Background: The conventional magnetic resonance imaging (MRI) grading system for foraminal stenosis (FS), known as the Lee classification, was introduced in 2010 and is widely utilized in clinical practice. Previous studies have reported that the conventional grading system for FS lacks prediction ability for surgical treatment. The purpose of this study was to develop a novel MRI grading system for lumbar FS with improved prediction ability for surgical treatment by incorporating facet effusion to indicate segmental instability. Methods: We retrospectively reviewed patients diagnosed with lumbar FS between 2011 and 2017 who had a follow-up period of at least 5 years. The FS severity was assessed using a conventional MRI grading system developed by Lee et al. We recorded whether the patient underwent surgical treatment for FS during the follow-up period and the time from the initial diagnosis to surgery. Survival analysis using a Kaplan-Meier curve and log-rank test was performed to verify the impact of FS severity on the surgical treatment. We performed additional survival analysis after modifying the grading system by incorporating the presence of excessive facet joint effusion assessed using axial MRI. We also compared the discrimination ability of the modified and conventional grading systems using Uno’s concordance index (C-index). Results: In total, 235 patients with a mean age of 63.7 years were included in this study. During the mean follow-up period of 8.1 years, 63 patients underwent surgical treatment for FS. The conventional grading system revealed no significant difference in survival between the grade 2 and 3 groups (p = 0.104). Conversely, the modified grading system revealed a significant difference in survival between the new grade 2 and 3 groups (p < 0.001). After modification, the discrimination ability, assessed using Uno’s Cindex, significantly improved from 0.69 to 0.73. Conclusions The Facet Effusion-Incorporating Grading System, which adds excessive facet joint effusion to the conventional MRI grading framework, demonstrated improved predictive value for surgical treatment and better discriminatory ability compared with the original system.

Background: and Purpose Induced hypertension therapy (IHT) is effective for treating early neurological deterioration (END) in patients with single subcortical infarction (SSI). However, the underlying pathophysiology of SSI is diverse and may affect the efficacy of IHT. Methods: We reviewed patients with SSI who experienced END and received IHT were enrolled. END was defined as ≥2-point increase in the National Institutes of Health Stroke Scale (NIHSS) score, ≥1 motor point increase. IHT was performed by using phenylephrine infusion to raise systolic blood pressure by 10%–20% over baseline. SSI was classified into three subtypes: distal (dSSI), proximal (pSSI), and SSI with parental artery disease (SSIPAD; with stenosis at parental artery disease <50%). Responders were defined as patients who showed neurological improvement (decrease of ≥2 points in NIHSS, ≥1 motor point decrease) within one day after IHT initiation. Multivariable analysis identified factors associated with responders. Results: Characteristics were compared between responders and non-responders, and factors associated with response to IHT were investigated. Among 96 patients (mean age, 64.7± 12.9 years; 61.5% male), 49 (51.0%) patients were categorized as responders. Multivariable analysis showed that pSSI (adjusted odds ratio [aOR]=18.10, 95% confidence interval [CI] 5.04–80.60, p<0.001) and SSIPAD (aOR=4.56, 95% CI 1.31–19.00, p=0.024) were associated with a positive response to IHT compared to dSSI. Additionally, less white matter changes (Fazekas scale 0–1) were associated with a better response to IHT (aOR=0.15, 95% CI 0.03–0.67,p=0.019). Conclusions Response to IHT varied according to SSI subtypes and the severity of small vessel changes.

Methods: Fifty patients who underwent UBE-TLIF and 50 patients who underwent W-TLIF, each with >2 years of follow-up, were retrospectively analyzed. Outcomes included operative parameters, time to postoperative mobilization, paravertebral muscle atrophy and fat infiltration rates, clinical scores (Visual Analog Scale [VAS], Oswestry Disability Index [ODI], Japanese Orthopaedic Association [JOA]), modified Macnab criteria, fusion rates, and complications. Results: Compared with W-TLIF, the UBE-TLIF group had significantly less intraoperative blood loss, shorter operative times, and lower postoperative drainage volumes (p <0.05). The UBE-TLIF group showed faster postoperative recovery and shorter hospital stays. At 6 months, 1 year, and 2 years, W-TLIF patients had higher multifidus and erector spinae atrophy, and greater paravertebral muscle fat infiltration (p <0.05). The UBE-TLIF group also had lower VAS and ODI scores at 1 year and 2 years (p <0.05) and fewer surgical complications (6% vs. 10%). Fusion rates (94% vs. 92%) and modified Macnab outcomes (88% vs. 86%) were comparable (p >0.05). Conclusions UBE-TLIF is associated with reduced intraoperative trauma, quicker recovery, and fewer complications. In the long-term, it better preserves paravertebral muscle integrity and provides superior pain and functional outcomes.

Alzheimer’s disease (AD) is characterized by progressive cognitive deterioration and significant depression. However, the mechanisms linking depression to AD pathology remain unclear. Here, we investigated whether Notch2 signaling mediates depressionlike behaviors in presenilin-2 (PS2) N141I mutant mice, an early-onset AD model. PS2 wild-type (WT) and mutant (MT) mice aged 12-15 months were subjected to unpredictable chronic mild stress (UCMS) for 4 weeks, followed by sucrose preference, tail-hanging, and forced swimming tests. Behavioral assessments showed that UCMS exacerbated anhedonia and immobility only in PS2 MT mice. Molecular analysis revealed concomitant increases in plasma corticosterone, hippocampal γ-secretase activity, and Notch2 expression, and elevated total and phosphorylated glucocorticoid receptor levels in PS2 MT-UCMS mice. Gene expression profiling of human hippocampal datasets confirmed upregulation of NOTCH2 in Alzheimer’s disease and depression.Pharmacological inhibition of γ-secretase and Notch signaling with DAPT normalizes depressive behavior, reduces corticosterone release, attenuates GR phosphorylation, and inhibits Notch2 signaling in PS2 MT mice. These findings identify Notch2 as a pivotal mediator linking chronic stress to molecular changes associated with depression and AD, and suggest that targeting Notch2 signaling may provide therapeutic benefits for comorbid mood and neurodegenerative disorders.

Purpose: The male predominance in the incidence of gastric cancer (GC) is established; however, sex differences in the prognosis of GC remain controversial. As such, this study analyzed the prognosis of patients with GC based on age and sex. Materials and Methods: Data from 14,739 patients diagnosed with GC at Seoul National University Bundang Hospital between 2003 and 2023 were analyzed. Baseline characteristics, histological types of GC, overall and GC-specific survival rates (age and stage stratification), and associated risk factors were analyzed. Results: Females were significantly younger (p < 0.001) and exhibited more gastric body cancers (p < 0.001) and tumors with diffuse-type or poorly differentiated histology (p < 0.001) than males. Females exhibited an advantage over males in terms of overall survival (p=0.004), but not in GC-specific survival. However, age stratification revealed significant sex differences, that females < 50 years of age exhibited survival disadvantages (p < 0.001); however, this trend was reversed with age, and females > 60 years exhibited survival advantages (p < 0.001) for both overall and GC-specific survival. This may be explained by the lower ratio of diffuse-type GC as females age. Furthermore, in the analysis according to stage, females with stage IV disease exhibited significant survival disadvantages, with significantly younger age and a higher proportion of diffuse-type GC which exhibits aggressive features, resulting in poorer survival than in males. Conclusion Age and stage stratification revealed significant differences in survival between the sexes, which can be helpful for public health strategies.

Objective:To explore the establishing Nomoplot prediction model for Kawasaki disease(KD)combined with coronary artery lesion(CAL)children was established based on logistic regression,and to evaluate its predictive efficacy.Methods:The clinical data of 367 KD children who were admitted to our hospital from January 2014 to June 2024 were retrospectively analyzed,the children were divided into CAL(n=91)and NCAL(n=276)groups according to whether they had concurrent CAL.The influencing factors for CAL children combine with KD were analyzed by univariate and multivariate logistic regression.Nomoplot prediction model for CAL children combine with KD was constructed.The predictive efficacy of the Nomoplot prediction model for CAL children combine with KD was evaluated by receiver operating characteristic(ROC)curve.Results:The incidence of CAL in 367 KD children was 24.80％(91/367),univariate analysis results showed that,the proportion of delayed intravenous immunoglobulin(IVIG)treatment and the proportion of IVIG resistance in CAL group were higher than those in NCAL group(P＜0.05),the fever duration was longer than that in NCAL group(P＜0.05),and the age,hemoglobin(Hb)and platelet count(PLT)levels were lower than those in NCAL group(P＜0.05),erythrocyte sedimentation rate(ESR),B-type natriuretic peptide(BNP),C-reactive protein(CRP),procalcitonin(PCT),albumin(ALB)and alanine aminotransferase(ALT)levels were higher than those in NCAL group(P＜0.05).Delayed IVIG treatment,prolonged fever duration,elevated CRP,ESR and BNP levels were independent risk factors for CAL children combine with KD(P＜0.05),Older age,elevated Hb level were protective factor(P＜0.05).The predicted curve of the Nomoplot prediction model was in good agreement with the ideal curve,the area under the curve(AUC)of the model for predicting CAL children combine with KD was 0.971.Conclusions:Delayed IVIG treatment,fever duration,age and Hb,CRP,ESR and BNP are independent Influencing factors for CAL children combine with KD,the Nomoplot prediction model constructed based on the above logistic regression results has a high predictive value for CAL children combined with KD.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To analyze the correlation between lesion volume,lesion mass,and maximum lesion diameter in the assessment of advanced hepatocarcinoma with lung metastasis,and to evaluate the application value of total volume response and total mass response of lung metastatic lesions in efficacy assessment.Methods A retrospective analysis was conducted on the CT imaging data of 20 patients clinically confirmed with hepatocarcinoma and lung metastases,followed by subsequent follow-up to monitor their survival outcomes.Volume measurement software was used to measure the volume of lesions before and after treatment.We recored lesion diameter,volume measurements and CT values,calculated the mass of the lesions.The correlation between lesion volume,mass and diameter was analyzed,as well as the correlation between the change rates of volume,mass and lesion diameter.Additionally,the total volume and total mass of all lesions were calculated.The correlation between the change rates of total volume/total mass and the change rate of pulmonary lesion diameter under the RECIST 1.1 criteria,as well as the correlation with changes in patients'tumor markers,were analyzed.Furthermore,the overall volume response and overall mass response of lesions were evaluated based on changes in total volume and total mass,and their consistencies with the RECIST 1.1 criteria for efficacy evaluation were analyzed.Finally,univariate Cox regression analysis was performed to explore the association between these variables and patient survival outcomes.Results There was strong correlation between lesion volume,mass and tumor diameter(r=0.771,0.775),between the rate of change in mass and the rate of change in lesion diameter(r=0.846),and between the rates of change in total volume/total mass and the rate of change in pulmonary lesion diameter under the RECIST 1.1 criteria(r=0.800,0.896).The correlation between the rates of change in total volume/total mass and patients'tumor markers was not statistically significant.There was moderate correlation between the rate of change in volume and the rate of change in lesion diameter(r=0.692).The evaluation results of total volume response and total mass response for pulmonary lesions in advanced hepatocarcinoma with lung metastasis were generally consistent with the RECIST 1.1 criteria(Kappa=0.486,0.426).Univariate Cox regression analysis revealed that total lesion volume(P=0.047)and total lesion mass(P=0.049)were independent prognostic factors for survival outcomes.Conclusion Lesion volume,mass,and diameter,as well as their respective change rates,were found to be interrelated.Furthermore,total lesion volume and total lesion mass were identified as independent prognostic factors for survival outcomes.The total volume response and total mass response are promising evaluation methods in evaluating the efficacy of lung metastasis of hepatocarcinoma,which are different from the RECIST 1.1 evaluation criteria.

Objective:To explore the establishing Nomoplot prediction model for Kawasaki disease(KD)combined with coronary artery lesion(CAL)children was established based on logistic regression,and to evaluate its predictive efficacy.Methods:The clinical data of 367 KD children who were admitted to our hospital from January 2014 to June 2024 were retrospectively analyzed,the children were divided into CAL(n=91)and NCAL(n=276)groups according to whether they had concurrent CAL.The influencing factors for CAL children combine with KD were analyzed by univariate and multivariate logistic regression.Nomoplot prediction model for CAL children combine with KD was constructed.The predictive efficacy of the Nomoplot prediction model for CAL children combine with KD was evaluated by receiver operating characteristic(ROC)curve.Results:The incidence of CAL in 367 KD children was 24.80％(91/367),univariate analysis results showed that,the proportion of delayed intravenous immunoglobulin(IVIG)treatment and the proportion of IVIG resistance in CAL group were higher than those in NCAL group(P＜0.05),the fever duration was longer than that in NCAL group(P＜0.05),and the age,hemoglobin(Hb)and platelet count(PLT)levels were lower than those in NCAL group(P＜0.05),erythrocyte sedimentation rate(ESR),B-type natriuretic peptide(BNP),C-reactive protein(CRP),procalcitonin(PCT),albumin(ALB)and alanine aminotransferase(ALT)levels were higher than those in NCAL group(P＜0.05).Delayed IVIG treatment,prolonged fever duration,elevated CRP,ESR and BNP levels were independent risk factors for CAL children combine with KD(P＜0.05),Older age,elevated Hb level were protective factor(P＜0.05).The predicted curve of the Nomoplot prediction model was in good agreement with the ideal curve,the area under the curve(AUC)of the model for predicting CAL children combine with KD was 0.971.Conclusions:Delayed IVIG treatment,fever duration,age and Hb,CRP,ESR and BNP are independent Influencing factors for CAL children combine with KD,the Nomoplot prediction model constructed based on the above logistic regression results has a high predictive value for CAL children combined with KD.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.