Objective The biomechanical performance of Ilizarov fixator models with different configurations for humeral shaft defect was compared,so as to provide a biomechanical basis for selecting the appropriate circular external fixation structure for the clinical treatment of humeral shaft defects using Ilizarov technology.Methods Based on CT data of the humerus from a healthy volunteer,the external fixators with three configurations,namely,hybrid frame,semi-ring frame and 90° fan frame were established.The finite element method was used to simulate the displacement and stress distribution under different loading conditions,and the finite element results were validated by biomechanical tests.Results Finite element analysis results:in terms of displacement,under compression,tensile and torque conditions,the displacement of 90° fan model was smaller than that of hybrid and semi-ring models.In terms of stress,the 90° fan model had the smallest displacement under tensile condition.In compression and torque tests,the semi-annular model had the lowest stress.Biomechanical test results:the semi-ring model exhibited the smallest displacement under axial compression,but there was no significant difference between the three models(P＞0.05).Conclusions The semi-ring and 90° fan frames can achieve a similar stability as the traditional hybrid frame through the strategy of'reducing the ring and increasing the stem'.The unilateral structure of the 90° fan frame has the advantages of small size,light weight,and structural stability,as well as a small impact on the shoulder and elbow joints,which makes it more valuable in clinical applications.

Objective The biomechanical performance of Ilizarov fixator models with different configurations for humeral shaft defect was compared,so as to provide a biomechanical basis for selecting the appropriate circular external fixation structure for the clinical treatment of humeral shaft defects using Ilizarov technology.Methods Based on CT data of the humerus from a healthy volunteer,the external fixators with three configurations,namely,hybrid frame,semi-ring frame and 90° fan frame were established.The finite element method was used to simulate the displacement and stress distribution under different loading conditions,and the finite element results were validated by biomechanical tests.Results Finite element analysis results:in terms of displacement,under compression,tensile and torque conditions,the displacement of 90° fan model was smaller than that of hybrid and semi-ring models.In terms of stress,the 90° fan model had the smallest displacement under tensile condition.In compression and torque tests,the semi-annular model had the lowest stress.Biomechanical test results:the semi-ring model exhibited the smallest displacement under axial compression,but there was no significant difference between the three models(P＞0.05).Conclusions The semi-ring and 90° fan frames can achieve a similar stability as the traditional hybrid frame through the strategy of'reducing the ring and increasing the stem'.The unilateral structure of the 90° fan frame has the advantages of small size,light weight,and structural stability,as well as a small impact on the shoulder and elbow joints,which makes it more valuable in clinical applications.

Objective:To study the risk factors and dosiomics-based prediction model of bone marrow suppression in patients with esophageal cancer during radiotherapy.Methods:Clinic data and radiotherapy planning documents of 107 patients with oesophageal cancer who underwent radiotherapy at the First Affiliated Hospital of Nanjing Medical University from January 2021 to May 2024 were retrospectively analyzed. Blood test results before and during radiotherapy were collected, and patients were classified into myelosuppressive groups (≤grade 1 and ≥grade 2). Clinical features, traditional dosimetric features and dosiomics features were collected, respectively. According to the stratified randomization grouping method, all patients were divided into the training and test sets in a 7 vs. 3 ratio. The region of interest was obtained by automatically outlining the thoracic skeleton (including the sternum, thoracic vertebrae and ribs) by AccuContour software. Dosiomics features were extracted from the dose distribution of the thoracic skeleton, and these features were screened using the independent samples t-test, the muse selector and the least absolute shrinkage operator. Subsequently, the dosiomic scores were calculated. Statistically significant clinical characteristics were screened using univariate and multivariate logistic regression analyses. Support vector machine method was used to construct a clinical model and a clinical combined with dosiomic model. Subsequently, nomogram was drawn for clinical prediction. The clinical efficacy and clinical benefit of predictive model were assessed by plotting the receiver operating characteristic (ROC) curve and evaluating its performance through the area under the ROC curve (AUC), the calibration curve and decision curve analysis (DCA). Results:Thirteen dosiomic features associated with bone marrow suppression were screened. Based on both univariate and multivariate logistic regression analyses, simultaneous chemotherapy, V 35 Gy and the average dose to bone were identified as statistically significant clinical predictors (all P<0.05). The AUC values of the combined model in the training and test sets were 0.800 and 0.776, higher than 0.709 and 0.650 of the clinical model. The calibration curves showed good agreement between the predicted and actual probabilities of the combined model. The DCA results showed that the net clinical benefit of the combined model was higher than that of the clinical model. Conclusions:The combined dosiomics-based model is effective in improving the predictive performance of bone marrow suppression occurring after radiotherapy for esophageal cancer.

Objective:To study the risk factors and dosiomics-based prediction model of bone marrow suppression in patients with esophageal cancer during radiotherapy.Methods:Clinic data and radiotherapy planning documents of 107 patients with oesophageal cancer who underwent radiotherapy at the First Affiliated Hospital of Nanjing Medical University from January 2021 to May 2024 were retrospectively analyzed. Blood test results before and during radiotherapy were collected, and patients were classified into myelosuppressive groups (≤grade 1 and ≥grade 2). Clinical features, traditional dosimetric features and dosiomics features were collected, respectively. According to the stratified randomization grouping method, all patients were divided into the training and test sets in a 7 vs. 3 ratio. The region of interest was obtained by automatically outlining the thoracic skeleton (including the sternum, thoracic vertebrae and ribs) by AccuContour software. Dosiomics features were extracted from the dose distribution of the thoracic skeleton, and these features were screened using the independent samples t-test, the muse selector and the least absolute shrinkage operator. Subsequently, the dosiomic scores were calculated. Statistically significant clinical characteristics were screened using univariate and multivariate logistic regression analyses. Support vector machine method was used to construct a clinical model and a clinical combined with dosiomic model. Subsequently, nomogram was drawn for clinical prediction. The clinical efficacy and clinical benefit of predictive model were assessed by plotting the receiver operating characteristic (ROC) curve and evaluating its performance through the area under the ROC curve (AUC), the calibration curve and decision curve analysis (DCA). Results:Thirteen dosiomic features associated with bone marrow suppression were screened. Based on both univariate and multivariate logistic regression analyses, simultaneous chemotherapy, V 35 Gy and the average dose to bone were identified as statistically significant clinical predictors (all P<0.05). The AUC values of the combined model in the training and test sets were 0.800 and 0.776, higher than 0.709 and 0.650 of the clinical model. The calibration curves showed good agreement between the predicted and actual probabilities of the combined model. The DCA results showed that the net clinical benefit of the combined model was higher than that of the clinical model. Conclusions:The combined dosiomics-based model is effective in improving the predictive performance of bone marrow suppression occurring after radiotherapy for esophageal cancer.

Objective To understand the current situation of breastfeeding duration in children aged 0-5 years in Yunnan Province,and to explore the influencing factors of breastfeeding duration.Methods Using the data of the 6th National Health Service Survey in Yunnan Province,1582 children aged 0～5 years in Yunnan Province were selected as the research subjects,and the Kaplan-Meier method and Cox regression were used to analyze the influencing factors of breastfeeding duration.Results The mean duration of breastfeeding for children aged 0～5 years in Yunnan Province was 9.29 months,and region,time of complementary food addition,time of suckling and family income were the main factors influencing the duration of breastfeeding.Conclusion The duration of breastfeeding for children aged 0～5 years in Yunnan Province deviates significantly from the recommendations provided by both the World Health Organization(WHO)and China's child breastfeeding guidelines.Given the current situation,the relevant departments must enhance their focus on this issue.

Objective:To investigate the prevalence of dental caries of 4-year-old children in Miyun District of Beijing by international caries detection and assessment system(ICDAS),to detect the caries activity Cariostat value and to analyze the correlation between the Cariostat value and dental caries.Methods:Totally 815 children aged 4 years in 7 kindergartens in Miyun District of Beijing were recrui-ted.The clinical examination of all children was conducted by one examiner using ICDAS.The oral de-birs and plaques were collected by one doctor who recorded the Cariostat scores.The results of clinical examination were compared between genders.At the same time,the prevalence of dental caries,the mean d3-6ft/d3_6fs and d1-6ft/d1-6fs among high Cariostat scores group(2.0-3.0),medium Cariostat scores group(1.5)and low Cariostat scores group(0-1.0)were compared.The distributions of incipi-ent caries in different Cariostat scores groups were compared among children with incipient caries only.Results:All the children had incipient caries,and 78.3％of the children had cavitated caries with ICDAS score of 3 or above.The mean d1-2t scores were 9.76±3.65,the mean d3-6ft scores was 4.64±4.43 and the mean d1-6ft scores were 14.41±3.42.The incipient caries with ICDAS score of 1-2 were widely distributed,accounting for 67.7％of the total numbers of caries.There was no significant diffe-rence in caries prevalence and caries experience between genders(P＞0.05).The proportion of children with high Cariostat scores in boys(43.6％)was higher than that in girls(33％)and the difference was statistically significant(P＜0.05).With the increase of Cariostat scores,the prevalence of cavitated caries,the mean d3-6ft/d3-6fs and d1-6ft/d,_6fs scores in children was on the increase and the difference among the three groups was statistically significant(P＜0.05).For children with incipient caries only,the distribution of incipient caries in different Cariostat scores groups was no significant difference(P＞0.05).Conclusion:ICDAS can detect early enamel demineralization of deciduous teeth in children.The prevalence of dental caries among 815 4-year-old children in Miyun District of Beijing is more serious,and incipient caries is widely distributed in children.Cariostat value reflects the status of cavi-tated caries and has no correlation with the distribution of incipient caries.Therefore,the combined ap-plication of ICDAS and Cariostat caries activity detection method is helpful for the detection of incipient caries and screening of caries high-risk children,which has great significance for the comprehensive ma-nagement of caries in children and the formulation of early preventive measures.

Objective:To assess the predictive value of dosiomics in predicting the occurrence of bone marrow suppression (BMS) in patients with pelvic tumors during radiotherapy.Methods:A retrospective analysis was conducted on the clinical data and radiotherapy planning documents of 129 patients with pelvic region tumors who underwent radiotherapy at the First Affiliated Hospital of Nanjing Medical University from January 2019 to January 2023. The region of interest (ROI) was outlined for bone marrow in the pelvic region by Accu Contour software in planning CT, and the ROI was exported together with the dose distribution file. According to a stratified randomization grouping method, the patients were divided into the training set and test set in an 8 vs. 2 ratio. The dosiomic features were extracted from the ROI, and the two independent samples t-test and the least absolute shrinkage and selection operator (LASSO) algorithm was employed to identify the best predictive characteristics. Subsequently, the dosiomic scores were calculated. Clinical predictors were identified through both univariant and multivariate logistic regression analyses. Predictive models were constructed by using clinical predictors alone and combining clinical predictors and dosiomic scores. The efficacy of predictive model was assessed by plotting the receiver operating characteristic (ROC) curve and evaluating its performance through the area under the ROC curve (AUC), the calibration curve, and decision curve analysis (DCA). Results:Fourteen dosiomic features that showed a strong correlation with the occurrence of BMS were screened and utilized to calculate the dosiomic scores. Based on both univariant and multivariate logistic regression analyses, chemotherapy, planning target volume (PTV) and V 5 Gy were identified as clinical predictors. According to the combined model, the AUC values for the training set and test set were 0.911 and 0.868, surpassing those of the clinical model (AUC=0.878 and 0.824). Furthermore, the analysis of both the calibration curve and DCA suggested that the combined model had higher calibration and net clinical benefit. Conclusion:The combined model has a high diagnostic value for predicting BMS in patients with pelvic tumors during radiotherapy.

Aristolochic acid (AA), extracted from Aristolochiaceae plants, plays an essential role in traditional herbal medicines and is used for different diseases. However, AA has been found to be nephrotoxic and is known to cause aristolochic acid nephropathy (AAN).AA-induced acute kidney injury (AKI) is a syndrome in AAN with a high morbidity that manifests mitochondrial damage as a key part of its pathological progression. Melatonin primarily serves as a mitochondria-targeted antioxidant. However, its mitochondrial protective role in AA-induced AKI is barely reported. In this study, mice were administrated 2.5 mg/kg AA to induce AKI. Melatonin reduced the increase in Upro and Scr and attenuated the necrosis and atrophy of renal proximal tubules in mice exposed to AA. Melatonin suppressed ROS generation, MDA levels and iNOS expression and increased SOD activities in vivo and in vitro. Intriguingly, the in vivo study revealed that melatonin decreased mitochondrial fragmentation in renal proximal tubular cells and increased ATP levels in kidney tissues in response to AA. In vitro, melatonin restored the mitochondrial membrane potential (MMP) in NRK-52E and HK-2 cells and led to an elevation in ATP levels. Confocal immunofluorescence data showed that puncta containing Mito-tracker and GFP-LC3A/B were reduced, thereby impeding the mitophagy of tubular epithelial cells. Furthermore, melatonin decreased LC3A/B-II expression and increased p62 expression. The apoptosis of tubular epithelial cells induced by AA was decreased. Therefore, our findings revealed that melatonin could prevent AA-induced AKI by attenuating mitochondrial damage, which may provide a potential therapeutic method for renal AA toxicity.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To investigate the genomic manifestation and pathogenesis of osteosarcoma with different relapse pattens, which were respectively initially presented with bone metastasis or pulmonary metastasis.Methods:From May 1, 2021 to October 1, 2021, 38 fresh tumor specimens and some paraffin-embedded specimens of high-grade osteosarcoma were collected in Peking University People's Hospital, including 29 males and 9 females, aged 19.6±2.2 years (range, 6-61 years). Among the 38 cases, 12 cases had initial bone metastasis (group A) and 26 cases had initial lung metastasis (group B), of which 15 cases (40%, 15/38) had paired specimens of primary and metastatic lesions. Based on Illumina NovaSeq 6000, we analyzed whole-exome sequencing (WES) as well as transcriptome for osteosarcoma with paired samples in different relapse patterns. During all their treatment courses, we also collected their paired samples to reveal these tumors' evolution. We sought to redefine disease subclassifications for osteosarcoma based on genetic alterations and correlate these genetic profiles with clinical treatment courses to elucidate potential evolving cladograms.Results:We found that osteosarcoma in group A mainly carried single-nucleotide variations (83%, 10/12), displaying higher tumor mutation burden [4.9 (2.8, 12.0) & 2.4 (1.4, 4.5), P=0.010] and neoantigen load [743.0 (316.5, 1,034.5) & 128.5 (49.0, 200.5), P=0.003], while those in group B mainly exhibit structural variants (58%, 15/26). The mutation spectrum showed that there was a significant difference in age-related gene imprinting 1 between the bone metastasis group and the lung metastasis group ( P=0.005). Samples were randomly selected from group A (3 patients) to investigate immunologic landscape by multiplex immunohistochemistry, from which we noticed tertiary lymphatic structure from one patient from group A. High conservation of reported genetic sequencing over time was found in their evolving cladograms. Conclusion:Osteosarcoma with mainly single-nucleotide variations other than structural variants might exhibit biological behavior predisposing toward bone metastases with older in age as well as better immunogenicity in tumor microenvironment.