Objective: To characterize longitudinal trajectories of testicular development in boys and breast development in girls, so as to provide reference data for understanding patterns of pubertal sexual maturation. Methods: Based on the Shanghai Pudong New Area Cohort Study on Growth, Development and Health in Children and Adolescents, a baseline survey was conducted in 2020 using a mult stage cluster random sampling method. A total of 2 184 children who completed all follow ups during the primary school period from 13 elementary schools in Pudong New Area,Shanghai,with annual follow ups during 2021-2025. Testicular volume and Tanner stage of breast development were assessed by professional physicians using standardized visual inspection and palpation. The age distribution of testicular volume and breast development was fitted by using cumulative link mixed models and Turnbull s nonparametric maximum likelihood estimation method. Results: Median ages for testicular volumes of 2, 3, 4 and 5 mL in boys were 7.07, 9.24, 10.29, and 11.57 years old, respectively. Median ages for Tanner breast stages Ⅱ, Ⅲ, Ⅳ, and Ⅴ in girls were 8.55 , 10.17, 11.18, and 13.78 years old, respectively. Based on overweight and obesity, stratified analysis showed that earlier pubertal onset among overweight/obesity children, and the key milestones for pubertal initiation were testicular volume reaching 4 mL in boys and breast Tanner II in girls for 10.29, 10.83; 8.18, 9.00 years. Conclusion Overweight and obesity are associated with earlier pubertal initiation,but there are certain gender and developmental stage specific patterns.

Echinococcosis is categorized into alveolar echinococcosis and cystic echinococcosis, a potentially fatal zoonotic disease that seriously damages the host. MicroRNA is a type of endogenous small non-coding RNA that binds specifically to the target gene mRNA sequence to promote mRNA degradation, or combines with other non-coding RNA to regulate or inhibit translation. Echinococcus-derived microRNA participates in various biological processes in the Echinococcus lifecycle and in the interaction and pathogenesis between Echinococcus and host through extracellular vesicle transport. This article reviews the research progress of parasite-derived microRNA in regulating the growth and development of echinococcosis and cross-species regulation of hosts, providing a reference for diagnosing and treating echinococcosis.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Tuberculosis(TB),a chronic infectious disease caused by infection with the Mycobacterium tuberculosis complex(MTBC),has re-emerged as the leading cause of death from a single infectious agent worldwide.Because of widespread use and mis-use of anti-tuberculosis drugs,the emergence of multidrug-resistant TB(MDR-TB)and extensively drug-resistant TB(XDR-TB)is increasing,thus posing a serious threat to global health.The current problem of drug resistance is a major prevention and treatment challenge;therefore,the search for new drug targets is urgently needed.In recent years,substantial progress has been made in re-search on anti-tuberculosis drug targets and novel therapeutic strategies.Herein,we summarize recent research progress in anti-tuberculosis drug targets,primarily cell wall synthesis,nucleic acid replication and transcription,and energy metabolism.We also provide an overview of research progress regarding two novel therapeutic strategies,to provide a theoretical basis and research ideas for the development of new clinical drugs.

Objective:To explore the application effect of high-fidelity intelligent simulator combined with scenario simulation for emergency response training in the Department of Radiology, and to improve the emergency preparedness of medical, nursing, and technical staff in managing contrast agent adverse reactions.Methods:From January to July 2024, 132 medical, nursing, and technical staff from the Department of Radiology of a tertiary hospital in Chengdu City, China were selected as the training subjects. The high-fidelity intelligent simulator combined with scenario simulation teaching mode was used to conduct emergency response training for the participants. The differences in theoretical knowledge and post competence regarding contrast agent adverse reactions among the staff were compared before and after the training. A self-made questionnaire was used to investigate their needs and satisfaction of the emergency response training. SPSS 26.0 was used for data analysis. The differences in theoretical knowledge and post competence scores before and after training were compared using the paired samples t test. Results:After the training, the average score of theoretical knowledge examination increased from (84.32±10.19) points to (90.34±7.87) points, and the difference was statistically significant ( P<0.001). After the training, the scores of knowledge reserve, operational skills, situational decision-making ability, professional literacy, comprehensive literacy, and overall post competency were all significantly higher than those before the training ( P<0.05). The satisfaction score of emergency response training was (4.17±0.25) points. Conclusions:High-fidelity intelligent simulator combined with scenario simulation training improved the emergency preparedness and teamwork of radiology staff in clinical emergencies. The training received high recognition and satisfaction from the participants, which is of great significance for clinical emergency response and patient safety.

Objective:To analyze and summarize the clinical features and short-medium term follow-up results of children with multisystem inflammatory syndrome（MIS-C）following coronavirus infection.Methods:The data of six children with MIS-C admitted to the Intensive Care Unit of Shanghai Children's Medical Center from January to March 2023 were retrospectively analyzed.Results:All six cases were in shock，requiring vasoactive drugs，and one case required invasive mechanical ventilation.All the six patients had multiple organ function injury and increased inflammation indicators.After admission，they received organ support，glucocorticoids and gamma globulin treatment.Two patients were treated with biological agents.Both organ function and inflammation indicators showed significantly improvement after therapy.Six patients had mild coronary artery widening.All patients had good prognosis following short-medium term follow-up.Conclusion:Children with severe MIS-C may suffer life-threatening hemodynamic instability.Timely assessment，active anti-inflammatory and organ support therapy can obtain favorable prognosis.

Objective:To explore the association between the gross motor development of children and Chi-nese developmental dyslexia.Methods:A total of 54 children were enrolled,indadit 27 children with Chinese DD and 27 age-gender-matched normal children.The Test of Gross Motor Development-Third Edition(TGMD-3),and the balance tests from the Movement Assessment Battery for Children(M-ABC)were used to evaluate the children's gross motor development level.Children's reading ability was evaluated by primary school students'literacy test question bank,one-minute reading task,and the Reading Test on Pupils.Results:The overall analysis of motortests showed that the grossmotor total score,locomotor score,object control score,and balance score in the DD group were significantly lower than those in the normal control group(P＜0.05).Logistic regression analysis revealed a negative association between balance scores,locomotor scores,object control scores,TGMD-3 total scores and indi-viduals with dyslexia(OR=3.08,1.35,1.16,1.13,Ps＜0.05).Conclusion:The delayed gross motor development of children is associated with the occurrence of Chinese developmental dyslexia.

The background of revising GB 19083-2023 Protective face mask for medical use was introduced.GB 19083-2023 was compared with GB 19083-2010 Technical requirements for protective face mask for medical use.The revision points were described in detail involving in dead space,total leakage rate,respiratory resistance,resistance to synthetic blood penetration,microbial indicators,biocompatibility and etc,and the convergence between GB 19083-2023 and international mainstream standards was analyzed.References were provided for the understanding of the standard for the production enterprises and consumers.[Chinese Medical Equipment Journal,2025,46(1):73-77]

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.