Objective:To evaluate the performance of deep learning reconstruction (DLR) in visualizing lenticulostriate arteries (LSAs) on cerebral CT angiography (CTA).Methods:This cross-sectional study retrospectively analyzed cerebral CTA from 38 patients who underwent cerebral CTA at the Fourth Affiliated Hospital of Harbin Medical University between January and December 2023. Images were reconstructed using filtered back projection (FBP), three-dimensional adaptive iterative dose reduction (AIDR), and DLR-advanced inteuigent clear-IQ engine(AiCE) algorithms (FBP group, AIDR group, DLR-AiCE group). On axial images, the mean CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus. Subjective evaluations were performed for overall vascular visualization and LSAs delineation. Comparisons of subjective and objective evaluation indexes among the 3 groups were performed using the complex measurement ANOVA, Friedman test, or χ2 test. Results:The CT, SD, SNR and CNR values at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus demonstrated statistically significance among DLR-AiCE group, AIDR group and FBP group ( P<0.001), in which, except for the difference between the FBP group and the AIDR group in the CT value of the head of the caudate nucleus and the CT value of the cerebrospinal fluid of the lateral ventricle which was not statistically significant ( P>0.05), the remaining pairwise comparisons between the groups for each site measurements were statistically significant ( P<0.001). The difference in the overall comparison of the subjective scores of the overall vessels and LSAs in the images of the DLR-AiCE group, the AIDR group, and the FBP group was statistically significant ( P<0.001), and the two-by-two comparisons showed a statistically significant difference ( P<0.001) except for the difference in the subjective scores of LSAs between the FBP group and the AIDR group. Conclusion:The DLR-AiCE algorithm significantly reduces image noise and improves image quality, enabling superior visualization of LSAs, thereby enhancing diagnostic confidence.

Objective:To evaluate the predictive value of the ROX index and its modified versions for assessing the therapeutic efficacy of high-flow nasal cannula oxygen therapy (HFNC) in pediatric patients with acute hypoxemic respiratory failure (AHRF).Methods:This retrospective study analyzed clinical data from 130 AHRF children admitted to the Pediatric Intensive Care Unit (PICU) of Nanjing Medical University Affiliated Children’s Hospital between January 2020 and December 2024. Patients were categorized into two groups: HFNC success ( n=99) and HFNC failure ( n=31). Clinical parameters were compared between groups, and the predictive performance of the ROX index and its modified variants (ROP, ROXH, ROPH) at various time points was assessed. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for HFNC failure. Results:The HFNC failure group exhibited significantly higher baseline PaCO 2 levels (37.2 mmHg vs. 34.1 mmHg, P<0.05) and prolonged PICU stays (21 days vs. 12 days, P<0.01) compared to the success group. During treatment, the success group demonstrated marked improvements in oxygenation parameters (S/F ratio, P/F ratio) and ROX-derived indices (ROX, ROP, ROXH, ROPH) ( P<0.01). Multivariate analysis identified FiO 2 within 12 hours post-treatment and the ROPH index as independent predictors of HFNC failure ( P<0.05). ROC curve analysis revealed that the relative change in ROPH before and after treatment had the highest predictive accuracy (AUC=0.836, optimal cutoff=0.053, sensitivity=95.3%, specificity=70%) among all evaluated indices. Conclusions:Modified ROX indices, particularly the ROPH index, serve as reliable predictors of HFNC outcomes in children with AHRF. Dynamic monitoring of these indices may enable early identification of patients at risk for treatment failure, facilitating timely clinical intervention.

The number of children's emergency patients has increased in recent years，yet only a tiny proportion are in a true emergency. As a result，it is very necessary to implement a children's triage system to promptly assess the patient's health and prioritize them for emergency treatment，ensuring that critically ill patients are treated promptly and medical resources are efficiently utilized. As an emerging technology，the integration of artificial intelligence and medicine has been used in emergency care aids in the rapid and accurate assessment of the patient's condition. Machine learning-based triage models for children's emergencies have been shown to aid triage decision-making，optimize resource allocation，and reduce emergency department crowding；however，there are still issues such as a lack of data and imperfect models. In this review，we provided an overview of advancements in using artificial intelligence algorithms in children's emergency triage.

The number of children's emergency patients has increased in recent years，yet only a tiny proportion are in a true emergency. As a result，it is very necessary to implement a children's triage system to promptly assess the patient's health and prioritize them for emergency treatment，ensuring that critically ill patients are treated promptly and medical resources are efficiently utilized. As an emerging technology，the integration of artificial intelligence and medicine has been used in emergency care aids in the rapid and accurate assessment of the patient's condition. Machine learning-based triage models for children's emergencies have been shown to aid triage decision-making，optimize resource allocation，and reduce emergency department crowding；however，there are still issues such as a lack of data and imperfect models. In this review，we provided an overview of advancements in using artificial intelligence algorithms in children's emergency triage.

Objective:To evaluate the performance of deep learning reconstruction (DLR) in visualizing lenticulostriate arteries (LSAs) on cerebral CT angiography (CTA).Methods:This cross-sectional study retrospectively analyzed cerebral CTA from 38 patients who underwent cerebral CTA at the Fourth Affiliated Hospital of Harbin Medical University between January and December 2023. Images were reconstructed using filtered back projection (FBP), three-dimensional adaptive iterative dose reduction (AIDR), and DLR-advanced inteuigent clear-IQ engine(AiCE) algorithms (FBP group, AIDR group, DLR-AiCE group). On axial images, the mean CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus. Subjective evaluations were performed for overall vascular visualization and LSAs delineation. Comparisons of subjective and objective evaluation indexes among the 3 groups were performed using the complex measurement ANOVA, Friedman test, or χ2 test. Results:The CT, SD, SNR and CNR values at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus demonstrated statistically significance among DLR-AiCE group, AIDR group and FBP group ( P<0.001), in which, except for the difference between the FBP group and the AIDR group in the CT value of the head of the caudate nucleus and the CT value of the cerebrospinal fluid of the lateral ventricle which was not statistically significant ( P>0.05), the remaining pairwise comparisons between the groups for each site measurements were statistically significant ( P<0.001). The difference in the overall comparison of the subjective scores of the overall vessels and LSAs in the images of the DLR-AiCE group, the AIDR group, and the FBP group was statistically significant ( P<0.001), and the two-by-two comparisons showed a statistically significant difference ( P<0.001) except for the difference in the subjective scores of LSAs between the FBP group and the AIDR group. Conclusion:The DLR-AiCE algorithm significantly reduces image noise and improves image quality, enabling superior visualization of LSAs, thereby enhancing diagnostic confidence.

Respiratory support is one of the key technologies for treating pediatric respiratory diseases. High-flow nasal cannula oxygen therapy (HFNC) represents a novel method of assisted respiratory support. The commonly used indicator for assessing HFNC efficacy in clinical practice is oxygenation index (PaO 2/FiO 2). However, this parameter has several limitations when evaluating the efficacy of HFNC in pediatrics. The ROX index, introduced in recent years, offers a more sensitive and specific assessment tool. This article reviews the application of the ROX index in assessing the efficacy of HFNC for pediatric respiratory failure, aiming to provide a more accurate assessment method.

Background::Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants.Methods::A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79 years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson’s correlation, multiple linear regression, and analyses of covariance.Results::All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5 μm/year in women and 5.8 μm/year in men) and diameter (0.03 mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. Conclusions::The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.

"Global cancer statistics 2022" based on the latest GLOBCAN data from the International Agency for Research on Cancer (IARC) was recently released, providing a systematic analysis of the incidence and mortality of 36 types of cancer across 185 countries worldwide. The international burden of cancer is expected to continue to increase over the next 30 years, posing a severe public health and social challenge for many countries, including China. This article offers a key point interpretation of the "Global cancer statistics 2022", focusing on the evolution of cancer epidemiology and future development trends. The aim is to broaden the international perspective on cancer prevention and treatment, with the hope of providing reference and guidance for cancer prevention and treatment efforts in our country.

Di-(2-ethylhexyl) phthalate (DEHP) is currently one of the most widely used plasticizers, widely found in all kinds of items, such as children’s toys and food packaging materials, but also added to wallpaper, cable protective agents and other building decoration materials. DEHP is toxic and absorbed by the human body through respiratory tract, digestive tract and skin contact, which can cause damage to multiple systems, especially the male reproductive system, and testis is an important target organ. Oxidative stress injury is the core mechanism of spermatogenesis disorder caused by DEHP. DEHP exposure can cause oxidative stress or reactive oxygen species (ROS) increase in germ cells, and on this basis, promote cell apoptosis or cause excessive autophagy. The toxicity of DEHP to Leydig cells is mainly to interfere with the synthesis of steroid hormones. For Sertoli cells, ferroptosis and destruction of the blood-testis barrier are common injury mechanisms. In addition, gene methylation caused by DEHP not only affects the spermatogenic process, but also has epigenetic effects on offspring. In this paper, we reviewed the pathological damage, germ cell toxicity and epigenetic effects of DEHP on testis, and focused on the damage and molecular mechanism on testicular spermatogenic cells, Leydig cells and Sertoli cells. Future research is required to elucidate the body’s clearance mechanism and treatment plan after exposure to DEHP and whether DEHP will damage the function of myoid cells. It is hoped that this can provide new ideas for prevention and treatment of male reproductive disorders resulting from long-term exposure to plastic products.

Objective:To establish a mortality risk prediction model of severe bacterial infection in children and compare it with the pediatric early warning score (PEWS), pediatric critical illness score (PCIS) and pediatric risk of mortality score Ⅲ (PRISM Ⅲ).Methods:A total of 178 critically ill children were selected from the PICU of the Children's Hospital of Nanjing Medical University from May 2017 to June 2022. After obtaining the informed consent of the parents/guardians, basic information such as sex, age, height and weight, as well as indicators such as heart rate, systolic blood pressure and respiratory rate were collected from all children. A standard questionnaire was used to score the child 24 h after admission to the PICU. The children were divided into the survival and death groups according to their survival status at 28 d after admission. A mortality risk prediction model was constructed and nomogram was drawn. The value of the mortality risk prediction model, PEWS, PCIS and PRISM in predicting the risk of death was assessed and compared using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC).Results:Among the 178 critically ill children, 11 cases were excluded due to severe data deficiencies and hospitalization not exceeding 24 h. A total of 167 children were included in the analysis, including 134 in the survival group and 33 in the death group. A mortality risk prediction model for children with severe bacterial infection was constructed using pupillary changes, state of consciousness, skin color, mechanical ventilation, total cholesterol and prothrombin time. ROC curve analysis showed that the AUCs of mortality risk prediction model was 0.888 ( P<0.05). The AUCs of PEWS, PCIS and PRISM Ⅲ in predicting death in children with severe bacterial infection were 0.769 ( P< 0.05), 0.575 ( P< 0.05) and 0.759 ( P< 0.05), respectively. Hosmer-Lemeshow goodness-of-fit test showed the best agreement between risk of death and PEWS predicted morbidity and mortality and actual morbidity and mortality (χ 2 = 5.180, P = 0.738; χ 2 = 4.939, P = 0.764), and the PCIS and PRISM Ⅲ predicted mortality rates fitted reasonably well with actual mortality rates (χ 2= 9.110, P= 0333; χ 2 = 8.943, P= 0.347). Conclusions:The mortality risk prediction model for predicting the death risk has better prognostic value than PEWS, PCIS and PRISM Ⅲ for children with severe bacterial infection.