Search Results (283)

Background: Malnutrition is a universal challenge in long-term care, significantly affecting vulnerable populations. Residents with Intellectual Developmental Disability (IDD) rely heavily on Direct Support Professionals (DSPs) for assisted feeding. Understanding DSP’s mealtime experiences is essential for improving nutritional care and well-being. Objective: To examine multilevel factors associated with DSPs’ mealtime experiences. Methods: This exploratory cross-sectional case study used a survey administrated to DSPs working in a long-term residential setting. Statistical analyses examined the associations between multilevel factors and DSP’s positive and negative mealtime experiences. Results: The sample included 46 DSP’s (98% women) from a single facility in Israel. Although DSPs reported high levels of positive feelings and satisfaction with their daily work efficacy, negative feelings were significantly associated with some organizational, environmental and resident-related factors. Negative feelings were higher among DSPs caring for residents who use wheelchairs compared to those working with residents who do not use wheelchairs (t = −2.99, p < 0.01). Negative feelings were negatively associated with institutional support (r = −0.49, p < 0.001), and perceived accessibility and adaptability of the environment (r = −0.46, p = 0.001), and showed a more modest association with communication with residents (r = −0.38, p = 0.01). DSPs’ seniority, education level, and prior feeding-related training were not significantly associated with mealtime experience. Conclusions: The findings highlight that negative mealtime experiences among DSPs are associated with organizational, environmental, and resident-related factors, rather than with individual DSP’s characteristics. Policy and practical adjustments to address mealtime experiences for residents with IDD are suggested. Full article

Objective: This study aims to analyze the dynamic changes in lymphatic endothelial cell (LEC) markers during the progression of intervertebral disk degeneration (IDD) and to investigate their association with the progression of IDD. Method: In this study, intervertebral disk (IVD) specimens were first collected from patients who underwent open lumbar fusion surgery for spinal fractures (control group, n = 10) and lumbar disk herniation (IDD group, n = 10). Concurrently, a mouse IDD model was established, and IVD specimens were collected from mouse in the Sham group and the IDD group 1, 3, and 6 weeks after modeling (n = 5 per group at each time point). Pathological morphological changes in human and mouse IVD specimens were observed using Hematoxylin and Eosin (H&E) and Masson’s Trichrome staining. The degree of degeneration in the mouse IVD specimens was quantified using a histopathological scoring system. Subsequently, real-time quantitative polymerase chain reaction (RT-qPCR), immunohistochemistry (IHC), and immunofluorescence (IF) staining were employed to examine LEC markers in IVD tissue, including lymphatic vessel endothelial hyaluronan receptor 1 (LYVE-1), podoplanin (PDPN), prospero homeobox protein 1 (PROX-1), and vascular endothelial growth factor receptor 3 (VEGFR-3), as well as matrix metabolism-related markers such as matrix metalloproteinase 13 (MMP-13) and collagen II (Col II). Finally, we performed Spearman’s rank correlation analysis between the histopathological scores of all mouse IVD specimens and the corresponding expression levels of LEC markers. Results: In human IVD tissue, expression levels of LYVE-1, PDPN, PROX-1, and VEGFR-3 were extremely low in the normal group. In contrast, expression of these markers was significantly upregulated in the IDD group. In the mouse IDD model, compared with the Sham group at the same time point, the IDD group exhibited higher histopathological scores in IVD tissue, accompanied by upregulation of LYVE-1, PDPN, PROX-1, and MMP-13, as well as downregulation of Col II. In-depth analysis revealed that these differences between the Sham and IDD groups were not static but exhibited a dynamic pattern of increasing magnitude over time. Concurrently, as the modeling period progressed, the histopathological scores of mouse IVD in the IDD group, as well as the expression levels of LYVE-1, PDPN, PROX-1, and MMP-13, showed a progressive upward trend, while Col II expression progressively decreased. In addition, Spearman’s rank correlation analysis revealed that the expression levels of LYVE-1, PDPN, and PROX-1 in mouse IVD tissue were all significantly positively correlated with histopathological scores. Conclusions: In the process of IDD, the dynamic upregulation of LEC markers is highly consistent with its severity in the time dimension. At the same time, there was also a significant positive correlation between the expression level of LEC markers and the severity of IDD. Taken together, these findings suggest that the dynamic upregulation of LEC markers may be potentially associated with the pathological progression of IDD. Full article

Background: INDETERMINATE DOMAIN proteins (IDDs) are a plant-specific transcription factor family, and members of this family play crucial roles in regulating growth and development as well as environmental adaptation. However, a comprehensive analysis of the IDD family in soybean [Glycine max (L.) Merrill] is limited. Methods and Results: A total of 27 GmIDD genes were identified in the soybean genome, unevenly distributed across 14 chromosomes, and their encoded proteins all harbor a conserved INDETERMINATE (ID) domain with two Cys2His2 (C2H2) and two Cys2HisCys (C2HC) zinc finger motifs. Phylogenetic analysis classified these GmIDD genes into three subgroups. Soybean GmIDD genes exhibit high homology with their Arabidopsis thaliana IDD counterparts. Cis-acting element analysis indicated that the promoters of GmIDD genes are enriched in light-responsive elements (such as Box4), hormone-responsive elements (such as ABRE and AuxRR-core), and abiotic stress-responsive elements (such as MBS and LTR). The qRT-PCR results showed that GmIDD3/5/14/22/26 were upregulated under salt stress, while GmIDD8/9/10/12/16/17/19/20/23/24/25/27 were obviously downregulated during treatment. Under drought stress, the expression levels of GmIDD4/6/7/10/14/16/19/22/24/25/26/27 were upregulated during the treatment. The expression levels of GmIDD1/2/3/4/12/14/15/16/17/18/22/23/25/26 were induced by short-day conditions, whereas GmIDD9/13/19/21 were induced by long-day conditions in soybean leaves. Conclusions: This study provides a theoretical basis for further understanding the functions of the soybean IDD gene family in abiotic stress tolerance and photoperiod adaptability. Full article

Background and Objectives: Low back pain (LBP) is a leading cause of disability worldwide and is frequently associated with intervertebral disc degeneration (IVDD). Current therapeutic strategies are primarily symptomatic and do not restore native disc biology, largely due to the avascular nature of the intervertebral disc and the hostile inflammatory and mechanical microenvironment that characterizes degeneration. The aim of this study is to provide an updated and clinically oriented overview of the pathophysiology of IVDD and to evaluate the current evidence on mesenchymal stem cells (MSCs) and platelet-rich plasma (PRP)-based therapies. Materials and Methods: A focused narrative literature review was performed to evaluate current evidence on MSC- and PRP-based therapies for intervertebral disc degeneration (IVDD). The search was conducted in PubMed. Only studies in English were considered eligible. Results: Mesenchymal stem cells (MSCs) demonstrated regenerative and immunomodulatory effects primarily through paracrine mechanisms, enhancing extracellular matrix synthesis and reducing inflammation and apoptosis. MSC-derived extracellular vesicles emerged as a promising cell-free alternative, potentially overcoming limitations related to cell survival and safety. Platelet-rich plasma (PRP) showed anabolic and anti-inflammatory properties, promoting disc cell proliferation and matrix production, particularly in early-stage degeneration. Clinical studies, including randomized trials, reported significant improvements in pain and function for both MSC and PRP therapies, with favourable safety profiles. However, heterogeneity in treatment protocols and limited long-term data remain significant limitations. Orthobiologic therapies represent a minimally invasive option for patients with discogenic low back pain refractory to conservative treatment. Patient selection is crucial and should consider degeneration stage, disc viability, and clinical presentation. PRP is primarily indicated in early-stage degeneration (Pfirrmann II–III), whereas MSC-based therapies may be considered in selected patients with more advanced but still viable discs. Based on current evidence, a stepwise approach is proposed, progressing from conservative management to PRP, MSCs, and ultimately surgery. Orthobiologics should be integrated within a multimodal strategy including rehabilitation. Conclusions: MSCs and PRP represent a promising and, eventually, complementary orthobiologic therapies for IVDD. PRP is primarily effective in early degenerative stages as a biologic stimulator, whereas MSCs may provide regenerative benefits in more advanced but still viable discs. Further studies are necessary to standardize protocols and confirm long-term efficacy and safety. Full article

In police culture, the danger imperative is the idea that the most important part of policing is to “come home at the end of the night.” Neurodivergence brings uncertainty to police encounters; because of the danger imperative, police officers may respond to that uncertainly with increased use of force. We examine the likelihood of being handcuffed and detained (low levels of use of force) for individuals with intellectual/developmental disabilities (I/DDs) (i.e., neurodiverse diagnoses) during discretionary stops using data from police stops in California (n = 3,300,671) and doubly-robust inverse-propensity weighted regression. Results show that the average effect of being I/DD on the likelihood of being handcuffed is nearly 6.5% percentage points higher than people without I/DD; similarly, the average effect of being I/DD on the likelihood of being detained is also nearly 7.5% percentage points higher than people without I/DD. Our findings point to officers’ perceptions of danger and safety (i.e., the danger imperative) during encounters with individuals with I/DD, creating disparate experiences with low levels of use for force for this population. Full article

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

Background: Individuals with intellectual and developmental disabilities (IDD) experience persistent health disparities that result in poorer health outcomes, reduced quality of life, and inequitable access to healthcare. Objective: This narrative review synthesized existing literature to identify key barriers to health equity and contributors to health disparities among individuals with IDD. Method: This study was a narrative (non-systematic) review that adopted a qualitative synthesis approach. A literature review was conducted across PubMed, CINAHL, PsycINFO, Medline, and Google Scholar to identify peer-reviewed articles published between 2010 and 2025 that address health disparities, health inequities, healthcare barriers, and social determinants of health among individuals with IDD. Thematic analysis was employed to synthesize the included studies and identify recurring patterns and themes. Results: A total of 88 articles were included. Two overarching domains shaping health disparities were identified: barriers to health equity and contributing factors. Seven barrier categories emerged: attitudinal, communication, policy, programmatic, social, physical, and transportation. Five key contributors were also identified: limited access to healthcare, comorbid conditions, low health literacy, adverse social determinants of health, and caregiver burden. Conclusions: Health disparities among individuals with IDD are driven by intersecting social, structural, and healthcare system barriers rather than individual limitations alone. This review informs policymakers, public health professionals, and interventionists on how to advance health equity for individuals with IDD through targeted, person-centered interventions. Full article

Semantic segmentation plays a pivotal role in autonomous driving, enabling pixel-level understanding of road scenes. Although transformer-based models such as SegFormer have shown exceptional performance on large datasets, their generalization to smaller and geographically diverse datasets remains underexplored. In this work, we analyze the scalability and transferability of SegFormer variants (B3, B4, B5) using CamVid as the base dataset. We perform cross-dataset transfer learning to KITTI and IDD, evaluate class-level performance, and explore explainable AI via confidence heatmaps. Our findings show that SegFormer-B5 achieves the highest accuracy ($82.4$% mIoU) on CamVid, while transfer learning from CamVid improves mIoU on KITTI by $2.57$% and enhances class-specific predictions in IDD by over 70%. These results highlight the practical potential of SegFormer in real-world segmentation systems and the interpretability benefits of confidence-based visual analysis. Full article

Surface defects in industrial production are complex and diverse. Therefore, deep learning-based defect detection models must consistently adapt to newly emerging defect categories. The trained models generally suffer from catastrophic forgetting as they learn new defect categories. To address this issue, we propose a selective distillation for incremental defect detection (SD-IDD) model based on GFLv1. Specifically, three selective distillation strategies are proposed, including high-confidence classification distillation, dual-stage cascaded regression distillation, and Intersection over Union (IoU)-driven difficulty-aware feature distillation. The high-confidence classification distillation aims to preserve critical discriminative knowledge of old categories within semantic confusion regions of the classification head, reducing interference from low-value regions. Dual-stage cascaded regression distillation focuses on high-quality anchors through geometric prior coarse filtering and statistical fine filtering, utilizing IoU-weighted KL divergence distillation loss to accurately transfer localization knowledge. IoU-driven difficulty-aware feature distillation adaptively allocates distillation resources, prioritizing features of high-difficulty targets. These selective distillation strategies significantly mitigate catastrophic forgetting while enhancing the detection accuracy of new classes, without requiring access to old training samples. Experimental results demonstrate that SD-IDD achieves superior performance, with mAP_old of 58.2% and 99.3%, mAP_new of 69.0% and 97.3%, and mAP_all of 63.6% and 98.3% on the NEU-DET and DeepPCB datasets, respectively, surpassing existing incremental detection methods. Full article

Aiming to address the challenges of excessive model parameters, high computational complexity, strong complex background interference, and inadequate small-target detection found in insulator defect detection when using UAV remote sensing imagery of transmission lines, we propose a lightweight multi-defect detection model—Insulator Defect Detection-DETR (IDD-DETR). Specifically, we introduce a lightweight multi-starblock feature extractor (LMS-FE) as the backbone network to enhance its feature extraction capacity. Next, in order to enhance small-defect detection performance, a multi-scale feature pyramid (SOEP) is constructed by integrating shallow high-resolution features into the neck network. Additionally, a lightweight multi-branch feature fusion module (LMB-FF) is designed to efficiently fuse spatial and semantic information of small defects, suppressing background interference while optimizing model complexity. Finally, experimental results demonstrate that IDD-DETR achieves a 2.2% improvement in mean average precision (mAP) on the insulator small-defect dataset compared with the baseline algorithm, with model parameters and computation reduced by 44.9% and 47.1%, respectively. It also reaches a detection speed of 61.2 frames per second, satisfying the lightweight and high-precision requirements for edge deployment in transmission line inspection scenarios. Full article

Background: Cardiometabolic risk is increasingly observed in young adults, particularly during university years, and is not limited to individuals with elevated body mass index. Emerging evidence highlights the presence of normal weight obesity—characterized by excess adiposity and unfavorable body composition despite normal BMI—which may confer early metabolic vulnerability. Dietary diversity is often promoted as a marker of dietary adequacy; however, its relationship with adiposity, body composition, and muscular health remains inconsistent, particularly in Latin American populations. Moreover, few studies have directly contrasted dietary diversity indicators with empirically derived dietary patterns in relation to cardiometabolic and functional outcomes. Objective: To examine the associations between dietary diversity, dietary patterns, and indicators of adiposity, muscular strength, and relative muscle mass in Ecuadorian university students. Methods: A cross-sectional study was conducted among 349 undergraduate students aged 18–26 years enrolled in health sciences programs in Ecuador. Dietary intake was assessed using a validated food frequency questionnaire. Dietary diversity was quantified using the Food and Agriculture Organization’s Individual Dietary Diversity Score, while dietary patterns were identified through principal component analysis followed by k-means clustering. Outcomes included excess body weight, relative muscle mass assessed by bioelectrical impedance analysis, and handgrip strength. Multivariable Poisson and linear regression models were fitted, adjusting for age, sex, academic program, physical activity level, and pre-existing conditions. Results: Despite their young age and low prevalence of diagnosed disease, approximately one-third of the participants exhibited markers of early cardiometabolic risk, including excess body weight and central adiposity. Higher dietary diversity was independently associated with a higher prevalence of excess body weight (adjusted prevalence ratio per one-unit increase in IDDS: 1.17; 95% CI: 1.06–1.30) and with greater relative muscle mass (adjusted β = 0.13; 95% CI: 0.05–0.22), whereas no association was observed with handgrip strength. In contrast, dietary patterns derived from multivariate analysis showed no significant associations with adiposity, muscular strength, or relative muscle mass after adjustment. Conclusions: In this young adult population, dietary diversity captured aspects of overall dietary exposure associated with both increased adiposity and greater lean mass, but not with muscular strength. Empirically derived dietary patterns demonstrated limited discriminatory capacity, likely reflecting dietary homogeneity within the cohort. These findings indicate that dietary diversity alone does not necessarily reflect diet quality and underscore the importance of interpreting diversity metrics alongside indicators of food quality, energy density, and body composition when evaluating early cardiometabolic risk in contemporary food environments. Full article

This study explored how mathematics teachers in Chinese special schools provide instructional support to primary-aged students with intellectual and developmental disabilities (IDD). The types, characteristics, and classroom implementation processes of such support were identified to address a gap in the literature regarding subject-specific instructional practices in special education settings. A qualitative research design using interpretative phenomenological analysis (IPA) was employed. Five mathematics teachers from special schools in Shanghai participated in the study. Data were collected through 15 video-recorded classroom observations and five semi-structured interviews. Thematic analysis was conducted to identify key patterns of instructional support. The analysis revealed five core domains of instructional support for students with IDD: (1) comprehension facilitation through simplified explanations, real-life connections, and visual scaffolding; (2) responding to tasks involving prompts, modeling, and hand-over-hand support; (3) maintaining attention using individual and collective cues; (4) sustaining motivation through praise, encouragement, and second-chance opportunities; and (5) regulating behavior such as verbal restraint, physical proximity, and attention redirection. The findings contribute to a deeper understanding of effective instructional support tailored to students with IDD. Full article

Intervertebral disc degeneration (IDD) is a leading cause of low back pain (LBP) and imposes a substantial social and economic burden. Current treatments mainly relieve symptoms but rarely halt or reverse disc degeneration, and key gaps remain in our understanding of its pathophysiology. Accordingly, promoting intervertebral disc regeneration (IVDR) has been proposed as a potential therapeutic aim. Immunometabolism, which refers to the bidirectional interplay between immune responses and cellular metabolism, is increasingly recognized as a key factor affecting the balance of disc homeostasis and degeneration and has become an emerging research focus. In this review, we synthesize evidence supporting a dual and context-specific role of immunometabolism in IDD and IVDR. On the one hand, certain immune cells and anabolic cytokines or growth factors may promote a regenerative microenvironment by supporting disc cell survival and extracellular matrix (ECM) synthesis. On the other hand, pro-inflammatory mediators and metabolic disorders, including oxidative stress, mitochondrial dysfunction, and lipid or amino acid imbalance, drive a catabolic cascade that accelerates ECM breakdown and cellular senescence. We summarize current knowledge regarding key immune cell subsets, cytokine networks, and metabolic pathways implicated in IDD pathogenesis and IVDR, and we discuss how these immunometabolic principles are being leveraged in emerging interventions such as stem cell-based therapies, gene therapy, and advanced biomaterials. By integrating mechanistic insights with translational advances, this review aims to clarify actionable immunometabolic targets and to inform the rational development of regenerative strategies for disc-related diseases. Full article

The Immersive Decentralized Digital space (IDDs), derived from blockchain technology and Massively Multiplayer Online Games (MMOGs), enables real-time multisensory interactions that support social connection under metaverse concepts. Although recognized as a technology with significant potential for educational innovation, IDDs remain underutilized in health professions education. Health profession students are often unaware of how IDDs’ features can be applied to their learning through in- or after-classroom activities. This study employs a quantitative research design to evaluate students’ perceptions of next-generation digital learning without any prior exposure to IDDs. An electronic survey was developed to examine four dimensions of learning facilitation: “Remote Learning” for capturing past experiences with digital competence during the COVID-19 era; “Digital Evolution,” reflecting preferences in utilizing digital spaces; “Interactive Communication” and “Knowledge Application” for applicability of IDDs in the health professions education. Statistical analyses revealed no significant differences in perceptions based on gender or major on all factors. Nevertheless, significant differences emerged based on nationality in “Digital Evolution”, “Interactive Communication”, and “Knowledge Application”, highlighting the influence of cultural and educational backgrounds on receptiveness to virtual learning environments. By recognizing the discrepancies and addressing barriers to digital inclusion, IDDs hold strong potential to enhance health professional learning experiences and educational outcomes. Full article

This study proposes an automated system using deep learning-based object detection to identify implant systems, leveraging recent progress in self-supervised learning, specifically masked image modeling (MIM). We advocate for self-pre-training, emphasizing that its advantages when acquiring suitable pre-training data is challenging. The proposed Masked Deep Embedding (MDE) pre-training method, extending the masked autoencoder (MAE) transformer, significantly enhances dental implant detection performance compared to baselines. Specifically, the proposed method achieves a best detection performance of AP = 96.1, outperforming supervised ViT and MAE baselines by up to +2.9 AP. In addition, we address the absence of a comprehensive dataset for implant design, enhancing an existing dataset under dental expert supervision. This augmentation includes annotations for implant design, such as coronal, middle, and apical parts, resulting in a unique Implant Design Dataset (IDD). The contributions encompass employing self-supervised learning for limited dental radiograph data, replacing MAE’s patch reconstruction with patch embeddings, achieving substantial performance improvement in implant detection, and expanding possibilities through the labeling of implant design. This study paves the way for AI-driven solutions in implant dentistry, providing valuable tools for dentists and patients facing implant-related challenges. Full article