Sci Rep. 2025 Jun 3;15(1):19415. doi: 10.1038/s41598-025-04259-y.

ABSTRACT

The pathological classification of nonspecific interstitial pneumonia (NSIP) with organizing pneumonia (OP) overlap (NSIP/OP overlap) remains complex due to overlapping pathological features, and its heterogeneity is not well understood. We retrospectively analysed adult patients with interstitial lung disease (ILD) diagnosed with NSIP/OP overlap via surgical lung biopsy. Patients were pathologically subclustered using an unbiased clustering method, and clinical, radiological, and prognostic differences were examined. Among 38 patients, two pathological clusters were identified: Cluster 1, characterized by fibrotic changes with mild inflammation, and Cluster 2, exhibiting intense inflammation with fibrosis. While both clusters initially responded well to treatment, Cluster 2 demonstrated progressive ILD deterioration and a higher frequency of pulmonary fibrosis. Cluster 2 was also associated with hypoxia, reduced pulmonary function, elevated erythrocyte sedimentation rate, and greater consolidation on chest computed tomography. Based on these findings, we have identified NSIP/OP overlap is a heterogeneous and progressive disease and pathological findings at diagnosis significantly influence both initial ILD severity and long-term prognosis. Our findings highlight the need for tailored long-term management strategies based on early histopathological evaluation.

PMID:40461660 | DOI:10.1038/s41598-025-04259-y

Nat Commun. 2025 Jun 3;16(1):5151. doi: 10.1038/s41467-025-60234-1.

ABSTRACT

Protection against pathogens is a major function of the gut microbiota. Although bacterial natural products have emerged as crucial components of host-microbiota interactions, their exact role in microbiota-mediated protection is largely unexplored. We addressed this knowledge gap with the nematode Caenorhabditis elegans and its microbiota isolate Pseudomonas fluorescens MYb115 that is known to protect against Bacillus thuringiensis (Bt) infection. We find that MYb115-mediated protection depends on sphingolipids (SLs) that are derived from an iterative type I polyketide synthase (PKS) cluster PfSgaAB, thereby revealing a non-canonical pathway for the production of bacterial SLs as secondary metabolites. SL production is common in eukaryotes but was thought to be limited to a few bacterial phyla that encode the serine palmitoyltransferase (SPT) enzyme, which catalyses the initial step in SL synthesis. We demonstrate that PfSgaB encodes a pyridoxal 5'-phosphate-dependent alpha-oxoamine synthase with SPT activity, and find homologous putative PKS clusters present across host-associated bacteria that are so far unknown SL producers. Moreover, we provide evidence that MYb115-derived SLs affect C. elegans defence against Bt infection by altering SL metabolism in the nematode host. This work establishes SLs as structural outputs of bacterial PKS and highlights the role of microbiota-derived SLs in host protection against pathogens.

PMID:40461452 | DOI:10.1038/s41467-025-60234-1

Bull Math Biol. 2025 Jun 3;87(7):91. doi: 10.1007/s11538-025-01471-9.

ABSTRACT

The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications often focus on model-based control, such as in biomedicine or metabolic engineering. In a recent paper, the authors developed a theoretical framework of modularity in Boolean networks, which led to a canonical semidirect product decomposition of these systems. In this paper, we present an approach to model-based control that exploits this modular structure, as well as the canalizing features of the regulatory mechanisms. We show how to identify control strategies from the individual modules, and we present a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally challenging. Our modular approach leads to an efficient approach to solving this problem. We apply it to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.

PMID:40461704 | DOI:10.1007/s11538-025-01471-9

Drug Ther Bull. 2025 Jun 3:dtb-2025-000023. doi: 10.1136/dtb.2025.000023. Online ahead of print.

NO ABSTRACT

PMID:40461176 | DOI:10.1136/dtb.2025.000023

Drug Ther Bull. 2025 Jun 3;63(6):85-93. doi: 10.1136/dtb.2025.000019.

ABSTRACT

Methylphenidate and lisdexamfetamine are recommended as first-line pharmacological treatment options for adults with attention deficit hyperactivity disorder (ADHD). Formulations of methylphenidate can generally be classified into three groups according to their duration of action: one group lasts 12 hours, another group lasts 8 hours and the immediate-release group lasts 3-4 hours. Patients are usually able to substitute brands with one of the equivalent release profiles without significant problems. Lisdexamfetamine is a prodrug which has a slow onset and long duration (approximately 12 hours), ensuring minimal potential for abuse compared with its active metabolite dexamfetamine. Second-line treatments such as atomoxetine are also available for those who cannot tolerate or do not respond to methylphenidate or lisdexamfetamine. In the UK, ADHD has been previously managed largely in tertiary clinics, but many cases could be managed by appropriately trained clinicians in secondary or primary care (as already happens in some countries), with great benefit for patients and job satisfaction for clinicians.

PMID:40461172 | DOI:10.1136/dtb.2025.000019

Drug Ther Bull. 2025 Jun 3:dtb-2025-000018. doi: 10.1136/dtb.2025.000018. Online ahead of print.

NO ABSTRACT

PMID:40461177 | DOI:10.1136/dtb.2025.000018

J Integr Bioinform. 2025 Jun 4. doi: 10.1515/jib-2024-0048. Online ahead of print.

ABSTRACT

Left ventricular non-compaction is a cardiac condition marked by excessive trabeculae in the left ventricle's inner wall. Although various methods exist to measure these structures, the medical community still lacks consensus on the best approach. Previously, we developed DL-LVTQ, a tool based on a UNet neural network, to quantify trabeculae in this region. In this study, we expand the dataset to include new patients with Titin cardiomyopathy and healthy individuals with fewer trabeculae, requiring retraining of our models to enhance predictions. We also propose ViTUNeT, a neural network architecture combining U-Net and Vision Transformers to segment the left ventricle more accurately. Additionally, we train a YOLOv8 model to detect the ventricle and integrate it with ViTUNeT model to focus on the region of interest. Results from ViTUNet and YOLOv8 are similar to DL-LVTQ, suggesting dataset quality limits further accuracy improvements. To test this, we analyze MRI images and develop a method using two YOLOv8 models to identify and remove problematic images, leading to better results. Combining YOLOv8 with deep learning networks offers a promising approach for improving cardiac image analysis and segmentation.

PMID:40460443 | DOI:10.1515/jib-2024-0048

Am J Respir Crit Care Med. 2025 Jun 3. Online ahead of print.

ABSTRACT

RATIONALE: One in ten children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood.

OBJECTIVE: We assessed the relationship between magnetic resonance imaging (MRI)-derived upper airway volume and children's cognition and regional cortical gray matter volumes.

METHODS: We used five-year data from the Adolescent Brain Cognitive Development study (n=11,875 children, 9-10 years at baseline). Upper airway volumes were derived using a deep learning model applied to 5,552,640 brain MRI slices. The primary outcome was the Total Cognition Composite score from the National Institutes of Health Toolbox (NIH-TB). Secondary outcomes included other NIH-TB measures and cortical gray matter volumes.

RESULTS: The habitual snoring group had significantly smaller airway volumes than non-snorers (mean difference=1.2 cm3; 95% CI, 1.0-1.4 cm3; P<0.001). Deep learning-derived airway volume predicted the Total Cognition Composite score (estimated mean difference=3.68 points; 95% CI, 2.41-4.96; P<0.001) per one-unit increase in the natural log of airway volume (~2.7-fold raw volume increase). This airway volume increase was also associated with an average 0.02 cm3 increase in right temporal pole volume (95% CI, 0.01-0.02 cm3; P<0.001). Similar airway volume predicted most NIH-TB domain scores and multiple frontal and temporal gray matter volumes. These brain volumes mediated the relationship between airway volume and cognition.

CONCLUSIONS: We demonstrate a novel application of deep learning-based airway segmentation in a large pediatric cohort. Upper airway volume is a potential biomarker for cognitive outcomes in pediatric SDB, offers insights into neurobiological mechanisms, and informs future studies on risk stratification. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

PMID:40460372

PLoS One. 2025 Jun 3;20(6):e0324816. doi: 10.1371/journal.pone.0324816. eCollection 2025.

ABSTRACT

The structural response of bridges involves a complex interplay of various coupled effects, rendering the identification of long-term variation trends inherently challenging. Consequently, effectively detecting and alerting abnormal monitoring data for bridge structures under complex coupled loads remains a significant difficulty. To address this issue, this study proposes a dynamic early-warning method for bridge structural safety, leveraging data reconstruction and deep learning-based prediction. First, the singular value decomposition (SVD) algorithm is employed to decompose and reconstruct the monitoring data based on the contribution rate of influencing factors, thereby decoupling the data from various coupled effects. Second, a deep learning architecture utilizing a long short-term memory (LSTM) network is applied to establish a prediction model for each group of decomposed monitoring data, significantly enhancing prediction accuracy. Building on this foundation, the dynamic early-warning system for bridge structural safety is realized by integrating anomaly diagnosis theory with both predicted and measured data. A validation case using measured strain data demonstrates that the proposed method accurately predicts bridge strain data and calculates real-time adaptive thresholds, enabling real-time detection of anomalous monitoring data.

PMID:40460166 | DOI:10.1371/journal.pone.0324816

J Am Soc Nephrol. 2025 Jun 3. doi: 10.1681/ASN.0000000778. Online ahead of print.

NO ABSTRACT

PMID:40459949 | DOI:10.1681/ASN.0000000778

J Clin Invest. 2025 Jun 3:e191107. doi: 10.1172/JCI191107. Online ahead of print.

ABSTRACT

BACKGROUND: Telomere biology disorders (TBDs) exhibit incomplete penetrance and variable expressivity, even among individuals harboring the same pathogenic variant. We assessed whether common genetic variants associated with telomere length combine with large-effect variants to impact penetrance and expressivity in TBDs.

METHODS: We constructed polygenic scores (PGS) for telomere length in the UK Biobank to quantify common variant burden, and assessed the PGS distribution across patient cohorts and biobanks to determine whether individuals with severe TBD presentations have increased polygenic burden causing short telomeres. We also characterized rare TBD variant carriers in the UK Biobank.

RESULTS: Individuals with TBDs in cohorts enriched for severe pediatric presentations have polygenic scores predictive of short telomeres. In the UK Biobank, we identify carriers of pathogenic TBD variants who are enriched for adult-onset manifestations of TBDs. Unlike individuals in disease cohorts, the PGS of adult carriers do not show a common variant burden for shorter telomeres, consistent with the absence of childhood-onset disease. Notably, TBD variant carriers are enriched for idiopathic pulmonary fibrosis diagnoses, and telomere length PGS stratifies pulmonary fibrosis risk. Finally, common variants affecting telomere length were enriched in enhancers regulating known TBD genes.

CONCLUSION: Common genetic variants combine with large-effect causal variants to impact clinical manifestations in rare TBDs. These findings offer a framework for understanding phenotypic variability in other presumed monogenic disorders.

FUNDING: This work was supported by National Institutes of Health grants R01DK103794, R01HL146500, R01CA265726, R01CA292941, and the Howard Hughes Medical Institute.

PMID:40459934 | DOI:10.1172/JCI191107

PLoS One. 2025 Jun 3;20(6):e0322226. doi: 10.1371/journal.pone.0322226. eCollection 2025.

ABSTRACT

Sarcocystis calchasi is a pathogenic apicomplexan parasite affecting avian species of several orders. To complete its heteroxenous life cycle, S. calchasi infects a wide range of avian intermediate hosts and accipitriform raptors serve as definitive hosts. The mechanism of invasion into host cells is largely understood in other apicomplexan parasites, particularly Toxoplasma gondii, which also belongs to the family of Sarcocystidae. However, Sarcocystis species exhibit several distinguishing features in their life cycles and in their secretory organelles. The composition of secretory pathogenesis determinants, including surface antigens and secretory organelle proteins, has been shown to differ between closely related species, as evidenced by Sarcocystis neurona. In this study, whole-genome sequencing was performed on S. calchasi, and transcriptomes were determined by RNA-seq of S. calchasi sporozoites and bradyzoites derived from intermediate and definitive hosts as well as from merozoites propagated in primary embryonal pigeon liver cells. The S. calchasi genome contains homologs of genes encoding proteins associated with the well-conserved host invasion machinery like AMA1 and rhoptry neck proteins, albeit with a markedly reduced number of genes encoding surface antigens, rhoptry and dense granule proteins in comparison to T. gondii. Our transcriptome analysis revealed different gene expression profiles between S. calchasi sporozoites, merozoites and bradyzoites. Factors associated with host cell attachment (surface antigens and micronemal proteins) were expressed predominantly either in sporozoites and merozoites or in bradyzoites. As sporozoites and merozoites invade various intermediate hosts and cell types whereas bradyzoites enter definitive host intestinal epithelium, their differential expression patterns indicate that S. calchasi utilizes different sets of secretory pathogenesis determinants for host cell attachment and invasion, depending on the type of host and cell.

PMID:40460112 | DOI:10.1371/journal.pone.0322226

PLoS One. 2025 Jun 3;20(6):e0324312. doi: 10.1371/journal.pone.0324312. eCollection 2025.

ABSTRACT

In the evolving landscape of technology, robots have emerged as social companions, prompting an investigation into social bonding between humans and robots. While human-animal interactions are well-studied, human-robot interactions (HRI) remain comparatively underexplored. Ethorobotics, a field of social robotic engineering based on ecology and ethology, suggests designing companion robots modeled on animal companions, which are simpler to emulate than humans. However, it is unclear whether these robots can match the social companionship provided by their original models. This study examined social bonding between humans and AIBOs, dog-inspired companion robots, compared to real dogs. Nineteen female participants engaged in 12 affiliative interactions with dogs and AIBOs across two counter-balanced, one-month bonding phases. Social bonding was assessed through urinary oxytocin (OXT) level change over an interaction, self-reported attachment using an adapted version of the Lexington Attachment to Pets Scale, and social companionship evaluations administering the Robot-Dog Questionnaire. To examine OXT level changes and self-reported attachment by comparing the two social companions, we conducted mixed-effects model analyses and planned follow-up comparisons. Frequency comparison, binary logistic regression, and thematic analysis were performed to analyze social companionship evaluations. Results revealed significant differences between dogs and AIBOs in fostering social bonds. OXT level change increased during interactions with dogs but decreased with AIBOs. Participants reported stronger attachment to dogs and rated them as better social companions. These findings highlight the current limitations of AIBOs in fostering social bonding immediately compared to dogs. Our study contributes to the growing HRI research by demonstrating an existing gap between AIBOs and dogs as social companions. It highlights the need for further investigation to understand the complexities of social bonding with companion robots, which is essential to implement successful applications for social robots in diverse domains such as the elderly and health care, education, and entertainment.

PMID:40460066 | DOI:10.1371/journal.pone.0324312

ACS Synth Biol. 2025 Jun 3. doi: 10.1021/acssynbio.4c00894. Online ahead of print.

ABSTRACT

Addressing the challenge of achieving a global circular bioeconomy requires efficient and robust bio-based processes operating at different scales. These processes should also be competitive replacements for the production of chemicals currently obtained from fossil resources, as well as for the production of new-to-nature compounds. To that end, genetic circuits can be used to control cellular behavior and are instrumental in developing efficient cell factories. Whole-cell biosensors harbor circuits that can be based on allosteric transcription factors (TFs) to detect and elicit a response depending on the target molecule concentrations. By modifying regulatory elements and testing various genetic components, the responsive behavior of genetic biosensors can be finely tuned and engineered. While previous models have described and characterized the behavior of naringenin biosensors, additional data and resources are required to predict their dynamic response and performance in different contexts, such as under various gene expression regulatory elements, media, carbon sources, or media supplements. Tuning these conditions is pivotal in optimizing biosensor design for applications operating in varying conditions, such as fermentation processes. In this study, we assembled a library of FdeR biosensors, characterized their performance under different conditions, and developed a mechanistic model to describe their dynamic behavior under reference conditions, which guided a machine learning-based predictive model that accounts for context-dependent dynamic parameters. Such a Design-Build-Test-Learn (DBTL) pipeline allowed us to determine optimal condition combinations for the desired biosensor specifications, both for automated screening and dynamic regulation. The findings of this work contribute to a deeper understanding of whole-cell biosensors and their potential for precise measurement, screening, and dynamic regulation of engineered production pathways for valuable molecules.

PMID:40460061 | DOI:10.1021/acssynbio.4c00894

ACS Nano. 2025 Jun 3. doi: 10.1021/acsnano.5c03590. Online ahead of print.

ABSTRACT

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

PMID:40460056 | DOI:10.1021/acsnano.5c03590

PLoS Biol. 2025 Jun 3;23(6):e3003172. doi: 10.1371/journal.pbio.3003172. eCollection 2025 Jun.

ABSTRACT

Inhaled anesthetics were first introduced into clinical use in the 1840s. Molecular and transgenic animal studies indicate that inhaled anesthetics act through several ion channels, including γ-aminobutyric acid type A receptors (GABAARs) and two-pore domain K+ (K2P) channels, but other targets may mediate anesthetic effects. Mutations in the type 1 ryanodine receptor (RyR1), which is a calcium release channel on the endoplasmic reticulum membrane, are relevant to malignant hyperthermia, a condition that can be induced by inhaled anesthetics. However, it was previously uncertain whether inhaled anesthetics directly interact with RyR1. In our study, we demonstrated that isoflurane and other inhaled anesthetics activate wild-type RyR1. By employing systematic mutagenesis, we discovered that altering just one amino acid residue negates the response to isoflurane, thus helping us to pinpoint the potential binding site. Knock-in mice engineered to express a mutant form of RyR1 that is insensitive to isoflurane exhibited resistance to the loss of righting reflex (LORR) when exposed to isoflurane anesthesia. This observation suggests a connection between RyR1 activation and the anesthetic effects in vivo. Moreover, it was shown that RyR1 is involved in the neuronal response to isoflurane. Additionally, administering new RyR1 agonists, which share the same binding site as isoflurane, resulted in a sedation-like state in mice. We propose that isoflurane directly activates RyR1, and this activation is pertinent to its anesthetic/sedative effects.

PMID:40460053 | DOI:10.1371/journal.pbio.3003172

Bioinformatics. 2025 Jun 3:btaf326. doi: 10.1093/bioinformatics/btaf326. Online ahead of print.

ABSTRACT

MOTIVATION: Drug combination therapy is an effective strategy for cancer treatment, enhancing drug efficacy and reducing toxic side effects. However, in vitro drug screening experiments are time-consuming and expensive, necessitating the development of computational methods for drug synergy prediction. While current methods focus on molecular chemical structures, they often overlook the biological context, limiting their ability to capture complex drug synergies.

RESULTS: In this work, we propose MADSP, a novel method for anti-cancer drug synergy prediction that integrates target and pathway knowledge for a more comprehensive understanding of systems biology. MADSP first incorporates chemical structure, target, and pathway features of drugs, using a multi-head self-attention mechanism to learn a unified drug representation. It then integrates protein-protein interaction (PPI) data with omics data from cell lines, extracting a low-dimensional dense embedding of cell lines via an autoencoder. Finally, the synergy scores for drug combinations are predicted using a fully connected neural network. Experiments on benchmark datasets demonstrate that MADSP outperforms state-of-the-art methods. The ablation study reveals that multi-source information fusion and attention mechanisms significantly enhance model performance. The case study further illustrates the practical applicability of MADSP as a powerful tool for drug synergy prediction, offering potential for advancing cancer treatment strategies.

AVAILABILITY AND IMPLEMENTATION: MADSP is available at https://github.com/Hhyqi/MADSP.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40460032 | DOI:10.1093/bioinformatics/btaf326

Naunyn Schmiedebergs Arch Pharmacol. 2025 Jun 3. doi: 10.1007/s00210-025-04317-2. Online ahead of print.

ABSTRACT

Psoriasis is a chronic inflammatory skin disorder that is triggered by immune-mediated, genetic, and environmental factors. Moxifloxacin is a fluoroquinolone antibiotic with extended non-expected anti-inflammatory and immune-modulating effects. This study aims to investigate the possible influence of two different concentrations of moxifloxacin emulgel on psoriasis induced via imiquimod in mice. Dividing 48 mice into six groups (8 mice for each group), all groups gated imiquimod to induce psoriasis (except group I) for 7 days. The induction group (Group II) received imiquimod cream for 7 days. The vehicle group obtained emulgel base for 7 days. The rest of the groups got calcipotriol 0.005% ointment, moxifloxacin 3% emulgel, and moxifloxacin 5% emulgel, respectively, once daily for a further 7 days after the induction period. Topical moxifloxacin had important anti-psoriatic activity by diminishing the Psoriasis Area Severity Index (PASI) scores and improving histological alterations during imiquimod application. Moreover, moxifloxacin significantly lowered the levels of inflammatory biomarkers like TGF-β, TNF-α, IL-17, IL-1β, IL-23, and VEFG while increasing levels of anti-inflammatory biomarkers IL-10 and IL-37. Moxifloxacin also suppressed oxidative indicators such as MDA and elevated antioxidant enzyme levels, such as catalase. Moxifloxacin has substantial anti-psoriatic action against imiquimod-induced psoriasis through its anti-proliferative and anti-inflammatory effects. Furthermore, moxifloxacin has a restorative effect on the histopathological alterations of mice's skin induced by imiquimod.

PMID:40459759 | DOI:10.1007/s00210-025-04317-2

Drug Metab Pers Ther. 2025 Jun 2. doi: 10.1515/dmpt-2025-0028. Online ahead of print.

NO ABSTRACT

PMID:40459564 | DOI:10.1515/dmpt-2025-0028

Future Oncol. 2025 Jun 3:1-9. doi: 10.1080/14796694.2025.2514417. Online ahead of print.

ABSTRACT

The DNAJ/HSP40 family consists of three distinct subfamilies (DNAJA, DNAJB, and DNAJC) and is the largest and most diverse co-chaperone proteins for HSP70. The DNAJA subfamily, comprising four members, assumes a pivotal role in various pathological conditions such as cystic fibrosis, neurodegenerative disorders, and cancer. This review comprehensively investigates the participation and underlying mechanisms of DNAJA proteins in tumor proliferation and metastasis, with a specific focus on their influence on the accumulation of mutant p53 proteins. Furthermore, we conducted an extensive examination of compounds utilizing computer-based techniques that specifically target DNAJA or its associated pathways, thereby offering novel insights for the development of cutting-edge combination therapies in the realm of cancer treatment. Our findings highlight the potential significance of targeting the DNAJA subfamily as a promising approach for anti-tumor therapy. Simultaneously, we also highlighted the limitations of current DNAJA research and proposed future directions for advancement in this field. We anticipate that DNAJA will emerge as a novel therapeutic target for anti-tumor interventions.

PMID:40459049 | DOI:10.1080/14796694.2025.2514417