Objective: To explore the application effectiveness of healthcare failure mode and effect analysis (HFMEA) in optimizing intelligent blood logistics transport pathways for safety assurance. Methods: Data from 1 851 cases of intelligent blood logistics transport were collected between September 2023 and March 2025. Based on the implementation phases of HFMEA measures, the cases were divided into a control group (n=120), observation group 1 (n=219), and observation group 2 (n=1 512). Through systematic analysis of the transport processes, hazard scoring and decision tree analysis were conducted for each process, and phased optimization measures were implemented for high-risk failure modes. Results: The transport duration of intelligent blood logistics was 35.5 (20.8, 71.1) min in the control group, 25.1 (10.9, 40.7) min in observation group 1, and 9.9 (4.2, 44.5) min in observation group 2. Observation group 2 exhibited significantly shorter transport time compared to both observation group 1 and the control group, with statistically significant differences between groups (P<0.000 1). Conclusion: The implementation of HFMEA-driven measures significantly reduced intelligent blood logistics transport duration, thereby fostering the evolution of smart hospital ecosystems while enhancing healthcare service quality and operational efficiency.

Background There is currently limited research on the antibiotic resistance of Legionella pneumophila in China. Establishing analytical methods for antibiotic resistance phenotypes and resistance genes of Legionella pneumophila in artificial water environment is of great significance. Objective To investigate the antibiotic resistance characteristics of Legionella pneumophila isolated from artificial water environment in Zhongshan, and to provide a scientific basis for the prevention and control of Legionella infection. Methods A total of 295 strains of Legionella pneumophila isolated from central air conditioning systems and hot water systems in Zhongshan from 2012 to 2024 were collected. The minimum inhibitory concentrations (MIC) of antimicrobial agents against the strains were determined using the E-test method. Whole genome sequencing combined with bioinformatics techniques was used to analyze the antibiotic resistance genes of 16 strains of Legionella pneumophila. The phylogenetic evolutionary relationships between these strains and clonal strains from different sources worldwide were analyzed based on whole genome single nucleotide polymorphisms (wgSNPs). Results The antimicrobial susceptibility testing results showed that the highest resistance rate of Legionella pneumophila was to erythromycin (ERY) (56.9%), followed by azithromycin (AZM) (27.1%) and levofloxacin (LEV) (12.2%). The antibiotic resistance rates to rifampicin (RIF) and cefotaxime (CTX) were low, and the strains were fully susceptible to clarithromycin (CLA), ciprofloxacin (CIP), and doxycycline (DOX). Compared to the isolates from the hot water systems, those from the central air conditioning systems exhibited higher resistance rates to ERY (70.9% vs. 28.1%) and AZM (34.2% vs. 12.5%), and the differences were statistically significant (χ²=48.226, P<0.001; χ²=15.388, P<0.001). The MIC values of Lp1 isolates for four antibiotics (AZM, CLA, DOX, and CTX) were significantly higher than those of Lp2-14 isolates (Z=−3.490, P <0.001; Z=−3.035, P <0.05; Z=−2.662, P<0.05; Z=−2.973, P <0.05). The MIC values of Lp2−14 isolates for RIF were significantly higher than those of Lp1 isolates (Z=−3.190, P<0.05). The strains isolated from 2019–2024 exhibited higher MIC values for both CLA and DOX than those isolated from 2012–2018 (Z=−4.847, P <0.001; Z=−6.057, P <0.001). Whole genome sequencing analysis showed that the 16 strains of Legionella pneumophila carried antibiotic resistance genes for aminoglycosides, macrolides, cephalosporins, and penicillins. The phylogenetic analysis showed that Legionella pneumophila in this region exhibited high genetic diversity and was consistent with the epidemic trends of strains from other regions worldwide. Conclusion The isolates of Legionella pneumophila in this study are highly susceptible to most antimicrobial agents. However, the antibiotic resistance should not be ignored, and continuous monitoring of antibiotic resistance is necessary.

Objective To investigate the epidemiological characteristics and spatial distribution characteristics of Clonorchis sinensis human infections in Guangdong Province from 2016 to 2022, so as to provide insights into formulation of the clonorchiasis control measures in the province. Methods Xinhui District of Jiangmen City, Longmen County of Huizhou City and Wengyuan County of Shaoguan City in Guangdong Province were selected as fixed surveillance sites for human clonorchiasis from 2016 to 2022, and additional 10% to 15% counties (districts) endemic for clonorchiasis were sampled from Guangdong Province as mobile surveillance sites each year from 2016 to 2022. A village (community) was randomly selected from each surveillance site according to the geographical orientations of east, west, south, north and middle, and subjects were randomly sampled from each village (community). C. sinensis eggs were detected in subjects’ stool samples using the Kato-Katz technique, and the prevalence and intensity of C. sinensis infections were calculated. In addition, subjects’ gender, age, ethnicity, educational level and occupation were collected. The Guangdong Provincial 1:1 million electronic map in vector format was downloaded from the National Geomatics Center of China, and kernel density analysis and spatial autocorrelation analysis of C. sinensis human infections in Guangdong Province from 2016 to 2022 were performed using the software ArcGIS 10.7. Results A total of 153 188 residents were tested for C. sinensis infections in Guangdong Province from 2016 to 2022, including 75 596 men (49.35%) and 77 592 women (50.65%), and there were 5 369 residents infected with C. sinensis, with 3.50% overall prevalence of infections. The prevalence rates of severe, moderate and mild C. sinensis infections were 0.76%, 7.26% and 91.97% among C. sinensis-infected residents in Guangdong Province from 2016 to 2022, and there were age-, gender-, ethnicity-, occupation- and educational level-specific prevalence of C. sinensis human infections (χ² = 2 578.31, 637.33, 52.22, 2 893.28 and 1 139.33, all P values < 0.05). Global spatial autocorrelation analysis showed a cluster in the prevalence of C. sinensis human infections in Guangdong Province (Moran’s I = 0.63, Z = 27.31, P < 0.05). Kernel density analysis showed that the prevalence of C. sinensis human infections with a high kernel density in Guangdong Province was mainly distributed along the Zhujiang River basin in Pearl River Delta areas, followed by in eastern and northern Guangdong Province. In addition, local spatial autocorrelation analysis identified 73 high-high clusters of the prevalence of C. sinensis human infections in Guangdong Province. Conclusions The prevalence of C. sinensis human infections was high in Guangdong Province from 2016 to 2022, and mild infection was predominant among all clonorchiasis cases, with spatial clusters identified in the prevalence of C. sinensis human infections. Targeted clonorchiasis control measures are required among high-risk populations and areas.

Objective:To investigate the clinical, pathological and immunophenotypic features, and differential diagnosis of angioimmunoblastic T-cell lymphoma (AITL) with B-cell proliferation or neoplasms.Methods:Eight qualified cases were collected from the Department of Pathology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China from January 2019 to July 2023. One case was diagnosed with AITL and diffuse large B-cell lymphoma (DLBCL) and the other seven cases were diagnosed with AITL and B-cell proliferation. Clinical characteristics and pathological morphology were summarized. Immunohistochemical analysis, fluorescence in situ hybridization and gene rearrangement detection were performed.Results:The patients′ average age was 58 years. Five of them were male. Biopsies of the enlarged cervical lymph nodes showed structural destruction and exhibited various histologic patterns. Some cases revealed Burkitt-like morphology, a moderate tumor volume and slightly irregular nuclei. Some cases showed prominent nucleoli. High endothelial venules and expanded follicular dendritic cells were detected. Tumor cells derived from T-follicular helper (TFH) cells were positive for two or more TFH biomarkers. Nodular or diffuse patchy proliferation of B cells was noted around the tumor tissue, which was initially considered as B-cell lymphoma. All of the 8 cases showed monoclonal rearrangements of the T-cell receptor genes while 5 of them also showed clonal rearrangements of the Ig genes. Seven of the 8 cases were subject to the detection of C-MYC gene breakage and were all negative. EBV-positive cells were seen in 6 cases. Neoplastic B cells were positive for C-MYC (>40%), while proliferative B cells were negative for C-MYC (<40%).Conclusions:The histological morphology of AITL with B-cell proliferation or lymphoma may be different from AITL. An integrated analysis, incorporating clinical, morphologic, immunophenotypic, and molecular assessment, helps reach an accurate diagnosis. This group of cases demonstrated the clinical and pathological characteristics of AITL accompanied by B-cell proliferation and B-cell lymphoma. The findings suggest that C-MYC maybe a feasible indicator for distinguishing B-cell proliferation from B-cell lymphoma, and provide a simple and feasible immunohistochemical marker for the diagnosis and research of composite lymphoma.

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.

Peptide‒drug conjugates (PDCs) are drug delivery systems consisting of a drug covalently coupled to a multifunctional peptide via a cleavable linker. As an emerging prodrug strategy, PDCs not only preserve the function and bioactivity of the peptides but also release the drugs responsively with the cleavable property of the linkers. Given the ability to significantly improve the circulation stability and targeting of drugs in vivo and reduce the toxic side effects of drugs, PDCs have already been extensively applied in drug delivery. Herein, we review the types and mechanisms of peptides, linkers and drugs used to construct PDCs, and summarize the clinical applications and challenges of PDC drugs.

Objective:To analyze the risk factors which may lead to tracheostomy in patients receiving invasive mechanical ventilation (IMV) in emergency intensive care unit (EICU).Methods:A case-control study was adopted to retrospectively analyze the clinical data of patients hospitalized in EICU receiving IMV from August 2016 to August 2019. The clinical data of patients were extracted through the electronic medical record system of the hospital information database. Patients were divided into the tracheostomy group and successful extubation group according to whether they received tracheostomy during hospitalization. The different clinical characteristics of the two groups were compared, and logistic regression was used to analyze the independent risk factors of tracheostomy.Results:A total of 109 patients were included in this study, among which, 53 patients underwent tracheotomy and 56 patients were successfully extubated. Logistic regression showed that GCS score ≤ 8 ( OR=5.10, 95% CI: 1.68-15.42, P < 0.01), cervical spinal cord injury ( OR=10.32, 95% CI: 2.74-38.82, P < 0.01), and sepsis ( OR=3.45, 95% CI: 1.39-8.54, P<0.01) were independent risk factors of tracheostomy for patients receiving IMV in EICU. Conclusions:If patients receiving IMV have GCS score ≤ 8, cervical spinal cord injury, or sepsis, they should be given more attention, because they may need early tracheostomy to save lives and improve the prognosis.

Objective:To analyze the relationship between preoperative deep vein thrombosis (DVT) and parameters of conventional coagulation tests (CCTs) and thromboelastography (TEG) in patients with acute trauma, and to establish a prediction model to screen out high-risk patients with preoperative DVT.Methods:The clinical data of patients with acute traumatic fracture admitted to the Emergency Intensive Care Unit of Shanghai Sixth People’s Hospital from January 2021 to June 2021 were retrospectively collected. According to whether preoperative DVT occurred, the patients were divided into the DVT group and non-DVT group. The differences of CCTs and TEG parameters at the same time were compared between the two groups. Logistic regression analysis was applied to identify independent risk factors for DVT after trauma. The receiver-operating characteristic curve analysis was employed to analyze the clinical value of those parameters to predict preoperative DVT.Results:Among 123 patients with acute traumatic fracture, 101 patients were treated with anticoagulation before operation, and 51 patients were diagnosed with DVT. There were significant differences in activated partial thromboplastin time, fibrinogen (Fib), D-Dimer, fibrinogen degradation products (FDPs), reaction time, clotting time, α angle, maximum amplitude (MA) and coagulation index between the DVT and non-DVT groups. D-Dimer and MA were independent risk factors for preoperative DVT in patients with traumatic fracture. Logistic regression equation was used to establish a prediction model: the predicting index = 0.101×D-Dimer +0.241×MA + (-18.190). The α angle (AUC=0.833, P<0.001), MA (AUC=0.904, P<0.001), coagulation index (AUC=0.914, P<0.001) of TEG versus Fib (AUC=0.684, P=0.001), D-Dimer (AUC=0.685, P<0.001) and FDPs (AUC=0.656, P=0.003) of CCTs had a higher diagnostic efficacy in developing DVT of lower extremity in patients with traumatic fracture, and of all the coagulation index was the best. However, the predictor of D-Dimer combined with MA had a better predictive value (AUC=0.926, P<0.001), and the best cut-off value was 0.32 with a sensitivity and specificity of 90.2% and 79.8%, respectively. Conclusions:Compared with CCTs, TEG has more advantages in predicting preoperative DVT in patients with traumatic fracture, and the predictor of D-Dimer combined with MA can screen out patients with high risk of DVT, which can be recommended for clinical application.

Objective To evaluate the rupture risk of carotid atherosclerotic plaque under cervical rotatory manipulation. Methods The fluid-structure interaction (FSI) model of carotid atherosclerotic plaque was established, and tensile deformation of the plaque and lumen under cervical rotatory manipulation was simulated.Mechanical parameters such as the maximum flow shear stress(FSS), the maximum wall shear stress (WSS), the maximum plaque wall stress (PWS), wall tensile stress (WTS) and wall pressure (WP) of the plaque and lumen were recorded. Results Under 16% carotid tensile deformation, the maximum WSS of the plaque was 40.54 Pa. The maximum PWS was 66.16 kPa, which was far smaller than the threshold of plaque rupture.The maximum WTS of fiber cap and the maximum strain were 156.75 kPa and 0.56, which were larger than the fracture strain range. The maximum WTS of the lumen was 1 040.30 kPa, which approached the threshold of medial membrane rupture and might cause vascular injury. Conclusions When the cervical spine rotates to the end range of motion, large carotid artery stretch may cause damage to epidermal tissues of the plaque, leading to abscission. Lesions, ulcers, bleeding and vascular damage may form inside the plaque, which will affect stability of the plaque. Cervical rotatory manipulation should be performed cautiously in patients with cervical diseases who also have carotid atherosclerotic plaques.The finite element assessment of plaques before manipulation may be an effective safety screening method.