Trends Plant Sci. 2025 Apr 1:S1360-1385(25)00063-9. doi: 10.1016/j.tplants.2025.03.003. Online ahead of print.

ABSTRACT

The field of mass spectrometry (MS)-based proteomics is rapidly advancing with technological and computational improvements, including leveraging the power of artificial intelligence (AI) to drive innovation. Such innovation has been particularly apparent in human disease research, where the intersection of these disciplines has pioneered a new age of disease diagnostics and pharmaceutical discovery. However, applications within plant sciences remains woefully under-represented and yet provides exceptional promise and potential to support new, interdisciplinary areas of research. Timely and novel examples of proteomics advancing plant science encompass biotechnology, climatic resiliency, agricultural production systems, and disease management. Herein, we propose new scientific avenues that leverage the power of proteomics and AI within plant science research to drive new discoveries and innovation.

PMID:40175191 | DOI:10.1016/j.tplants.2025.03.003

Adv Pharmacol. 2025;103:287-311. doi: 10.1016/bs.apha.2025.01.012. Epub 2025 Feb 26.

ABSTRACT

Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.

PMID:40175046 | DOI:10.1016/bs.apha.2025.01.012

Adv Pharmacol. 2025;103:231-263. doi: 10.1016/bs.apha.2025.01.003. Epub 2025 Feb 6.

ABSTRACT

Computational precision in drug discovery integrates algorithms and high-performance computing to analyze complex biological data with unprecedented accuracy, revolutionizing the identification of therapeutic targets. This process encompasses diverse computational and experimental approaches that enhance drug discovery's speed and precision. Advanced techniques like next-generation sequencing enable rapid genetic characterization, while proteomics explores protein expression and interactions driving disease progression. In-silico methods, including molecular docking, virtual screening, and pharmacophore modeling, predict interactions between small molecules and biological targets, accelerating early drug candidate identification. Structure-based drug design and molecular dynamics simulations refine drug designs by elucidating target structures and molecular behaviors. Ligand-based methods utilize known chemical properties to anticipate new compound activities. AI and machine learning optimizes data analysis, offering novel insights and improving predictive accuracy. Systems biology and network pharmacology provide a holistic view of biological networks, identifying critical nodes as potential drug targets, which traditional methods might overlook. Computational tools synergize with experimental techniques, enhancing the treatment of complex diseases with personalized medicine by tailoring therapies to individual patients. Ethical and regulatory compliance ensures clinical applicability, bridging computational predictions to effective therapies. This multi-dimensional approach marks a paradigm shift in modern medicine, delivering safer, more effective treatments with precision. By integrating bioinformatics, genomics, and proteomics, computational drug discovery has transformed how therapeutic interventions are developed, ensuring an era of personalized, efficient healthcare.

PMID:40175044 | DOI:10.1016/bs.apha.2025.01.003

Adv Pharmacol. 2025;103:1-22. doi: 10.1016/bs.apha.2025.01.006. Epub 2025 Feb 13.

ABSTRACT

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

PMID:40175036 | DOI:10.1016/bs.apha.2025.01.006

J Am Coll Radiol. 2025 Mar 31:S1546-1440(25)00195-4. doi: 10.1016/j.jacr.2025.03.006. Online ahead of print.

NO ABSTRACT

PMID:40174871 | DOI:10.1016/j.jacr.2025.03.006

Nephron. 2025 Apr 2:1-11. doi: 10.1159/000544754. Online ahead of print.

ABSTRACT

BACKGROUND: Although there have been reports of relapse or worsening of membranous nephropathy after receiving vaccines against coronavirus disease 2019 (COVID-19), the causal relationship or association between them has not been established. This study aimed to investigate the occurrence of relapse or worsening of membranous nephropathy following inactivated COVID-19 vaccination.

METHODS: Patients who had been diagnosed with membranous nephropathy before receiving their first dose of vaccination, or before March 1, 2021, for unvaccinated patients, were included in the study. All patients were monitored at the Membranous Nephropathy Clinic of Huashan Hospital, Fudan University. The reasons for not receiving vaccines were investigated. The impact of COVID-19 vaccination on membranous nephropathy was assessed by comparing the relapse or worsening of membranous nephropathy within 12 months in vaccinated and unvaccinated patients with proteinuria <3.5 g/d. The baseline variables were balanced using cardinality matching.

RESULTS: A total of 353 patients with membranous nephropathy were included in the study, with 186 (53%) having received inactivated COVID-19 vaccines. Among the 167 unvaccinated participants, 114 (68%) expressed concerns about the possibility of disease relapse, and 47 (28%) were worried about the vaccine's efficacy due to their immunosuppressive therapy. Of the 239 participants with proteinuria <3.5 g/d, 152 were vaccinated, and 16 (11%) experienced a relapse or worsening of the disease during the follow-up period, which was similar to the 14 (16%) observed in the unvaccinated group. Following cardinality matching, there was no difference in the rate of relapse or worsening between the two groups, with 10 (13%) in the vaccinated group and 11 (15%) in the unvaccinated group (hazard ratio 0.98, 95% confidence interval 0.42-2.33).

CONCLUSION: Getting the inactivated COVID-19 vaccine may not increase risk of relapse or worsening in patients with membranous nephropathy.

PMID:40174580 | DOI:10.1159/000544754

Reprod Biomed Online. 2024 Nov 14;50(5):104707. doi: 10.1016/j.rbmo.2024.104707. Online ahead of print.

ABSTRACT

Infertility, adverse pregnancy outcomes and genital infections are global concerns. The reproductive tract microbiome appears to play a crucial role in the physiology of both the female and male reproductive tracts. Despite the presence of thousands of microbes in body fluids shared during unprotected sexual intercourse, they have traditionally been studied separately, with greater emphasis on the female (mostly vaginal) microbiome, and the interaction between these microbiomes in a sexually active couple has been overlooked. This review introduces the concept of the 'seminovaginal microbiome' - the collective microbiota of both partners, transferred and shared during sexual interaction. By synthesizing the existing body of next-generation sequencing-based literature, this review establishes the first holistic view of how these microbiomes interact, influence reproductive health and affect assisted reproductive technique outcomes, as well as the occurrence of microbe-associated diseases such as sexually transmitted infections, prostatitis, bacterial vaginosis and candidiasis. Additionally, the microbial interplay in homosexual couples and transgender individuals is discussed.

PMID:40174296 | DOI:10.1016/j.rbmo.2024.104707

PLoS Comput Biol. 2025 Apr 2;21(4):e1012876. doi: 10.1371/journal.pcbi.1012876. Online ahead of print.

ABSTRACT

Computational approaches for drug repurposing for viral diseases have mainly focused on a small number of antivirals that directly target pathogens (virus centric therapies). In this work, we combine ideas from collaborative filtering and network medicine for making predictions on a much larger set of drugs that could be repurposed for host centric therapies, that are aimed at interfering with host cell factors required by a pathogen. Our idea is to create matrices quantifying the perturbation that drugs and viruses induce on human protein interaction networks. Then, we decompose these matrices to learn embeddings of drugs, viruses, and proteins in a low dimensional space. Predictions of host-centric antivirals are obtained by taking the dot product between the corresponding drug and virus representations. Our approach is general and can be applied systematically to any compound with known targets and any virus whose host proteins are known. We show that our predictions have high accuracy and that the embeddings contain meaningful biological information that may provide insights into the underlying biology of viral infections. Our approach can integrate different types of information, does not rely on known drug-virus associations and can be applied to new viral diseases and drugs.

PMID:40173200 | DOI:10.1371/journal.pcbi.1012876

PLoS Pathog. 2025 Apr 2;21(4):e1013021. doi: 10.1371/journal.ppat.1013021. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa is an opportunistic pathogen responsible for airway infections in immunocompromised individuals, including those with cystic fibrosis (CF). P. aeruginosa has a type III secretion system (T3SS) that translocates effectors into host cells. ExoS is a T3SS effector with ADP ribosyltransferase (ADPRT) activity. ExoS ADPRT activity promotes P. aeruginosa virulence by inhibiting phagocytosis and limiting oxidative burst in neutrophils. The P. aeruginosa T3SS also translocates flagellin, which can activate the NLRC4 inflammasome, resulting in: 1) gasdermin-D pores, release of IL-1β and pyroptosis; and 2) histone 3 citrullination (CitH3), nuclear DNA decondensation and expansion into the neutrophil cytosol with incomplete NET extrusion. However, studies with P. aeruginosa PAO1 indicate that ExoS ADPRT activity inhibits the NLRC4 inflammasome in neutrophils. Here, we identified an ExoS+ CF clinical isolate of P. aeruginosa with a hyperactive T3SS. Variants of the hyperactive T3SS mutant or PAO1 were used to infect neutrophils from C57BL/6 mice that were wildtype or engineered to have a CF genotype or defects in inflammasome assembly. Responses to NLRC4 inflammasome assembly or ExoS ADPRT activity were assayed and found to be similar for C57BL/6 or CF neutrophils. ExoS ADPRT activity in the hyperactive T3SS mutant regulated inflammasome, nuclear DNA decondensation and incomplete NET extrusion responses, like PAO1, but promoted enhanced CitH3 and plasma membrane rupture (PMR). Glycine supplementation inhibited PMR by the hyperactive T3SS mutant, suggesting ninjurin-1 is required for this process. These results identify enhanced neutrophil PMR as a pathogenic activity of ExoS ADPRT in hypervirulent P. aeruginosa.

PMID:40173191 | DOI:10.1371/journal.ppat.1013021

Invest Radiol. 2025 Apr 2. doi: 10.1097/RLI.0000000000001182. Online ahead of print.

ABSTRACT

OBJECTIVES: Respiratory motion poses a significant challenge for clinical workflows in diagnostic imaging and radiation therapy. Many technologies such as motion artifact reduction and tumor tracking have been developed to compensate for its effect. To assess these technologies, respiratory motion phantoms (RMPs) are required as preclinical testing environments, for instance, in computed tomography (CT). However, current CT RMPs are highly simplified and do not exhibit realistic tissue structures or deformation patterns. With the rise of more complex motion compensation technologies such as deep learning-based algorithms, there is a need for more realistic RMPs. This work introduces PixelPrint4D, a 3D printing method for fabricating lifelike, patient-specific deformable lung phantoms for CT imaging.

MATERIALS AND METHODS: A 4DCT dataset of a lung cancer patient was acquired. The volumetric image data of the right lung at end inhalation was converted into 3D printer instructions using the previously developed PixelPrint software. A flexible 3D printing material was used to replicate variable densities voxel-by-voxel within the phantom. The accuracy of the phantom was assessed by acquiring CT scans of the phantom at rest, and under various levels of compression. These phantom images were then compiled into a pseudo-4DCT dataset and compared to the reference patient 4DCT images. Metrics used to assess the phantom structural accuracy included mean attenuation errors, 2-sample 2-sided Kolmogorov-Smirnov (KS) test on histograms, and structural similarity index (SSIM). The phantom deformation properties were assessed by calculating displacement errors of the tumor and throughout the full lung volume, attenuation change errors, and Jacobian errors, as well as the relationship between Jacobian and attenuation changes.

RESULTS: The phantom closely replicated patient lung structures, textures, and attenuation profiles. SSIM was measured as 0.93 between the patient and phantom lung, suggesting a high level of structural accuracy. Furthermore, it exhibited realistic nonrigid deformation patterns. The mean tumor motion errors in the phantom were ≤0.7 ± 0.6 mm in each orthogonal direction. Finally, the relationship between attenuation and local volume changes in the phantom had a strong correlation with that of the patient, with analysis of covariance yielding P = 0.83 and f = 0.04, suggesting no significant difference between the phantom and patient.

CONCLUSIONS: PixelPrint4D facilitates the creation of highly realistic RMPs, exceeding the capabilities of existing models to provide enhanced testing environments for a wide range of emerging CT technologies.

PMID:40173424 | DOI:10.1097/RLI.0000000000001182

Aesthet Surg J. 2025 Apr 2:sjaf047. doi: 10.1093/asj/sjaf047. Online ahead of print.

ABSTRACT

BACKGROUND: Breast Implant Illness (BII) is a spectrum of symptoms some people attribute to breast implants. While causality remains unproven, patient interest has grown significantly. Understanding patient perceptions of BII on social media is crucial as these platforms increasingly influence healthcare decisions.

OBJECTIVES: The purpose of this study is to analyze patient perceptions and emotional responses to BII on social media using RoBERTa, a natural processing model trained on 124 million X posts.

METHODS: Posts mentioning BII from 2014-2023 were analyzed using two NLP models: one for sentiment (positive/negative) and another for emotions (fear, sadness, anger, disgust, neutral, surprise, and joy). Posts were then classified by their highest-scoring emotion. Results were compared over across 2014-2018 and 2019-2023, with correlation analysis (Pearson correlation coefficient) between published implant explantation and augmentation data.

RESULTS: Analysis of 6,099 posts over 10 years showed 75.4% were negative, with monthly averages of 50.85 peaking at 213 in March 2019. Fear and neutral emotions dominated, representing 35.9% and 35.6% respectively. The strongest emotions were neutral and fear, with an average score of 0.293 and 0.286 per post, respectively. Fear scores increased from 0.219 (2014-2018) to 0.303 (2019-2023). Strong positive correlations (r>0.70) existed between annual explantation rates/explantation-to-augmentation ratios and total, negative, neutral, and fear posts.

CONCLUSIONS: BII discourse on X peaked in 2019, characterized predominantly by negative sentiment and fear. The strong correlation between fear/negative-based posts and explantation rates suggests social media discourse significantly influences patient decisions regarding breast implant removal.

PMID:40173420 | DOI:10.1093/asj/sjaf047

PLoS One. 2025 Apr 2;20(4):e0319078. doi: 10.1371/journal.pone.0319078. eCollection 2025.

ABSTRACT

Hepatitis is a widespread inflammatory condition of the liver, presenting a formidable global health challenge. Accurate and timely detection of hepatitis is crucial for effective patient management, yet existing methods exhibit limitations that underscore the need for innovative approaches. Early-stage detection of hepatitis is now possible with the recent adoption of machine learning and deep learning approaches. With this in mind, the study investigates the use of traditional machine learning models, specifically classifiers such as logistic regression, support vector machines (SVM), decision trees, random forest, multilayer perceptron (MLP), and other models, to predict hepatitis infections. After extensive data preprocessing including outlier detection, dataset balancing, and feature engineering, we evaluated the performance of these models. We explored three modeling approaches: machine learning with default hyperparameters, hyperparameter-tuned models using GridSearchCV, and ensemble modeling techniques. The SVM model demonstrated outstanding performance, achieving 99.25% accuracy and a perfect AUC score of 1.00 with consistency in other metrics with 99.27% precision, and 99.24% for both recall and F1-measure. The MLP and Random Forest proved to be in pace with the superior performance of SVM exhibiting an accuracy of 99.00%. To ensure robustness, we employed a 5-fold cross-validation technique. For deeper insight into model interpretability and validation, we employed an explainability analysis of our best-performed models to identify the most effective feature for hepatitis detection. Our proposed model, particularly SVM, exhibits better prediction performance regarding different performance metrics compared to existing literature.

PMID:40173410 | DOI:10.1371/journal.pone.0319078

Sci Adv. 2025 Apr 4;11(14):eads5185. doi: 10.1126/sciadv.ads5185. Epub 2025 Apr 2.

ABSTRACT

Atlantic and Benguela Niño events substantially affect the tropical Atlantic region, with far-reaching consequences on local marine ecosystems, African climates, and El Niño Southern Oscillation. While accurate forecasts of these events are invaluable, state-of-the-art dynamic forecasting systems have shown limited predictive capabilities. Thus, the extent to which the tropical Atlantic variability is predictable remains an open question. This study explores the potential of deep learning in this context. Using a simple convolutional neural network architecture, we show that Atlantic/Benguela Niños can be predicted up to 3 to 4 months ahead. Our model excels in forecasting peak-season events with remarkable accuracy extending lead time to 5 months. Detailed analysis reveals our model's ability to exploit known physical precursors, such as long-wave ocean dynamics, for accurate predictions of these events. This study challenges the perception that the tropical Atlantic is unpredictable and highlights deep learning's potential to advance our understanding and forecasting of critical climate events.

PMID:40173237 | DOI:10.1126/sciadv.ads5185

Sci Adv. 2025 Apr 4;11(14):eadp0558. doi: 10.1126/sciadv.adp0558. Epub 2025 Apr 2.

ABSTRACT

Historical records of climate fields are often sparse because of missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we use a recently introduced deep learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach, we are able to realistically reconstruct large and irregular areas of missing data and to reproduce known historical events, such as strong El Niño or La Niña events, with very little given information. Our method outperforms the widely used statistical kriging method, as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

PMID:40173235 | DOI:10.1126/sciadv.adp0558

Global Spine J. 2025 Apr 2:21925682251332556. doi: 10.1177/21925682251332556. Online ahead of print.

ABSTRACT

Study DesignRetrospective cohort.ObjectiveDespite numerous studies highlighting patient comorbidities and surgical factors in postoperative success, the role of social determinants of health (SDH) in anterior cervical discectomy and fusion (ACDF) outcomes remains unexplored. This study evaluates the predictive impact of SDH on 90-day readmission and health utilization (HU) in ACDF patients using machine learning (ML).MethodsWe analyzed 3127 ACDF patients (2003-2023) from a multisite academic center, incorporating over 35 clinical and demographic variables. SDH characteristics were assessed using the Social Vulnerability Index. Primary outcomes included 90-day readmission and postoperative HU. ML models were developed and validated by the area under the curve (AUC) for readmission and mean absolute error (MAE) for HU. Feature importance analysis identified key predictors.ResultsBalanced Random Forest (AUC = 0.75) best predicted 90-day readmission, with length of stay, Elixhauser score, and Medicare status as top predictors. Among SDH factors, minority status & language, household composition & disability, socioeconomic status, and housing type & transportation were influential. Support Vector Regression (MAE = 1.96) best predicted HU, with perioperative duration, socioeconomic status, and minority status & language as key predictors.ConclusionsFindings highlight SDH's role in ACDF outcomes, suggesting the value of stratifying for interventions such as targeted resource allocation, language-concordant care, and tailored follow-up. While reliance on a single healthcare system and proxy SDH measures are limitations, this is the first study to apply ML to assess SDH in ACDF patients. Further validation with direct patient-reported SDH data is needed to refine predictive models.

PMID:40173192 | DOI:10.1177/21925682251332556

IEEE Trans Pattern Anal Mach Intell. 2025 Apr 2;PP. doi: 10.1109/TPAMI.2025.3557245. Online ahead of print.

ABSTRACT

This paper introduces a one-stage deep uncalibrated photometric stereo (UPS) network, namely Fourier Uncalibrated Photometric Stereo Network (FUPS-Net), for non-Lambertian objects under unknown light directions. It departs from traditional two-stage methods that first explicitly learn lighting information and then estimate surface normals. Two-stage methods were deployed because the interplay of lighting with shading cues presents challenges for directly estimating surface normals without explicit lighting information. However, these two-stage networks are disjointed and separately trained so that the error in explicit light calibration will propagate to the second stage and cannot be eliminated. In contrast, the proposed FUPS-Net utilizes an embedded Fourier transform network to implicitly learn lighting features by decomposing inputs, rather than employing a disjointed light estimation network. Our approach is motivated from observations in the Fourier domain of photometric stereo images: lighting information is mainly encoded in amplitudes, while geometry information is mainly associated with phases. Leveraging this property, our method "decomposes" geometry and lighting in the Fourier domain as guidance, via the proposed Fourier Embedding Extraction (FEE) block and Fourier Embedding Aggregation (FEA) block, which generate lighting and geometry features for the FUPS-Net to implicitly resolve the geometry-lighting ambiguity. Furthermore, we propose a Frequency-Spatial Weighted (FSW) block that assigns weights to combine features extracted from the frequency domain and those from the spatial domain for enhancing surface reconstructions. FUPS-Net overcomes the limitations of two-stage UPS methods, offering better training stability, a concise end-to-end structure, and avoiding accumulated errors in disjointed networks. Experimental results on synthetic and real datasets demonstrate the superior performance of our approach, and its simpler training setup, potentially paving the way for a new strategy in deep learning-based UPS methods.

PMID:40173071 | DOI:10.1109/TPAMI.2025.3557245

Respir Investig. 2025 Apr 1;63(3):431-437. doi: 10.1016/j.resinv.2025.03.016. Online ahead of print.

ABSTRACT

BACKGROUND: The effect of combining high fraction of inspired oxygen (FIO2) and high flow through a high-flow nasal cannula (HFNC) on exercise tolerance in chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) remains unclear.

METHODS: This prospective, single-blind, randomized, crossover study included patients with COPD (n = 25) and IPF (n = 25). The patients performed a 6-min walking test (6 MWT) while attached to a battery-supplied portable HFNC device under the following four conditions: FIO2 set to a minimum percutaneous oxygen saturation (SpO2) of 86-88 % during 6 MWT with a flow rate of 10 L/min (LOLF) or 50 L/min (LOHF); and FIO2 set to a minimum SpO2 of 92-94 % with a flow rate of 10 L/min (HOLF) or 50 L/min (HOHF).

RESULTS: In both groups, the 6-min walking distance (6 MWD) was significantly greater for HOHF than for LOLF (COPD: 323.2 ± 77.6 m vs. 268.6 ± 87.3 m, respectively, p < 0.0001 and IPF: 406 ± 50.7 m vs. 372.3 ± 50.9 m, respectively, p < 0.0001). In the analysis of the interaction effects for the 6 MWD, the combination of high FIO2 and high flow resulted in an additional 15.9-m extension of the 6 MWD (95 % confidence interval: 0.34-31.5; p = 0.050). The interaction between IPF and high-flow was -14.0 m, suggesting a less pronounced extension effect compared with COPD (95 % confidence interval: -29.5-1.6; p = 0.085).

CONCLUSION: The combination of high FIO2 and high flow through an HFNC may improve exercise tolerance in patients with COPD and IPF.

PMID:40174242 | DOI:10.1016/j.resinv.2025.03.016

Plant Physiol. 2025 Apr 2:kiaf126. doi: 10.1093/plphys/kiaf126. Online ahead of print.

ABSTRACT

Potato (Solanum tuberosum) is highly water and space efficient but susceptible to abiotic stresses such as heat, drought, and flooding, which are severely exacerbated by climate change. Our understanding of crop acclimation to abiotic stress, however, remains limited. Here, we present a comprehensive molecular and physiological high-throughput profiling of potato (Solanum tuberosum, cv. Désirée) under heat, drought, and waterlogging applied as single stresses or in combinations designed to mimic realistic future scenarios. Stress responses were monitored via daily phenotyping and multi-omics analyses of leaf samples comprising proteomics, targeted transcriptomics, metabolomics, and hormonomics at several timepoints during and after stress treatments. Additionally, critical metabolites of tuber samples were analyzed at the end of the stress period. We performed integrative multi-omics data analysis using a bioinformatic pipeline that we established based on machine learning and knowledge networks. Waterlogging produced the most immediate and dramatic effects on potato plants, interestingly activating ABA responses similar to drought stress. In addition, we observed distinct stress signatures at multiple molecular levels in response to heat or drought and to a combination of both. In response to all treatments, we found a downregulation of photosynthesis at different molecular levels, an accumulation of minor amino acids, and diverse stress-induced hormones. Our integrative multi-omics analysis provides global insights into plant stress responses, facilitating improved breeding strategies toward climate-adapted potato varieties.

PMID:40173380 | DOI:10.1093/plphys/kiaf126

Sci Adv. 2025 Apr 4;11(14):eado0173. doi: 10.1126/sciadv.ado0173. Epub 2025 Apr 2.

ABSTRACT

Understanding the heterogeneity of fibroblasts depends on decoding the complexity of cell subtypes, their origin, distribution, and interactions with other cells. Here, we integrated 249,156 fibroblasts from 73 studies across 10 tissues to present a single-cell atlas of fibroblasts. We provided a high-resolution classification of 18 fibroblast subtypes. In particular, we revealed a previously undescribed cell population, TSPAN8+ chromatin remodeling fibroblasts, characterized by high expression of genes with functions related to histone modification and chromatin remodeling. Moreover, TSPAN8+ chromatin remodeling fibroblasts were detectable in spatial transcriptome data and multiplexed immunofluorescence assays. Compared with other fibroblast subtypes, TSPAN8+ chromatin remodeling fibroblasts exhibited higher scores in cell differentiation and resident fibroblast, mainly interacting with endothelial cells and T cells through ligand VEGFA and receptor F2R, and their presence was associated with poor prognosis. Our analyses comprehensively defined the shared and specific characteristics of fibroblast subtypes across tissues and provided a user-friendly data portal, Fibroblast Atlas.

PMID:40173240 | DOI:10.1126/sciadv.ado0173

Neurodegener Dis Manag. 2025 Apr 2:1-3. doi: 10.1080/17582024.2025.2488230. Online ahead of print.

NO ABSTRACT

PMID:40173153 | DOI:10.1080/17582024.2025.2488230