Triatoma rubrofasciata is currently the most widely distributed species of Triatoma worldwide, and it is also widespread in southern China. T. rubrofasciata has been proven to transmit Trypanosoma cruzi, and is one of vectors transmitting Chagas disease, which poses a potential risk for transmission of imported Chagas disease in China. Findings from latest studies have shown that T. rubrofasciata naturally infects T. lewisi, T. conorhini, and T. rangeli, which undoubtedly increases significant risks of and challenges to trypanosomiasis control in China. This article briefly describes the species of T. rubrofasciata-transmitted trypanosomes, and summarizes the epidemiological characteristics of trypanosomiasis, so as to provide insights into T. rubrofasciata-transmitted trypanosomiasis surveillance and control, and prevention of trypanosomiasis development and transmission in China.

Pregnancy with chronic kidney disease (CKD), particularly in stages 4-5, carries high risks of adverse outcomes including maternal renal failure, preeclampsia/eclampsia, fetal growth restriction, and preterm birth. This report described a 26-year-old woman with congenital solitary kidney, polycystic ovaries, and uterine septum due to PAX2 gene variant, complicated by CKD stage 4. Through multidisciplinary team precision management and individualized treatment strategies, including timely initiation of dialysis, the patient successfully maintained pregnancy until 34 +1 weeks and delivered a female infant via cesarean section. This case summarizes key management experiences for end-stage renal disease in pregnancy, highlighting early risk assessment, precise nutritional management, hemodialysis protocol optimization, and the crucial role of multidisciplinary collaboration, providing valuable references for managing CKD-complicated pregnancies.

Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

Due to advances in treatment methods, the survival rate and quality of life of cancer patients have been improved. Radiation-induced vascular injury (RIVI) is a common adverse reaction following radiotherapy, mainly manifested as capillary injury and atherosclerosis in the irradiated area. Radiotherapy induces RIVI in the cerebral vessels, carotid arteries, coronary arteries, and large arteries through mechanisms such as endothelial cell injury and senescence, oxidative stress and inflammatory responses, angiogenesis, and vascular remodeling. In this review research progress in the pathological features, pathophysiological mechanisms, clinical manifestations, prevention and treatment strategies of RIVI was summarized, aiming to provide insights for future research on RIVI.

Objective To investigate the correlation of serum levels of soluble programmed death ligand 1(sPD-L1),tumor necrosis factor-associated apoptosis-inducing ligand(TRAIL)and fibroblast growth factor 23(FGF-23)with risk stratification and death in patients with acute pulmonary embolism(APE).Methods A total of 113 pa-tients with APE admitted to the hospital from January 2022 to January 2024 were selected as APE group,and 50 healthy subjects were selected as control group.The 113 patients with APE were divided into high risk group(39 cases),medium risk group(45 cases)and low risk group(29 cases)by risk stratification.Accord-ing to the death of APE patients,they were divided into survival group(83 cases)and death group(30 cases).The levels of serum sPD-L1,TRAIL and FGF-23 were measured by enzyme-linked immunosorbent assay(ELISA).Multiple Logistic regression was used to analyze the risk factors affecting death of APE patients.Receiver operating characteristic(ROC)curve was drawn to analyze the value of serum sPD-L1,TRAIL and FGF-23 levels in evaluating mortality in APE patients.Pearson correlation analysis was used to investigate the correlation between serum sPD-L1,TRAIL and FGF-23 levels and cardiac function indicators in APE patients.Results The levels of serum sPD-L1,TRAILand FGF-23 in the APE group were significantly higher than those in the control group(P＜0.001).The serum sPD-L1,TRAIL and FGF-23 levels in the death group were significantly higher than those in the survival group(P＜0.001).The levels of serum sPD-L1,TRAIL and FGF-23 in the high-risk group were higher than those in the medium and low-risk groups,and the medium risk group was higher than the low-risk group,and the differences were statistically significant(P＜0.001).Multiple Logistic regression analysis showed that elevated levels of cardiac troponin Ⅰ(cTnⅠ),B type brain na-triuretic peptide(BNP),sPD-L1,TRAIL and FGF-23 were risk factors for mortality in APE patients(P＜0.05).ROC curve analysis showed that the combination of sPD-L1,TRAIL and FGF-23 had the largest area under curve for predicting death in APE patients,which was 0.924(95％CI:0.861-0.986),with a sensitivity of 96.8％and a specificity of 81.2％.Correlation analysis showed that the levels of serum sPD-L1,TRAIL and FGF-23 were positively correlated with cTnⅠ and BNP in APE patients(P＜0.001).Conclusion The lev-els of serum sPD-L1,TRAIL and FGF-23 are significantly elevated in APE patients,and their high expression is associated with high-risk stratification and mortality.The combination of these three indexes has good eval-uation value for APE patient mortality.

Objective This study aims to investigate controversial cases of forensic clinical re-identification of fractures,exploring the characteristics,causes,and countermeasures related to identification errors in primary bone injuries,complications,and subsequent changes.The goal is to provide identification strategies for similar cases regarding the collection of identification materials,timing,and examination method selection,ultimately establishing a paradigm for such identifications.Methods A total of 103 cases of clinical re-identification of fractures accepted by the West China Forensic Identification Center from 2020 to 2024 were collected,and the data from initial identifications and re-identifications were retrospectively analyzed.Results Male cases accounted for 69.90%of the re-identifications,with disability grade(67.96%)and injury degree(30.10%)being the primary concerns.Individual requests represented a high proportion(92.86%)in the initial assessment of disability levels,while unit or joint requests dominated the re-assessment(92.86%).The agreement rates for disability grade and injury degree were 55.26%and 59.38%,respectively.The reassessment of disability grades primarily involved fractures of limb long bones,spine,and ribs,with 75.53%of opinions resulting in downgraded disability levels.Rib,orbital,and nasal bone fractures were the main focus in injury degree reassessments,with 84.62%of opinions indicating aggravated injuries.The consistency rates for fracture identification in disability grade assessments was 92.21%,while rates for injury degree and sequelae were 65.63%and 48.94%,respectively.Inconsistencies in identifying damage facts—including the presence of fractures,distinguishing between fresh and old fractures,and determining the nature of fractures and sequelae—were primarily noted in rib,orbital,and nasal bone fractures.The utilization rate of CT metadata in initial evaluations(25.00%)was significantly lower than in re-evaluations(95.00%).The identification time for joint mobility dysfunction after fracture in re-identifications was significantly longer than in initial identifications(P=0.0002),and the identification time for cases with agreement was significantly shorter than for cases with disagreement(P=0.036).Conclusion Image data type and identification timing are critical factors that may influence the accuracy and consistency of forensic clinical identification of bone injuries.When necessary,dynamic CT metadata in conjunction with image post-processing technology can be routinely employed to identify fractures of the ribs,orbital bones,or nasal bones,thereby reducing the risk of misidentification.

Objective This study aims to investigate controversial cases of forensic clinical re-identification of fractures,exploring the characteristics,causes,and countermeasures related to identification errors in primary bone injuries,complications,and subsequent changes.The goal is to provide identification strategies for similar cases regarding the collection of identification materials,timing,and examination method selection,ultimately establishing a paradigm for such identifications.Methods A total of 103 cases of clinical re-identification of fractures accepted by the West China Forensic Identification Center from 2020 to 2024 were collected,and the data from initial identifications and re-identifications were retrospectively analyzed.Results Male cases accounted for 69.90%of the re-identifications,with disability grade(67.96%)and injury degree(30.10%)being the primary concerns.Individual requests represented a high proportion(92.86%)in the initial assessment of disability levels,while unit or joint requests dominated the re-assessment(92.86%).The agreement rates for disability grade and injury degree were 55.26%and 59.38%,respectively.The reassessment of disability grades primarily involved fractures of limb long bones,spine,and ribs,with 75.53%of opinions resulting in downgraded disability levels.Rib,orbital,and nasal bone fractures were the main focus in injury degree reassessments,with 84.62%of opinions indicating aggravated injuries.The consistency rates for fracture identification in disability grade assessments was 92.21%,while rates for injury degree and sequelae were 65.63%and 48.94%,respectively.Inconsistencies in identifying damage facts—including the presence of fractures,distinguishing between fresh and old fractures,and determining the nature of fractures and sequelae—were primarily noted in rib,orbital,and nasal bone fractures.The utilization rate of CT metadata in initial evaluations(25.00%)was significantly lower than in re-evaluations(95.00%).The identification time for joint mobility dysfunction after fracture in re-identifications was significantly longer than in initial identifications(P=0.0002),and the identification time for cases with agreement was significantly shorter than for cases with disagreement(P=0.036).Conclusion Image data type and identification timing are critical factors that may influence the accuracy and consistency of forensic clinical identification of bone injuries.When necessary,dynamic CT metadata in conjunction with image post-processing technology can be routinely employed to identify fractures of the ribs,orbital bones,or nasal bones,thereby reducing the risk of misidentification.

Pregnancy with chronic kidney disease (CKD), particularly in stages 4-5, carries high risks of adverse outcomes including maternal renal failure, preeclampsia/eclampsia, fetal growth restriction, and preterm birth. This report described a 26-year-old woman with congenital solitary kidney, polycystic ovaries, and uterine septum due to PAX2 gene variant, complicated by CKD stage 4. Through multidisciplinary team precision management and individualized treatment strategies, including timely initiation of dialysis, the patient successfully maintained pregnancy until 34 +1 weeks and delivered a female infant via cesarean section. This case summarizes key management experiences for end-stage renal disease in pregnancy, highlighting early risk assessment, precise nutritional management, hemodialysis protocol optimization, and the crucial role of multidisciplinary collaboration, providing valuable references for managing CKD-complicated pregnancies.

Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

Due to advances in treatment methods, the survival rate and quality of life of cancer patients have been improved. Radiation-induced vascular injury (RIVI) is a common adverse reaction following radiotherapy, mainly manifested as capillary injury and atherosclerosis in the irradiated area. Radiotherapy induces RIVI in the cerebral vessels, carotid arteries, coronary arteries, and large arteries through mechanisms such as endothelial cell injury and senescence, oxidative stress and inflammatory responses, angiogenesis, and vascular remodeling. In this review research progress in the pathological features, pathophysiological mechanisms, clinical manifestations, prevention and treatment strategies of RIVI was summarized, aiming to provide insights for future research on RIVI.