage.There is a lack of calibration methods and internal validation for most prediction models,and only the univariate analysis is used to for the screening of variables,which leads to the high risk of bias.In the future,it is necessary to improve the methods for model establishment,so as to develop risk prediction models with a higher prediction accuracy.

Objective To investigate the efficacy and safety of retroperitoneal single-port laparoscopic (LESS) adrenal tumor resection.Methods The clinical data of 130 patients receiving multi-port and single-port laparoscopic adrenal tumor resection in this hospital from January 2015 to March 2023 were retrospec-tively analyzed.There were 50 cases in the LESS group and 80 cases in the traditional laparoscopic adrenal tumor resection (LA) group.The age,gender,tumor location,complicating underlying diseases and tumor size were collected in the two groups.The related perioperative indicators were compared between the two groups. Results The operation time[(95±52)min vs. (101±58)min],drainage tube indwelling time[(4.9±1.5)d vs. (6.7±1.0)d],postoperative incision satisfaction degree and pain situation had statistically significant differences between the LESS group and LA group (P<0.05).The intraoperative bleeding volume[(20.2±13.2)mL vs. (25.6±11.3)mL]and hospitalization duration[(5.9±1.3)d vs. (7.8±1.0)d]had no statisti-cal differences between the two groups (P>0.05).No intraoperative and postoperative complications oc-curred in the two groups.There was 1 case of conversion to open operation in the LA group.Conclusion The LESS adrenal tumor resection is safe and effective,and the incision is more beautiful compared with multi-port laparoscopic operation.

Objective·To develop a machine learning approach for early identification of metabolic syndromes associated with inflammatory metabolic state changes in breast cancer patients after neoadjuvant therapy,using common laboratory and transthoracic echocardiography indices.Methods·Female patients with primary invasive breast cancer diagnosed at the Department of Breast Surgery,Renji Hospital,Shanghai Jiao Tong University School of Medicine,between September 2020 and September 2022,were included.General patient information,laboratory test results,and transthoracic echocardiography data were collected.After feature extraction,five machine learning algorithms,including random forest(RF),gradient boosting(GB),support vector machine(SVM),K-nearest neighbor(KNN),and decision tree(DT),were applied to construct a prediction model for the changes of the patients' metabolic state after neoadjuvant therapy,and the prediction performances of the five models were compared.Results·A total of 232 cases with valid clinical data were included,comprising 135 cases before neoadjuvant therapy and 97 cases after completing 4 cycles of neoadjuvant therapy.Feature extraction identified five key features:white blood cell count,hemoglobin,high-density lipoprotein(HDL),interleukin-2 receptor,and interleukin-8.In the multi-feature analysis,the area under the receiver operating characferistic curve(AUC)was higher in the combination of white blood cell count,hemoglobin and HDL compared to the combination of interleukin-2 receptor and interleukin-8(RF:0.928 vs 0.772,GB:0.900 vs 0.792,SVM:0.941 vs 0.764,KNN:0.907 vs 0.762,DT:0.799 vs 0.714).The RF,SVM,and GB models showed higher AUC(0.928,0.941,0.900)and accuracy(0.914,0.897,0.776).The SVM model exhibited superior accuracy in the training data compared to the RF and GB models(P=0.394,0.122 and 0.097,respectively).Conclusion·The SVM model can be used to establish a prediction model for identifying breast cancer patients at high risk of developing inflammatory metabolic state-related metabolic syndrome after neoadjuvant therapy by incorporating five common clinical indicators,namely,white blood cell count,hemoglobin,high-density lipoprotein,interleukin-2 receptor,and interleukin-8.SVM modeling may be useful for clinicians to establish individualized screening protocols based on a patient's inflammatory metabolic state.

This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.
Male ; Humans ; Prostate/pathology* ; Prostate-Specific Antigen/analysis* ; Prostatic Neoplasms/diagnostic imaging* ; Biopsy ; Nomograms ; Retrospective Studies