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.

Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.

An essential step for cancer vaccination is to break the immunosuppression and elicit a tumor-specific immunity. A major hurdle against cancer therapeutic vaccination is the insufficient immune stimulation of the cancer vaccines and lack of a safe and efficient adjuvant for human use. We discovered a novel cancer immunostimulant, trichosanthin (TCS), that is a clinically used protein drug in China, and developed a well-adaptable protein-engineering method for making recombinant protein vaccines by fusion of an antigenic peptide, TCS, and a cell-penetrating peptide (CPP), termed an "all-in-one" vaccine, for transcutaneous cancer immunization. The TCS adjuvant effect on antigen presentation was investigated and the antitumor immunity of the vaccines was investigated using the different tumor models. The vaccines were prepared