Objective To investigate the effect of secondary preventive scheme on recurrence rate of ce-rebrovascular event based on China ischemic stroke subclassification(CISS)of intracranial arterial stenosis is-chemic stroke(IS).Methods A total of 192 patients with intracranial arterial stenosis IS were prospectively included and the secondary preventive scheme was formulated according to whether or not based on the patho-genesis.Then the patients were divided into the personalized treatment group and conventional treatment group.The personalized group conducted the classification and was given different the secondary preventive schemes the artery-artery embolism group was given the active lipid-lowering scheme to make the low density lipoprotein(LDL)reaching the standard;the low hypoperfusion group was given the smooth pressure reduc-tion program;the carrier artery occlusion perforating artery group was given the routine secondary prevention program;the mixture mechanism group was given the corresponding schemes superposition according to dif-ferent mechanisms].The conventional treatment group was given the conventional secondary preventive scheme.The difference in the recurrence rate of cerebrovascular events on 90 d was compared between the two groups.Meanwhile the univariate and multivariate logistic regression were used to analyze the influencing fac-tors of recurrence of cerebrovascular events on 90 d in intracranial arterial stenosis IS.Results Among 192 study subjects,there were 90 cases in the personalized treatment group(20 cases of vector artery occlusion and perforator artery,44 cases of arterial-arterial embolization,6 cases of hypoperfusion and 20 cases of mixed mechanism).There were 102 cases in the conventional treatment group(16 cases of perforator artery occlu-sion of the vector artery,52 cases of arterial-arterial embolism,8 cases of hypoperfusion and 26 cases of mixed mechanism).The occurrence rate of cerebrovascular events on 90 d in the personalized treatment group was significantly decreased compared to the conventional treatment group(7.8%vs.17.6%,χ2=4.112,P=0.043).The Logistic regression analysis revealed that the active lipid-lowering scheme for LDL reaching the standard was the independent protective factor of the cerebrovascular event recurrence on 90 d(OR=0.128,95%CI:1.150-71.170).Conclusion The personalized secondary prevention scheme based on pathogenesis reduces the recurrence rate of cerebrovascular event in intracranial arterial stenosis IS,in which making the LDL reaching the standard by the active lipid-lowering scheme is the independent protective factor for cerebro-vascular event recurrence in intracranial arterial stenosis IS.

Fatigue driving is a major cause of traffic accidents,which poses a great threat to public safety and property.In order to reduce the losses caused by fatigue driving,many researchers have devoted themselves to the study about fatigue driving,such as driver behavior monitoring,brainwave monitoring,eye tracking and facial expression analysis.Each of these methods has its own advantages and disadvantages.Behavioral monitoring reflects the fatigue state by analyzing the driver's driving habits and facial expression,which is easy to operate but prone to be affected by the external environment.Brainwave monitoring is more accurate and can detect the fatigue state in real time,but the equipment is complicated and costly,which limits its large-scale application.The detection based on eye-tracking and facial expression analysis also has a certain potential for application,but errors may occur under different light conditions.Herein the review introduces the effects of fatigue on driving ability and compares the research results of various fatigue driving detection methods by searching,collating,analyzing and summarizing the relevant literatures at home and abroad.Various detection methods are analyzed and summarized,and it is pointed out that the fatigue driving detection method based on multi-feature information fusion will become a research hotspot.

Alzheimer's disease(AD)is a progressive,irreversible neurodegenerative disorder characterized by gradual brain cell degeneration,leading to progressive decline in cognitive function and ultimately death.Early identification and intervention are critical to AD diagnosis.In recent years,deep learning has further advanced image-based AD classification methods and facilitated the application of deep models in the early AD diagnosis.To achieve accurate early diagnosis and subsequent classification of AD,researchers have integrated deep learning with magnetic resonance imaging to develop more precise models.By analyzing and synthesizing relevant domestic and international literature,this review introduces commonly used public datasets and evaluation criteria for AD,analyzes the application of magnetic resonance imaging in AD classification and its integration with deep learning methods,and highlights the roles of techniques such as convolutional neural networks,transfer learning,attention mechanisms,and multimodal approaches in AD classification.It also discusses the advantages,limitations,and developmental trends of deep learning in AD classification,aiming to provide new insights for the application of deep learning in AD research.

Congenital hyperinsulinism is the most common cause of hypoglycemia in children, characterized by a complex genetic basis, diverse pathogenic mechanisms, and significant clinical heterogeneity.Delayed diagnosis and treatment can lead to irreversible neurological sequelae, including epilepsy and psychomotor retardation.In this review, recent advancements in the molecular genetics, imaging localization, therapeutic strategies, and follow-up outcomes of congenital hyperinsulinism were summarized so as to offer valuable insights for its clinical management.

Fatigue driving is a major cause of traffic accidents,which poses a great threat to public safety and property.In order to reduce the losses caused by fatigue driving,many researchers have devoted themselves to the study about fatigue driving,such as driver behavior monitoring,brainwave monitoring,eye tracking and facial expression analysis.Each of these methods has its own advantages and disadvantages.Behavioral monitoring reflects the fatigue state by analyzing the driver's driving habits and facial expression,which is easy to operate but prone to be affected by the external environment.Brainwave monitoring is more accurate and can detect the fatigue state in real time,but the equipment is complicated and costly,which limits its large-scale application.The detection based on eye-tracking and facial expression analysis also has a certain potential for application,but errors may occur under different light conditions.Herein the review introduces the effects of fatigue on driving ability and compares the research results of various fatigue driving detection methods by searching,collating,analyzing and summarizing the relevant literatures at home and abroad.Various detection methods are analyzed and summarized,and it is pointed out that the fatigue driving detection method based on multi-feature information fusion will become a research hotspot.

Alzheimer's disease(AD)is a progressive,irreversible neurodegenerative disorder characterized by gradual brain cell degeneration,leading to progressive decline in cognitive function and ultimately death.Early identification and intervention are critical to AD diagnosis.In recent years,deep learning has further advanced image-based AD classification methods and facilitated the application of deep models in the early AD diagnosis.To achieve accurate early diagnosis and subsequent classification of AD,researchers have integrated deep learning with magnetic resonance imaging to develop more precise models.By analyzing and synthesizing relevant domestic and international literature,this review introduces commonly used public datasets and evaluation criteria for AD,analyzes the application of magnetic resonance imaging in AD classification and its integration with deep learning methods,and highlights the roles of techniques such as convolutional neural networks,transfer learning,attention mechanisms,and multimodal approaches in AD classification.It also discusses the advantages,limitations,and developmental trends of deep learning in AD classification,aiming to provide new insights for the application of deep learning in AD research.

Congenital hyperinsulinism is the most common cause of hypoglycemia in children, characterized by a complex genetic basis, diverse pathogenic mechanisms, and significant clinical heterogeneity.Delayed diagnosis and treatment can lead to irreversible neurological sequelae, including epilepsy and psychomotor retardation.In this review, recent advancements in the molecular genetics, imaging localization, therapeutic strategies, and follow-up outcomes of congenital hyperinsulinism were summarized so as to offer valuable insights for its clinical management.

Objective:To analyze the genetic and clinical characteristics, treatment and prognosis of patients diagnosed with maturity onset of diabetes of the young (MODY) 12 subtype.Methods:This retrospective study collected and analyzed data from 5 children with MODY12 subtype caused by ABCC8 gene variants who underwent inpatient and outpatient genetic testing at Beijing Children′s Hospital from January 2016 to December 2023. Their clinical and genetic features, treatment, and follow-up results were analyzed.Results:Among the 5 patients with MODY12 subtype, 4 were male and 1 was female, with an age of 13.4 (5.5, 14.6) years. Four of the patients were born large for gestational age, while one was born small for gestational age. Two patients were overweight or obese. Three patients exhibited typical symptoms of diabetes, while 2 were incidentally found to have elevated blood glucose level. One patient was found to have diabetic ketoacidosis at onset, who was diagnosed with congenital hyperinsulinism during the neonatal period and received diazoxide treatment, and experienced intellectual developmental delay. All 5 patients had autosomal dominant inherited diabetes within 3 generations. The fasting blood glucose at onset was 7.5 (6.5, 10.0) mmol/L, the haemoglobin A1c (HbA1c) was 11.8% (7.5%, 13.5%), and the fasting C-peptide was 1.2 (1.1, 2.2) μg/L. The duration of follow-up was 15 (9, 32) months. One patient underwent lifestyle intervention, 2 received metformin orally, 1 received insulin therapy, and the other received subcutaneous injection of insulin combined with sulfonylurea orally. At the last follow-up, the median fasting blood glucose was 6.1 (5.1, 7.0) mmol/L, the HbA1c was 5.9% (5.7%, 7.1%), and the fasting C-peptide was 1.7 (0.9, 2.9) μg/L. One patient developed diabetic retinopathy. There were 4 missense variations in ABCC8 gene and one in-frame deletion, all of which were maternally inherited heterozygotes.Conclusions:MODY12 subtype is a heterogeneous disorder with the age of onset from infancy to adolescence. It can present as mild hyperglycemia or diabetic ketoacidosis, and has a high incidence of obesity. Definitive diagnosis can be achieved through genetic test, and individualized treatment is recommended based on glucose levels.

Objective:To describe the prevalence of food allergy among children aged 0-5 years in China and to explore related influencing factors.Methods:Multistage stratified random sampling method was used to collect data from 275 surveillance sites of the China National Nutrition and Health Survey of Chinese children and lactating mothers programs in 31 provinces (autonomous regions and municipalities) of China in 2016-2017. A total of 70 107 participants aged 0-5 years were included in this study. The study collected information of participants' demographic characteristics and food allergies by face-to-face questionnaire. The prevalence of food allergy was analyzed, using the complex data weighting method. The logistic regression models were used to analyze the influencing factors related to food allergy.Results:The overall prevalence of self-reported food allergy among children aged 0-5 years was 4.81%. Prevalence rates in infants aged 0-5 months, and 6-23 months and preschool children aged 2-5 years were 0.81%, 4.68% and 5.26%, respectively. The results of logistic analysis showed that there was a significantly positive correlation between factors including children from 6 months to 5 years old, urban area, southwest area, first-born, mothers with college education or above, and the prevalence of food allergy in children. Shrimp, poultry eggs, crab shellfish, fruit, milk and fish appeared the common allergic foods in children aged 0-5 years, with prevalence rates of self-reported food allergy as 1.55%, 1.25%, 0.99%, 0.97%, 0.87% and 0.86%, respectively. The proportion of single food allergy in children with allergies was 69.85%.Conclusions:Among children aged 0-5 years, the prevalence of self-reported food allergy increases with age, in China. Foods that is prone to allergies include fish, shrimp, crab, shellfish, poultry eggs, milk and fruits, etc. Most allergies were only caused by single food in children, under observation.

Objective To compare the effectiveness of Defocus Incorporated Multiple Segments (DIMS) and High Aspheric Lenslet (HAL) defocus lenses in controlling myopia. Methods A total of 214 children and adolescents who were fitted with defocus lenses (DIMS or HAL) in our hospital from January to June 2023 were selected, including 100 cases in DIMS group and 114 cases in the HAL group. Changes in parameters such as spherical lens power, cylindrical lens power, spherical equivalent (SE), corneal curvature (K), axial length (AL), binocular accommodative response (BCC), positive/negative relative accommodation (PRA/NRA), distance/near latent phoria (DLP/NLP), and accommodative convergence to accommodation ratio (AC/A) were compared between the two groups before wearing and after wearing the lenses for 6 months, and the effectiveness of myopia control between DIMS and HAL was further compared. Results After wearing the lenses (DIMS or HAL) for 6 months, the average increase in axial length was (0.09±0.14) mm, the average increase in spherical lens power was (-0.11±0.23) D, the average increase in astigmatism was (-0.07±0.16) DC, and the average increase in spherical equivalent was (-0.15±0.24) D, with statistically significant differences observed for all these changes (t=9.24, -7.27, -6.60, -9.03, respectively, P < 0.001). Compared with parameters before wearing the lenses, statistically significant differences were noted after wearing for 6 months in spherical lens power, cylindrical lens power, SE, BCC, PRA, DLP, average K, and AL (P≤0.001), except for NRA (t=-0.12, P>0.05), NLP (t=-0.28, P>0.05), and AC/A (t=0.33, P>0.05). The differences in parameters between the two groups after wearing the lenses for 6 months showed a statistically significant difference only in the AL difference (t=4.38, P≤0.001), with no statistically significant differences observed for other parameter differences (P>0.05). Conclusion Wearing defocus lenses has a positive effect on controlling myopia, delaying axial length elongation, and maintaining binocular visual function balance.