Front Nutr. 2025 Feb 21;12:1553674. doi: 10.3389/fnut.2025.1553674. eCollection 2025.

ABSTRACT

Respiratory diseases are a leading cause of morbidity in children globally, with significant healthcare costs. The overuse of conventional treatments like antibiotics has raised concerns about antibiotic resistance and side effects. Lacticaseibacillus rhamnosus GG (LGG), one of the most extensively studied probiotics, has gained attention as a potential adjunct therapies due to their ability to modulate the gut microbiota and immune responses. This review aims to assess the effectiveness of LGG in managing pediatric respiratory diseases, including respiratory tract infections (RTI), cystic fibrosis (CF), and asthma. Clinical trials suggest LGG can reduce the incidence and severity of RTI, improving CF symptoms, and enhancing quality of life in children. However, evidence for its benefits in asthma remains inconclusive. Its mechanisms include modulating immune responses, enhancing gut barrier function, and maintaining a microbial homeostasis via the gut-lung axis. Existing studies are often limited by small sample sizes, heterogeneity in intervention protocols, and short follow-up periods. Emerging technologies and novel formulations, hold promise for unraveling the complex interactions among LGG, the gut-lung axis, and respiratory health. These advancements could pave the way for personalized probiotic therapies, highlighting the potential of LGG as a cost-effective, adjunctive therapy for pediatric respiratory diseases. This review underscores the broader significance of integrating LGG into pediatric healthcare, while calling for future research to overcome current limitations, optimize clinical protocols, and explore innovative therapeutic strategies.

PMID:40062233 | PMC:PMC11885142 | DOI:10.3389/fnut.2025.1553674

Cureus. 2025 Feb 5;17(2):e78551. doi: 10.7759/cureus.78551. eCollection 2025 Feb.

ABSTRACT

Introduction Non-cystic fibrosis bronchiectasis (bronchiectasis) is an increasingly recognized but understudied disease in children. National data on this disease are scarce. This study aimed to describe the clinical, radiological, and microbiological characteristics of Portuguese children with bronchiectasis. Methods A retrospective observational study was conducted at a tertiary pediatric pulmonology center in northern Portugal. Pediatric patients diagnosed with bronchiectasis and followed between July 2020 and September 2023 were included. Results A total of 38 patients were included, of whom 19 (50.0%) were male, with a median age at diagnosis of 6.3 years (3.8-11.0 years). Recurrent wheezing (n = 30, 78.9%) and chronic wet cough (n = 18, 47.4%) were the most common symptoms. An underlying etiology was identified in 36 (94.7%) patients, primarily postinfectious bronchiectasis (n = 18, 47.4%) and primary ciliary dyskinesia (n = 10, 26.3%). Multilobar involvement was observed in 25 (65.8%) patients, most frequently affecting the middle and lower lobes. Spirometry showed a mixed obstructive-restrictive pattern in 10 (33.3%) patients and a predominantly obstructive pattern in nine (30.0%) patients. Haemophilus influenzae and Streptococcus pneumoniae were the most frequently isolated microorganisms, both in bronchoalveolar lavage and sputum cultures. Pseudomonas aeruginosa was detected in nine (7.4%) sputum samples. Conclusion This study highlights the diverse clinical presentations, etiologies, and microbiological findings in pediatric bronchiectasis. Identifiable causes were present in most cases, emphasizing the importance of clinical vigilance for early diagnosis and intervention. Further research is warranted to explore long-term outcomes and refine treatment approaches based on microbiological profiles.

PMID:40062141 | PMC:PMC11887590 | DOI:10.7759/cureus.78551

World J Clin Pediatr. 2025 Mar 9;14(1):97537. doi: 10.5409/wjcp.v14.i1.97537. eCollection 2025 Mar 9.

ABSTRACT

BACKGROUND: Neonatal screening (NS) is a public health policy to identify genetic pathologies such as cystic fibrosis (CF), sickle cell disease, and other diseases. Sickle cell disease is the comprehensive term for a group of hemoglobinopathies characterized by the presence of hemoglobin S. CF is an autosomal recessive multisystemic disease with pathophysiology involving deleterious mutations in the transmembrane regulatory gene that encodes a protein that regulates the activity of chloride and sodium channels in the cell surface epithelium. NS is crucial for early diagnosis and management, which ensures a better quality of life.

AIM: To report a case of the coexistence of sickle cell anemia (SCA) and CF and perform an integrative literature review.

METHODS: This is an observational study and a review of the literature focusing on two rare genetic pathologies identified simultaneously in NS from the perspective of a clinical case. The authors identified only 5 cases of SCA associated with CF. No clinical trials or review articles were identified considering the rarity of the coexistence of these two pathologies.

RESULTS: Herein, the authors reported the case of a girl who after undergoing NS on day 8 of life was diagnosed with SCA with an alteration in the dosage of immunoreactive trypsin. The diagnosis of CF was confirmed by the Coulometry Sweat Test. The rarity of the co-occurrence of these two severe genetic pathologies (CF and SCA) is a challenge for medical science.

CONCLUSION: This study adds to the few case reports present in the literature that highlight the identification of two severe diseases via NS.

PMID:40059892 | PMC:PMC11686575 | DOI:10.5409/wjcp.v14.i1.97537

mSystems. 2025 Mar 10:e0125824. doi: 10.1128/msystems.01258-24. Online ahead of print.

ABSTRACT

Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec) in the active sites, which is encoded by an in-frame UGA codon. Compared to eukaryotes, identification of selenoprotein genes in bacteria remains challenging due to the absence of an effective strategy for distinguishing the Sec-encoding UGA codon from a normal stop signal. In this study, we have developed a deep learning-based algorithm, deep-Sep, for quickly and precisely identifying selenoprotein genes in bacterial genomic sequences. This algorithm uses a Transformer-based neural network architecture to construct an optimal model for detecting Sec-encoding UGA codons and a homology search-based strategy to remove additional false positives. During the training and testing stages, deep-Sep has demonstrated commendable performance, including an F1 score of 0.939 and an area under the receiver operating characteristic curve of 0.987. Furthermore, when applied to 20 bacterial genomes as independent test data sets, deep-Sep exhibited remarkable capability in identifying both known and new selenoprotein genes, which significantly outperforms the existing state-of-the-art method. Our algorithm has proved to be a powerful tool for comprehensively characterizing selenoprotein genes in bacterial genomes, which should not only assist in accurate annotation of selenoprotein genes in genome sequencing projects but also provide new insights for a deeper understanding of the roles of selenium in bacteria.IMPORTANCESelenium is an essential micronutrient present in selenoproteins in the form of Sec, which is a rare amino acid encoded by the opal stop codon UGA. Identification of all selenoproteins is of vital importance for investigating the functions of selenium in nature. Previous strategies for predicting selenoprotein genes mainly relied on the identification of a special cis-acting Sec insertion sequence (SECIS) element within mRNAs. However, due to the complexity and variability of SECIS elements, recognition of all selenoprotein genes in bacteria is still a major challenge in the annotation of bacterial genomes. We have developed a deep learning-based algorithm to predict selenoprotein genes in bacterial genomic sequences, which demonstrates superior performance compared to currently available methods. This algorithm can be utilized in either web-based or local (standalone) modes, serving as a promising tool for identifying the complete set of selenoprotein genes in bacteria.

PMID:40062874 | DOI:10.1128/msystems.01258-24

Invest Ophthalmol Vis Sci. 2025 Mar 3;66(3):22. doi: 10.1167/iovs.66.3.22.

ABSTRACT

PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable screening tool. This study compares two optical coherence tomography angiography algorithms for quantifying NPA.

METHODS: Adults with (N = 101) and without (N = 274) DR were recruited from 20 U.S. sites. We collected 3 × 3-mm macular scans using an Optovue RTVue-XR. Rules-based (RB) and deep-learning-based artificial intelligence (AI) algorithms were used to segment the NPA into four anatomical slabs. For comparison, a subset of scans (n = 50) NPA was graded manually.

RESULTS: The AI method outperformed the RB method in intersection over union, recall, and F1 score, but the RB method has better precision relative to manual grading in all anatomical slabs (all P ≤ 0.001). The AI method had a stronger rank correlation with Early Treatment of Diabetic Retinopathy Study DR severity than the RB method in all slabs (all P < 0.001). NPAs graded using the AI method had a greater area under the receiver operating characteristic curve for diagnosing referable DR than the RB method in the superficial vascular complex, intermediate capillary plexus, and combined inner retina (all P ≤ 0.001), but not in the deep capillary plexus (P = 0.92).

CONCLUSIONS: Our results indicate that output from the AI-based method agrees better with manual grading and can better distinguish between clinically relevant DR severity levels than a RB approach using most plexuses.

PMID:40062815 | DOI:10.1167/iovs.66.3.22

Biochemistry. 2025 Mar 10. doi: 10.1021/acs.biochem.4c00465. Online ahead of print.

ABSTRACT

Chemical peptide engineering, enabled by residue insertion, backbone cyclization, and incorporation of an additional disulfide bond, led to a unique cyclic peptide that efficiently inhibits the invasion of red blood cells by malaria parasites. The engineered peptide exhibits a 20-fold enhanced affinity toward its receptor (PfAMA1) compared to the native peptide ligand (PfRON2), as determined by surface plasmon resonance. In-vitro parasite growth inhibition assay revealed augmented potency of the engineered peptide. The structure of the PfAMA1-cyclic peptide complex, predicted by the deep learning-based structure prediction tool ColabFold-AlphaFold2 with protein-cyclic peptide complex offset, provided valuable insights into the observed activity of the peptide analogs. Rational editing of the peptide backbone and side chain described here proved to be an effective strategy for designing peptide-based inhibitors to interfere with disease-related protein-protein interactions.

PMID:40062812 | DOI:10.1021/acs.biochem.4c00465

J Chem Phys. 2025 Mar 14;162(10):104105. doi: 10.1063/5.0255288.

ABSTRACT

Self-consistent field theory (SCFT) is a powerful polymer field-theoretic simulation tool that plays a crucial role in the study of block copolymer (BCP) self-assembly. However, the computational cost of implementing SCFT simulations is comparatively high, particularly in computationally demanding applications where repeated forward simulations are needed. Herein, we propose a deep learning-based method to accelerate the SCFT simulations. By directly mapping early SCFT results to equilibrium structures using a deep neural network (DNN), this method bypasses most of the time-consuming SCFT iterations, significantly reducing the simulation time. We first applied this method to two- and three-dimensional large-cell bulk system simulations. Both results demonstrate that a DNN can be trained to predict equilibrium states based on early iteration outputs accurately. The number of early SCFT iterations can be tailored to optimize the trade-off between computational speed and predictive accuracy. The effect of training set size on DNN performance was also examined, offering guidance on minimizing dataset generation costs. Furthermore, we applied this method to the more computationally demanding inverse directed self-assembly-lithography problem. A covariance matrix adaptation evolution strategy-based inverse design method was proposed. By replacing the forward simulation model in this method with a trained DNN, we were able to determine the guiding template shapes that direct the BCP to self-assemble into the target structure with certain constraints, eliminating the need for any SCFT simulations. This improved the inverse design efficiency by a factor of 100, and the computational cost for training the network can be easily averaged out over repeated tasks.

PMID:40062757 | DOI:10.1063/5.0255288

Turk J Obstet Gynecol. 2025 Mar 10;22(1):75-82. doi: 10.4274/tjod.galenos.2025.12689.

ABSTRACT

The use of machine learning (ML) in biomarker analysis for predicting Down syndrome exemplifies an innovative strategy that enhances diagnostic accuracy and enables early detection. Recent studies demonstrate the effectiveness of ML algorithms in identifying genetic variations and expression patterns associated with Down syndrome by comparing genomic data from affected individuals and their typically developing peers. This review examines how ML and biomarker analysis improve prenatal screening for Down syndrome. Advancements show that integrating maternal serum markers, nuchal translucency measurements, and ultrasonographic images with algorithms, such as random forests and deep learning convolutional neural networks, raises detection rates to above 85% while keeping false positive rates low. Moreover, non-invasive prenatal testing with soft ultrasound markers has increased diagnostic sensitivity and specificity, marking a significant shift in prenatal care. The review highlights the importance of implementing robust screening protocols that utilize ultrasound biomarkers, along with developing personalized screening tools through advanced statistical methods. It also explores the potential of combining genetic and epigenetic biomarkers with ML to further improve diagnostic accuracy and understanding of Down syndrome pathophysiology. The findings stress the need for ongoing research to optimize algorithms, validate their effectiveness across diverse populations, and incorporate these cutting-edge approaches into routine clinical practice. Ultimately, blending advanced imaging techniques with ML shows promise for enhancing prenatal care outcomes and aiding informed decision-making for expectant parents.

PMID:40062699 | DOI:10.4274/tjod.galenos.2025.12689

Brief Bioinform. 2025 Mar 4;26(2):bbaf089. doi: 10.1093/bib/bbaf089.

ABSTRACT

The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, and negative binomial (NB)-based loss functions, to infer GRNs. GRANGER was evaluated using multiple popular benchmarking datasets, where it demonstrated superior performance compared to eight well-known GRN inference methods. The integration of a NB-based loss function and sparsity-inducing penalties in GRANGER significantly enhanced its capacity to address dropout noise and sparsity in scRNA-seq data. Additionally, GRANGER exhibited robustness against high levels of dropout noise. We applied GRANGER to scRNA-seq data from the whole mouse brain obtained through the BRAIN Initiative project and identified GRNs for five transcription regulators: E2f7, Gbx1, Sox10, Prox1, and Onecut2, which play crucial roles in diverse brain cell types. The inferred GRNs not only recalled many known regulatory relationships but also revealed sets of novel regulatory interactions with functional potential. These findings demonstrate that GRANGER is a highly effective tool for real-world applications in discovering novel gene regulatory relationships.

PMID:40062616 | DOI:10.1093/bib/bbaf089

Brief Bioinform. 2025 Mar 4;26(2):bbaf088. doi: 10.1093/bib/bbaf088.

ABSTRACT

Parkinson's disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD's diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD's underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, proteomics, RNA sequencing, metabolomics, and dopamine transporter scan imaging, sourced from the Parkinson's Progression Markers Initiative. Several model architectures were investigated and evaluated, including support vector machine, eXtreme Gradient Boosting, fully connected neural networks with concatenation and joint modeling (FCNN_C and FCNN_JM), and a multimodal encoder-based model with multi-head cross-attention (MMT_CA). The MMT_CA model demonstrated superior predictive performance, achieving a balanced classification accuracy of 97.7%, thus highlighting its ability to capture and leverage cross-modality inter-dependencies to aid predictive analytics. Furthermore, feature importance analysis using SHapley Additive exPlanations not only identified crucial diagnostic biomarkers to inform the predictive models in this study but also holds potential for future research aimed at integrated functional analyses of PD from a multi-omics perspective, ultimately revealing targets required for precision medicine approaches to aid treatment of PD aimed at slowing down its progression.

PMID:40062615 | DOI:10.1093/bib/bbaf088

Brief Bioinform. 2025 Mar 4;26(2):bbaf083. doi: 10.1093/bib/bbaf083.

ABSTRACT

Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment (QA) or estimation of model accuracy models that can evaluate the quality of the predicted protein-complexes without knowing their native structures are of key importance for protein structure generation and model selection. In this paper, we leverage persistent homology (PH) to capture the atomic-level topological information around residues and design a topological deep learning-based QA method, TopoQA, to assess the accuracy of protein complex interfaces. We integrate PH from topological data analysis into graph neural networks (GNNs) to characterize complex higher-order structures that GNNs might overlook, enhancing the learning of the relationship between the topological structure of complex interfaces and quality scores. Our TopoQA model is extensively validated based on the two most-widely used benchmark datasets, Docking Benchmark5.5 AF2 (DBM55-AF2) and Heterodimer-AF2 (HAF2), along with our newly constructed ABAG-AF3 dataset to facilitate comparisons with AF3. For all three datasets, TopoQA outperforms AF-Multimer-based AF2Rank and shows an advantage over AF3 in nearly half of the targets. In particular, in the DBM55-AF2 dataset, a ranking loss of 73.6% lower than AF-Multimer-based AF2Rank is obtained. Further, other than AF-Multimer and AF3, we have also extensively compared with nearly-all the state-of-the-art models (as far as we know), it has been found that our TopoQA can achieve the highest Top 10 Hit-rate on the DBM55-AF2 dataset and the lowest ranking loss on the HAF2 dataset. Ablation experiments show that our topological features significantly improve the model's performance. At the same time, our method also provides a new paradigm for protein structure representation learning.

PMID:40062613 | DOI:10.1093/bib/bbaf083

Photoacoustics. 2025 Feb 17;42:100697. doi: 10.1016/j.pacs.2025.100697. eCollection 2025 Apr.

ABSTRACT

Imaging speed is critical for photoacoustic microscopy as it affects the capability to capture dynamic biological processes and support real-time clinical applications. Conventional approaches for increasing imaging speed typically involve high-repetition-rate lasers, which pose a risk of thermal damage to samples. Here, we propose a deep-learning-driven optical-scanning undersampling method for photoacoustic remote sensing (PARS) microscopy, accelerating imaging acquisition while maintaining a constant laser repetition rate and reducing laser dosage. We develop a hybrid Transformer-Convolutional Neural Network, HTC-GAN, to address the challenges of both nonuniform sampling and motion misalignment inherent in optical-scanning undersampling. A mouse ear vasculature image dataset is created through our customized galvanometer-scanned PARS system to train and validate HTC-GAN. The network successfully restores high-quality images from 1/2-undersampled and 1/4-undersampled data, closely approximating the ground truth images. A series of performance experiments demonstrate that HTC-GAN surpasses the basic misalignment compensation algorithm, and standalone CNN or Transformer networks in terms of perceptual quality and quantitative metrics. Moreover, three-dimensional imaging results validate the robustness and versatility of the proposed optical-scanning undersampling imaging method across multiscale scanning modes. Our method achieves a fourfold improvement in PARS imaging speed without hardware upgrades, offering an available solution for enhancing imaging speed in other optical-scanning microscopic systems.

PMID:40062321 | PMC:PMC11889609 | DOI:10.1016/j.pacs.2025.100697

PeerJ Comput Sci. 2025 Feb 4;11:e2574. doi: 10.7717/peerj-cs.2574. eCollection 2025.

ABSTRACT

Human pose estimation (HPE) is designed to detect and localize various parts of the human body and represent them as a kinematic structure based on input data like images and videos. Three-dimensional (3D) HPE involves determining the positions of articulated joints in 3D space. Given its wide-ranging applications, HPE has become one of the fastest-growing areas in computer vision and artificial intelligence. This review highlights the latest advances in 3D deep-learning-based HPE models, addressing the major challenges such as accuracy, real-time performance, and data constraints. We assess the most widely used datasets and evaluation metrics, providing a comparison of leading algorithms in terms of precision and computational efficiency in tabular form. The review identifies key applications of HPE in industries like healthcare, security, and entertainment. Our findings suggest that while deep learning models have made significant strides, challenges in handling occlusion, real-time estimation, and generalization remain. This study also outlines future research directions, offering a roadmap for both new and experienced researchers to further develop 3D HPE models using deep learning.

PMID:40062308 | PMC:PMC11888865 | DOI:10.7717/peerj-cs.2574

Cureus. 2025 Feb 5;17(2):e78594. doi: 10.7759/cureus.78594. eCollection 2025 Feb.

ABSTRACT

Although several studies have reported that poor nutritional status is associated with a worse prognosis in patients with interstitial lung disease (ILD), the beneficial impact of nutritional therapy has not yet been established. We report a case of idiopathic pulmonary fibrosis (IPF) in which nutritional therapy played an important role alongside drug therapy. A 71-year-old Japanese male was diagnosed with IPF and started on nintedanib. However, he experienced appetite loss, leading to significant weight loss and disease progression. Consequently, nintedanib was discontinued, and a dietitian introduced a high-fat, high-protein nutritional therapy. His condition improved, allowing nintedanib to be restarted after a period of cessation. Following multiple nutritional education sessions, his condition stabilized without further appetite loss. These findings suggest that when determining treatment strategies for patients with ILD, clinicians should incorporate appropriate nutritional management during long-term treatment with effective anti-ILD agents to optimize patient outcomes.

PMID:40062119 | PMC:PMC11889364 | DOI:10.7759/cureus.78594

bioRxiv [Preprint]. 2025 Feb 26:2025.02.24.639173. doi: 10.1101/2025.02.24.639173.

ABSTRACT

INTRODUCTION: The aging lung enters into a state of irreversible cellular growth arrest characterized by senescence. While senescence is beneficial in preventing oncogenic cell proliferation, it becomes detrimental when persistent, promoting chronic inflammation and fibrosis through the senescence-associated secretory phenotype (SASP). Such senescence-related pathophysiological processes play key roles in lung diseases like chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF). However, few models accurately represent senescence in the human lung.

METHODS: To generate a human lung senescence in vitro model, we first generated a human induced pluripotent stem cell (hiPSC)-derived lung organoid (LO) system which was dissociated into monolayers and air-liquid interface (ALI) cultures to enhance visualization and allow uniform exposure to agents. Cellular senescence was induced using doxorubicin, a DNA-damaging agent. Senescence markers, such as β-galactosidase (β-gal) activity, SASP cytokine production and secretion, cell morphology, proliferative capacity, and barrier integrity were evaluated to validate the senescent phenotype.

RESULTS: The doxorubicin-induced senescent hiPSC-derived lung cells demonstrated the hallmark characteristics of cellular senescence, including increased β-gal activity and increased production of the pro-inflammatory SASP cytokine IL-6 and increased secretion of TNF-α. Senescent cells displayed enlarged morphology, decreased proliferation, and reduced wound repair capacity. Barrier integrity was impaired with decreased electrical resistance, and increased permeability, as well as expression of abnormal tight junction proteins and increased fibrosis, all consistent with the senescent lung.

CONCLUSION: Our hiPSC-derived lung cell senescent model reproduces key aspects of human lung senescence and offer an in vitro tool for studying age-related lung disease mechanisms and therapeutic interventions. This model has potential applications in exploring the impact of environmental factors (e.g., toxins, infectious pathogens, etc.) on the senescent lung and assessing treatments that could mitigate pathologies associated with pulmonary aging including barrier impairment, inflammation and fibrosis.

PMID:40060424 | PMC:PMC11888323 | DOI:10.1101/2025.02.24.639173

JCI Insight. 2025 Mar 10;10(5):e187172. doi: 10.1172/jci.insight.187172.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) causes remodeling of the distal lung. Pulmonary remodeling is histologically characterized by fibrosis, as well as appearance of basal cells; however, the involvement of basal cells in IPF remains unclear. Here, we focus on the long noncoding RNA MIR205HG, which is highly expressed in basal cells, using RNA sequencing. Through RNA sequencing of genetic manipulations using primary cells and organoids, we discovered that MIR205HG regulates IL-33 expression. Mechanistically, the AluJb element of MIR205HG plays a key role in IL-33 expression. Additionally, we identified a small molecule that targets the AluJb element, leading to decreased IL-33 expression. IL-33 is known to induce type 2 innate lymphoid cells (ILC2s), and we observed that MIR205HG expression was positively correlated with the number of ILC2s in patients with IPF. Collectively, these findings provide insights into the mechanisms by which basal cells contribute to IPF and suggest potential therapeutic targets.

PMID:40059822 | DOI:10.1172/jci.insight.187172

Liver Int. 2025 Apr;45(4):e70058. doi: 10.1111/liv.70058.

ABSTRACT

BACKGROUND: This study utilised the Global Burden of Disease data (2010-2021) to analyse the rates and trends in point prevalence, annual incidence and years lived with disability (YLDs) for major chronic liver diseases, such as hepatitis B, hepatitis C, metabolic dysfunction-associated liver disease, cirrhosis and other chronic liver diseases.

METHODS: Age-standardised rates per 100,000 population for prevalence, annual incidence and YLDs were compared across regions and countries, as well as the socio-demographic index (SDI). Trends were expressed as percentage changes (PC) and estimates were reported with uncertainty intervals (UI).

RESULTS: Globally, in 2021, the age-standardised rates per 100,000 population for the prevalence of hepatitis B, hepatitis C, MASLD and cirrhosis and other chronic liver diseases were 3583.6 (95%UI 3293.6-3887.7), 1717.8 (1385.5-2075.3), 15018.1 (13756.5-16361.4) and 20302.6 (18845.2-21791.9) respectively. From 2010 to 2021, the PC in age-standardised prevalence rates were-20.4% for hepatitis B, -5.1% for hepatitis C, +11.2% for MASLD and + 2.6% for cirrhosis and other chronic liver diseases. Over the same period, the PC in age-standardized incidence rates were -24.7%, -6.8%, +3.2%, and +3.0%, respectively. Generally, negative associations, but with fluctuations, were found between age-standardised prevalence rates for hepatitis B, hepatitis C, cirrhosis and other chronic liver diseases and the SDI at a global level. However, MASLD prevalence peaked at moderate SDI levels.

CONCLUSIONS: The global burden of chronic liver diseases remains substantial. Hepatitis B and C have decreased in prevalence and incidence in the last decade, while MASLD, cirrhosis and other chronic liver diseases have increased, necessitating targeted public health strategies and resource allocation.

PMID:40062742 | DOI:10.1111/liv.70058

iScience. 2025 Jan 23;28(3):111873. doi: 10.1016/j.isci.2025.111873. eCollection 2025 Mar 21.

ABSTRACT

The COVID-19 pandemic was the most dramatic in the newest history with nearly 7 million deaths and global impact on mankind. Here, we report binding index of 305 human leukocyte antigen (HLA) class I molecules from 18,771 unique haplotypes of 28,104 individuals to 821 peptides experimentally observed from spike protein receptor binding domain (RBD) of five main SARS-CoV-2 strains hydrolyzed by human proteasomes with constitutive and immune catalytic phenotypes. Our data read that mutations in the human angiotensin-converting enzyme 2 (hACE2)-binding region RBD496-513 of Omicron B.1.1.529 strain results in a dramatic increase of proteasome-mediated release of two public HLA class I epitopes. Global population analysis of HLA class I haplotypes, specific to these peptides, demonstrated decreased mortality of human populations enriched in these haplotypes from COVID-19 after but not before December, 2021, when Omicron became dominant SARS-CoV-2 strain. Noteworthy, currently circulating BA.2.86 and JN.1 strains contain same amino acid substitutions at key proteasomal cleavage sites, thus preserving identified core epitopes.

PMID:40060909 | PMC:PMC11889684 | DOI:10.1016/j.isci.2025.111873

iScience. 2025 Feb 4;28(3):111876. doi: 10.1016/j.isci.2025.111876. eCollection 2025 Mar 21.

ABSTRACT

T cell-redirecting bispecific antibodies (bsAbs) to treat advanced stage solid tumors are gaining interest after recent clinical successes. The immune checkpoint human leukocyte antigen G (HLA-G) is expressed in several tumor types while in normal tissues expression is limited. Here, we describe JNJ-78306358, a T cell-redirecting bispecific antibody (bsAb) to treat advanced stage solid tumors. JNJ-78306358 binds with high affinity to the α3 subunit of HLA-G on cancer cells and with purposely engineered weaker affinity to CD3ε on T cells. JNJ-78306358 induced potent T cell-mediated cytotoxicity of HLA-G-expressing solid tumors in vitro and in vivo. JNJ-78306358 also blocked the interaction of HLA-G with its receptors in vitro, indicating that immune checkpoint blocking may contribute to its anti-tumor activity. These results suggest that T cell-redirection against HLA-G could be a potent and effective treatment for a wide range of solid tumor indications.

PMID:40060890 | PMC:PMC11889666 | DOI:10.1016/j.isci.2025.111876

bioRxiv [Preprint]. 2025 Feb 28:2025.02.24.639810. doi: 10.1101/2025.02.24.639810.

ABSTRACT

Biological sex plays a crucial role in the outcomes of cardiac health and therapies. Sex hormones are known to strongly influence cardiac remodeling through intracellular signaling pathways, yet their underlying mechanisms remain unclear. To address this need, we developed and validated a logic-based systems biology model of cardiomyocyte hypertrophy that, for the first time, incorporates the effects of both estradiol (E2) and testosterone (T) alongside well-established hypertrophic stimuli (Strain, angiotensin II (AngII), and endothelin-1 (ET-1)). We qualitatively validated the model to literature data with 84% agreement. Quantitative validation was done by simulating the impact of the inputs (E2, T, Strain, AngII, and ET-1) on cardiac hypertrophy, captured as change in CellArea. We perturbed the validated model to examine the differential response to hypertrophy and identify changes in influential and sensitive downstream nodes for a male, pre-menopausal female, and post-menopausal female condition. Our results suggest that T has a greater impact on hypertrophy than E2. This model increases our understanding of the mechanisms through which sex hormones influence cardiac hypertrophy and can aid with developing more effective cardiac therapies for all patients.

AUTHOR SUMMARY: Differences between female and male hearts extend far beyond size and structure. Sex hormones estradiol and testosterone play key roles in sex-specific cardiac remodeling via intracellular pathways. Understanding how these sex hormones impact cardiac remodeling is critical for developing more effective, sex-specific approaches to cardiovascular care. Logic-based systems biology models have proven useful in quantifying and analyzing complex and intricate intracellular signaling network dynamics in various cell types. We leverage this method to develop a model of cardiomyocyte hypertrophy, which, for the first time, includes the effect of both estradiol and testosterone. Considering the combined influence of these hormones is important because both women and men have varying concentrations of these hormones throughout their lives. The model was developed and validated based on previously published studies. We then investigated differences in cardiomyocyte hypertrophy in pre- and post-menopausal women and men.

PMID:40060665 | PMC:PMC11888296 | DOI:10.1101/2025.02.24.639810