The purpose of this study was to examine how low-energy LED red light influences the early to middle stage of osteogenic differentiation of periodontal ligament stem cells (PDLSCs) via the ERK5 signaling pathway. PDLSCs were extracted from periodontal membrane tissue using enzymatic digestion. At three time points of 7, 10, and 14 days after irradiation with 5J/cm2 LED red light, the expression levels of early to middle-stage osteogenic-related genes ALP, Col-1, BSP, and OPN were detected by real-time fluorescence quantitative PCR(qRT-PCR) in both control and osteogenesis experimental groups. The addition of BIX02189 could block the ERK5 signaling pathway. Under irradiation with 5J/cm2 LED red light, the expression levels of the ERK5 gene, related proteins ERK5, p-ERK5, as well as early to middle-stage osteogenic-related genes ALP, Col-1, BSP, and OPN were detected by qRT-PCR and Western blot in the osteogenic medium group and the osteogenic medium + BIX02189 group. Both low-energy LED red light and osteogenic medium could induce osteogenesis and differentiation of PDLSCs, upregulating the expression of ALP, Col-1, BSP, and OPN genes in PDLSCs. Their combination also produced a synergistic effect. Moreover, the ERK5 signaling pathway participated in the promoting effect of LED red light on the early to middle-stage osteogenic differentiation of PDLSCs, indicating a positive role of LED red light in this process. The ERK5 signaling pathway can mediate the promotion of early to middle-stage osteogenic differentiation of PDLSCs by low-energy LED red light.

Background:Remimazolam is an ultra-short-acting benzodiazepine with sedative effects, but its impact on postoperative delirium (POD) and cognitive function in elderly patients remains unclear. This study aimed to compare the incidence of POD and cognitive function between remimazolam and other sedatives in elderly patients undergoing general anesthesia or procedural sedation. Methods:This study included randomized controlled trials (RCTs) comparing remimazolam with other sedatives in elderly patients undergoing general anesthesia or procedural sedation. A comprehensive search was conducted in Ovid MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, and the China National Knowledge Infrastructure (CNKI) from inception to January 2, 2025, without language restrictions. Data were pooled quantitatively using a random-effects model. The primary outcomes were the incidence of POD and cognitive function. Results:A total of 1,808 elderly patients from 11 RCTs were included. Compared with other sedatives, remimazolam did not increase the incidence of POD (OR: 0.62, 95% CI [0.23, 1.68], p = 0.35, I 2 = 73%), but improve cognitive function, as measured by Mini-Mental State Examination scores, the seventh postoperative day (MD: 0.53, 95% CI [0.16, 0.91], p = 0.005, I 2 = 28). Additionally, remimazolam significantly reduced the incidence of hypotension (OR: 0.27, 95% CI [0.21, 0.35], p < 0.001, I 2 = 0%) and respiratory depression (OR: 0.35, 95% CI [0.17, 0.69], p = 0.003, I 2 = 0%) compared to other sedatives. However, no significant differences were observed between remimazolam and other sedatives for postoperative nausea and vomiting (OR: 1.31, 95% CI [0.91, 1.89], p = 0.15, I 2 = 0%) or hypoxemia (OR: 0.69, 95% CI [0.35, 1.34], p = 0.28, I2 = 0%). Conclusion:Overall, the use of remimazolam in the elderly population appears to pose fewer risks than other sedatives. It does not increase the incidence of postoperative delirium following general anesthesia or sedation, but it improves postoperative cognitive function and provides more stable hemodynamics. However, further well-designed RCTs with long-term follow-up are needed to establish a standardized medication regimen and optimal dosage tailored to elderly patients. Systematic review registration:https://www.crd.york.ac.uk/PROSPERO/myprospero, registration number (CRD4202563620).

INTRODUCTION:Cerebral small vessel disease (CSVD) is highly prevalent in elder individuals, and its variable cognitive outcomes indicate some cognitive reserve mechanisms. Contribution from functional network features is still unclear. Here we explore how functional segregation-integration preference influences the cognitive changes against CSVD. MATERIALS AND METHODS:A total of, 271 CSVD patients were included, all underwent MRI scans including routine and resting-state functional MRI (rs-fMRI). Hierarchical balance index (HB) was obtained from the rs-fMRI connectivity using eigenmode-based approach. Individuals were classified into segregated and integrated groups according to negative and positive HB. A composite CSVD lesion score was calculated from imaging findings. Global and five specific cognitive functions were assessed. RESULTS:Hierarchical regression analysis revealed negative contribution from lesion load to global and all cognitive domains (β = -0.22~-0.35, ∆R2 = 0.046~0.112, all p < 0.001). Inclusion of HB did not show significant contribution (all p > 0.05), but interaction between HB and lesion score was significantly associated with global (β = -0.27, ∆R2 = 0.013, p = 0.034) and execution score (β = -0.34, ∆R2 = 0.023, p = 0.002). Integrated patients show significant better global cognitive (23.9 ± 3.9 vs. 25.5 ± 3.1, p = 0.044) and executive ability (0.235 ± 0.678 vs. 0.535 ± 0.688, p = 0.049) at mild damage stage, visuospatial (-0.001 ± 0.804 vs. 0.379 ± 0.249, p = 0.034) and language ability (-0.133 ± 0.849 vs. 0.218 ± 0.704, p = 0.037) at moderate damage stage. Cross-overs of cognitive scores were observed. Significant better execution (-0.277 ± 0.717 vs. -0.675 ± 0.883, p = 0.027) was found in severe damage stage for segregated patients. CONCLUSION:Thus, we concluded that integrated network contributes to cognitive resilience in mild and moderate but not in severe damage stages.

Neuroimaging studies revealed that smoking is associated with abnormal white matter (WM) microstructure. However, results are controversial, and the impact of smoking on the WM integrity in young smokers is still unclear. In this study, we used diffusion tensor imaging to investigate the smoking-related WM alterations in young adult smokers. One hundred and twenty-six subjects (60 current smokers and 66 nonsmokers) aged 18-29 years participated in the study. The tract-based spatial statistics with multiple diffusion indices was applied to explore diffusivity patterns associated with smoking. Correlation analysis was performed to evaluate relationships between fractional anisotropy (FA) and smoking-related variables in young adult smokers. Compared with nonsmokers, young adult smokers showed higher FA dominantly in the anterior cerebral WM regions, while lower FA mainly in the posterior cerebral WM areas. The dominant diffusivity pattern for regions with larger FA was characterized by lower radial and axial diffusion (Dr and Da), while in areas with smaller FA, higher Dr without significant difference in Da was the main diffusivity pattern. Moreover, diffusion indices in the genu and body of the corpus callosum were related with smoking-related variables. Our findings indicate that smoking may have differential effects on the WM integrity in the anterior and posterior parts of the brain, and may also accelerate brain aging in young adult smokers.

In this research, we introduce an enhanced Convolutional Neural Network (CNN) model designed to address the limitations of traditional CNNs in processing multimodal medical images, such as inadequate data fusion and imprecise feature extraction. By employing advanced deep learning techniques, our model significantly improves the fusion and feature extraction processes of multimodal data, leading to a marked increase in the accuracy and efficiency of medical image segmentation. Through cross-validation tests conducted on three diverse datasets, our enhanced CNN model demonstrated an average segmentation accuracy between 92% and 94%, outperforming established models like U-Net and V-Net. Moreover, the model shows exceptional robustness across different noise levels, maintaining a high segmentation accuracy of 95% in noise-free conditions and 81% under high noise scenarios. These results underscore the superior performance and reliability of our improved CNN model in medical image segmentation, showcasing its potential for advancing multimodal medical image processing techniques in clinical applications.

Steel surface defect detection in industrial quality control has always been a challenging objective detection task in the field of computer vision. However, unlike other detection problems, some surface defects on steel are relatively small compared to the entire inspection object, leading to less prominent defect features in the detection. To address these issues, we propose a YOLOv5-based steel defect detection method enhanced with multi-scale feature extraction and contextual augmentation (MSCA-YOLO). Specifically, adopting the YOLOv5 as the backbone network, we first add the C3-RFE to expand the receptive. Then, we design a neck network structure via combining multi-scale guided upsampling, which effectively enhances the model’s ability to handle multi-scale features and improves the model’s feature extraction ability for small defects. Finally, we propose a context mechanism that provides the model with a deeper context analysis capability, offering richer up-and-down information. The experiments on the NEU-DET dataset show that MSCA-YOLO achieves a mean Average Precision of 0.645 while maintaining rapid detection, especially at an Intersection over Union threshold of 0.5. It also exhibits substantial improvements in Precision compared to YOLOv5 across six defect types: Crazing (18.5% increase), Inclusion (1.2% increase), Patches (1.9% increase), Pitted_Surface (7.8% increase), Rolled-in_Scale (8.9% increase), and Scratches (6.5% increase). This achievement marks the efficiency and reliability of MSCA-YOLO in automated steel surface defect detection, providing a new solution for real-time inspection of steel surface defects.

Background: White-matter hyperintensity (WMH) is the key magnetic resonance imaging (MRI) marker of cerebral small-vessel disease (CSVD). This study aimed to investigate whether habitat analysis based on physiologic MRI parameters can predict the progression of WMH and cognitive decline in CSVD. Methods: Diffusion- and perfusion-weighted imaging data were obtained from 69 patients with CSVD at baseline and at 1-year of follow-up. The white-matter region was classified into constant WMH, growing WMH, shrinking WMH, and normal-appearing white matter (NAWM) according to the T2-fluid-attenuated inversion recovery (FLAIR) sequences images at the baseline and follow-up. We employed k-means clustering on a voxel-wise basis to delineate WMH habitats, integrating multiple diffusion metrics and cerebral blood flow (CBF) values derived from perfusion data. The WMH at the baseline and the predicted WMH from the habitat analysis were used as regions of avoidance (ROAs). The decreased rate of global efficiency for the whole brain structural connectivity was calculated after removal of the ROA. The association between the decreased rate of global efficiency and Montreal Cognitive Assessment (MoCA) and mini-mental state examination (MMSE) scores was evaluated using Pearson correlation coefficients. Results: We found that the physiologic MRI habitats with lower fractional anisotropy and CBF values and higher mean diffusivity, axial diffusivity, and radial diffusivity values overlapped considerably with the new WMH (growing WMH of baseline) after a 1-year follow-up; the accuracy of distinguishing growing WMH from NAWM was 88.9%±12.7% at baseline. Similar results were also found for the prediction of shrinking WMH. Moreover, after the removal of the predicted WMH, a decreased rate of global efficiency had a significantly negative correlation with the MoCA and MMSE scores at follow-up. Conclusions: This study revealed that a habitat analysis combining perfusion with diffusion parameters could predict the progression of WMH and related cognitive decline in patients with CSVD.

ObjectivesTo investigate the potential trade-offs among brain structural network characteristics across different stages of cognitive impairment in cerebral small vessel disease (CSVD) based on diffusion tensor imaging (DTI).MethodsA total of 264 CSVD patients, including 95 patients with non-cognitive impairment (NCI), 142 with mild cognitive impairment (MCI), 27 with vascular dementia (VaD), and 30 healthy controls (HC) underwent cognitive test and brain diffusion magnetic resonance imaging (MRI). The brain structural network was constructed using connections between 90 cortical and subcortical regions. Network characteristics, including sparsity, redundancy, global efficiency (Eg), and local efficiency (Eloc), were calculated.ResultsSparsity and redundancy significantly declined in the NCI group compared to the HC group. Eg was significantly reduced in the MCI group compared to the NCI group. All network characteristics declined in the VaD group compared to the MCI group. In the NCI group, both sparsity and redundancy were significantly positively correlated with Montreal Cognitive Assessment (MoCA). In the MCI group, there was significant positive correlation between Eg and MoCA. In the VaD group, there was significant negative correlation between Eloc and MoCA. When controlling for sparsity, Eloc exhibited a significant negative correlation with Eg in all three CSVD groups, while redundancy displayed a significant negative correlation with Eg specifically in MCI group.ConclusionOur study provides evidence for the heterogeneous alterations in brain structural network across different stages of cognitive impairment in CSVD. The disconnection of brain structural network at NCI stage is mainly the loss of redundant connections. The decline of Eg is the vital factor for cognitive impairment at MCI stage. The decline of all network characteristics is the prominent manifestation at VaD stage. Throughout the cognitive decline process in CSVD, there are trade-offs among the brain network wiring cost, integration, and segregation.

PURPOSE:To compare the 26-week cost-effectiveness of adalimumab-corticosteroids (ADA-CS) and cyclosporine-corticosteroids (CSA-CS) for Vogt-Koyanagi-Harada (VKH). METHODS:A preplanned cost-effectiveness analysis based on the per-protocol population of a randomized-controlled trial. VKH subjects were randomized to receive either cyclosporine (100-200 mg daily) combined with corticosteroids or adalimumab (40 mg twice monthly) combined with corticosteroids. The primary outcome of this cost-effectiveness study was the incremental cost-effectiveness ratio (ICER). Costs and quality-adjusted life-years (QALYs) data were calculated by the medical records and health utility, respectively. Subgroup (early and late-phase VKH) analysis and sensitivity analyses were performed. RESULTS:The ICER at 26 weeks was $62,425/QALY for the total participants. Compared to the CSA-CS group, costs in the ADA-CS group were more expensive (mean difference [ΔA-C]: $2,497) with more gains in QALYs (mean difference [ΔA-C]: 0.04). The probability of ADA-CS being cost-effective was 0.17 and 0.41 at willingness to pay (WTP) thresholds of $12,000/QALY and $36,000/QALY, respectively. Subgroup analysis and sensitivity analyses showed consistent findings with the primary analysis. CONCLUSIONS:Regardless of early or late-phase VKH, the CSA-CS strategy may be recommended as the preferred initial choice for the majority of VKH.

Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key neuroimaging evidence remains unrevealed for elucidating such commonness and the relationships among these disorders. To explore this puzzle, we build a restricted single-branch deep learning model, using multi-site functional magnetic resonance imaging data (N=4,410, 6 sites), for classifying 5 different early- and late-life brain disorders from healthy controls (cognitively unimpaired). Our model achieves 62.6±1.9% overall classification accuracy and thus supports us in detecting a set of commonly affected functional subnetworks, including default mode, executive control, visual, and limbic networks. In the deep-layer representation of data, we observe young and aging patients with disorders are continuously distributed, which is in line with the clinical concept of the “spectrum of disorders”. The relationships among brain disorders from the revealed spectrum promote the understanding of disorder comorbidities and time associations in the lifespan.

Fuchs' uveitis syndrome (FUS) is one of the most under- or misdiagnosed uveitis entities. Many undiagnosed FUS patients are unnecessarily overtreated with anti-inflammatory drugs, which may lead to serious complications. To offer assistance for ophthalmologists in the screening and diagnosis of FUS, we developed seven deep convolutional neural networks (DCNNs) to detect FUS using slit-lamp images. We also proposed a new optimized model with a mixed "attention" module to improve test accuracy. In the same independent set, we compared the performance between these DCNNs and ophthalmologists in detecting FUS. Seven different network models, including Xception, Resnet50, SE-Resnet50, ResNext50, SE-ResNext50, ST-ResNext50, and SET-ResNext50, were used to predict FUS automatically with the area under the receiver operating characteristic curves (AUCs) that ranged from 0.951 to 0.977. Our proposed SET-ResNext50 model (accuracy = 0.930; Precision = 0.918; Recall = 0.923; F1 measure = 0.920) with an AUC of 0.977 consistently outperformed the other networks and outperformed general ophthalmologists by a large margin. Heat-map visualizations of the SET-ResNext50 were provided to identify the target areas in the slit-lamp images. In conclusion, we confirmed that a trained classification method based on DCNNs achieved high effectiveness in distinguishing FUS from other forms of anterior uveitis. The performance of the DCNNs was better than that of general ophthalmologists and could be of value in the diagnosis of FUS.

Background/aims Fuchs' uveitis syndrome (FUS) is one of the frequently misdiagnosed uveitis entities, which is partly due to the absence of internationally recognised diagnostic criteria. This study was performed to develop and evaluate a set of revised diagnostic criteria for FUS. Methods The clinical data of Chinese patients with FUS and patients with non-FUS were collected and analysed from a tertiary referral centre between April 2008 and December 2020. A total of 593 patients with FUS and 625 patients with non-FUS from northern China were enrolled for the development of diagnostic criteria for FUS. Three hundred and seventy-seven patients with FUS and 503 patients with non-FUS from southern China were used to validate the criteria. Clinical symptoms and ocular signs were collected from all patients with FUS and patients with non-FUS. Multivariate two-step cluster analysis, logistic regression and decision tree algorithms in combination with the clinical judgement of uveitis experts were used to revise diagnostic criteria for FUS. Results Three essential findings including diffuse iris depigmentation, absence of posterior synechiae, mild inflammation in the anterior chamber at presentation and five associated findings including mostly unilateral involvement, cataract, vitreous opacities, absence of acute symptoms and characteristic iris nodules were used in the development of FUS diagnostic criteria. All essential findings were required for the diagnosis of FUS, and the diagnosis was further strengthened by the presence of associated findings. Conclusion Revised diagnostic criteria for FUS were developed and validated by analysing data from Chinese patients and showed a high sensitivity (96.55%) and specificity (97.42%).