1.Toxicokinetics of Chlorfenapyr and Its Metabolites in Rats
Wen-Yan LI ; Jin-Feng ZHAO ; Wei-Chen LIU ; Shi-Jing LÜ ; Jia-Xin ZHANG ; Xu-Dong ZHANG ; Zhi-Wen WEI ; Ke-Ming YUN ; Chao ZHANG
Journal of Forensic Medicine 2025;41(4):380-386
Objective To establish a chromatography-tandem mass spectrometry method for detecting chlorfenapyr and its metabolite tralopyril in blood,and to investigate the toxicokinetics in rats.Methods Chlorfenapyr(8 mg/kg)was administered orally to rats,and blood samples were collected from rats'canthus vein at 5 min,15 min,30 min,1 h,3 h,6 h,12 h,24 h and 48 h after administration.The blood samples were extracted using 100 μL of 5%formic acid solution and 400 μL of acetonitrile.Chlorfena-pyr was qualitatively and quantitatively detected by triple quadrupole gas chromatography-tandem mass spectrometry(GC-MS/MS)and tralopyril was detected by triple quadrupole liquid chromatography-tandem mass spectrometry(LC-MS/MS).The DAS 3.0 software was used to fit the toxicokinetic equa-tions and calculate the toxicokinetic parameters.Results Chlorfenapyr was detectable from 5 min to 24 h with a peak time of 1 h.Tralopyril was detectable from 15 min to 48 h with a peak time of 3 h.The toxicokinetic process of chlorfenapyr in rat blood conformed to a first-order absorption one-compartment open model,with the toxicokinetic equation described as C=e-0.265t-e-0.175t.Tralopyril con-formed to the first-order absorption three-compartment model,and the toxicokinetic equation was C=47 361.069e-2.209t-35 404.962e-1.486t+11 956.363e-0.512t.In the equations,C stands for the concentration of the target substance in the blood,e is the natural constant(≈2.718 28),and t stands for time.Conclu-sion This study optimized the detection method for chlorfenapyr and its metabolite tralopyril in blood.The toxicokinetic equations and parameters of chlorfenapyr and tralopyril can provide a reference for the estimation of oral intake time of chlorfenapyr.
2.Cation Channel TMEM63A Autonomously Facilitates Oligodendrocyte Differentiation at an Early Stage.
Yue-Ying WANG ; Dan WU ; Yongkun ZHAN ; Fei LI ; Yan-Yu ZANG ; Xiao-Yu TENG ; Linlin ZHANG ; Gui-Fang DUAN ; He WANG ; Rong XU ; Guiquan CHEN ; Yun XU ; Jian-Jun YANG ; Yongguo YU ; Yun Stone SHI
Neuroscience Bulletin 2025;41(4):615-632
Accurate timing of myelination is crucial for the proper functioning of the central nervous system. Here, we identified a de novo heterozygous mutation in TMEM63A (c.1894G>A; p. Ala632Thr) in a 7-year-old boy exhibiting hypomyelination. A Ca2+ influx assay suggested that this is a loss-of-function mutation. To explore how TMEM63A deficiency causes hypomyelination, we generated Tmem63a knockout mice. Genetic deletion of TMEM63A resulted in hypomyelination at postnatal day 14 (P14) arising from impaired differentiation of oligodendrocyte precursor cells (OPCs). Notably, the myelin dysplasia was transient, returning to normal levels by P28. Primary cultures of Tmem63a-/- OPCs presented delayed differentiation. Lentivirus-based expression of TMEM63A but not TMEM63A_A632T rescued the differentiation of Tmem63a-/- OPCs in vitro and myelination in Tmem63a-/- mice. These data thus support the conclusion that the mutation in TMEM63A is the pathogenesis of the hypomyelination in the patient. Our study further demonstrated that TMEM63A-mediated Ca2+ influx plays critical roles in the early development of myelin and oligodendrocyte differentiation.
Animals
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Cell Differentiation/physiology*
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Oligodendroglia/metabolism*
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Mice, Knockout
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Mice
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Male
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Myelin Sheath/metabolism*
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Humans
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Child
;
Cells, Cultured
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Oligodendrocyte Precursor Cells/metabolism*
3.Association between Per and Polyfluoroalkyl Substance and Abdominal Fat Distribution: A Trait Spectrum Exposure Pattern and Structure-Based Investigation.
Zhi LI ; Shi Lin SHAN ; Chen Yang SONG ; Cheng Zhe TAO ; Hong QIAN ; Qin YUAN ; Yan ZHANG ; Qiao Qiao XU ; Yu Feng QIN ; Yun FAN ; Chun Cheng LU
Biomedical and Environmental Sciences 2025;38(1):3-14
OBJECTIVE:
To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles.
METHODS:
Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk.
RESULTS:
Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk.
CONCLUSION
PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans
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Fluorocarbons/blood*
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Female
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Adult
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Middle Aged
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Male
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Environmental Pollutants/blood*
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Abdominal Fat
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Nutrition Surveys
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Alkanesulfonic Acids/blood*
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Obesity
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Environmental Exposure
4.Effect of Health Failure Mode and Effect Analysis in Optimizing the Management Process of Postoperative Diabetes Insipidus in Children Undergoing Neurosurgery.
Hui-Yun ZHAO ; Xiao-Ying XU ; Bo WU ; Shi TANG ; Xin-Meng LI
Acta Academiae Medicinae Sinicae 2025;47(4):582-589
Objective To investigate the effect of health failure mode and effect analysis(HFMEA)in optimizing the management process of postoperative diabetes insipidus in children undergoing neurosurgery.Methods Based on HFMEA,a management flowchart for postoperative diabetes insipidus in children undergoing neurosurgery was created.Brainstorming was adopted to identify failure modes in the workflow,analyze risk factors,and develop improvement measures,thereby refining the management flowchart.The amelioration and prognosis of diabetes insipidus in these children before(October 2022 to November 2023)and after(January 2024 to February 2025)implementation of the management flowchart were compared.Results The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery alleviated the symptoms of diabetes insipidus regarding the number of diabetes insipidus in the pediatric intensive care unit(P=0.006),the average daily urine output in the pediatric intensive care unit(P=0.001),the proportion of electrolyte abnormalities at discharge/transfer(P=0.037),the duration of mechanical ventilation(P=0.007),and the length of stay in the intensive care unit(P=0.001).Conclusion The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery is beneficial to the optimization of the management process,the alleviation of postoperative diabetes insipidus,and the improvement of prognosis in these children.
Humans
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Diabetes Insipidus/etiology*
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Neurosurgical Procedures/adverse effects*
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Child
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Postoperative Complications/therapy*
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Healthcare Failure Mode and Effect Analysis
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Intensive Care Units, Pediatric
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Risk Factors
5.Clinical features and immunotherapy for children with loss-of-function/gain-of-function mutations in the STAT gene: an analysis of 10 cases.
Hong-Wei LI ; Yan-Hong WANG ; Shang-Zhi WU ; Bi-Yun ZHANG ; Shi-Hui XU ; Jia-Xing XU ; Zhan-Hang HUANG ; Cheng-Yu LU ; De-Hui CHEN
Chinese Journal of Contemporary Pediatrics 2025;27(8):951-958
OBJECTIVES:
To investigate the clinical features of children with STAT gene mutations, and to explore corresponding immunotherapy strategies.
METHODS:
A retrospective analysis was performed for the clinical data of 10 children with STAT gene mutations who were admitted to the Department of Pediatrics of the First Affiliated Hospital of Guangzhou Medical University, from October 2015 to October 2024. Exploratory immunotherapy was implemented in some refractory cases, and the changes in symptoms, imaging manifestations, and cytokine levels were assessed after treatment.
RESULTS:
For the 10 children, the main clinical manifestations were recurrent rash since birth (7/10), cough (8/10), wheezing (5/10), expectoration (4/10), and purulent nasal discharge (4/10). Genotyping results showed that there was one child with heterozygous loss-of-function (LOF) mutation in the STAT1 gene, four children with heterozygous LOF mutation in the STAT3 gene, and five children with heterozygous gain-of-function (GOF) mutation in the STAT3 gene. Two children with LOF mutation in the STAT3 gene showed decreased interleukin-6 levels and improved clinical symptoms and imaging findings after omalizumab treatment. Three children with GOF mutation in the STAT3 gene achieved effective disease control after treatment with methylprednisolone (0.5 mg/kg per day). Two children with GOF mutation in the STAT3 gene received treatment with JAK inhibitor and then showed some improvement in symptoms.
CONCLUSIONS
STAT gene mutation screening should be considered for children with recurrent rash and purulent respiratory tract infections. Targeted immunotherapy may improve prognosis in patients with no response to conventional treatment.
Humans
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Male
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Immunotherapy
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Female
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Child, Preschool
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Child
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Gain of Function Mutation
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Retrospective Studies
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Infant
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Loss of Function Mutation
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STAT Transcription Factors/genetics*
6.Impact of admission-blood-glucose-to-albumin ratio on all-cause mortality and renal prognosis in critical patients with coronary artery disease: insights from the MIMIC-IV database.
Yong HONG ; Bo-Wen ZHANG ; Jing SHI ; Ruo-Xin MIN ; Ding-Yu WANG ; Jiu-Xu KAN ; Yun-Long GAO ; Lin-Yue PENG ; Ming-Lu XU ; Ming-Ming WU ; Yue LI ; Li SHENG
Journal of Geriatric Cardiology 2025;22(6):563-577
BACKGROUND:
Blood glucose and serum albumin have been associated with cardiovascular disease prognosis, but the impact of admission-blood-glucose-to-albumin ratio (AAR) on adverse outcomes in critical ill coronary artery disease (CAD) patients was not investigated.
METHODS:
Patients diagnosed with CAD were non-consecutively selected from the MIMIC-IV database and categorized into quartiles based on their AAR. The primary outcome was 1-year mortality, and secondary endpoints were in-hospital mortality, acute kidney injury (AKI), and renal replacement therapy (RRT). A restricted cubic splines model and Cox proportional hazard models assessed the association between AAR and adverse outcomes in CAD patients. Kaplan-Meier survival analysis determined differences in endpoints across subgroups.
RESULTS:
A total of 8360 patients were included. There were 726 patients (8.7%) died in the hospital and 1944 patients (23%) died at 1 year. The incidence of AKI and RRT was 63% and 4.3%, respectively. High AAR was markedly associated with in-hospital mortality (HR = 1.587, P = 0.003), 1-year mortality (HR = 1.502, P < 0.001), AKI incidence (HR = 1.579, P < 0.001), and RRT (HR = 1.640, P < 0.016) in CAD patients in the completely adjusted Cox proportional hazard model. Kaplan-Meier survival analysis noted substantial differences in all endpoints based on AAR quartiles. Stratified analysis and interaction test demonstrated stable correlations between AAR and outcomes.
CONCLUSIONS
The results highlight that AAR may be a potential indicator for assessing in-hospital mortality, 1-year mortality, and adverse renal prognosis in critical CAD patients.
7.Construction and validation of a prognostic model of hepatocellular carcinoma based on immune-related genes
Dong-Dong CHEN ; Jin-Jin LOU ; Yan-Yan HUANG ; Lu ZHOU ; Shi-Bo LI ; Li-Yun XU
Acta Anatomica Sinica 2024;55(3):319-326,328
Objective To construct a prognostic model for liver hepatocellular carcinoma(LIHC)based on immune-related genes.Methods LIHC and normal tissue samples were downloaded from the UCSC Xena database and The Cancer Genome Atlas(TCGA)database.Differential analysis was performed on the gene data of LIHC samples and adjacent/normal samples.Enrichment analysis was conducted on differentially expressed genes.Kaplan-Meier survival analysis was performed on liver cancer samples from the TCGA cohort to obtain survival-and immune-related differentially expressed genes.LASSO Cox and multivariate Cox regression analysis were used to identify hub genes and construct a gene risk prognostic model.Data from a high-throughput gene expression(GEO)database was obtained for external validation.The sensitivity of hub genes to common anticancer drugs was investigated using the CellMiner database.Results Enrichment analysis result indicated that differentially expressed genes were mainly associated with metabolic pathways.Through differential analysis and Kaplan-Meier survival analysis,25 survival-and immune-related differentially expressed genes were obtained.Based on the result of LASSO Cox and multivariate Cox regression analysis,five hub genes(FYN,CSF3R,HLA-G,FOS,BIRC5)were identified and a nomogram was constructed.The concordance index(C-index)value for the training cohort and validation cohort were 0.739 and 0.625,respectively.Based on the sensitivity of hub genes to anticancer drugs,12 types of anticancer drugs were selected for subsequent experiments.Conclusion This model can effectively predict the prognosis of LIHC patients and provide a new insights for immune therapy in LIHC.
8.Different methods in predicting mortality of pediatric intensive care units sepsis in Southwest China
Rong LIU ; Zhicai YU ; Changxue XIAO ; Shufang XIAO ; Juan HE ; Yan SHI ; Yuanyuan HUA ; Jimin ZHOU ; Guoying ZHANG ; Tao WANG ; Jianyu JIANG ; Daoxue XIONG ; Yan CHEN ; Hongbo XU ; Hong YUN ; Hui SUN ; Tingting PAN ; Rui WANG ; Shuangmei ZHU ; Dong HUANG ; Yujiang LIU ; Yuhang HU ; Xinrui REN ; Mingfang SHI ; Sizun SONG ; Jumei LUO ; Juan LIU ; Juan ZHANG ; Feng XU
Chinese Journal of Pediatrics 2024;62(3):204-210
Objective:To investigate the value of systemic inflammatory response syndrome (SIRS), pediatric sequential organ failure assessment (pSOFA) and pediatric critical illness score (PCIS) in predicting mortality of pediatric sepsis in pediatric intensive care units (PICU) from Southwest China.Methods:This was a prospective multicenter observational study. A total of 447 children with sepsis admitted to 12 PICU in Southwest China from April 2022 to March 2023 were enrolled. Based on the prognosis, the patients were divided into survival group and non-survival group. The physiological parameters of SIRS, pSOFA and PCIS were recorded and scored within 24 h after PICU admission. The general clinical data and some laboratory results were recorded. The area under the curve (AUC) of the receiver operating characteristic curve was used to compare the predictive value of SIRS, pSOFA and PCIS in mortality of pediatric sepsis.Results:Amongst 447 children with sepsis, 260 patients were male and 187 patients were female, aged 2.5 (0.8, 7.0) years, 405 patients were in the survival group and 42 patients were in the non-survival group. 418 patients (93.5%) met the criteria of SIRS, and 440 patients (98.4%) met the criteria of pSOFA≥2. There was no significant difference in the number of items meeting the SIRS criteria between the survival group and the non-survival group (3(2, 4) vs. 3(3, 4) points, Z=1.30, P=0.192). The pSOFA score of the non-survival group was significantly higher than that of the survival group (9(6, 12) vs. 4(3, 7) points, Z=6.56, P<0.001), and the PCIS score was significantly lower than that of the survival group (72(68, 81) vs. 82(76, 88) points, Z=5.90, P<0.001). The predictive value of pSOFA (AUC=0.82) and PCIS (AUC=0.78) for sepsis mortality was significantly higher than that of SIRS (AUC=0.56) ( Z=6.59, 4.23, both P<0.001). There was no significant difference between pSOFA and PCIS ( Z=1.35, P=0.176). Platelet count, procalcitonin, lactic acid, albumin, creatinine, total bilirubin, activated partial thromboplastin time, prothrombin time and international normalized ratio were all able to predict mortality of sepsis to a certain degree (AUC=0.64, 0.68, 0.80, 0.64, 0.68, 0.60, 0.77, 0.75, 0.76, all P<0.05). Conclusion:Compared with SIRS, both pSOFA and PCIS had better predictive value in the mortality of pediatric sepsis in PICU.
9.Analysis of In Vitro Mirtazapine Metabolites in Human Liver Microsomes by LC-HRMS
Ying ZHANG ; Wen-Fang ZHANG ; Duo-Qi XU ; Shi-Yang QIN ; Shi-Yun YANG ; Jing QIAO
Journal of Forensic Medicine 2024;40(6):569-574
Objective To establish and optimize an in vitro incubation system with human liver micro-somes and investigate the in vitro metabolites and possible metabolic pathways of mirtazapine.Methods Three major metabolites of mirtazapine were selected to optimize the incubation conditions of liver mi-crosomes.The metabolites of mirtazapine were analyzed by liquid chromatography-high resolution mass spectrometry(LC-HRMS)to identify the in vitro metabolites and metabolic pathways of mir-tazapine.Results Ten metabolites,including nine phase Ⅰ metabolites and one phase Ⅱ metabolite,were identified in the in vitro liver microsome incubation.Among them,five new metabolites and one new metabolic pathway were discovered.The pathways involved in phase Ⅰ metabolic included methyla-tion,hydroxylation,oxidation,reduction,etc.,while the phase Ⅱ biotransformation was mainly glucuroni-dation.Conclusion The metabolites discovered in this study are consistent with the main metabolites of mirtazapine reported in literature,which are N-desmethylmetazapine,8-hydroxy mirtazapine and mirtazapine-N-oxide.The results can provide basis for the confirmation of mirtazapine cases and pro-vide reference for the study of other substances.
10.Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data.
Cong LI ; Xiao-Yan ZHANG ; Yun-Hong WU ; Xiao-Lei YANG ; Hua-Rong YU ; Hong-Bo JIN ; Ying-Bo LI ; Zhao-Hui ZHU ; Rui LIU ; Na LIU ; Yi XIE ; Lin-Li LYU ; Xin-Hong ZHU ; Hong TANG ; Hong-Fang LI ; Hong-Li LI ; Xiang-Jun ZENG ; Zai-Xing CHEN ; Xiao-Fang FAN ; Yan WANG ; Zhi-Juan WU ; Zun-Qiu WU ; Ya-Qun GUAN ; Ming-Ming XUE ; Bin LUO ; Ai-Mei WANG ; Xin-Wang YANG ; Ying YING ; Xiu-Hong YANG ; Xin-Zhong HUANG ; Ming-Fei LANG ; Shi-Min CHEN ; Huan-Huan ZHANG ; Zhong ZHANG ; Wu HUANG ; Guo-Biao XU ; Jia-Qi LIU ; Tao SONG ; Jing XIAO ; Yun-Long XIA ; You-Fei GUAN ; Liang ZHU
Acta Physiologica Sinica 2024;76(6):937-942
As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence*
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Humans
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Consensus
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Computer Security/standards*
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Confidentiality/ethics*
;
Informed Consent/ethics*

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