The patient was a boy aged 1 year and 9 months who presented with 46,XY disorder of sex development (DSD), with severe undermasculinization of the external genitalia. Laboratory tests and ultrasound examinations showed normal functions of Leydig cells and Sertoli cells in the testes. Genetic testing revealed a novel pathogenic heterozygous variant, c.1186dupA (p.T396Nfs*17), in the PPP1R12A gene. Thirteen cases of PPP1R12A gene variants have been reported previously. These variants may cause isolated involvement of the genitourinary or neurological systems, or affect other systems/organs including the digestive tract, eyes, heart, etc. Patients with DSD typically present with a 46,XY karyotype and variable degrees of undermasculinization involving the external genitalia, gonads, and reproductive tract. This article reports a child with 46,XY DSD accompanied by growth retardation caused by a heterozygous variant in the PPP1R12A gene, which expands the clinical disease spectrum associated with PPP1R12A gene variants.
Humans ; Male ; Infant ; Disorder of Sex Development, 46,XY/etiology* ; Protein Phosphatase 1/genetics*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To analyze the cellular and histopathological characteristics of underdiagnosed thyroid nodules of Chinese thyroid imaging reporting and data system(C-TIRADS) categories 3 and 4a,thus improving the understanding of these lesions. Methods The data of ultrasound and fine needle aspiration cytology were collected from 683 nodules diagnosed based on pathological evidence in 549 patients undergoing thyroid surgery.The cellular and histopathological characteristics of C-TIRADS 3 and 4a nodules were analyzed. Results Two hundred and sixty-eight nodules were classified as C-TIRADS category 3,including 236 benign nodules,12 low-risk ones,and 20 (7.46%) malignant ones.Two hundred and twenty-one nodules were classified as C-TIRADS category 4a,including 133 benign nodules,7 low-risk ones,and 81 (36.65%) malignant ones.The malignancy rates differed between C-TIRADS 3 and 4a nodules (χ²=58.93,P<0.001),and both were higher than the recommended malignancy rate in the guidelines for malignancy risk stratification of thyroid nodules (C-TIRADS) (both P<0.001).According to the pathological evidence,the underdiagnosed C-TIRADS 3/4a nodules were mainly papillary thyroid carcinoma,especially in patients with Hashimoto thyroiditis.There was not a consistent one-to-one match between each ultrasound result and each cytological classification of low-risk thyroid nodules.Conclusions When the malignant features in preoprative ultrasound imaging are atypical or absent,papillary thyroid carcinoma (especially with Hashimoto thyroiditis),follicular carcinoma,and medullary carcinoma are likely to be underdiagnosed as C-TIRADS 3 or 4a nodules.Therefore,efforts should be made to fully understand the cellular and pathological characteristics of these lesions.
Humans ; Thyroid Nodule/diagnostic imaging* ; Female ; Male ; Middle Aged ; Adult ; Ultrasonography ; Biopsy, Fine-Needle ; Aged ; Young Adult ; Thyroid Neoplasms/diagnostic imaging* ; Adolescent

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the clinical manifestations,endoscopic findings,histopathological changes,treatment,and prognosis of eosinophilic gastrointestinal disease(EGID)in children,with the aim of enhancing awareness among pediatricians about this condition.Methods Data of 267 children with EGID were prospectively collected from January 2019 to July 2022 at Jiangxi Children's Hospital,Hunan Children's Hospital,and Henan Children's Hospital.The age of onset,symptoms,physical signs,laboratory examination results,endoscopic findings,histopathological changes,and treatment outcomes were observed.Results Among the 267 children with EGID,the majority had mild(164 cases,61.4% )or moderate(96 cases,35.6% )clinical severity.The disease occurred at any age,with a higher prevalence observed in school-age children(178 cases).The main symptoms in infants were vomiting and hematemesis,while in toddlers,vomiting and bloody stools were prominent.Abdominal pain and vomiting were the primary symptoms in preschool and school-age children.Nearly half(49.4% )of the affected children showed elevated platelet counts on hematological examination,but there was no significant difference in platelet counts among children with mild,moderate,and severe EGID(P>0.05).Endoscopic findings in EGID children did not reveal significant specificity,and histopathological examination showed no specific structural damage.Among them,85.0% (227 cases)received acid suppression therapy,34.5% (92 cases)practiced dietary avoidance,20.9% (56 cases)received anti-allergic medication,and a small proportion(24 cases,9.0% )were treated with prednisone.Clinical symptoms were relieved in all patients after treatment,but three cases with peptic ulcers experienced recurrence after drug discontinuation.Conclusions Mild and moderate EGID are more common in children,with no specific endoscopic findings.Dietary avoidance,acid suppression therapy,and anti-allergic medication are the main treatment methods.The prognosis of EGID is generally favorable in children.[Chinese Journal of Contemporary Pediatrics,2024,26(2):139-144]

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Electric vertical takeoff and landing(eVTOL)aircraft was introduced from the aspects of basic concept,development history,classification mode,characteristic and advantage.The feasibility of eVTOL aircraft used in aeromedical rescue was discussed in terms of time,space,transport capacity and safety.It's pointed out that eVTOL aircraft would be applied widely in pre-hospital emergency care,inter-hospital transfer and emergency medical rescue.[Chinese Medical Equipment Journal,2024,45(2):82-86]

Objective To propose an improved YOLOv5s-based lesion area detection method for ophthalmic ultrasound images so as to solve the problems due to high complexity,difficult deployment and low accuracy of the model during ophthalmic ultrasound imaging detection and diagnosis.Methods Firstly,an ophthalmic ultrasound image dataset was established contai-ning Lhe images of stellate vitreous degeneration,retinal detachment,vitreous hemorrhage,posterior vitreous detachment and posterior scleral staphyloma.Secondly,a YOLOv5s-MobileNetV2 model was constructed based on YOLOv5s with the original backbone feature extraction network CSPDarkNet replaced by the lightweight network MobileNet.Thirdly,the model's performance in recognizing lesion areas in ophthalmic ultrasound images was evaluated by multi-category mean average precision(mAP),number of parameters and frames per second(FPS).Finally,the intelligent detection software for ophthalmic ultrasound images was designed based on PyQt5 library.Results The YOLOv5s-MobileNetV2 model had the mAP,number of parameters and FPS being 97.73％,4.61×106 and 47 f/s respectively,which gained advantages in timeliness over YOLOv5s by decreasing the mAP by 0.22％and the number of parameters by 34.98％.The developed intelligent detection software for ophthalmic ultrasound images behaved in human-computer interaction and clinical applicability of YOLOv5s-MobileNetV2 model.Conclusion The improved YOLOv5s-based lesion area detection method for ophthalmic ultrasound images meets clinical diagnosis requirements for ophthalmic diseases by involving in lightweight models and detecting lesion areas accurately.[Chinese Medical Equipment Journal,2024,45(11):1-7]

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.