BMJ Open. 2024 Dec 22;14(12):e087798. doi: 10.1136/bmjopen-2024-087798.

ABSTRACT

INTRODUCTION: Living with a chronic disease impacts many aspects of life, including the ability to participate in activities that enable interactions with others in society, that is, social participation (SP). Despite efforts to monitor the quality of care and life of chronically ill people in Belgium, no disease-specific patient-reported measures (PRMs) have been used. These tools are essential to understand SP and to develop evidence-based recommendations to support its improvement. This protocol presents the phases for the disease-specific development of patient-reported outcome and experience measures to assess SP and its potential determinants among people living in Belgium with cancer, cystic fibrosis, diabetes, HIV or a neuromuscular disease.

METHODS AND ANALYSIS: This protocol applies the PROMIS Instrument Development and Validation Scientific Standards and COnsensus-based Standards for the selection of health Measurement INstruments to develop PRMs in a disease-specific manner to quantify the components of the International Classification of Functioning, Disability and Health (ICF). A mixed-method approach is used to create broad initial item pools based on patient (focus groups) and literature perspectives which are compared within ICF-standardised language by applying the refined ICF linking rules. An item set is first created based on this cross-matching exercise and then validated by multidisciplinary expert panels. Cognitive assessment and pilot testing are followed by the dissemination of the survey to a representative sample in Belgium. Advanced psychometric testing (classical test theory and item response theory) is applied to inform an item reduction strategy for the final measures and to develop scales for the ICF components.

ETHICS AND DISSEMINATION: Ethical approval was granted by the Ethics Committee of the Ghent University Hospital on 20 February 2023 to organise the patient focus groups (ONZ-2022-0470). Ethical approval for dissemination of the PRMs and psychometric testing will be sought at the Ghent University Hospital Ethics Committee at the start of Phase 6. Results will be disseminated through peer-reviewed journals and professional conferences.

PMID:39806694 | DOI:10.1136/bmjopen-2024-087798

BMJ Open. 2024 Dec 20;14(12):e092747. doi: 10.1136/bmjopen-2024-092747.

ABSTRACT

INTRODUCTION: Cystic fibrosis (CF) is an autosomal recessive genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, primarily affecting the respiratory and digestive systems. Respiratory rehabilitation techniques play a crucial role in managing pulmonary symptoms and maintaining lung function in CF patients. Although various techniques have been developed and applied, there is currently no globally recognised optimal respiratory rehabilitation regimen. This study intends to conduct a network meta-analysis to comprehensively evaluate and compare the effectiveness of different respiratory rehabilitation techniques in CF patients.

METHODS AND ANALYSIS: The following key electronic bibliographic databases will be searched from inception to September 2024: Medline, Embase, Cochrane Library, Web of Science, CINAHL and Physiotherapy Evidence Database. We will include randomised controlled trials (RCTs) and quasi-RCTs that compare the efficacy of various respiratory rehabilitation techniques in CF patients, such as airway clearance techniques, exercise training and inspiratory muscle training. The primary outcomes will be lung function (forced expiratory volume in 1 s and forced vital capacity) and exercise capacity (VO2 max and 6 min walk test). Secondary outcomes will include quality of life, frequency of pulmonary exacerbations, hospitalisation rates and adverse events. If permitted, data will be synthesised using traditional pairwise meta-analysis and network meta-analysis, with the quality of evidence assessed using the Grading of Recommendations Assessment, Development and Evaluation approach.

ETHICS AND DISSEMINATION: Ethical approval will not be required for this protocol. The results of the final review will be disseminated via peer-reviewed journals and conference presentations.

PROSPERO REGISTRATION NUMBER: CRD42024574551.

PMID:39806674 | DOI:10.1136/bmjopen-2024-092747

Heliyon. 2024 Dec 12;11(1):e41147. doi: 10.1016/j.heliyon.2024.e41147. eCollection 2025 Jan 15.

ABSTRACT

Sleep stages classification one of the essential factors concerning sleep disorder diagnoses, which can contribute to many functional disease treatments or prevent the primary cognitive risks in daily activities. In this study, A novel method of mapping EEG signals to music is proposed to classify sleep stages. A total of 4.752 selected 1-min sleep records extracted from the capsleep database are applied as the statistical population for this assessment. In this process, first, the tempo and scale parameters are extracted from the signal according to the rules of music, and next by applying them and changing the dominant frequency of the pre-processed single-channel EEG signal, a sequence of musical notes is produced. A total of 19 features are extracted from the sequence of notes and fed into feature reduction algorithms; the selected features are applied to a two-stage classification structure: 1) the classification of 5 classes (merging S1 and REM-S2-S3-S4-W) is made with an accuracy of 89.5 % (Cap sleep database), 85.9 % (Sleep-EDF database), 86.5 % (Sleep-EDF expanded database), and 2) the classification of 2 classes (S1 vs. REM) is made with an accuracy of 90.1 % (Cap sleep database),88.9 % (Sleep-EDF database), 90.1 % (Sleep-EDF expanded database). The overall percentage of correct classification for 6 sleep stages are 88.13 %, 84.3 % and 86.1 % for those databases, respectively. The other objective of this study is to present a new single-channel EEG sonification method, The classification accuracy obtained is higher or comparable to contemporary methods. This shows the efficiency of our proposed method.

PMID:39807512 | PMC:PMC11728888 | DOI:10.1016/j.heliyon.2024.e41147

Heliyon. 2024 Dec 15;11(1):e41209. doi: 10.1016/j.heliyon.2024.e41209. eCollection 2025 Jan 15.

ABSTRACT

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our method achieves high performance, with a validation F1-score of 94 % and IoU of 90.78 %. Besides, the morphometric analysis that shows strong alignment between manual annotations and automated segmentation with nerve length and tortuosity closely matching manual measurements. Furthermore, our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories revealing a significant decline in axon density correlating with higher CAPRA-S prostate cancer risk scores. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

PMID:39807499 | PMC:PMC11728976 | DOI:10.1016/j.heliyon.2024.e41209

Vis Intell. 2024;2(1):36. doi: 10.1007/s44267-024-00070-x. Epub 2024 Dec 30.

ABSTRACT

The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width. This exploration can potentially provide new insights and challenges for the low-bit quantization of LLaMA3 and other future LLMs, especially in addressing performance degradation issues that suffer in LLM compression. Specifically, we comprehensively evaluate the 10 existing post-training quantization and LoRA fine-tuning (LoRA-FT) methods of LLaMA3 on 1-8 bits and various datasets to reveal the low-bit quantization performance of LLaMA3. To uncover the capabilities of low-bit quantized MLLM, we assessed the performance of the LLaMA3-based LLaVA-Next-8B model under 2-4 ultra-low bits with post-training quantization methods. Our experimental results indicate that LLaMA3 still suffers from non-negligible degradation in linguistic and visual contexts, particularly under ultra-low bit widths. This highlights the significant performance gap at low bit-width that needs to be addressed in future developments. We expect that this empirical study will prove valuable in advancing future models, driving LLMs and MLLMs to achieve higher accuracy at lower bit to enhance practicality.

PMID:39807379 | PMC:PMC11728678 | DOI:10.1007/s44267-024-00070-x

Anim Cells Syst (Seoul). 2025 Jan 10;29(1):72-83. doi: 10.1080/19768354.2024.2449518. eCollection 2025.

ABSTRACT

Dynamic modeling of cellular states has emerged as a pivotal approach for understanding complex biological processes such as cell differentiation, disease progression, and tissue development. This review provides a comprehensive overview of current approaches for modeling cellular state dynamics, focusing on techniques ranging from dynamic or static biomolecular network models to deep learning models. We highlight how these approaches integrated with various omics data such as transcriptomics, and single-cell RNA sequencing could be used to capture and predict cellular behavior and transitions. We also discuss applications of these modeling approaches in predicting gene knockout effects, designing targeted interventions, and simulating organ development. This review emphasizes the importance of selecting appropriate modeling strategies based on scalability and resolution requirements, which vary according to the complexity and size of biological systems under study. By evaluating strengths, limitations, and recent advancements of these methodologies, we aim to guide future research in developing more robust and interpretable models for understanding and manipulating cellular state dynamics in various biological contexts, ultimately advancing therapeutic strategies and precision medicine.

PMID:39807350 | PMC:PMC11727055 | DOI:10.1080/19768354.2024.2449518

Int J Exerc Sci. 2024 Dec 1;17(1):1629-1647. doi: 10.70252/PRVV4165. eCollection 2024.

ABSTRACT

In weightlifting, quantitative kinematic analysis is essential for evaluating snatch performance. While marker-based (MB) approaches are commonly used, they are impractical for training or competitions. Markerless video-based (VB) systems utilizing deep learning-based pose estimation algorithms could address this issue. This study assessed the comparability and applicability of VB systems in obtaining snatch kinematics by comparing the outcomes to an MB reference system. 21 weightlifters (15 Male, 6 Female) performed 2-3 snatches at 65%, 75%, and 80% of their one-repetition maximum. Snatch kinematics were analyzed using an MB (Vicon Nexus) and VB (Contemplas along with Theia3D) system. Analysis of 131 trials revealed that corresponding lower limb joint center positions of the systems on average differed by 4.7 ± 1.2 cm, and upper limb joint centers by 5.7 ± 1.5 cm. VB and MB lower limb joint angles showed highest agreement in the frontal plane (root mean square difference (RMSD): 11.2 ± 5.9°), followed by the sagittal plane (RMSD: 13.6 ± 4.7°). Statistical Parametric Mapping analysis revealed significant differences throughout most of the movement for all degrees of freedom. Maximum extension angles and velocities during the second pull displayed significant differences (p < .05) for the lower limbs. Our data showed significant differences in estimated kinematics between both systems, indicating a lack of comparability. These differences are likely due to differing models and assumptions, rather than measurement accuracy. However, given the rapid advancements of neural network-based approaches, it holds promise to become a suitable alternative to MB systems in weightlifting analysis.

PMID:39807293 | PMC:PMC11728585 | DOI:10.70252/PRVV4165

Kidney Med. 2024 Nov 28;7(1):100939. doi: 10.1016/j.xkme.2024.100939. eCollection 2025 Jan.

NO ABSTRACT

PMID:39807248 | PMC:PMC11728938 | DOI:10.1016/j.xkme.2024.100939

Eur J Radiol Open. 2024 Dec 19;14:100626. doi: 10.1016/j.ejro.2024.100626. eCollection 2025 Jun.

ABSTRACT

OBJECTIVE: Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.

METHOD: Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.

RESULT: A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, P < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.

CONCLUSION: The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.

PMID:39807092 | PMC:PMC11728962 | DOI:10.1016/j.ejro.2024.100626

Math Biosci Eng. 2024 Dec 11;21(12):7830-7853. doi: 10.3934/mbe.2024344.

ABSTRACT

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span.

PMID:39807055 | DOI:10.3934/mbe.2024344

Curr Med Imaging. 2025 Jan 9. doi: 10.2174/0115734056326685250101113959. Online ahead of print.

ABSTRACT

OBJECTIVE: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.

METHODS: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model. The CNN layers perform initial feature extraction, capturing local patterns within the images. At the same time, the modified Swin Transformer blocks handle long-range dependencies and global context through window-based self-attention mechanisms. Preprocessing steps included resizing images to 224x224 pixels and applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image features. Data augmentation techniques, such as horizontal flipping, rotation, and zooming, were utilized to prevent overfitting and ensure model robustness. Hyperparameter optimization was conducted using Optuna, employing Bayesian optimization (Tree-structured Parzen Estimator) to fine-tune key parameters of both the CNN and Swin Transformer components, ensuring optimal model performance.

RESULTS: The proposed hybrid model was trained and validated on a dataset provided by the Guangzhou Women and Children's Medical Center. The model achieved an overall accuracy of 98.72% and a loss of 0.064 on an unseen dataset, significantly outperforming a baseline CNN model. Detailed performance metrics indicated a precision of 0.9738 for the normal class and 1.0000 for the pneumonia class, with an overall F1-score of 0.9872. The hybrid model consistently outperformed the CNN model across all performance metrics, demonstrating higher accuracy, precision, recall, and F1-score. Confusion matrices revealed high sensitivity and specificity with minimal misclassifications.

CONCLUSION: The proposed hybrid CNN-ViT model, which integrates modified Swin Transformer blocks within the CNN architecture, provides a significant advancement in pneumonia detection by effectively capturing both local and global features within chest X-ray images. The modifications to the Swin Transformer blocks enable them to work seamlessly with the CNN layers, enhancing the model's ability to understand complex visual patterns and dependencies. This results in superior classification performance. The lightweight design of the model eliminates the need for extensive hardware, facilitating easy deployment in resource-constrained settings. This innovative approach not only improves pneumonia diagnosis but also has the potential to enhance patient outcomes and support healthcare providers in underdeveloped regions. Future research will focus on further refining the model architecture, incorporating more advanced image processing techniques, and exploring explainable AI methods to provide deeper insights into the model's decision-making process.

PMID:39806960 | DOI:10.2174/0115734056326685250101113959

New Phytol. 2025 Jan 14. doi: 10.1111/nph.20384. Online ahead of print.

ABSTRACT

Lupins are promising protein crops that accumulate toxic quinolizidine alkaloids (QAs) in the seeds, complicating their end-use. QAs are synthesized in green organs (leaves, stems, and pods) and a subset of them is transported to the seeds during fruit development. The exact sites of biosynthesis and accumulation remain unknown; however, mesophyll cells have been proposed as sources, and epidermal cells as sinks. We investigated the exact sites of QA biosynthesis and accumulation in biosynthetic organs of narrow-leafed lupin (Lupinus angustifolius) using mass spectrometry-based imaging (MSI), laser-capture microdissection coupled to RNA-Seq, and precursor feeding studies coupled to LC-MS and MSI. We found that the QAs that accumulate in seeds ('core' QAs) were evenly distributed across tissues; however, their esterified versions accumulated primarily in the epidermis. Surprisingly, RNA-Seq revealed strong biosynthetic gene expression in the epidermis, which was confirmed in leaves by quantitative real-time polymerase chain reaction. Finally, feeding studies using a stably labeled precursor showed that the lower leaf epidermis is highly biosynthetic. Our results indicate that the epidermis is a major site of QA biosynthesis in narrow-leafed lupin, challenging the current assumptions. Our work has direct implications for the elucidation of the QA biosynthesis pathway and the long-distance transport network from source to seed.

PMID:39807565 | DOI:10.1111/nph.20384

Comput Struct Biotechnol J. 2024 Mar 1;23:1076-1087. doi: 10.1016/j.csbj.2024.02.019. eCollection 2024 Dec.

ABSTRACT

DNA holds immense potential as an emerging data storage medium. However, the recovery of information in DNA storage systems faces challenges posed by various errors, including IDS errors, strand breaks, and rearrangements, inevitably introduced during synthesis, amplification, sequencing, and storage processes. Sequence reconstruction, crucial for decoding, involves inferring the DNA reference from a cluster of erroneous copies. While most methods assume equal contributions from all reads within a cluster as noisy copies of the same reference, they often overlook the existence of contaminated sequences caused by DNA breaks, rearrangements, or mis-clustering reads. To address this issue, we propose RobuSeqNet, a robust multi-read reconstruction neural network specifically designed to robustly reconstruct multiple reads, accommodating noisy clusters with strand breakage, rearrangements, and mis-clustered strands. Leveraging the attention mechanism and an elaborate network design, RobuSeqNet exhibits resilience to highly-noisy clusters and effectively deals with in-strand IDS errors. The effectiveness and robustness of the proposed method are validated on three representative next-generation sequencing datasets. Results demonstrate that RobuSeqNet maintains high sequence reconstruction success rates of 99.74%, 99.58%, and 96.44% across three datasets, even in the presence of noisy clusters containing up to 20% contaminated sequences, outperforming known sequence reconstruction models. Additionally, in scenarios without contaminated sequences, it exhibits comparable performance to existing models, achieving success rates of 99.88%, 99.82%, and 97.68% across the three datasets.

PMID:39807110 | PMC:PMC11725466 | DOI:10.1016/j.csbj.2024.02.019

Endocr Metab Immune Disord Drug Targets. 2025 Jan 9. doi: 10.2174/0118715303313654241101042033. Online ahead of print.

ABSTRACT

BACKGROUND: There is still no conclusive understanding of whether the glucokinase regulator (GCKR) gene rs780094 and rs1260326 polymorphisms predispose to gestational diabetes mellitus (GDM).

OBJECTIVE: This systematic review and meta-analysis aimed to determine the effect of the GCKR polymorphisms on GDM susceptibility.

METHODS: Seven literature databases were searched (from inception to February 17, 2024) to locate relevant studies included in further meta-analysis. Odds ratio (OR) and 95% confidence intervals (CI) in the pooled population were estimated to assess the effects of the variant allele on GDM risk.

RESULTS: For the rs780094 polymorphism, 13 datasets with 3443 GDM cases and 5930 nondiabetic controls were included. The pooled estimates in the allele model (OR: 1.19, 95% CI: 1.07~1.32), homozygote model (OR: 1.27, 95% CI: 1.10~1.47), dominant model (OR: 1.16, 95% CI: 1.03~1.31), and recessive model (OR: 1.31, 95% CI: 1.09~1.57) suggested that the C allele carriers were prone to GDM. For the rs1260326 polymorphism, five datasets with 1495 cases and 2678 controls were integrated. The statistically significant effect of the C allele was evident in the allele model (OR: 1.12, 95% CI: 1.01~1.24) and the homozygote model (OR: 1.26, 95% CI: 1.03~1.54).

CONCLUSION: This meta-analysis suggested that the C allele of the rs780094 and rs1260326 polymorphisms in the GCKR gene are significantly associated with increased risk of GDM.

PMID:39806965 | DOI:10.2174/0118715303313654241101042033

Notice NOT-HL-24-032 from the NIH Guide for Grants and Contracts

Notice NOT-NS-25-009 from the NIH Guide for Grants and Contracts

Funding Opportunity PAR-25-313 from the NIH Guide for Grants and Contracts. The purpose of the NCI Pathway to Independence Award (K99/R00) program is to facilitate a timely transition of talented postdoctoral researchers with a research and/or clinical doctorate degree from mentored, postdoctoral research positions to independent, tenure-track or equivalent faculty positions. The program will provide independent NCI research support during this transition in order to help awardees to launch competitive, independent research careers.

Notice NOT-CA-25-022 from the NIH Guide for Grants and Contracts

Notice NOT-OD-25-064 from the NIH Guide for Grants and Contracts

Notice NOT-EY-25-005 from the NIH Guide for Grants and Contracts