Objective Low-dose CT(LDCT)images significantly affect clinical diagnosis due to the substantial presence of noise and artifacts.To address the challenges such as over-smoothing of images,loss of texture details,and the presence of residual noise artifacts,a deconvolution-convolution residual network integrating multi-scale dilated convolution(MDC-DCRN)is proposed for LDCT denoising.Methods The network employed deconvolution-convolution architecture to better preserve image details and integrated an MDC module to enhance the feature extraction capabilities at different scales.Moreover,the issue of excessive image smoothing was effectively mitigated by the composite loss function combining L1 loss and perceptual loss.Results The experimental results on the Mayo dataset demonstrated that MDC-DCRN outperformed 4 classic denoising methods,namely RED-CNN,EDCNN,WGAN-RAM,and CTformer.MDC-DCRN effectively eliminated noise and artifacts while recovering more texture detail information.Compared with LDCT images,the images processed by MDC-DCRN had an average increase of 13.64%in peak signal-to-noise ratio,an average increase of 4.57%in structural similarity index,and an average decrease of 37.40%in root mean square error.Conclusion The proposed MDC-DCRN model can effectively preserve details while reducing noise from low-dose scanning,offering a novel approach to clinical LDCT image denoising.

Objective:To analyze the predictive value of the prognostic nutritional index combined with inflammatory indicators for clinical outcomes in patients with ischemic stroke, providing a basis for targeted therapeutic interventions and nursing care for patients at potential risk of poor outcomes.Methods:This retrospective cohort study recruited 424 ischemic stroke patients admitted to the Stroke Center of the First Hospital of Jilin University from June 2021 to May 2024 by the convenience sampling method. Collect the general demographic data, routine laboratory examination results within 24 hours of admission, as well as the National Institutes of Health Stroke Scale (NIHSS) scores at admission and discharge of the patients. Based on 3-month functional outcomes, patients were divided into a favorable prognosis group and a poor prognosis group. Univariate analysis, least absolute shrinkage and selection operator regression, and binary Logistic regression were employed to screen predictive variables and develop a nomogram model. Internal validation was performed using the Bootstrap method. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drew to evaluate the predictive performance of the model.Results:The favorable prognosis group comprised 256 patients, including 57 females and 199 males, with an age of 61(54, 68) years. The poor prognosis group included 168 patients, with 55 females and 113 males, and an age of 66(58, 72) years. Binary Logistic regression identified age, sex, hyperlipidemia, NIHSS score at discharge, prognostic nutritional index, systemic inflammatory response index, and systemic immune-inflammation index as independent predictors of prognosis in ischemic stroke patients ( χ2 values were 4.52-56.18, all P<0.05). The prediction model demonstrated the area under the curve of ROC of 0.806 (95% CI 0.764-0.849), with an optimal cutoff value of 0.406, achieving specificity of 78% and sensitivity of 68%. The Hosmer-Lemeshow test yielded a non-significant P>0.05, and the calibration curve showed good agreement between predicted and observed outcomes, with a mean absolute error of 0.012. Decision curve analysis confirmed the clinical utility of the model across a wide range of threshold probabilities. Conclusions:The integrated prognostic model combining the prognostic nutritional index and inflammatory indicators demonstrated favorable discriminative ability, robust calibration, and substantial clinical utility. This risk stratification tool shows high applicability for predicting ischemic stroke outcomes, facilitating early identification of high-risk patients with poor prognosis and guiding personalized intervention strategies.

Objective Low-dose CT(LDCT)images significantly affect clinical diagnosis due to the substantial presence of noise and artifacts.To address the challenges such as over-smoothing of images,loss of texture details,and the presence of residual noise artifacts,a deconvolution-convolution residual network integrating multi-scale dilated convolution(MDC-DCRN)is proposed for LDCT denoising.Methods The network employed deconvolution-convolution architecture to better preserve image details and integrated an MDC module to enhance the feature extraction capabilities at different scales.Moreover,the issue of excessive image smoothing was effectively mitigated by the composite loss function combining L1 loss and perceptual loss.Results The experimental results on the Mayo dataset demonstrated that MDC-DCRN outperformed 4 classic denoising methods,namely RED-CNN,EDCNN,WGAN-RAM,and CTformer.MDC-DCRN effectively eliminated noise and artifacts while recovering more texture detail information.Compared with LDCT images,the images processed by MDC-DCRN had an average increase of 13.64%in peak signal-to-noise ratio,an average increase of 4.57%in structural similarity index,and an average decrease of 37.40%in root mean square error.Conclusion The proposed MDC-DCRN model can effectively preserve details while reducing noise from low-dose scanning,offering a novel approach to clinical LDCT image denoising.

Objective:To analyze the predictive value of the prognostic nutritional index combined with inflammatory indicators for clinical outcomes in patients with ischemic stroke, providing a basis for targeted therapeutic interventions and nursing care for patients at potential risk of poor outcomes.Methods:This retrospective cohort study recruited 424 ischemic stroke patients admitted to the Stroke Center of the First Hospital of Jilin University from June 2021 to May 2024 by the convenience sampling method. Collect the general demographic data, routine laboratory examination results within 24 hours of admission, as well as the National Institutes of Health Stroke Scale (NIHSS) scores at admission and discharge of the patients. Based on 3-month functional outcomes, patients were divided into a favorable prognosis group and a poor prognosis group. Univariate analysis, least absolute shrinkage and selection operator regression, and binary Logistic regression were employed to screen predictive variables and develop a nomogram model. Internal validation was performed using the Bootstrap method. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drew to evaluate the predictive performance of the model.Results:The favorable prognosis group comprised 256 patients, including 57 females and 199 males, with an age of 61(54, 68) years. The poor prognosis group included 168 patients, with 55 females and 113 males, and an age of 66(58, 72) years. Binary Logistic regression identified age, sex, hyperlipidemia, NIHSS score at discharge, prognostic nutritional index, systemic inflammatory response index, and systemic immune-inflammation index as independent predictors of prognosis in ischemic stroke patients ( χ2 values were 4.52-56.18, all P<0.05). The prediction model demonstrated the area under the curve of ROC of 0.806 (95% CI 0.764-0.849), with an optimal cutoff value of 0.406, achieving specificity of 78% and sensitivity of 68%. The Hosmer-Lemeshow test yielded a non-significant P>0.05, and the calibration curve showed good agreement between predicted and observed outcomes, with a mean absolute error of 0.012. Decision curve analysis confirmed the clinical utility of the model across a wide range of threshold probabilities. Conclusions:The integrated prognostic model combining the prognostic nutritional index and inflammatory indicators demonstrated favorable discriminative ability, robust calibration, and substantial clinical utility. This risk stratification tool shows high applicability for predicting ischemic stroke outcomes, facilitating early identification of high-risk patients with poor prognosis and guiding personalized intervention strategies.

Objective:To investigate the multimorbidity of myopia and obesity, as well as myopia and malnutrition, among children and adolescents aged 7-18 in Guangdong Province and analyze their epidemiological characteristics and related factors.Methods:A stratified random cluster sampling method was used to select 274 939 children and adolescents aged 7-18 from 21 cities in Guangdong Province in 2023. Physical examination information such as height, weight, distance vision, and diopter, as well as questionnaire survey information on dietary behavior, physical activity, screen behavior, sleep time, etc., were collected to analyze the current status and trends of multimorbidity between myopia and obesity, myopia and malnutrition. The multivariate logistic regression model was used to analyze the related factors of multimorbidity.Results:The multimorbidity rates of myopia and obesity, myopia and malnutrition in children and adolescents aged 7-18 in Guangdong Province in 2023 were 4.43% and 6.40%, respectively. The multimorbidity rates for males were 5.44% and 6.88%, respectively, which were higher than those for females, about 3.31% and 5.88% (both P<0.001). The multimorbidity rates of urban students were 5.03% and 6.73%, respectively, which were higher than those of county students at 4.03% and 6.18% (both P<0.001). The multimorbidity rates of myopia and obesity, myopia and malnutrition increased with the increase of academic stage (all P<0.001). The multimorbidity rates of myopia and obesity, as well as myopia and malnutrition, fluctuated with age, with the first decrease occurring at the age of 12. The multivariate logistic regression analysis showed that compared to children and adolescents aged 7-18 who had daily after-school tutoring <2 hours, daily screen time <2 hours, did not consume sugary drinks every day, sleep time that could meet health requirements daily, and exercised≥60 minutes of moderate-to vigorous-physical activity ≥60 minutes for at least 3 days per week, those who had daily after-school tutoring ≥2 hours ( OR=1.18, 95% CI: 1.11-1.26), daily screen time ≥2 hours ( OR=1.09, 95% CI: 1.02-1.16), consumed sugary drinks every day ( OR=1.20, 95% CI: 1.11-1.30), daily sleep time that could not meet the health requirements ( OR=1.16, 95% CI: 1.09-1.23), and no exercise per week ( OR=1.09, 95% CI: 1.01-1.18) had a higher risk of multimorbidity of myopia and obesity. Compared to children and adolescents who exercised≥60 minutes of moderate-to vigorous-physical activity ≥60 minutes for at least 3 days per week, those who exercised <3 days per week ( OR=1.20, 95% CI: 1.17-1.34) had a higher risk of multimorbidity of myopia and malnutrition. Conclusion:The multimorbidity rates of myopia and obesity, as well as myopia and malnutrition, in children and adolescents aged 7-18 in Guangdong Province are relatively low and fluctuate with age. Physical activity, screen time, consumption of sugary drinks, and sleep time may be associated with these multimorbidities.

Objective To explore the value of transvaginal ultrasound five-view method for diagnosing fetal congenital heart disease(CHD)in early pregnancy of high-risk pregnancy.Methods Totally 428 singleton fetuses at 11-13+6 weeks of gestation in high-risk pregnancies were prospectively enrolled.Fetal cardiac examinations were performed using two-dimensional grey scale imaging combined with CDFI via transabdominal ultrasound method,transvaginal ultrasound five-view method(upper abdominal transverse view,four-chamber view,three-vessel and trachea[3VT]view,ventricular outflow tract view and bilateral subclavian artery view)and transvaginal ultrasound two-view method(four-chamber view and 3VT view).Taken results of pregnant metaphase or late pregnancy fetal echocardiography,post-abortion pathology or postnatal neonatal echocardiography as gold standards,the diagnostic performances of the above 3 methods for diagnosing fetal CHD in early pregnancy were compared.Results CHD was diagnosed in 120(120/428,28.04％)fetuses.Transvaginal ultrasound five-view method made 11 false positives and 19 false negatives,with sensitivity,specificity,positive predictive value and negative predictive values for diagnosing fetal CHD in the first trimester of high-risk pregnancy of 84.17％,96.43％,90.18％and 93.99％,respectively,which were all significantly higher than of transvaginal ultrasound two-view method(70.83％,91.88％,77.27％and 88.99％)and transabdominal ultrasound method(72.50％,91.23％,76.32％and 89.49％;all P＜0.05).Conclusion Transvaginal ultrasound five-view method could be used to effectively diagnose fetal CHD in the first trimester of high-risk pregnancy.

The clinical data of 2 pediatric patients with atypical hemolytic uremic syndrome (aHUS) who were admitted to the Department of Nephrology at the Children′s Hospital of Nanjing Medical University on July 2018 to June 2023 were retrospectively analyzed.Both patients had combined CFH and CD46 gene mutations.One patient, a 2-year-old boy, presented jaundice and darkened urine following mumps.The other patient, a 7-month-old girl and the younger sister of the boy, developed fever, cough, vomiting, and thrombocytopenia without any apparent cause.Laboratory tests revealed hemolytic anemia, thrombocytopenia, and acute kidney injury in both patients.The genetic test results revealed mutations in both CFH (c.3572C>T, p.Ser1191Leu) and CD46 genes (c.293C>T, p.Thr98Ile) in both patients.The patients′ mother is a heterozygous carrier of the CFH gene mutation, while their father is a heterozygous carrier of the CD46 gene mutation.Both parents exhibit normal phenotypes and are currently receiving regular infusions of Eculizumab.The pediatric aHUS caused by combined CFH and CD46 gene mutations is reported in this study for the first time in China.The clinical features of these patients are summarized and analyzed.

Objective　To explore the parainfluenza virus (PIV) infection in children hospitalized in Hainan between March 2012 and December 2022, and to analyze its epidemiological characteristics. Methods　The samples were obtained from 62 553 kids with respiratory infections who were hospitalized in the Department of Pediatrics of multiple hospitals in various regions of Hainan from March 2012 to December 2022. Indirect immunofluorescence was employed to detect IgM antibodies in serum for nine respiratory pathogens, including PIV, adenovirus, influenza A virus, Legionella pneumophila, respiratory syncytial virus, Mycoplasma pneumoniae, influenza B virus, Coxiella burnetii, and Chlamydia pneumoniae. Epidemiological and clinical data (time, gender, age, season, etc.) of PIV-IgM antibody-positive cases were analyzed in a descriptive study. Results　The total PIV-IgM antibody positive rate of 62 553 respiratory tract infected children was 3.29% (2 015/62 553), with the highest positive rate of 11.01% (385/3 496) in 2017, and the second highest positive rate of 8.37% (351/4 196) in 2016, which were significantly higher than the positive rate of the rest of the years (P<0.001). The PIV positive rate was 3.18% (1 248/39 225) in males and 3.29% (767/23 328) in females, with no statistically significant difference (P>0.05). PIV infection occurred in all age groups, with the highest positive rate in the 6 to <12 years group at 4.50% (357/7 941), followed by the 3 to <6 years group at 4.47% (656/14 689), significantly higher than other age groups (P<0.001). The highest positive rate for PIV was in summer at 4.30% (693/16 093), followed by 3.78% (598/15 804) in spring, and the lowest rate of 2.27% (342/15 065) in winter, with statistically significant differences (P<0.001). Single PIV infection accounted for 63.08% (1 271/2 015), while mixed infections accounted for 36.92% (744/2 015), and the most common co-infection being with Mycoplasma pneumoniae infection at 23.13% (466/2 015). Conclusions　PIV is an important pathogen for children's acute respiratory infections in Hainan Province, exhibiting year-round sporadic occurrence with alternating high and low periods characteristics. PIV infection is to the gender of the child, predominantly affects preschool and school-age children, peaks in spring and summer, and commonly co-infects with Mycoplasma pneumoniae infection.

Diabetic retinopathy(DR)is a prevalent microvascular complication of diabetes and a leading cause of vision loss globally.Although anti-vascular endothelial growth factor(anti-VEGF)therapies remain the clinical mainstay, a significant proportion of patients exhibit suboptimal responses, highlighting the urgent need for novel therapeutic targets. Calcitonin gene-related peptide(CGRP), a multifunctional neuropeptide, is gaining attention due to its roles in vascular regulation, neuroprotection, and immunomodulation. This review summarizes the biological characteristics of CGRP and its receptor-mediated signaling, and explores emerging evidence of CGRP's involvement in DR through its vasodilatory effects and regulatory effect on neurodegenerative disorders and release of inflammatory cytokines. Furthermore, the therapeutic potential of targeting the CGRP pathway in DR is evaluated, especially in cases unresponsive to VEGF inhibition. Despite currently the lack of CGRP-targeted drugs applied for DR, the peptide demonstrates efficacy and safety in other diseases, such as migraine, suggests promising translational opportunities. However, CGRP may play a dual role in different pathological stages of DR, thus its treatment strategy needs to be considered precisely. Future research elucidating the precise mechanisms of CGRP in DR may pave the way for innovative intervention strategies.