Comput Biol Med. 2025 Apr 11;191:110170. doi: 10.1016/j.compbiomed.2025.110170. Online ahead of print.

ABSTRACT

BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable biomarkers for MASH diagnosis and progression remain elusive. This study aimed to identify hepatic transcriptomic and circulating proteomic signatures specific to MASH, and to develop a machine learning-based biomarker discovery model.

METHODS: A systematic review of RNA-Seq and proteomic datasets was conducted, retrieving 7 hepatic transcriptomics and 3 circulating proteomics studies, encompassing 483 liver samples and 169 serum/plasma samples, respectively. Differential gene and protein expression analyses were performed, and pathways were enriched using gene set enrichment analysis. A machine learning (ML) model was developed to identify MASH-specific biomarkers, utilizing biologically significant protein ratios.

KEY FINDINGS: Hepatic transcriptomic analysis identified 5017 differentially expressed genes (DEGs), with significant enrichment of extracellular matrix (ECM) pathways. Serum proteomics revealed six differentially expressed proteins (DEPs), including complement-related proteins. Integration of transcriptomic and proteomic data highlighted the complement cascade as a key pathway in MASH, with discordant regulation between the liver and circulation. The ML-based biomarker discovery model, utilizing protein ratios, achieved an F1 scores of 0.83 and 0.64 in the training sets and 0.67 in an external validation set.

CONCLUSION: Our findings indicate ECM deregulation and complement system involvement in MASH progression. The novel ML model incorporating protein ratios offers a potential tool for MASH diagnosis. However, further refinement and validation across larger and more diverse cohorts is needed to generalize these results.

PMID:40220593 | DOI:10.1016/j.compbiomed.2025.110170

J Plant Physiol. 2025 Apr 5;308:154487. doi: 10.1016/j.jplph.2025.154487. Online ahead of print.

ABSTRACT

Low-resource environments, such as dry or infertile soils, result in limited plant growth and development, which in turn constrain crop productivity. Water shortage is a significant threat to agricultural productivity all over the world. Drought may also affect plant nutrient uptake and assimilation capability causing nutrient deficiencies even in fertilized fields. Durum wheat is an important staple food crop for ensuring food security in the Mediterranean area, which is increasingly subjected to periods of severe drought due to global changes. Thus, identifying wheat cultivars/genotypes able to cope with suboptimal water, and with unbalanced nutrient availability deriving from drought is crucial to mitigate climate change's adverse effects on agriculture. In this study, a detailed analysis of the phenome, including biomass production, proline production, and characterization of root system architecture, and the ionome, was performed on a panel of 15 Triticum turgidum genotypes, differing for drought tolerance, in order to understand the genotype-specific physiological responses to drought and to identify those genotypes characterised by a positive correlation between ion homeostasis and drought response. The characterization of root system architecture helped our understanding of the morphological responses of wheat plants to drought. Our findings demonstrated that drought exposure for 7 days significantly impacted the ionomic profiles of most genotypes in both shoot and root tissues, albeit to varying degrees. The Lcye A-B- genotype showed the highest accumulation efficiency for most nutrients in shoots, while Bulel tritordeum and Karim in roots. It is also important to understand how micronutrients interact with each other and with macronutrients. Thus, we performed a nutrient correlation network analysis, which showed that drought altered the interactions between nutrients in most genotypes. These findings underscore the importance of understanding the mechanisms regulating nutrient homeostasis, as these mechanisms can either mitigate or exacerbate the impact of drought stress. Understanding the interplay between ionomic profiles and environmental conditions can provide valuable insights into developing more resilient crops that can thrive in challenging environments, ultimately contributing to global food security in the face of climate change.

PMID:40220515 | DOI:10.1016/j.jplph.2025.154487

Orphanet J Rare Dis. 2025 Apr 12;20(1):176. doi: 10.1186/s13023-025-03676-6.

ABSTRACT

BACKGROUND: Treatment satisfaction can be described as the patient's experience in patients with cystic fibrosis (CF). It can be influenced using modulators and clinical characteristics. The aims of this study were to evaluate and compare adult CF patients with and without modulators regarding treatment satisfaction, related factors and to manage their drug related problems (DRPs).

METHODS: A single-center prospective cohort study was conducted between June 2023 and January 2024. Treatment Satisfaction Questionnaire for Medication (TSQM 1.4), including effectiveness, side effects, convenience, global satisfaction domains, CF Questionnaire-Revised (CFQ-R), and Medication Adherence Report Scale were applied and assessed with and without modulator therapy groups. The relationship between clinical characteristics and TSQM was analyzed by correlations and regression analysis. Recommendations on DRPs identified by clinical pharmacists were made to the physicians and patients and classified according to Pharmaceutical Care Network Europe (PCNE v9.1).

RESULTS: A total of 110 patients with 51 modulator therapy and 59 without modulator therapy were included. The mean global satisfaction score of modulator users was found to be 19.733 (p < 0.001) points higher than non-users. When the CFQ-R treatment burden score improved by 1point, global satisfaction score increased by 0.233 points (p < 0.001). When the number of hospitalizations increased by 1 day, the global satisfaction score decreased by 4.751 points (p < 0.001). A total of 84 DRPs were identified, and 69 (82.1%) of them were resolved.

CONCLUSIONS: Treatment satisfaction in adult CF patients is influenced by modulators, treatment burden, and clinical factors, so access to modulators is important. This is the first study to classify DRPs according to PCNE in CF. Clinical pharmacists contribute to the management of CF.

PMID:40221761 | DOI:10.1186/s13023-025-03676-6

J Clin Med. 2025 Mar 21;14(7):2142. doi: 10.3390/jcm14072142.

ABSTRACT

Background/Objectives: Polymorbidity and polypharmacy are major challenges in geriatric care, resulting in a reduced quality of life and increased health care costs. Methods: We evaluated the proactive medication management of nursing home residents through personal visits and the use of a clinical decision support system (CDSS) with an integrated Beers Criteria list. Results: Among 56 nursing home residents, we observed a high prevalence of polypharmacy with an average of 7.9 regular and 5.1 on-demand prescriptions. Proactive medication management led to persistent medication changes in 87.5% of patients. Regular prescriptions were reduced in 21 residents and increased in 18 residents, resulting in a reduced use of cardiovascular drugs and antacids (p < 0.05), but no significant overall reduction in polypharmacy. CDSS alerts based on Beers Criteria made no clinically relevant contribution to medication reduction. Conclusions: Proactive geriatric medication management led to persistent medication changes and no reduction in overall polypharmacy but reduced the use of selected drug classes that are associated with an increased risk of adverse reactions and costs. The clinical relevance and implementability of Beers Criteria were low, revealing major limitations of algorithm-based alerts for older patients, who require additional personalized evaluations of their individual complex healthcare needs.

PMID:40217592 | DOI:10.3390/jcm14072142

Clin Pediatr (Phila). 2025 Apr 12:99228251330121. doi: 10.1177/00099228251330121. Online ahead of print.

ABSTRACT

In 2020, Kaftrio, a combination of Elexacaftor, Tezacaftor, and Ivacaftor, gained approval for treating cystic fibrosis (CF) in patients from the age of 12 years. This study aims to analyze 1 year of treatment with Kaftrio in pediatric patients, comparing their clinical characteristics with pre-treatment data. This is an observational, descriptive, and longitudinal study in patients with CF older than 12 years with at least 1 F508del mutation treated with Kaftrio for 1 year. Forced expiratory volume in 1 second (FEV1) z-score increased by +1.1 (95% confidence interval [CI] = 0.55 to 1.64), forced vital capacity (FVC) by +0.56 (95% CI = 0.10 to 1.04), and maximal mid-expiratory flow (MMEF) 25/75 improved by +1.53 (95% CI = 0.59 to 2.47). In addition, a reduction of 25.50 points (95% CI = -37.95 to -13.06) in sweat chloride levels was observed. Body mass index (BMI)-for-age z-score (WHO 2006/2007) increased +0.39 (95% CI = 0.02 to 0.77). A transient increase in cough and secretions was noted in 61.53% after starting treatment. Kaftrio improves lung function and BMI and also reduces respiratory exacerbations and sweat chloride levels.

PMID:40219783 | DOI:10.1177/00099228251330121

Nutrients. 2025 Apr 2;17(7):1239. doi: 10.3390/nu17071239.

ABSTRACT

Background/Objective: Cystic Fibrosis (CF) is a common, life-threatening genetic disorder that leads to progressive lung function decline, respiratory failure, and premature death. Musculoskeletal complications, affecting both peripheral and respiratory muscles, are major concerns in CF patients. Inflammatory cytokines seem to be responsible for the activation of the molecular pathways involved in the imbalance between protein synthesis and catabolism, with consequent loss of muscle mass and function. This study aims to assess the effects of amino acid supplements on functional status, muscle mass and strength, inflammation, and quality of life in adult CF patients. Methods: We conducted a randomized, double-blind, placebo-controlled pilot trial with 60 adult CF patients, aged 18 or older. Participants were randomly assigned to receive either amino acid supplementation or a placebo for 4 weeks. Physical function tests and self-assessment questionnaires on quality of life, global health, and sleep status, as well as blood samples to measure pro-inflammatory cytokines, were performed at baseline and after the treatment period. Results: The amino acid supplementation group showed a significant improvement in self-perceived physical performance and health status. Interleukin-6 serum levels were significantly reduced in this group compared to those who received the placebo (p = 0.042). Conclusions: Amino acid supplementation in adult CF patients improves self-perception of health status and may reduce systemic inflammation, significantly decreasing serum levels of Interleukin-6. This suggests potential benefits for the overall well-being of CF patients and a reduction in their inflammatory status.

PMID:40218996 | DOI:10.3390/nu17071239

J Clin Med. 2025 Apr 3;14(7):2437. doi: 10.3390/jcm14072437.

ABSTRACT

Background/Objectives: The prevalence of Achromobacter xylosoxidans is increasing in people with Cystic Fibrosis (pwCF), yet its clinical pathogenicity remains controversial. The objective of this study was to chart the longitudinal prevalence and examine clinical associations before and after infection. Methods: This observational, retrospective study was conducted at a single CF center over a 14-year period. Data were collated from patient charts and clinic databases. Patients with Achromobacter sputum cultures were compared to those without the bacterium and analyzed according to whether they had single, intermittent, or chronic infections. Results: During the study period, an annual average of 124 pwCF were followed up at our clinic, with a median age of 13.6 years (IQR = 7.6-27.7). The Achromobacter detection rate increased from 0 to 6.1%. Twenty-three percent (29/124) of patients had at least one positive culture. The median age at acquisition was 17 years (IQR = 14.5-33). At the time of acquisition, the median FEV1 was 81% (IQR = 46-94), compared to 90% (IQR = 72-99) for patients without Achromobacter, p < 0.001. Patients with Achromobacter tended to demonstrate more chronic Pseudomonas (55% vs. 27%, p = 0.06) and pancreatic insufficiency (66% vs. 47%, p = 0.07). At two years post-acquisition, the median FEV1 for patients with intermittent and chronically infected decreased by 11.5% (IQR = -3.75-7.5), compared to 1.5% (IQR = -2.5-12.5) for those with a single positive culture, p = 0.03. Similarly, pulmonary exacerbations per year became more frequent post-acquisition in intermittent and chronically infected patients: Median (range) 2.5 (0-8) pre-, versus 3.0 (0-9) post-acquisition, p = 0.036. Conclusions: Chronic and intermittent infection with Achromobacter were associated with accelerated lung function decline and increased exacerbation frequency. Larger prospective studies are needed to confirm these findings and examine the effect of eradication on the clinical course.

PMID:40217889 | DOI:10.3390/jcm14072437

J Clin Med. 2025 Mar 28;14(7):2320. doi: 10.3390/jcm14072320.

ABSTRACT

Background/Objectives: Cystic Fibrosis (CF) is an autosomal recessive genetic disorder caused by variants in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Recently, a targeted therapy for CF has been developed, represented by the CFTR modulators that enhance or restore the function of the CFTR protein. The most recent is the combination of three modulators, Elexacaftor, Tezacaftor, and Ivacaftor (ETI). This study describes the presentation, management, and follow-up of tracheal diverticulum (TD) in pwCF receiving ETI therapy. Methods: This retrospective study included people with CF (pwCF) on ETI treatment and followed up in two CF Italian centers who developed an asymptomatic TD, diagnosed incidentally at chest CT scan. Results: Among 268 pwCF receiving ETI, three (1.19%) were diagnosed with TD identified after chest CT and were included in this study. Endoscopic confirmation was obtained in one patient. All patients were on inhaled colistimethate, two of them for chronic Pseudomonas aeruginosa colonization, and one undergoing eradication therapy. Conclusions: TD may be identified in chest CT obtained in pwCF in treatment with ETI. Further studies and a longer follow up are needed to confirm these findings.

PMID:40217771 | DOI:10.3390/jcm14072320

J Clin Med. 2025 Mar 25;14(7):2241. doi: 10.3390/jcm14072241.

ABSTRACT

Objectives: Chronic kidney disease (CKD) among lung transplant (LTx) recipients has increased in recent decades. However, there is insufficient evidence regarding clinical outcomes, and current guidelines lack specific recommendations for its management. Methods: This single-center retrospective study included all patients who underwent LTx and were subsequently referred to a dedicated nephrology outpatient clinic. Major adverse renal events were defined as a composite event. Results: Eighty LTx recipients with underlying lung disease etiology such as cystic fibrosis, chronic obstructive pulmonary disease, or interstitial lung disease were included. The mean time from LTx to first nephrologist evaluation was 4.7 years with an eGFR of 31.7 mL/min/1.73 m2. LTx recipients experienced a 48% reduction in eGFR within the first few months after LTx. Rapid progressors require renal replacement therapy earlier than the slow progressors. Patients requiring dialysis had higher all-cause mortality compared to those who did not require dialysis. Conclusions: Early post-LTx functional impairment appears to be the most significant predictor for CKD progression and the eventual need for RRT. Although CNI toxicity is the most common cause of CKD, early nephrology evaluation can uncover other causes and promote early renoprotective measures. For this patient population, specific guidelines addressing CKD after LTx and a multidisciplinary approach are essential.

PMID:40217693 | DOI:10.3390/jcm14072241

Neurol Sci. 2025 Apr 12. doi: 10.1007/s10072-025-08167-x. Online ahead of print.

ABSTRACT

BACKGROUND: Alzheimer's disease is the most prevalent kind of age-associated dementia among older adults globally. Traditional diagnostic models for predicting Alzheimer's disease risks primarily rely on demographic and clinical data to develop policies and assess probabilities. However, recent advancements in machine learning (ML) and other artificial intelligence (AI) have shown promise in developing personalized risk models. These models use specific patient data from medical imaging and related reports. In this systematic review, different studies comprehensively examined the use of ML in magnetic resonance imaging (MRI), genetics, radiomics, and medical data for Alzheimer's disease risk assessment. I highlighted the results of our rigorous analysis of this research and emphasized the exciting potential of ML methods for Alzheimer's disease risk prediction. We also looked at current research projects and possible uses of AI-driven methods to enhance Alzheimer's disease risk prediction and enable more efficient investigating and individualized risk mitigation strategies.

AIM AND METHODS: This review integrates both conventional and AI-based models to thoroughly analyze neuroimaging and non-neuroimaging features used in Alzheimer's disease prediction. This study examined factors related to imaging, radiomics, genetics, and clinical aspects. In addition, this study comprehensively presented machine learning for predicting the risk of Alzheimer's disease detection to benefit both beginner and expert researchers.

RESULTS: A total of 700 publications from 2000 and 2024, were initially retrieved, out of which 120 studies met the inclusion criteria and were elected for review. The diagnosis of neurological disorders, along with the application of deep learning (DL) and machine learning (ML) were central themes in studies on the subject. When analyzing the medical implementation or design of innovative models, various machine learning models applied to neuroimaging and non-neuroimaging data may help researchers and clinicians become more informed. This review provides an extensive guide to the state of Alzheimer's disease risk assessment with artificial AI.

CONCLUSION: By integrating diverse neuroimaging and non-neuroimaging data sources, this study provides researchers with an alternative viewpoint on the application of AI in Alzheimer's disease risk prediction emphasizing its potential to improve early diagnosis and personalized intervention strategies.

PMID:40220257 | DOI:10.1007/s10072-025-08167-x

Methods Mol Biol. 2025;2904:21-50. doi: 10.1007/978-1-0716-4414-0_3.

ABSTRACT

Fluorescence microscopy is a key method for the visualization of cellular, subcellular, and molecular live-cell dynamics, enabling access to novel insights into mechanisms of health and disease. However, effects like phototoxicity, the fugitive nature of signals, photo bleaching, and method-inherent noise can degrade the achievable signal-to-noise ratio and image resolution. In recent years, deep learning (DL) approaches have been increasingly applied to remove these degradations. In this review, we give a brief overview over existing classical and DL approaches for denoising, deconvolution, and computational super-resolution of fluorescence microscopy data. We summarize existing open-source databases within these fields as well as code repositories related to corresponding publications and further contribute an example project for DL-based image denoising, which provides a low barrier entry into DL coding and respective applications. In summary, we supply interested researchers with tools to apply or develop DL applications in live-cell imaging and foster research participation in this field.

PMID:40220224 | DOI:10.1007/978-1-0716-4414-0_3

Int J Comput Assist Radiol Surg. 2025 Apr 12. doi: 10.1007/s11548-025-03353-w. Online ahead of print.

ABSTRACT

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a noninvasive technique that enables the direct visualization of tissue at a microscopic level in real time. One of the main challenges in using pCLE is maintaining the probe within a working range of micrometer scale. As a result, the need arises for automatically regressing the probe-tissue distance to enable precise robotic tissue scanning.

METHODS: In this paper, we propose the spatial frequency bidirectional structured state space model (SF-BiS4D) for pCLE probe-tissue distance regression. This model advances traditional state space models by processing image sequences bidirectionally and analyzing data in both the frequency and spatial domains. Additionally, we introduce a guided trajectory planning strategy that generates pseudo-distance labels, facilitating the training of sequential models to generate smooth and stable robotic scanning trajectories. To improve inference speed, we also implement a hierarchical guided fine-tuning (GF) approach that efficiently reduces the size of the BiS4D model while maintaining performance.

RESULTS: The performance of our proposed model has been evaluated both qualitatively and quantitatively using the pCLE regression dataset (PRD). In comparison with existing state-of-the-art (SOTA) methods, our approach demonstrated superior performance in terms of accuracy and stability.

CONCLUSION: Our proposed deep learning-based framework effectively improves distance regression for microscopic visual servoing and demonstrates its potential for integration into surgical procedures requiring precise real-time intraoperative imaging.

PMID:40220066 | DOI:10.1007/s11548-025-03353-w

Int J Comput Assist Radiol Surg. 2025 Apr 12. doi: 10.1007/s11548-025-03344-x. Online ahead of print.

ABSTRACT

PURPOSE: Deep learning methods are commonly used to generate context understanding to support surgeons and medical professionals. By expanding the current focus beyond the operating room (OR) to postoperative workflows, new forms of assistance are possible. In this article, we propose a novel multi-target multi-camera tracking (MTMCT) architecture for postoperative phase recognition, location tracking, and automatic timestamp generation.

METHODS: Three RGB cameras were used to create a multi-camera data set containing 19 reenacted postoperative patient flows. Patients and beds were annotated and used to train the custom MTMCT architecture. It includes bed and patient tracking for each camera and a postoperative patient state module to provide the postoperative phase, current location of the patient, and automatically generated timestamps.

RESULTS: The architecture demonstrates robust performance for single- and multi-patient scenarios by embedding medical domain-specific knowledge. In multi-patient scenarios, the state machine representing the postoperative phases has a traversal accuracy of 84.9 ± 6.0 % , 91.4 ± 1.5 % of timestamps are generated correctly, and the patient tracking IDF1 reaches 92.0 ± 3.6 % . Comparative experiments show the effectiveness of using AFLink for matching partial trajectories in postoperative settings.

CONCLUSION: As our approach shows promising results, it lays the foundation for real-time surgeon support, enhancing clinical documentation and ultimately improving patient care.

PMID:40220065 | DOI:10.1007/s11548-025-03344-x

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf277. doi: 10.1093/nar/gkaf277.

ABSTRACT

Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human transcripts, our model learned hidden rules of translation control and achieved a near perfect prediction of canonical translation sites across entire human transcriptome. Surprisingly, this model revealed a new role of codon usage in regulating translation termination, which was experimentally validated. We also identified thousands of new open reading frames in mRNAs or lncRNAs, some of which were confirmed experimentally. The model trained with human mRNAs achieved high prediction accuracy of canonical translation sites in all eukaryotes and good prediction in polycistronic transcripts from prokaryotes or RNA viruses, suggesting a high degree of conservation in translation control. Collectively, we present TranslationAI (https://www.biosino.org/TranslationAI/), a general and efficient deep learning model for RNA translation that generates new insights into the complexity of translation regulation.

PMID:40219965 | DOI:10.1093/nar/gkaf277

J Cereb Blood Flow Metab. 2025 Apr 12:271678X251333256. doi: 10.1177/0271678X251333256. Online ahead of print.

ABSTRACT

Cerebral small vessel disease (SVD) is diagnosed through imaging hallmarks like white matter hyperintensities (WMH). Novel hypotheses imply that endothelial dysfunction, blood-brain barrier (BBB) disruption and neurovascular inflammation contribute to conversion of normal-appearing white matter (NAWM) into WMH in hypertensive individuals. Aiming to unravel the association between chronic hypertension and the earliest WMH pathogenesis, we characterized microvascular pathology in periventricular NAWM into WMH in post-mortem brains of individuals with and without hypertension. Our second aim was to delineate the NAWM-WMH transition from NAWM towards the center of WMH using deep learning, refining WMH segmentation capturing increases in FLAIR signal. Finally, we aimed to demonstrate whether these processes may synergistically contribute to WMH pathogenesis by performing voxel-wise correlations between MRI and microvascular pathology. Larger endothelium disruption, BBB damage and neurovascular inflammation were observed in individuals with hypertension. We did not observe gradual BBB damage nor neurovascular inflammation along the NAWM-WMH transition. We found a strong correlation between BBB damage and neurovascular inflammation in all individuals in both periventricular NAWM and WMH. These novel findings suggest that endothelium disruption, BBB damage and neurovascular inflammation are major contributors to SVD progression, but being already present in NAWM in hypertension.

PMID:40219923 | DOI:10.1177/0271678X251333256

J Med Eng Technol. 2025 Apr 12:1-25. doi: 10.1080/03091902.2025.2484691. Online ahead of print.

ABSTRACT

The most common cause of dementia, which includes significant cognitive impairment that interferes with day-to-day activities, is Alzheimer's Disease (AD). Deep learning techniques performed better on diagnostic tasks. However, current methods for detecting Alzheimer's disease lack effectiveness, resulting in inaccurate results. To overcome these challenges, a novel deep ensemble architecture for AD classification is proposed in this research. The proposed model involves key phases, including Preprocessing, Segmentation, Feature Extraction, and Classification. Initially, Median filtering is employed for preprocessing. Subsequently, an improved U-Net architecture is employed for segmentation, and then the features including Improved Shape Index Histogram (ISIH), Multi Binary Pattern (MBP), and Multi Texton are extracted from the segmented image. Then, an En-LeCILSTM is proposed, which combines the LeNet, CNN and improved LSTM models. Finally, the resultant output is obtained by averaging the intermediate output of each model, leading to improved detection accuracy. Finally, the proposed model's efficiency is assessed through various analyses, including classifier comparison, and performance metric evaluation. As a result, the En-LeCILSTM model scored a higher accuracy of 0.963 and an F-measure of 0.908, which surpasses the result of traditional methods. The outcomes demonstrate that the proposed model is notably more effective in detecting Alzheimer's disease.

PMID:40219912 | DOI:10.1080/03091902.2025.2484691

Sleep. 2025 Apr 11:zsaf091. doi: 10.1093/sleep/zsaf091. Online ahead of print.

ABSTRACT

Insufficient sleep quality is directly linked to various diseases, making reliable sleep monitoring crucial for prevention, diagnosis, and treatment. As sleep laboratories are cost- and resource-prohibitive, wearable sensors offer a promising alternative for long-term unobtrusive sleep monitoring at home. Current unobtrusive sleep detection systems are mostly based on actigraphy (ACT) that tend to overestimate sleep due to a lack of movement in short periods of wakefulness. Previous research established sleep stage classification by combining ACT with cardiac information but has not investigated the incorporation of respiration in large-scale studies. For that reason, this work aims to systematically compare ACT-based sleep-stage classification with multimodal approaches combining ACT, heart rate variability (HRV) as well as respiration rate variability (RRV) using state-of-the-art machine- and deep learning algorithms. The evaluation is performed on a publicly available sleep dataset including more than 1,000 recordings. Respiratory information is introduced through ECG-derived respiration (EDR) features, which are evaluated against traditional respiration belt data. Results show that including RRV features improves the Matthews Correlation Coefficient (MCC), with long short-term memory (LSTM) algorithms performing best. For sleep staging based on AASM standards, the LSTM achieved a median MCC of 0.51 (0.16 IQR). Respiratory information enhanced classification performance, particularly in detecting Wake and Rapid eye movement (REM) sleep epochs. Our findings underscore the potential of including respiratory information in sleep analysis to improve sleep detection algorithms and, thus, help to transfer sleep laboratories into a home monitoring environment. The code used in this work can be found online at https://github.com/mad-lab-fau/sleep_analysis.

PMID:40219765 | DOI:10.1093/sleep/zsaf091

Med Phys. 2025 Apr 11. doi: 10.1002/mp.17828. Online ahead of print.

ABSTRACT

BACKGROUND: Radiation therapy (RT) planning is a time-consuming process involving the contouring of target volumes and organs at risk, followed by treatment plan optimization. CT is typically used as the primary planning image modality as it provides electron density information needed for dose calculation. MRI is widely used for contouring after registration to CT due to its high soft tissue contrast. However, there exists uncertainties in registration, which propagate throughout treatment planning as contouring errors, and lead to dose inaccuracies. MR-only RT planning has been proposed as a solution to eliminate the need for CT scan and image registration, by synthesizing CT from MRI. A challenge in deploying MR-only planning in clinic is the lack of a method to estimate the reliability of a synthetic CT in the absence of ground truth. While methods have used sampling-based approaches to estimate model uncertainty over multiple inferences, such methods suffer from long run time and are therefore inconvenient for clinical use.

PURPOSE: To develop a fast and robust method for the joint synthesis of CT from MRI, estimation of model uncertainty related to the synthesis accuracy, and segmentation of organs at risk (OARs), in a single model inference.

METHODS: In this work, deep evidential regression is applied to MR-only brain RT planning. The proposed framework uses a multi-task vision transformer combining a single joint nested encoder with two distinct convolutional decoder paths for synthesis and segmentation separately. An evidential layer was added at the end of the synthesis decoder to jointly estimate model uncertainty in a single inference. The framework was trained and tested on a dataset of 119 (80 for training, 9 for validation, and 30 for test) paired T1-weighted MRI and CT scans with OARs contours.

RESULTS: The proposed method achieved mean ± SD SSIM of 0.820 ± 0.039, MAE of 47.4 ± 8.49 HU, and PSNR of 23.4 ± 1.13 for the synthesis task and dice similarity coefficient of 0.799 ± 0.132 (lenses), 0.945 ± 0.020 (eyes), 0.834 ± 0.059 (optic nerves), 0.679 ± 0.148 (chiasm), 0.947 ± 0.014 (temporal lobes), 0.849 ± 0.027 (hippocampus), 0.953 ± 0.024 (brainstem), 0.752 ± 0.228 (cochleae) for segmentation-in a total run time of 6.71 ± 0.25 s. Additionally, experiments on challenging test cases revealed that the proposed evidential uncertainty estimation highlighted the same uncertain regions as Monte Carlo-based epistemic uncertainty, thus highlighting the reliability of the proposed method.

CONCLUSION: A framework leveraging deep evidential regression to jointly synthesize CT from MRI, predict the related synthesis uncertainty, and segment OARs in a single model inference was developed. The proposed approach has the potential to streamline the planning process and provide clinicians with a measure of the reliability of a synthetic CT in the absence of ground truth.

PMID:40219601 | DOI:10.1002/mp.17828

Network. 2025 Apr 11:1-50. doi: 10.1080/0954898X.2025.2476081. Online ahead of print.

ABSTRACT

Efficient data transmission in Wireless Sensor Networks (WSNs) is a critical challenge. Traditional routing protocols focus on energy efficiency but do not consider other factors that might degrade performance. This research proposes a novel Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm to address these issues, focusing on optimizing energy-efficient data transmission. In the proposed approach, initially, sensor nodes and field dimensions are initialized. Then, K-means clustering is applied to grouping nodes. The Deep Q-Net model is used to predict energy levels of nodes. CH is selected as per the node having higher energy. Multipath routing is performed through the HBWCO algorithm, which optimally selects the best routing paths by considering factors like reliability, residual energy, predicted energy, throughput, and traffic intensity. If link breakage occurs, a route maintenance phase is initiated using Source Link Breakage Warning (SLBW) message strategy to notify the source node about the issue of choosing another path. This work offers a comprehensive approach to enhancing energy efficiency in networks. The suggested HBWCO approach is in contrast to the traditional methods. The HBWCO approach has achieved the highest reliability of 0.948 and the highest throughput of 3496. Therefore, the HBWCO algorithm offers an effective solution for data transmission and routing reliability.

PMID:40219585 | DOI:10.1080/0954898X.2025.2476081

J Clin Med. 2025 Mar 24;14(7):2205. doi: 10.3390/jcm14072205.

ABSTRACT

In recent years, several studies have examined the impact of sleep-disordered breathing (SBD) on the quality of life and prognosis of patients with idiopathic pulmonary fibrosis (IPF). Among these disorders, obstructive sleep apnea (OSA) and nocturnal hypoxemia (NH) are the most prevalent and extensively studied, whereas central sleep apnea (CSA) has only been documented in recent research. The mechanisms underlying the relationship between IPF and SBDs are complex and remain an area of active investigation. Despite growing recognition of SBDs in IPF, no standardized guidelines exist for their management and treatment, particularly in a population characterized by distinct structural pulmonary abnormalities. This review outlines the pathophysiological connections between sleep-breathing disorders (SBDs) and idiopathic pulmonary fibrosis (IPF), as well as current therapeutic options. A comprehensive literature search using PubMed identified relevant studies, confirming the efficacy of CPAP in treating severe OSA and CSA. While high-flow oxygen therapy has not been validated in this patient cohort, it may offer a potential solution for select patients, particularly the elderly and those with low compliance. Conventional oxygen therapy, however, is limited to cases of isolated nocturnal hypoxemia or mild central sleep apnea.

PMID:40217656 | DOI:10.3390/jcm14072205