The Internet of Bodies （IoB） refers to an embodied technology that treats the physical body as a network interface and embeds technological objects into the human body， aiming to collect massive body data. As an emerging technology， the widespread application of IoB in the field of smart healthcare will bring both advantages and disadvantages. It poses ethical risks in terms of physicality， psychology， and sociability， primarily manifested in the uncertainty of technology that may harm patients’ bodies， the easy restriction of patients’ autonomy by external technologies， and issues of fairness and equality caused by the technology gap. Faced with the realistic ethical dilemmas arising from IoB technology empowering smart healthcare， solutions were proposed across several levels. On the ethical mechanism level， an interdisciplinary and multi-field expert alliance should be established to promote the optimization of ethical governance mechanisms. In terms of ethical governance methods， a governance strategy prioritizing “pre-control” should be adopted to front-load ethical risks. On the practical application level， it was vital to clarify the reasonable application boundaries of technology in practice and integrate ethical morality into technology application behavior. Regarding social justice in technology resources， a dual approach of strengthening grassroots investment and educational guidance should be implemented to ensure ethical justice and accessibility in medical practice， thereby guiding the development of IoB technology to align with the fundamental principles of bioethics.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective:To analyze and explore the possible mechanism of anti-tumor metastasis of Notoginseng Radix et Rhizoma using Internet pharmacology. Methods:The active components and targets of Notoginseng Radix et Rhizoma were screened by retrieving Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP). GeneCards database was used to screen the anti-tumor metastasis-related targets, and compounds and disease targets were under mapping analysis. Key targets of Notoginseng Radix et Rhizoma for anti-tumor metastasis were screened through Venn map. With the help of Cytoscape 3.7.2 software, a compound-disease network diagram was constructed. String platform was used to build a PPI network. Bioconductor was used to enrich the target genes for KEGG signaling pathway and GO biological process analysis. Results:Totally 119 active components were selected from Notoginseng Radix et Rhizoma. There were 8 eligible active components, corresponding to 162 related targets, 121 targets related to anti-tumor metastasis, and 30 key targets screened by PPI network, including AKT1, MAPK1, JUN, RELA, IL6, etc. GO enrichment analysis mainly involved biological processes such as cytokine receptor binding, heme binding, RNA polymerase Ⅱ transcription factor binding, ubiquitin protein ligase binding, and steroid hormone receptor activity. 149 signal pathways related to Notoginseng Radix et Rhizoma anti-tumor metastasis were obtained by KEGG enrichment analysis, mainly involving multiple signal pathways, such as AGE-RAGE and PI3K-Akt, and hepatitis B, Kaposi's sarcoma-associated herpes virus infection, human cytomegalovirus infection and other viral infections and various tumors. Conclusion:Notoginseng Radix et Rhizoma can pass multiple active components, such as ginsenoside f2, ginsenoside rh2 β-, sitosterol, stigmasterol and quercetin, and multiple targets, such as AKT1, MAPK1, JUN, RELA and IL6, acting on multiple pathways such as PI3K-Akt, thereby playing the role of anti-tumor metastasis.

Objective:To compare the short-term efficacy between our self-designed intelligent robot-assisted minimally invasive reduction system and conventional freehand reduction assisted by fluoroscopy in the treatment of unstable pelvic fractures by robot or fluoroscopy-assisted internal fixation with percutaneous screws.Methods:A prospective randomized controlled trial was conducted to include eligible 35 patients with unstable pelvic fracture who were admitted to Department of Orthopaedic Trauma, Beijing Jishuitan Hospital from December 2021 to October 2022. They were randomized into 2 groups. The observation group[17 cases, 10 males and 7 females with an age of (44.0±17.4) years] was treated with robot-assisted minimally invasive reduction, followed by robot-assisted or fluoroscopic internal fixation with percutaneous screws; the control group[18 cases, 12 males and 6 females with an age of (38.8±15.0) years] was treated with freehand reduction assisted by fluoroscopy, followed by robot-assisted or fluoroscopic internal fixation with percutaneous screws. The 2 groups were compared in terms of operation time, intraoperative bleeding, successful reduction, reduction quality, incidence of surgical complications and postoperative functional scores.Results:The 2 groups were comparable because there were no significant differences in the preoperative general data between them ( P>0.05). The intraoperative fluoroscopy frequency[(32.4±17.5) times] and fluoroscopy time [(19.8±10.4) s] in the observation group were significantly lower or shorter than those in the control group [(60.8±26.6) times and (38.2±16.1) s], and the rate of successful reduction in the observation group was 100.0% (17/17), significantly higher than that in the control group[72.2% (13/18)] ( P<0.05). There was no significant difference between the 2 groups in intraoperative bleeding, operation time, reduction error, excellent and good rate of reduction after operation by Matta scoring, or Majeed functional score at 12 weeks after operation ( P>0.05). Conclusion:In the treatment of unstable pelvic fractures, since our self-designed intelligent robot-assisted minimally invasive reduction system can plan autonomously the reduction paths and accomplish minimally invasive reduction of the fracture with 3D images real-time monitoring, it is advantageous over conventional reduction methods in a higher success rate and less radiation exposure.

In addition to the typical respiratory symptoms, COVID-19 patients present with clinical manifestations of cardiovascular system damage, and they are at higher risk of intensive care or mortality. The mechanism of COVID-19's impact on the cardiovascular system is still unclear, therefore we need to pay close attention to it. Based on the existing research, this paper focused on the concurrent characteristics of the cardiovascular system diseases, summarized the possible mechanisms of cardiovascular system damage, including the coronavirus invaders directly into cardiomyocytes and its conduction system, with complications or accompanying conditions such as cytokine storm, imbalance of oxygen supply and demand, drug influence, stress and other damage to cardiovascular system. Meanwhile, current clinical manifestations and treatment methods of COVID-19 were summarized.

Objective:To evaluate a self-designed intelligent robot-assisted minimally invasive reduction system in the reduction of unstable pelvic fractures by a cadaveric anatomic study.Methods:Ten unembalmed cadavers (7 male and 3 female ones) were used in this study. In each cadaveric specimen an unstable pelvic fracture was created in accordance with clinical case models (3 cases of type B1, 4 cases of type B2 and 3 cases of type C1 by the Tile classification). A self-designed intelligent robot-assisted minimally invasive reduction system was used to assist the reduction in the cadaveric models. Intraoperative registration and navigation time, autonomous reduction time, total operation time and reduction error were measured.Results:Effective reduction was completed in 10 bone models with the assistance of our self-designed intelligent robot-assisted minimally invasive reduction system. The time for intraoperative registration and navigation averaged 47.4 min (from 32 to 74 min), the autonomous reduction time 73.9 min (from 48 to 96 min), and the total operation time 121.3 min (from 83 to 170 min). The reduction error averaged 2.02 mm (from 1.67 to 2.62 mm), and the reduction results met the clinical requirements.Conclusion:Our self-designed intelligent robot-assisted minimally invasive reduction system is a new clinical solution for unstable pelvic fractures, showing advantages of agreement with clinical operative procedures, high reduction accuracy and operational feasibility, and reduced radiation exposure compared to a conventional operation.

OBJECTIVE: By introducing the Medical Device Reporting (MDR) system and related inspection practice of the US, this paper puts forward some suggestions on implementing reporting responsibility of manufactures in China. METHODS: The MDR system and the related inspection system in the US were systematically analyzed. RESULTS: The US had established a sound system for discovering and reporting MDR, and a mechanism for inspecting the implementing of manufactures, forming an effective post-market surveillance system. CONCLUSIONS By learning from the experience of the US, we can carry out the post-market surveillance of medical devices adverse events in China from the aspects of implementing the existing system, strengthening the reporting ability and perfecting the inspection mechanism.
China ; Equipment and Supplies/standards* ; Product Surveillance, Postmarketing

OBJECTIVETo analyze mutations of SLC26A4 gene and explore their origins for a patient with enlarge vestibuar aqueduct syndrome.

METHODSClinical data and peripheral venous blood samples were collected from the patient and her parents. Genome DNA was extracted from the peripheral blood. All of the 21 exons of the SLC26A4 gene were amplified with PCR and subjected to directly sequencing.

RESULTSThe patient was found to have carried two mutant alleles of the SLC26A4 gene, namely c.1522A to G and c.1229C to T, which were inherited from her father and mother, respectively.

CONCLUSIONSLC26A4 c.1522A to G is likely to be a pathogenic mutation. Above results may facilitate genetic counseling and prenatal diagnosis for this family.

Adult ; Amino Acid Sequence ; Child ; Exons ; Female ; Hearing Loss, Sensorineural ; genetics ; Humans ; Male ; Membrane Transport Proteins ; genetics ; Molecular Sequence Data ; Pedigree ; Vestibular Aqueduct ; abnormalities