Nevin Manimala

BMC Public Health. 2025 Mar 3;25(1):838. doi: 10.1186/s12889-025-22040-8.

ABSTRACT

BACKGROUND: Evidence on psychological outcomes of gaming disorder (GD) is still scarce. This study aimed to investigate the mediating role of sleep hygiene in the relationship between GD and psychological distress (depression and anxiety) among Iranian adolescents.

METHODS: This was a cross-sectional study among school students in Qazvin city, Iran. We administered GD, anxiety, and depression questionnaires in a paper-and-pencil format. GD was measured using the GD S4-SF scale, and anxiety and depression were evaluated using the DASS-21. We assessed sleep health as a mediator using the Sleep Hygiene Behaviors scale. Covariance-Based Structural Equation Modeling (CB-SEM) was employed for data analysis, accounting for sex and physical activity as the main confounders. Statistical significance was determined using various fit indices and confidence intervals.

RESULTS: The sample consisted of 600 adolescents (41% female). CB-SEM revealed a positive but not statistically significant association between GD and depression, along with a negative statistically significant association with anxiety. Notably, sleep hygiene was identified as a partial mediator in the relationship between GD and depression, indicating that poor sleep practices may exacerbate depressive symptoms among adolescents with GD. However, no mediating effect was observed for anxiety.

CONCLUSION: Our data supported a mediating role for sleep hygiene in the association between GD and depression among participants. Our results highlight the critical need for targeted policy interventions to improve sleep hygiene among adolescents with GD.

PMID:40033319 | DOI:10.1186/s12889-025-22040-8

BMC Musculoskelet Disord. 2025 Mar 3;26(1):217. doi: 10.1186/s12891-025-08389-2.

ABSTRACT

BACKGROUND: Osteoporosis is common in patients with knee osteoarthritis (KOA) awaiting total knee arthroplasty (TKA) and varies in different regional and ethnic. However, it is unclear whether the prevalence of osteoporosis and osteopenia in these patients is different from that in the general population. This study aims to investigate the prevalence of osteoporosis and osteopenia in both populations to help exploring the relationship between the osteoporosis and osteoarthritis, and to explore whether knee function and radiological assessments of KOA are associated with osteoporosis.

METHODS: In total, 249 patients diagnosed with KOA awaiting TKA were investigated in this cross-sectional study. The mean age was 70.9 ± 6.4 years. Bone mineral density (BMD) and T scores at the hip and lumbar spine were used to assess bone status using dual X-ray absorptiometry. A matched cohort from 2448 individuals in the Health Examination Center of our hospital was set as controls by matching sex, age (± 3.0 years) and BMI (± 1.0). The Kellgren-Lawrence grades (K-L grades), mechanical femorotibial angle (mFTA), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and range of motion (ROM) of the knee were measured to evaluate radiological assessments and knee function in patients awaiting TKA and used to explore the association between KOA and BMD or T score. Prevalence of osteoporosis and osteopenia were investigated in the two cohorts, and inferential statistical analyses were undertaken. The chi-squared test or Fisher’s exact test was used for categorical variables while comparisons of scores were examined by ANOVA with/without Bonferroni correction or the Kruskal‒Wallis test.

RESULTS: The prevalence of osteoporosis and osteopenia in patients awaiting TKA was 30.5% (76/249) and 44.2% (110/249), respectively. In the matched cohort, 72/249 (28.9%) had osteoporosis, while 98/249 (39.4%) had osteopenia. There was no significant difference in the prevalence of osteoporosis or osteopenia between the two groups (χ2 = 2.603, P = 0.272). mFTA was significantly correlated with BMD and T score (P < 0.05), while no correlation was found between K-L grade, ROM or WOMAC and BMD or T score (P > 0.05).

CONCLUSIONS: The prevalence of osteoporosis in patients awaiting TKA was similar to that in the general population. BMD and T score were not correlated with WOMAC score or K-L grade but were correlated with mFTA.

PMID:40033308 | DOI:10.1186/s12891-025-08389-2

BMC Med Inform Decis Mak. 2025 Mar 3;25(1):109. doi: 10.1186/s12911-025-02945-5.

ABSTRACT

BACKGROUND: Algorithms and models increasingly support clinical and shared decision-making. However, they may be limited in effectiveness, accuracy, acceptance, and comprehensibility if they fail to consider patient preferences. Addressing this gap requires exploring methods to integrate patient preferences into model-based clinical decision-making.

OBJECTIVES: This scoping review aimed to identify and map applications of computational methods for incorporating patient preferences into individualized medical decision models and to report on the types of models where these methods are applied.

INCLUSION CRITERIA: This review includes articles without restriction on publication date or language, focusing on practical applications. It examines the integration of patient preferences in models for individualized clinical decision-making, drawing on diverse sources, including both white and gray literature, for comprehensive insights.

METHODS: Following the Joanna Briggs Institute (JBI) methodology, a comprehensive search was conducted across databases such as PubMed, Web of Science, ACM Digital Library, IEEE Xplore, Cochrane Library, OpenGrey, National Technical Reports Library, and the first 20 pages of Google Scholar. Keywords related to patient preferences, medical models, decision-making, and software tools guided the search strategy. Data extraction and analysis followed the JBI framework, with an explorative analysis.

RESULTS: From 7074 identified and 7023 screened articles, 45 publications on specific applications were reviewed, revealing significant heterogeneity in incorporating patient preferences into decision-making tools. Clinical applications primarily target neoplasms and circulatory diseases, using methods like Multi-Criteria Decision Analysis (MCDA) and statistical models, often combining approaches. Studies show that incorporating patient preferences can significantly impact treatment decisions, underscoring the need for shared and personalized decision-making.

CONCLUSION: This scoping review highlights a wide range of approaches for integrating patient preferences into medical decision models, underscoring a critical gap in the use of cohesive frameworks that could enhance consistency and clinician acceptance. While the flexibility of current methods supports tailored applications, the limited use of existing frameworks constrains their potential. This gap, coupled with minimal focus on clinician and patient engagement, hinders the real-world utility of these tools. Future research should prioritize co-design with clinicians, real-world testing, and impact evaluation to close this gap and improve patient-centered care.

PMID:40033306 | DOI:10.1186/s12911-025-02945-5

IEEE Trans Pattern Anal Mach Intell. 2025 Jan 9;PP. doi: 10.1109/TPAMI.2025.3527498. Online ahead of print.

ABSTRACT

Existing methods for integerized training speed up deep learning by using low-bitwidth integerized weights, activations, gradients, and optimizer buffers. However, they overlook the issue of full-precision latent weights, which consume excessive memory to accumulate gradient-based updates for optimizing the integerized weights. In this paper, we propose the first latent weight quantization schema for general integerized training, which minimizes quantization perturbation to training process via residual quantization with optimized dual quantizer. We leverage residual quantization to eliminate the correlation between latent weight and integerized weight for suppressing quantization noise. We further propose dual quantizer with optimal nonuniform codebook to avoid frozen weight and ensure statistically unbiased training trajectory as full-precision latent weight. The codebook is optimized to minimize the disturbance on weight update under importance guidance and achieved with a three-segment polyline approximation for hardware-friendly implementation. Extensive experiments show that the proposed schema allows integerized training with lowest 4-bit latent weight for various architectures including ResNets, MobileNetV2, and Transformers, and yields negligible performance loss in image classification and text generation. Furthermore, we successfully fine-tune Large Language Models with up to 13 billion parameters on one single GPU using the proposed schema.

PMID:40030978 | DOI:10.1109/TPAMI.2025.3527498

IEEE Trans Neural Syst Rehabil Eng. 2025 Jan 16;PP. doi: 10.1109/TNSRE.2025.3530154. Online ahead of print.

ABSTRACT

Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in δ and θ activity and decreases in α and β activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.

PMID:40030934 | DOI:10.1109/TNSRE.2025.3530154

IEEE J Biomed Health Inform. 2025 Jan 15;PP. doi: 10.1109/JBHI.2025.3530107. Online ahead of print.

ABSTRACT

Employing a minimal array of electroencephalography (EEG) channels for neonatal sleep stage classification is essential for data acquisition in the Internet of Medical Things (IoMT), as single-channel and edge-based features can reduce data transfer and processing requirements, enhancing cost-effectiveness and practicality. In this paper, we evaluate the efficacy of a single channel and the viability of a binary classification scheme for discerning awake and sleep states and transitions to quiet sleep. For this, two datasets of EEG signals for neonate sleep analysis were recorded from Children’s Hospital of Fudan University, Shanghai, comprising recordings from 64 and 19 neonates, respectively. From each epoch, a diverse ensemble of 490 features was extracted through a blend of discrete and continuous wavelet transforms (DWT, CWT), spectral statistics, and temporal features. In addition, we introduced an innovative hybrid univariate and ensemble feature selection approach with multidomain feature fusion, a stacking-based ensemble classifier that outperforms existing work. We achieved 90.37%, 91.13%, and 94.88% accuracy for sleep/awake, quiet sleep/non-quiet sleep, and quiet sleep/awake, respectively. This was corroborated by significant Kappa values of 77.5%, 80.29%, and 89.76%. Using SelectPercentile, we devised three distinct feature selection mechanisms: one using DWT, one with CWT, and another incorporating both spectral and temporal features. Subsequently, SelectKBest was used to determine the most effective features. For our stacked model, we incorporated a trifecta of the ExtraTree model with variable estimators, a Random Forest, and an Artificial Neural Network (ANN) as base classifiers, and for the final prediction phase, ANN was implemented again. The model’s performance was evaluated using K-fold and leave-one-subject cross-validation.

PMID:40030895 | DOI:10.1109/JBHI.2025.3530107

IEEE Trans Pattern Anal Mach Intell. 2025 Jan 14;PP. doi: 10.1109/TPAMI.2025.3529711. Online ahead of print.

ABSTRACT

Subspace learning and Support Vector Machine (SVM) are two critical techniques in pattern recognition, playing pivotal roles in feature extraction and classification. However, how to learn the optimal subspace such that the SVM classifier can perform the best is still a challenging problem due to the difficulty in optimization, computation, and algorithm convergence. To address these problems, this paper develops a novel method named Optimal Discriminant Support Vector Machine (ODSVM), which integrates support vector classification with discriminative subspace learning in a seamless framework. As a result, the most discriminative subspace and the corresponding optimal SVM are obtained simultaneously to pursue the best classification performance. The efficient optimization framework is designed for binary and multi-class ODSVM. Moreover, a fast sequential minimization optimization (SMO) algorithm with pruning is proposed to accelerate the computation in multi-class ODSVM. Unlike other related methods, ODSVM has a strong theoretical guarantee of global convergence, highlighting its superiority and stability. Numerical experiments are conducted on thirteen datasets and the results demonstrate that ODSVM outperforms existing methods with statistical significance.

PMID:40030888 | DOI:10.1109/TPAMI.2025.3529711

IEEE Trans Pattern Anal Mach Intell. 2025 Jan 6;PP. doi: 10.1109/TPAMI.2024.3523422. Online ahead of print.

ABSTRACT

Recent methods for synthesizing 3D-aware face images have achieved rapid development thanks to neural radiance fields, allowing for high quality and fast inference speed. However, existing solutions for editing facial geometry and appearance independently usually require retraining and are not optimized for the recent work of generation, thus tending to lag behind the generation process. To address these issues, we introduce NeRFFaceEditing, which enables editing and decoupling geometry and appearance in the pretrained tri-plane-based neural radiance field while retaining its high quality and fast inference speed. Our key idea for disentanglement is to use the statistics of the tri-plane to represent the high-level appearance of its corresponding facial volume. Moreover, we leverage a generated 3D-continuous semantic mask as an intermediary for geometry editing. We devise a geometry decoder (whose output is unchanged when the appearance changes) and an appearance decoder. The geometry decoder aligns the original facial volume with the semantic mask volume. We also enhance the disentanglement by explicitly regularizing rendered images with the same appearance but different geometry to be similar in terms of color distribution for each facial component separately. Our method allows users to edit via semantic masks with decoupled control of geometry and appearance. Both qualitative and quantitative evaluations show the superior geometry and appearance control abilities of our method compared to existing and alternative solutions.

PMID:40030881 | DOI:10.1109/TPAMI.2024.3523422

IEEE Trans Med Imaging. 2025 Jan 3;PP. doi: 10.1109/TMI.2024.3525095. Online ahead of print.

ABSTRACT

Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the generalization of deep models by alleviating domain gaps caused by inconsistency across equipment, imaging protocols, and patient conditions. However, existing UDA works remain insufficiently explored and present great limitations: (i) Exhibit cumbersome designs that prioritize aligning statistical metrics and distributions, which limits the model’s flexibility and generalization while also overlooking the potential knowledge embedded in unlabeled data; (ii) More applicable in a certain domain, lack the generalization capability to handle diverse shifts encountered in clinical scenarios. To overcome these limitations, we introduce MedCon, a unified framework that leverages general unsupervised contrastive pre-training to establish domain connections, effectively handling diverse domain shifts without tailored adjustments. Specifically, it initially explores a general contrastive pre-training to establish domain connections by leveraging the rich prior knowledge from unlabeled images. Thereafter, the pre-trained backbone is fine-tuned using source-based images to ultimately identify per-pixel semantic categories. To capture both intra- and inter-domain connections of anatomical structures, we construct positive-negative pairs from a hybrid aspect of both local and global scales. In this regard, a shared-weight encoder-decoder is employed to generate pixel-level representations, which are then mapped into hyper-spherical space using a non-learnable projection head to facilitate positive pair matching. Comprehensive experiments on diverse medical image datasets confirm that MedCon outperforms previous methods by effectively managing a wide range of domain shifts and showcasing superior generalization capabilities.

PMID:40030864 | DOI:10.1109/TMI.2024.3525095

IEEE Trans Haptics. 2024 Dec 26;PP. doi: 10.1109/TOH.2024.3522897. Online ahead of print.

ABSTRACT

In recent years, systems that utilize immersive space have been developed in various fields. Immersive spaces often contain considerable amounts of visual information; therefore, users often fail to obtain their desired information. Therefore, various methods have been developed to guide users toward haptic sensations. However, many of these methods have limitations in terms of the intuitive perception of haptic sensation and require practice for familiarization with haptic sensation. Fabric actuators are wearable haptic devices that combine fabric and McKibben artificial muscles to provide wearers with surface haptic sensation. These sensations can be provided to a wide area of the body with intuitive perception, instead of only to a part of the body. This paper presents a novel air pressure adjustment method for whole-body motion guidance using surface haptic sensations provided by a wearable fabric actuator. The proposed system can provide users with a directional sense without visual information in an immersive space. The effectiveness of the proposed system was evaluated through subject experiments and statistical data analysis. Finally, a directional sense presentation was conducted for users performing micromanipulations in a mixed-reality space to demonstrate the applicability of the proposed system for teleoperation.

PMID:40030816 | DOI:10.1109/TOH.2024.3522897