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

STAR Protoc. 2025 Apr 11;6(2):103763. doi: 10.1016/j.xpro.2025.103763. Online ahead of print.

ABSTRACT

Noise regulatory proteins are key to understanding the dynamic regulation of cell-to-cell heterogeneity in gene expression. Here, we present a protocol for identifying novel candidate proteins with noise regulatory functions. We describe steps for inhibiting translation in cells, performing single-cell RNA sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and utilizing known regulator-target interactions to integrate obtained data in a regulator enrichment analysis. This protocol has the potential to be applied in any cell line and under culture conditions of choice. For complete details on the use and execution of this protocol, please refer to García-Blay et al.1.

PMID:40220303 | DOI:10.1016/j.xpro.2025.103763

Methods Mol Biol. 2025;2910:105-125. doi: 10.1007/978-1-0716-4446-1_7.

ABSTRACT

The expression pattern of proteins defines the range of biological processes in cellular subcompartments. A core aim in cell biology is therefore to determine the localization and composition of protein complexes within cells. Proximity labeling methodologies offer an unbiased and efficient way to unravel the cellular micro-environment of proteins, providing insights into the molecular networks they participate in. In this chapter, we present a protocol for conducting proximity labeling experiments in primary murine neuronal cultures in vitro based on the proximity-dependent biotinylation identification (BioID) approach. Data acquired through this protocol can be utilized to identify the composition of protein complexes in neurons and to create molecular maps of neuronal subcompartments. This will aid in determining the spatial distribution of biological processes within neurons, and in unraveling fundamental principles of neuronal function and plasticity.

PMID:40220096 | DOI:10.1007/978-1-0716-4446-1_7

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf266. doi: 10.1093/nar/gkaf266.

ABSTRACT

For a few years, it has been possible to experimentally probe the universe of cis and trans RNA-RNA interactions in a transcriptome-wide manner. These experiments give rise to so-called duplex data, i.e. short reads generated via high-throughput sequencing that each encode information on a cis or trans RNA-RNA interaction. These raw duplex data require complex, subsequent computational analyses in order to be interpreted as solid evidence for actual cis and trans RNA-RNA interactions. While several methods have already been proposed to tackle this challenge, almost all of them lack one or more desirable feature-computational efficiency, ability to readily alter the main processing steps and parameter values, p-value estimation for predictions, and interoperability with the common bioinformatics tools for transcriptomics. To overcome these challenges, we present DuplexDiscoverer-a computational method and R package that allows for the efficient, adjustable, and conceptually coherent analysis of duplex data. DuplexDiscoverer is readily adaptable to analysing data from different experimental protocols and its results seamlessly integrate with the most commonly used bioinformatics tools for transcriptomics in R. Most importantly, DuplexDiscoverer generates predictions that are of superior or comparable quality to those of the existing methods while significantly improving time and memory efficiency.

PMID:40219963 | DOI:10.1093/nar/gkaf266

Animals (Basel). 2025 Apr 4;15(7):1045. doi: 10.3390/ani15071045.

ABSTRACT

The frequency, duration and intensity of heat events in Australia are forecast to increase. Different grain types result in different heat loads on animals, so grain selection could reduce the impact of heat exposure. Thirty-two multiparous Holstein cows at 86 days in milk were offered a basal forage diet plus one of four supplements: (1) BLY, rolled barley; (2) CAN, canola meal and rolled wheat; (3) CRN, disk-milled corn; or (4) WHT, rolled wheat. Cows were exposed to a 2-day heat wave in controlled-climate chambers. Overall, cows offered CAN had the lowest dry matter intake (DMI; 16.2 vs. 17.7 kg) but produced more energy-corrected milk (ECM; 34.9 vs. 29.6 kg) when compared with the other treatments. The results were similar during heat exposure. Cows fed CRN and CAN had the greatest body temperature (38.9 °C), and cows fed BLY had the lowest (38.4 °C). Despite this, cows fed BLY had the greatest reduction in DMI from the pre-challenge to the heat-challenge periods (-2.8 vs. -0.4 kg DM/d). There appears to be a small advantage to offering cows a concentrate with a greater protein concentration compared to one that has a greater concentration of fat or starch. The choice of grain to include in a dairy cow's ration during summers with acute heat events may simply be an economic one.

PMID:40218438 | DOI:10.3390/ani15071045

Expert Opin Drug Saf. 2025 Apr 12. doi: 10.1080/14740338.2025.2493781. Online ahead of print.

ABSTRACT

BACKGROUND: Guillain-Barré Syndrome (GBS) is a rare but severe neurological disorder often precipitated by infections, vaccines, and potentially by certain medications. Drug-induced GBS, though less commonly reported, presents significant diagnostic and therapeutic challenges. This study investigates the correlation between various medications and the onset of GBS.

RESEARCH DESIGN AND METHODS: We conducted a retrospective pharmacovigilance analysis using data from the FDA Adverse Event Reporting System (FAERS) from Q1 2004 to Q1 2024. The analysis focused on identifying drugs frequently associated with GBS and examining the time-to-onset and severity of these events.

RESULTS: From over 17 million adverse events, 1,869 cases were identified as drug-induced GBS. Monoclonal antibodies and immunomodulators were the most frequently implicated drug classes. The median time-to-onset for GBS was within the first 30 days following drug exposure. Approximately 51.8% of the cases resulted in severe outcomes, such as hospitalization or disability. Drugs like brentuximab vedotin and efalizumab showed strong associations with GBS occurrences.

CONCLUSIONS: This study highlights the importance of monitoring for GBS symptoms following the administration of certain medications, particularly those that affect immune function, and underscores the need for healthcare providers to be aware of the potential neurological risks associated with these medications.

PMID:40220275 | DOI:10.1080/14740338.2025.2493781