Objective As primary Sj?gren's syndrome(pSS)primarily affects the salivary glands,saliva can serve as an indicator of the glands'pathophysiology and the disease's status.This study aims to illustrate the salivary proteomic profiles of pSS patients and identify potential candidate biomarkers for diagnosis. Methods The discovery set contained 49 samples(24 from pSS and 25 from age-and gender-matched healthy controls[HCs])and the validation set included 25 samples(12 from pSS and 13 from HCs).Totally 36 pSS patients and 38 HCs were centrally randomized into the discovery set or to the validation set at a 2:1 ratio.Unstimulated whole saliva samples from pSS patients and HCs were analyzed using a data-independent acquisition(DIA)strategy on a 2D LC-HRMS/MS platform to reveal differential proteins.The crucial proteins were verified using DIA analysis and annotated using gene ontology(GO)and International Pharmaceutical Abstracts(IPA)analysis.A prediction model for SS was established using random forests. Results A total of 1,963 proteins were discovered,and 136 proteins exhibited differential representation in pSS patients.The bioinformatic research indicated that these proteins were primarily linked to immunological functions,metabolism,and inflammation.A panel of 19 protein biomarkers was identified by ranking order based on P-value and random forest algorichm,and was validated as the predictive biomarkers exhibiting good performance with area under the curve(AUC)of 0.817 for discovery set and 0.882 for validation set. Conclusions The candidate protein panel discovered may aid in pSS diagnosis.Salivary proteomic analysis is a promising non-invasive method for prognostic evaluation and early and precise treatments for pSS patients.DIA offers the best time efficiency and data dependability and may be a suitable option for future research on the salivary proteome.

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 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]

This study was aimed at studying the phenotypic and molecular characteristics of a Salmonella Grumpensis isolate from a patient with diarrhea in Shanghai,to provide evi-dence for the prevention of salmonellosis.Biochemical identifi-cation,serum agglutination testing,antimicrobial susceptibility testing,and whole genome sequencing(WGS)were performed on isolate 2023JD76.Global Salmonella Grumpensis genome sequences were searched and downloaded for serotyping predic-tion,multilocus sequence typing(MLST),prediction of anti-microbia resistance genes and virulence genes,and phylogenetic analysis of 2023JD76.The 2023JD76 strain was identified as Salmonella Grumpensis(13,23:d:1,7)with ST2060,and was susceptible to 20 antimicrobial agents.Strain 2023JD76 carried the aminoglycoside resistance gene aac(6')-Iaa and five types of virulence genes:the adhesion genes csg and rat;the secretion and transport genes sip and inv;the typhoid toxin genes cdt and plt;the invasive gene nutrient metabolism factor mgt;and the antimicrobial peptide resistance factor mig.Global S.Grumpensis strains harbored ten types of antimicrobial resistance genes whose prevalence ranged from 58.33％to 100％.The global genome sequences of S.Grumpensis were divided into two lineages.Lineage I was dominated by ST751(88.89％,16/18),and lineage Ⅱ was dominated by ST2060(89.47％,17/19).The genome sequence of strain 2023JD76 belonged to lineage Ⅱ,and was closely related to the genome sequences from human fecal and human cerebrospinal fluid.This study provides the first report of a S.Grumpensis isolate from the stool of a patient with diarrhea in China.Considerable variability in antimicrobial resistance genes was observed among genome sequences from different sources,and the strains harbored a substantial number of virulence genes.Enhanced surveillance should be emphasized to prevent a potential risk of global dissemination.

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.

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.

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.