PLoS Biol. 2025 Apr 2;23(4):e3003072. doi: 10.1371/journal.pbio.3003072. eCollection 2025 Apr.

ABSTRACT

An alga that abandoned photosynthesis? This Primer explores a PLOS Biology study showing that a single horizontal gene transfer event allowed the diatom Nitzschia sing1 to evolve a complete enzymatic machinery to break down alginate from brown algae, unlocking a new ecological niche.

PMID:40173128 | DOI:10.1371/journal.pbio.3003072

J Agric Food Chem. 2025 Apr 2. doi: 10.1021/acs.jafc.4c12671. Online ahead of print.

ABSTRACT

Methanol is an ideal one-carbon (C1) feedstock for bioconversion into multicarbon value-added compounds. Biocatalytic approaches to methanol conversion provide sustainable and environmentally friendly alternatives to conventional methods. This process is facilitated by methanol-oxidizing enzymes, including alcohol oxidase (AOx). Here, we report an AOx from Pestalotiopsis fici (PfAOx) with the highest methanol oxidation activity and efficient heterologous expression compared to other AOxs. To investigate the bioconversion of a multicarbon compound (C4 chemical, 2-keto-4-hydroxybutyrate, 2-KHB) from cost-effective methanol, we developed a one-pot enzyme system including PfAOx and pyruvate aldolase from Deinococcus radiodurans (DrADL) with catalase from Bos taurus (BtCAT, commercially available enzyme) to remove toxic H2O2. The optimal reaction conditions for 2-KHB production using PfAOx, DrADL, and BtCAT were determined as pH 8.0, 35 °C, 0.9 mg mL-1 PfAOx, 0.3 mg mL-1 DrADL, 1.5 mg mL-1 BtCAT, 150 mM methanol, 100 mM pyruvate, and 5 mM Mg2+ with shaking at 200 rpm. Under these reaction conditions, 88.8 mM (10.4 g L-1) of 2-KHB was produced for 75 min, representing a 74.0-fold higher yield compared to previously reported 2-KHB production systems from methanol and pyruvate. This study demonstrates a promising multi-enzyme cascade approach for the bioconversion of methanol into valuable compounds.

PMID:40173089 | DOI:10.1021/acs.jafc.4c12671

J Med Virol. 2025 Apr;97(4):e70319. doi: 10.1002/jmv.70319.

ABSTRACT

Papaverine (PV) has been previously identified as a promising candidate in SARS-CoV-2 repurposing screens. In this study, we further investigated both its antiviral and immunomodulatory properties. PV displayed antiviral efficacy against SARS-CoV-2 and influenza A viruses H1N1 and H5N1 in single infection as well as in co-infection. We demonstrated PV's activity against various SARS-CoV-2 variants and identified its action at the post-entry stage of the viral life cycle. Notably, treatment of air-liquid interface (ALI) cultures of primary bronchial epithelial cells with PV significantly inhibited SARS-CoV-2 levels. Additionally, PV was found to attenuate interferon (IFN) signaling independently of viral infection. Mechanistically, PV decreased the activation of the IFN-stimulated response element following stimulation with all three IFN types by suppressing STAT1 and STAT2 phosphorylation and nuclear translocation. Furthermore, the combination of PV with approved COVID-19 therapeutics molnupiravir and remdesivir demonstrated synergistic effects. Given its immunomodulatory effects and clinical availability, PV shows promising potential as a component for combination therapy against COVID-19.

PMID:40171981 | DOI:10.1002/jmv.70319

Pediatr Blood Cancer. 2025 Apr 2:e31697. doi: 10.1002/pbc.31697. Online ahead of print.

ABSTRACT

Parents of children with complex vascular anomalies (VAs) struggle to locate credible information. We explored whether their perceptions of the adequacy of VA-related information were associated with caregiver burden, anxiety, child health, and their ability to navigate the healthcare system and seek information. We also examined how their perceptions of clinician knowledge and communication affect their perceptions of information adequacy. A total of 86 parents completed our online survey. Perceived information adequacy was associated with lower anxiety, greater ability to navigate the healthcare system, greater clinician knowledge, and better clinician communication. These data identify important communication barriers that future research studies should address.

PMID:40172173 | DOI:10.1002/pbc.31697

Allergy. 2025 Apr 2. doi: 10.1111/all.16544. Online ahead of print.

NO ABSTRACT

PMID:40171941 | DOI:10.1111/all.16544

Am J Health Syst Pharm. 2025 Apr 2:zxaf071. doi: 10.1093/ajhp/zxaf071. Online ahead of print.

ABSTRACT

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE: A customized Epic scoring tool for monitoring medications requiring renal dose adjustment utilizing Epic Bugsy and a custom renal function trend scoring column was developed and implemented in June 2023 at UT Southwestern Medical Center (UTSW) to replace the manual review and intervention (i-Vent) documentation process.

METHODS: This retrospective, observational cohort study evaluated pharmacist interventions and antimicrobial dosing before and after implementation of the UTSW renal clinical pharmacist responsibility (CPR) dose adjustment tool. Adult patients (aged 18 years or older) requiring renal dose adjustment were included. The preintervention group included patients admitted between July 1 and August 31, 2022, whereas the postintervention group included patients admitted from July 1 through August 31, 2023. Patients exempt from the institutional automatic adult renal dosing guideline (ie, those with cystic fibrosis, solid organ transplantation, or bone marrow transplantation) or actively receiving renal replacement therapy during the index encounter were excluded.

RESULTS: In a comparable 2-month timespan, implementation of the renal CPR dose adjustment tool resulted in a 68.2% increase in the number of renal dosing interventions completed (P < 0.0001), a 47.2% reduction in the number of unique alerts requiring pharmacist review (P < 0.0001), and an increase in the proportion of actionable interventions per alert requiring review from 11.1% before implementation to 39.4% after implementation (P < 0.0001). Pharmacist satisfaction with the renal monitoring workflow also improved with implementation.

CONCLUSION: In a comparable 2-month timespan, implementation of the renal CPR dose adjustment tool at UTSW resulted iin improvements in interventions completed, a reduction in alerts requiring review, an increased total duration that selected antimicrobials were dosed appropriately, and improved pharmacist satisfaction.

PMID:40172577 | DOI:10.1093/ajhp/zxaf071

Curr Opin Pediatr. 2025 Mar 27. doi: 10.1097/MOP.0000000000001458. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: The field of cystic fibrosis is experiencing dramatic changes, as the translation of a massive body of scientific knowledge accumulated from the day of the cloning of the CFTR gene has led to the identification of effective therapies to correct the basic defect. This has also allowed care providers and people with cystic fibrosis in low-income and middle-income countries (LMICs) to become more knowledgeable and proficient in cystic fibrosis cares.

RECENT FINDINGS: This review focuses on two main aspects highly relevant to understand the current status of cystic fibrosis in LMICs: The recognition of the universal occurrence of cystic fibrosis, as well as the varying incidence in different regions of the world, and the collaborative international efforts for dissemination of best care practices as an attempt to close gaps in care.

SUMMARY: As the field continues to change rapidly, multiple international efforts are attempting to close gaps and disparities clearly apparent between affluent countries and LMICs. However, these efforts are seriously hampered by limited access to effective therapies and most dramatically to CFTR modulator drugs.

PMID:40172290 | DOI:10.1097/MOP.0000000000001458

mBio. 2025 Apr 2:e0037425. doi: 10.1128/mbio.00374-25. Online ahead of print.

ABSTRACT

Staphylococcus aureus is one of the most common pathogens isolated from the lungs of people with cystic fibrosis (CF), but little is known about its ability to colonize this niche. We performed a transposon-sequencing (Tn-seq) screen to identify genes necessary for S. aureus growth in media prepared from ex vivo CF sputum. We identified 19 genes that were required for growth in all sputum media tested and dozens more that were required for growth in at least one sputum medium. Depleted mutants of interest included insertions in many genes important for surviving metal starvation, as well as the primary regulator of cysteine metabolism, cymR. To investigate the mechanisms by which these genes contribute to S. aureus growth in sputum, we quantified low-molecular-weight thiols, nutrient transition metals, and the host metal-sequestration protein calprotectin in sputum from 11 individuals with CF. In all samples, the abundance of calprotectin exceeded nutrient metal concentration, explaining the S. aureus requirement for metal-starvation genes. Furthermore, all samples contain potentially toxic quantities of cysteine and sufficient glutathione to satisfy the organic sulfur requirements of S. aureus. Deletion of the cysteine importer genes tcyA and tcyP in the ∆cymR background restored growth to wild-type levels in CF sputum, suggesting that the mechanism by which cymR is required for growth in sputum is to prevent uncontrolled import of cysteine or cystine from this environment. Overall, this work demonstrates that calprotectin and cysteine limit S. aureus growth in CF sputum.IMPORTANCEStaphylococcus aureus is a major cause of lung infections in people with cystic fibrosis (CF). This work identifies genes required for S. aureus growth in this niche, which represent potential targets for anti-Staphylococcal treatments. We show that genes involved in surviving metal starvation are required for growth in CF sputum. We also found that the primary regulator of cysteine metabolism, CymR, plays a critical role in preventing cysteine intoxication during growth in CF sputum. To support these models, we analyzed sputum from 11 individuals with CF to determine concentrations of calprotectin, nutrient metals, and low-molecular-weight thiols, which have not previously been quantified together in the same samples.

PMID:40172197 | DOI:10.1128/mbio.00374-25

Mol Divers. 2025 Apr 2. doi: 10.1007/s11030-025-11145-2. Online ahead of print.

ABSTRACT

Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certain cancers, while clinically approved FXR antagonists remain unavailable and underrepresented in current treatment strategies. To address this, we developed deep learning models for predicting FXR antagonistic activity (ANTCL) and toxicity (TOXCL). Screening 217,345 compounds from the HMDB database identified eleven human metabolite candidates with significant FXR binding potential. Molecular dynamics simulations and binding free energy calculations revealed five more stable complexes compared to the reference compound Gly-MCA, with HMDB0253354 (Fulvestrant) and HMDB0242367 (ZM 189154) standing out for their binding free energies. Hydrophobic interactions, particularly involving residues MET328, PHE329, and ALA291, contributed to their stability. These results demonstrate the effectiveness of deep learning in FXR antagonist discovery and highlight the potential of HMDB0253354 and HMDB0242367 as promising candidates for metabolic disease treatment.

PMID:40172823 | DOI:10.1007/s11030-025-11145-2

Med Biol Eng Comput. 2025 Apr 2. doi: 10.1007/s11517-025-03347-5. Online ahead of print.

ABSTRACT

Fetal congenital heart disease (FCHD) represents a serious and prevalent congenital malformation. However, there exist notable regional disparities in the detection rates of fetal heart abnormalities. To enhance the diagnostic capabilities of ultrasound physicians in primary hospitals regarding fetal heart structures, the adoption of artificial intelligence technology to assist in acquiring high-quality, standard fetal echocardiographic images is of paramount importance. Currently, primary hospitals face challenges in recognizing standard views in fetal echocardiography, particularly under resource-constrained conditions. Efficient and accurate identification of fetal heart structures has become an urgent issue to address. Despite existing research efforts dedicated to the recognition of standard views in fetal echocardiography, current methods still suffer from limitations in computational complexity, feature extraction capabilities, and long-distance feature capturing, hindering their widespread application in ultrasound diagnosis at primary hospitals. Specifically, the literature lacks an efficient and robust model that can effectively balance high accuracy in standard view recognition with low computational complexity and fast inference times. The need for a model that can accurately capture long-distance features while maintaining efficiency is particularly acute in the context of primary hospitals, where resources are limited and the demand for accurate fetal heart assessments is high. To address these issues, the present study proposes an efficient network based on a state-space model trained with evidence for standard view recognition in fetal echocardiography. This method integrates a visual state space (VSS) model, which boasts powerful feature extraction capabilities and effective long-distance feature capturing, while significantly reducing computational complexity and facilitating efficient model inference. In the collected dataset, the proposed model achieved an accuracy of 99.32% and an F1-score of 99.29% in identifying eight standard views of fetal echocardiography. Furthermore, the model exhibited the lowest floating point operations per second (FLOPs), parameters, and inference time, while achieving the highest frames per second (FPS). This achievement not only provides a solid technical foundation for intelligent diagnosis of FCHD but also serves as an auxiliary tool for junior or novice sonographers at primary hospitals in acquiring basic views of fetal heart structures.

PMID:40172789 | DOI:10.1007/s11517-025-03347-5

Med Biol Eng Comput. 2025 Apr 2. doi: 10.1007/s11517-025-03323-z. Online ahead of print.

ABSTRACT

Ultrasound (US) imaging enables the evaluation of vascular structures in real time, and it can provide morphological and pathological information during US-guided procedures. Automatic prediction of vascular structure boundaries can help clinicians in locating and measuring target structures more accurately and efficiently. Most existing US segmentation methods use per-pixel classification or regression, which require post-processing to obtain contour coordinates. In this work, we present ACE-Net, a novel approach that directly predicts the contour coordinates for every scanning line (A-line) in US images. ACE-Net combines two main modules: a boundary regression module that predicts the upper and lower coordinates of the target area for each A-line, and an A-line classification module that determines whether an A-line belongs to the target area or not. We evaluated our method on three clinical US datasets using, among others, dice similarity coefficient (DSC) and inference time as performance metrics. Our method outperformed state-of-the-art segmentation methods in inference time while achieving superior or comparable performance in DSC. ACE-Net is publicly available at https://github.com/bfarolabarata/ace-net .

PMID:40172788 | DOI:10.1007/s11517-025-03323-z

Eur Radiol. 2025 Apr 2. doi: 10.1007/s00330-025-11552-x. Online ahead of print.

NO ABSTRACT

PMID:40172639 | DOI:10.1007/s00330-025-11552-x

Eur Radiol. 2025 Apr 2. doi: 10.1007/s00330-025-11550-z. Online ahead of print.

NO ABSTRACT

PMID:40172638 | DOI:10.1007/s00330-025-11550-z

An Acad Bras Cienc. 2025 Mar 31;97(2):e20240623. doi: 10.1590/0001-3765202520240623. eCollection 2025.

ABSTRACT

The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various conditions. This scenario necessitates the development of suitable methods that integrate appropriate computational techniques, such as convolutional neural networks (CNN) to address the diversity of drone captures and improve accuracy in detection and re-identification. In this paper, a solution for vehicle detection and Re-ID is proposed, combining CNN techniques VGG16, VGG19, ResNet50, InceptionV3 and EfficientNetV2L. YOLOv4 was selected for detection, while the DeepSORT algorithm was chosen for tracking. The proposed solution considers the generalization capabilities of these techniques with varied images from different drones in different positions. Two datasets were employed: the first is a public dataset from Mendeley used for method evaluation, while the second consists of images and data collected by a swarm of drones. In the first experiment, the best performing network was ResNet50, with an average accuracy of 55%. In the second experiment, the highest accuracy CNN was VGG19, with 91% accuracy. Overall, the techniques were able to distinguish vehicles of different models and adapted to the data captured by drones.

PMID:40172334 | DOI:10.1590/0001-3765202520240623

Radiol Artif Intell. 2025 Apr 2:e240628. doi: 10.1148/ryai.240628. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs (CXR) algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up CXRs. Materials and Methods This single-center, retrospective study analyzed CXRs from individuals who underwent health screenings between January 2004 and June 2018. The CXR-Lung-Risk scores from baseline CXRs were externally tested for predicting mortality due to lung disease or lung cancer, using competing risk analysis, with adjustments made for clinical factors. The additional value of these risk scores beyond clinical factors was evaluated using the likelihood ratio test. An exploratory analysis was conducted on the CXR-Lung-Risk trajectory over a three-year follow-up period for individuals in the highest quartile of baseline respiratory disease mortality risk, using a time-series clustering algorithm. Results Among 36,924 individuals (median age, 58 years [interquartile range: 53-62 years]; 22,352 male), 264 individuals (0.7%) died of respiratory illness, over a median follow-up period of 11.0 years (interquartile range: 7.8- 12.7 years). CXR-Lung-Risk predicted respiratory disease mortality (adjusted hazard ratio [HR] per 5 years: 2.01, 95% CI: 1.76-2.39, P < .001), offering a prognostic improvement over clinical factors (P < .001). The trajectory analysis identified a subgroup with a continuous increase in CXR-Lung-Risk, which was associated with poorer outcomes (adjusted HR for respiratory disease mortality: 3.26, 95% CI: 1.20-8.81, P = .02) compared with the subgroup with a continuous decrease in CXR-Lung-Risk. Conclusion The open-source CXR-Lung-Risk model predicted respiratory disease mortality in an Asian cohort, enabling a two-layer risk stratification approach through an exploratory longitudinal analysis of baseline and follow-up CXRs. ©RSNA, 2025.

PMID:40172326 | DOI:10.1148/ryai.240628

Radiol Artif Intell. 2025 Apr 2:e240507. doi: 10.1148/ryai.240507. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier (BBB) leakage detection using dynamic contrast-enhanced (DCE) MRI, without requiring pharmacokinetic (PK) models and arterial input function (AIF) estimation. Materials and Methods This retrospective study included data from patients who underwent DCE MRI between April 2010 and December 2020. An autoencoder-based anomaly detection (AEAD) identified 1D voxel-wise time-series abnormal signals through reconstruction residuals, separating them into residual leakage signals (RLS) and residual vascular signals (RVS). The RLS maps were evaluated and compared with the volume transfer constant (Ktrans) using the structural similarity index (SSIM) and correlation coefficient (r). Generalizability was tested on subsampled data, and IDH status classification performance was assessed using areas under the receiver operating characteristic curves (AUCs). Results A total of 274 patients were included (164 male; mean age 54.23 ± [SD] 14.66 years). RLS showed high structural similarity (SSIM = 0.91 ± 0.02) and correlation (r = 0.56, P < .001) with Ktrans. On subsampled data, RLS maps showed better correlation with RLS values from original data (0.89 versus 0.72, P < .001), higher PSNR (33.09 dB versus 28.94 dB, P < .001), and higher SSIM (0.92 versus 0.87, P < .001) compared with Ktrans maps. RLS maps also outperformed Ktrans maps in predicting IDH mutation status (AUC = 0.87 [95% CI: 0.83-0.91] versus 0.81 [95% CI: 0.76-0.85], P = .02). Conclusion The unsupervised framework effectively detected blood-brain barrier leakage without PK models and AIF. ©RSNA, 2025.

PMID:40172325 | DOI:10.1148/ryai.240507

JASA Express Lett. 2025 Apr 1;5(4):045201. doi: 10.1121/10.0036356.

ABSTRACT

Laser doppler vibrometers (LDVs) facilitate noncontact speech acquisition; however, they are prone to material-dependent spectral distortions and speckle noise, which degrade intelligibility in noisy environments. This study proposes a data augmentation method that incorporates material-specific and impulse noises to simulate LDV-induced distortions. The proposed approach utilizes a gated convolutional neural network with HiFi-GAN to enhance speech intelligibility across various material and low signal-to-noise ratio (SNR) conditions, achieving a short-time objective intelligibility score of 0.76 at 0 dB SNR. These findings provide valuable insights into optimized augmentation and deep-learning techniques for enhancing LDV-based speech recordings in practical applications.

PMID:40172315 | DOI:10.1121/10.0036356

AJR Am J Roentgenol. 2025 Apr 2. doi: 10.2214/AJR.25.33012. Online ahead of print.

NO ABSTRACT

PMID:40172167 | DOI:10.2214/AJR.25.33012

Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2412310122. doi: 10.1073/pnas.2412310122. Epub 2025 Apr 2.

ABSTRACT

High tumor mutational burden (TMB-H) is a predictive biomarker for the responsiveness of cancer to immune checkpoint inhibitor (ICI) therapy that indicates whether immune cells can sufficiently recognize cancer cells as nonself. However, about 30% of all cancers from The Cancer Genome Atlas (TCGA) are classified as immune-desert tumors lacking T cell infiltration despite TMB-H. Since the underlying mechanism of these immune-desert tumors has yet to be unraveled, there is a pressing need to transform such immune-desert tumors into immune-inflamed tumors and thereby enhance their responsiveness to anti-PD1 therapy. Here, we present a systems framework for identifying immuno-oncotargets, based on analysis of gene regulatory networks, and validating the effect of these targets in transforming immune-desert into immune-inflamed tumors. In particular, we identify DEAD-box helicases 54 (DDX54) as a master regulator of immune escape in immune-desert lung cancer with TMB-H and show that knockdown of DDX54 can increase immune cell infiltration and lead to improved sensitivity to anti-PD1 therapy.

PMID:40172969 | DOI:10.1073/pnas.2412310122

Elife. 2025 Apr 2;13:RP92536. doi: 10.7554/eLife.92536.

ABSTRACT

In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications, including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

PMID:40172959 | DOI:10.7554/eLife.92536