Development. 2025 Feb 10:dev.204555. doi: 10.1242/dev.204555. Online ahead of print.

ABSTRACT

Forty first-trimester human hearts were studied to lay groundwork for further studies of principles underlying congenital heart defects. We first sampled 49,227 cardiac nuclei from three fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw) for single-nucleus RNA sequencing, enabling distinction of six classes comprising 21 cell types. Improved resolution led to identification of novel cardiomyocytes and minority autonomic and lymphatic endothelial transcriptomes, among others. After integration with 5-7 pcw heart single-cell RNAseq, we identified a human cardiomyofibroblast progenitor preceding diversification of cardiomyocyte and stromal lineages. Analysis of six Visium sections from two additional hearts was aided by deconvolution, and key spatial markers validated on sectioned and whole hearts in two- and three-dimensional space and over time. Altogether, anatomical-positional features including innervation, conduction and subdomains of the atrioventricular septum translate latent molecular identity into specialized cardiac functions. This atlas adds unprecedented spatial and temporal resolution to the characterization of human-specific aspects of early heart formation.

PMID:39927812 | DOI:10.1242/dev.204555

Hum Mol Genet. 2025 Feb 10:ddaf018. doi: 10.1093/hmg/ddaf018. Online ahead of print.

ABSTRACT

The rising prevalence of chronic kidney disease (CKD), affecting an estimated 37 million adults in the United States, presents a significant global health challenge. CKD is typically assessed using estimated Glomerular Filtration Rate (eGFR), which incorporates serum levels of biomarkers such as creatinine and cystatin C. However, these biomarkers do not directly measure kidney function; their elevation in CKD results from diminished glomerular filtration. Genome-wide association studies (GWAS) based on eGFR formulas using creatinine (eGFRcre) or cystatin C (eGFRcys) have identified distinct non-overlapping loci, raising questions about whether these loci govern kidney function or biomarker metabolism. In this study, we show that GWAS on creatinine and cystatin C levels in healthy individuals reveal both nonoverlapping genetic loci impacting their metabolism as well as overlapping genetic loci associated with kidney function; whereas GWAS on elevated levels of these biomarkers uncover novel loci primarily associated with kidney function in CKD patients.

PMID:39927731 | DOI:10.1093/hmg/ddaf018

Front Oncol. 2025 Jan 24;14:1536406. doi: 10.3389/fonc.2024.1536406. eCollection 2024.

ABSTRACT

INTRODUCTION: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer resistant to endocrine and targeted therapies. Immune checkpoint inhibitors (ICIs) have shown significant efficacy in various cancers. Taraxacum officinale, commonly known as dandelion, has traditionally been used to treat breast-related diseases and is recognized for its beneficial composition and low side effects. FDA-approved drugs, having undergone rigorous validation for their safety, efficacy, and quality, provide a foundation for drug repurposing research. Researchers may explore FDA-approved drugs targeting the potential target NANOS1 for TOE (Taraxacum officinale extract) treatment to develop innovative therapeutic strategies. In this context, Dig (Digoxin) and AA (Algestone acetophenide) have been identified as potential drug candidates for further exploration of their therapeutic effects and application potential in targeting NANOS1.

METHODS: RNA sequencing (RNA-seq) was employed to identify potential targets for triple-negative breast cancer (TNBC) from TOE. Bioinformatics tools, including bc-GenExMiner v4.8, the Human Protein Atlas, and the TIMER database, were utilized for target identification. Molecular docking studies assessed FDA-approved drugs interacting with these targets, with Dig and AA selected as candidate drugs. The therapeutic efficacy of Dig and AA in combination with PD-1 inhibitors was evaluated using the 4T1 mouse model. Flow cytometry was applied to assess lymphocyte infiltration in the tumor immune microenvironment. RNA-seq analysis after target silencing by small interfering RNA (siRNA) was performed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Validation of findings was conducted through quantitative PCR and Western blot analysis.

RESULTS: TOE inhibited TNBC cell growth, migration, and invasion, as assessed by CCK-8 and transwell assays. RNA-seq indicated the effects may be due to NANOS1 down-regulation. Survival analysis showed lower NANOS1 expression correlated with better prognosis. Immunoinfiltration analysis indicated a negative correlation between NANOS1 levels and activated NK cells. Molecular docking identified Dig and AA as high-affinity binders of NANOS1. Animal experiments showed Dig and PD-1 inhibitor combination enhanced immunotherapy efficacy for TNBC.

DISCUSSION: The findings from this study suggest that TOE may offer a novel therapeutic approach for TNBC by targeting NANOS1, a protein whose down-regulation is associated with improved patient outcomes. The negative correlation between NANOS1 and activated NK cells highlights the potential role of the immune system in TNBC pathogenesis and response to treatment. The identification of Dig as potential drugs targeting NANOS1 provides a new direction for drug repurposing in TNBC. The synergistic effect of Dig and PD-1 inhibition observed in animal models is promising and warrants further investigation into the role of immunotherapy in TNBC treatment. Overall, this study identifies NANOS1 as a new target for TNBC therapy and suggests a combination therapy approach that could enhance immunotherapy effectiveness and improve patient outcomes.

PMID:39927118 | PMC:PMC11802438 | DOI:10.3389/fonc.2024.1536406

Drug Dev Res. 2025 Feb;86(1):e70057. doi: 10.1002/ddr.70057.

ABSTRACT

Acne is a skin disease that impacts 9.4% of the world's population. Available treatments for managing acne include retinoid-like drugs, antibiotics, corticosteroids, photo, and radiotherapy. Howevere, the aforementioned treatments have certain limitations such as possibility of developing skin cancer from tetracycline, doxycycline, and corticosteroids, microbial resistance to antibiotics, and deadly side effects, and so forth. Repurposing of existing therapeutics having excellent safety profile can be promising way to treat acne efficiently. The repurposed drugs and phytoceuticals from diverse classes have demonstrated promising effects in treating acne. These repurposed drugs have displayed antiacne effectiveness by targeting single or multiple signaling pathways. Various repurposed therapeutics undergoing clinical trials at different phases demonstrated their safety and efficacy in treating acne. Despite being a very good, safe, and less time-consuming strategy, drug repurposing (DR) faces multiple challenges such as lack of regulatory guidelines, preservation of intellectual property, and clinical validation of claimed therapeutic indication. DR appears to be a viable approach and is likely to offer effective treatment at a reasonable cost in alleviating acne.

PMID:39925109 | DOI:10.1002/ddr.70057

J Alzheimers Dis. 2025 Feb 9:13872877251314777. doi: 10.1177/13872877251314777. Online ahead of print.

ABSTRACT

BACKGROUND: Nearly two-thirds of Alzheimer's disease (AD) patients are women. Therapeutics for women are critical to lowering their elevated risk of developing this major cause of adult dementia. Moreover, targeting epigenetic processes such as histone acetylation that regulate multiple cellular pathways is advantageous given the multifactorial nature of AD. Histone acetylation takes part in memory consolidation, and its disruption is linked to AD.

OBJECTIVE: Determine whether the investigational drug RG2833 has repurposing potential for AD. RG2833 is a histone deacetylase HDAC1/3 inhibitor that is orally bioavailable and permeates the blood-brain-barrier.

METHODS: RG2833 effects were determined on cognition, transcriptome, and AD-like pathology in 11-month TgF344-AD female and male rats. Treatment started early in the course of pathology when therapeutic intervention is predicted to be most effective.

RESULTS: RG2833-treatment of 11-month TgF344-AD rats: (1) Significantly improved hippocampal-dependent spatial memory in females but not males. (2) Upregulated expression of immediate early genes, such as Arc, Egr1 and c-Fos, and other genes involved in synaptic plasticity and memory consolidation in females. Remarkably, out of 17,168 genes analyzed for each sex, no significant changes in gene expression were detected in males at p < 0.05, false discovery rate <0.05, and fold-change equal or > 1.5. (3) Failed to improve amyloid beta accumulation and microgliosis in female and male TgF344-AD rats.

CONCLUSIONS: Our study highlights the potential of histone-modifying therapeutics such as RG2833 to improve cognitive behavior and drive the expression of immediate early, synaptic plasticity and memory consolidation genes, especially in female AD patients.

PMID:39924842 | DOI:10.1177/13872877251314777

Chron Respir Dis. 2025 Jan-Dec;22:14799731241310897. doi: 10.1177/14799731241310897.

ABSTRACT

OBJECTIVES: To assess real-world survival and healthcare resource utilization (HCRU) in US patients with non-cystic fibrosis bronchiectasis (NCFBE).

METHODS: This retrospective analysis, using data from the STATinMED RWD Insights database from Jan 2015-Oct 2022, included adults with NCFBE (from Jan 2015-Oct 2021) and non-NCFBE comparators (from Jan 2015-Aug 2020); baseline characteristics were balanced by inverse probability treatment weighting. Outcomes included survival through end of study. HCRU was assessed over 12 months.

RESULTS: 117,718 patients with NCFBE and 306,678 comparators were included. Patients with NCFBE had a 77% higher risk of death than comparators (hazard ratio [HR] 1.77 [95% CI 1.74-1.80]). Risk of death was higher among patients aged ≥65 years (vs 18-34 years; HR 11.03 [95% CI 10.36-11.74]), among Black patients (vs White; HR 1.53 [95% CI 1.50-1.55]), and among patients with comorbid COPD (HR 1.42 [95% CI 1.40-1.44]). Patients with NCFBE incurred higher all-cause and respiratory-related HCRU than comparators for outpatient office, outpatient hospital, emergency department (ED), inpatient and respiratory-related pulmonologist visits (all p < .0001); HCRU increased with exacerbations.

CONCLUSIONS: Patients with NCFBE have high mortality burden and incur high HCRU, both of which are further increased with exacerbations. Prevention and delay of exacerbations are key areas for improvement of disease management.

PMID:39925084 | DOI:10.1177/14799731241310897

Front Neurorobot. 2025 Jan 24;18:1513354. doi: 10.3389/fnbot.2024.1513354. eCollection 2024.

ABSTRACT

INTRODUCTION: In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly been deployed in various applications such as autonomous navigation, surveillance, and object detection. Traditional methods for UAV navigation and object detection have often relied on either handcrafted features or unimodal deep learning approaches. While these methods have seen some success, they frequently encounter limitations in dynamic environments, where robustness and computational efficiency become critical for real-time performance. Additionally, these methods often fail to effectively integrate multimodal inputs, which restricts their adaptability and generalization capabilities when facing complex and diverse scenarios.

METHODS: To address these challenges, we introduce NavBLIP, a novel visual-language model specifically designed to enhance UAV navigation and object detection by utilizing multimodal data. NavBLIP incorporates transfer learning techniques along with a Nuisance-Invariant Multimodal Feature Extraction (NIMFE) module. The NIMFE module plays a key role in disentangling relevant features from intricate visual and environmental inputs, allowing UAVs to swiftly adapt to new environments and improve object detection accuracy. Furthermore, NavBLIP employs a multimodal control strategy that dynamically selects context-specific features to optimize real-time performance, ensuring efficiency in high-stakes operations.

RESULTS AND DISCUSSION: Extensive experiments on benchmark datasets such as RefCOCO, CC12M, and Openlmages reveal that NavBLIP outperforms existing state-of-the-art models in terms of accuracy, recall, and computational efficiency. Additionally, our ablation study emphasizes the significance of the NIMFE and transfer learning components in boosting the model's performance, underscoring NavBLIP's potential for real-time UAV applications where adaptability and computational efficiency are paramount.

PMID:39927288 | PMC:PMC11802496 | DOI:10.3389/fnbot.2024.1513354

Front Med (Lausanne). 2025 Jan 24;11:1506686. doi: 10.3389/fmed.2024.1506686. eCollection 2024.

ABSTRACT

INTRODUCTION: Bones are a fundamental component of human anatomy, enabling movement and support. Bone fractures are prevalent in the human body, and their accurate diagnosis is crucial in medical practice. In response to this challenge, researchers have turned to deep-learning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection.

METHODS: Nevertheless, it is essential to develop an automated approach for identifying fractures using the multi-region X-ray dataset from Kaggle, which contains a comprehensive collection of 10,580 radiographic images. This study advocates for the use of DL techniques, including VGG16, ResNet152V2, and DenseNet201, for the detection and diagnosis of bone fractures.

RESULTS: The experimental findings demonstrate that the proposed approach accurately identifies and classifies various types of fractures. Our system, incorporating DenseNet201 and VGG16, achieved an accuracy rate of 97% during the validation phase. By addressing these challenges, we can further improve DL models for fracture detection. This article tackles the limitations of existing methods for fracture detection and diagnosis and proposes a system that improves accuracy.

CONCLUSION: The findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.

PMID:39927268 | PMC:PMC11803505 | DOI:10.3389/fmed.2024.1506686

Phys Imaging Radiat Oncol. 2025 Jan 20;33:100701. doi: 10.1016/j.phro.2025.100701. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for rectal dose mapping, limiting their analysis. We developed a method to standardise rectum contours, unfold them into 2D cylindrical surface maps, and identify subregions where higher doses increase rectal toxicities.

MATERIALS AND METHODS: Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. Rectum length was standardised using linear transformations superior and inferior to the prostate. The automatic contours were validated against the manual contours through contour variation assessment with cylindrical mapping. Voxel-based analysis of the dose surface maps for the manual and automatic contours against individual rectal toxicities was performed using Student's t permutation test and Cox Proportional Hazards Model (CPHM). Significance was defined by permutation testing.

RESULTS: Our method enabled the analysis of 1,048 patients using automatic segmentation. Student's t-test showed significance (p < 0.05) in the lower posterior for clinical-reported proctitis and patient-reported bowel urgency. Univariable CPHM identified a 3 % increased risk per Gy for clinician-reported proctitis and a 2 % increased risk per Gy for patient-reported bowel urgency in the lower posterior. No other endpoints were significant.

CONCLUSION: We developed a methodology that unfolds the rectum to a 2D surface map. The lower posterior was significant for clinician-reported proctitis and patient-reported bowel urgency, suggesting that reducing the dose in the region could decrease toxicity risk.

PMID:39927213 | PMC:PMC11803856 | DOI:10.1016/j.phro.2025.100701

Nanophotonics. 2025 Jan 27;14(2):121-151. doi: 10.1515/nanoph-2024-0536. eCollection 2025 Feb.

ABSTRACT

Nanophotonics, which explores significant light-matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photonics. While conventional brute-force, intuition-based forward design methods have produced successful nanophotonic solutions over the past several decades, recent developments in optimization methods and artificial intelligence offer new potential to expand these capabilities. In this review, we delve into the latest progress in the inverse design of nanophotonic devices, where AI and optimization methods are leveraged to automate and enhance the design process. We discuss representative methods commonly employed in nanophotonic design, including various meta-heuristic algorithms such as trajectory-based, evolutionary, and swarm-based approaches, in addition to adjoint-based optimization. Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. Additionally, we summarize the open-source inverse design tools and commercial foundries. Finally, we provide our perspectives on the current challenges of inverse design, while offering insights into future directions that could further advance this rapidly evolving field.

PMID:39927200 | PMC:PMC11806510 | DOI:10.1515/nanoph-2024-0536

Open Med (Wars). 2025 Feb 4;20(1):20241110. doi: 10.1515/med-2024-1110. eCollection 2025.

ABSTRACT

BACKGROUND: The highly infectious coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, the seventh coronavirus. It is the longest pandemic in recorded history worldwide. Many countries are still reporting COVID-19 cases even in the fifth year of its emergence.

OBJECTIVE: The performance of various machine learning (ML) and deep learning (DL) models was studied for image-based classification of the lungs infected with COVID-19, pneumonia (viral and bacterial), and normal cases from the chest X-rays (CXRs).

METHODS: The K-nearest neighbour and logistics regression as the two ML models, and Visual Geometry Group-19, Vision transformer, and ConvMixer as the three DL models were included in the investigation to compare the brevity of the detection and classification of the cases.

RESULTS: Among the investigated models, ConvMixer returned the best result in terms of accuracy, recall, precision, F1-score and area under the curve for both binary as well as multiclass classification. The pre-trained ConvMixer model outperformed the other four models in classifying. As per the performance observations, there was 97.1% accuracy for normal and COVID-19 + pneumonia-infected lungs, 98% accuracy for normal and COVID-19 infected lungs, 82% accuracy for normal + bacterial + viral infected lungs, and 98% accuracy for normal + pneumonia infected lungs. The DL models performed better than the ML models for binary and multiclass classification. The performance of these studied models was tried on other CXR image databases.

CONCLUSION: The suggested network effectively detected COVID-19 and different types of pneumonia by using CXR imagery. This could help medical sciences for timely and accurate diagnoses of the cases through bioimaging technology and the use of high-end bioinformatics tools.

PMID:39927166 | PMC:PMC11806240 | DOI:10.1515/med-2024-1110

J Pharm Bioallied Sci. 2024 Dec;16(Suppl 4):S3080-S3082. doi: 10.4103/jpbs.jpbs_1106_24. Epub 2024 Dec 10.

ABSTRACT

This systematic review examines the diverse applications of AI in all areas of dentistry. The search was conducted using the terms "Artificial Intelligence," "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Out of 607 publications analyzed from 2010 to 2024, only 13 were selected for inclusion based on their relevance and publication year. AI in dentistry offers both advantages and challenges. It enhances diagnosis, therapy, and patient outcomes through complex algorithms and massive datasets. However, issues such as data privacy, dental professional job displacement, and the necessity for thorough validation and regulation to ensure safety and efficacy remain significant concerns.

PMID:39926925 | PMC:PMC11804999 | DOI:10.4103/jpbs.jpbs_1106_24

Front Med (Lausanne). 2025 Jan 24;12:1510357. doi: 10.3389/fmed.2025.1510357. eCollection 2025.

ABSTRACT

PURPOSE: Acute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.

METHODS: A retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. Clinical data (medical history and laboratory indicators) was included in a multivariate logistic regression analysis to identify the independent factors associated with AMI and establish a clinical factors model. The arterial and venous CTA images were utilized to construct DL model. A Fusion Model was constructed by integrating clinical factors into the DL model. The performance of the models was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

RESULTS: Albumin and International Normalized Ratio (INR) were associated with AMI by univariate and multivariate logistic regression (P < 0.05). In the test set, the area under ROC curve (AUC) of the clinical factor model was 0.60 (sensitivity 0.47, specificity 0.86). The AUC of the DL model based on CTA images reached 0.90, which was significantly higher than the AUC values of the clinical factor model, as confirmed by the DeLong test (P < 0.05). The Fusion Model also showed exceptional performance in terms of AUC, accuracy, sensitivity, specificity, and precision, with values of 0.96, 0.94, 0.94, 0.95, and 0.98, respectively. DCA indicated that the Fusion Model provided a greater net benefit than those of models based solely on imaging and clinical information across the majority of the reasonable threshold probabilities.

CONCLUSION: The incorporation of CTA images and clinical information into the model markedly enhances the diagnostic accuracy and efficiency of AMI. This approach provides a reliable tool for the early diagnosis of AMI and the subsequent implementation of appropriate clinical intervention.

PMID:39926426 | PMC:PMC11802816 | DOI:10.3389/fmed.2025.1510357

Front Oncol. 2025 Jan 24;15:1516264. doi: 10.3389/fonc.2025.1516264. eCollection 2025.

ABSTRACT

The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions.

PMID:39926279 | PMC:PMC11802434 | DOI:10.3389/fonc.2025.1516264

RSC Adv. 2025 Feb 7;15(6):4262-4273. doi: 10.1039/d4ra07951e. eCollection 2025 Feb 6.

ABSTRACT

Virtual screening (VS) methodologies have become key in the drug discovery process but are also applicable to other fields including catalysis, material design, and, more recently, insecticide solutions. Indeed, the search for effective pest control agents is a critical industrial objective, driven by the need to meet stringent regulations and address public health concerns. Cockroaches, known vectors of numerous diseases, represent a major challenge due to the toxicity of existing control measures to humans. In this article, we leverage an Artificial Intelligence (AI)-based screening of the Drug Bank (DB) database to identify novel acetylcholinesterase (AChE) inhibitors, a previously uncharacterized target in the American cockroach (Periplaneta americana). Our AI-based VS pipeline starts with the deep-learning-based AlphaFold to predict the previously unknown 3D structure of AChE based on its amino acid sequence. This first step enables the subsequent ligand-receptor VS of potential inhibitors, the development of which is performed using a consensus VS protocol based on two different tools: Glide, an industry-leading solution, and METADOCK 2, a metaheuristic-based tool that takes advantage of GPU acceleration. The proposed VS pipeline is further refined through rescoring to pinpoint the most promising biocide compounds against cockroaches. We show the search space explored by different metaheuristics generated by METADOCK 2 and how this search is more exhaustive, but complementary, than the one offered by Glide. Finally, we applied Molecular Mechanics Generalized Born Surface Area (MMGBSA) to list the most promising compounds to inhibit the AChE enzyme.

PMID:39926230 | PMC:PMC11804414 | DOI:10.1039/d4ra07951e

World J Gastroenterol. 2025 Feb 7;31(5):102692. doi: 10.3748/wjg.v31.i5.102692.

ABSTRACT

In this letter, we comment on a recent article published in the World Journal of Gastroenterology by Xiao et al, where the authors aimed to use a deep learning model to automatically detect gastrointestinal lesions during capsule endoscopy (CE). CE was first presented in 2000 and was approved by the Food and Drug Administration in 2001. The indications of CE overlap with those of regular diagnostic endoscopy. However, in clinical practice, CE is usually used to detect lesions in areas inaccessible to standard endoscopies or in cases of bleeding that might be missed during conventional endoscopy. Since the emergence of CE, many physiological and technical challenges have been faced and addressed. In this letter, we summarize the current challenges and briefly mention the proposed methods to overcome these challenges to answer a central question: Do we still need CE?

PMID:39926220 | PMC:PMC11718605 | DOI:10.3748/wjg.v31.i5.102692

Health Sci Rep. 2025 Feb 6;8(2):e70328. doi: 10.1002/hsr2.70328. eCollection 2025 Feb.

ABSTRACT

BACKGROUND AND AIMS: Interactions between the lung microbiome and pulmonary epithelium plays a pivotal role in shaping immunity in the lung. Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease (ILD). Some patients with IPF develop episodic acute exacerbations often associated with microbial dysbiosis in the lungs. This study aimed to investigate etiologic agents as well as the lung microbiome in patients with ILDs and sarcoidosis.

METHODS: This study analyzed 31 patients divided into the IPF (IPF-stable, n = 12), acute exacerbation of ILDs (AE-ILDs, n = 6), and sarcoidosis (n = 13) groups. Bronchoalveolar lavage fluid (BALF) samples were analyzed by RNA-based metagenomic next-generation sequencing (NGS) on an Illumina platform.

RESULTS: In total, 87 pathogens were detected using metagenomic NGS at the genus level. Prevotella, Streptococcus, and Veillonella dominated the BALF microbial communities, and sequence reads of these bacteria were abundant, especially in the sarcoidosis group. Conversely, only a small number of bacterial reads were detected in the AE-ILDs group, and the overall proportion of microbial composition differed from that of the other groups. No significant difference was found in community diversity (α-diversity) among the groups, whereas the structural similarity of the microflora (β-diversity) differed significantly between the AE-ILDs and sarcoidosis groups.

CONCLUSIONS: Bacterial sequence reads in BALF were smaller in both the IPF-stable and AE-ILD groups than in the sarcoidosis group. Dysbiosis in the lung microbiome has been observed in patients with AE-ILD and may be related to the progression of inflammation.

PMID:39927182 | PMC:PMC11803077 | DOI:10.1002/hsr2.70328

Clin Exp Vaccine Res. 2025 Jan;14(1):67-76. doi: 10.7774/cevr.2025.14.e3. Epub 2025 Jan 13.

ABSTRACT

PURPOSE: Calcium phosphate (CaP) and iron oxide (IO) nanoparticles (NPs) are promising adjuvants and delivery systems for vaccination. Furthermore, it has been shown that the chimeric antigen TF/Bp26/Omp31 (TBO) is a good candidate for stimulating protection against virulent Brucella melitensis. Our aim in the present study was to compare the roles of CaP and IO NPs for induction of the immune response and protection against B. melitensis 16M by using the TBO antigen as a model protein.

MATERIALS AND METHODS: The tbo gene was expressed in the bacterial host and was evaluated by SDS-PAGE and western blot. The recombinant TBO was loaded onto CaP (CaP/TBO) and IO (IO/TBO) NPs. CaP/TBO and IO/TBO NPs were administered subcutaneously.

RESULTS: Antibody levels showed that immunization with both CaP/TBO and IO/TBO NPs stimulated mixed Th1-Th2 immune responses. In addition, immunized mice were challenged with a virulent strain of B. melitensis 16M. Immunized mice with CaP/TBO NPs showed a higher degree of protection than vaccinated animals with IO/TBO NPs.

CONCLUSION: Altogether, our results indicated that the CaP NPs are a potent adjuvant and delivery system for subcutaneously administered Brucella antigens.

PMID:39927221 | PMC:PMC11799579 | DOI:10.7774/cevr.2025.14.e3

Front Oncol. 2025 Jan 24;15:1496735. doi: 10.3389/fonc.2025.1496735. eCollection 2025.

ABSTRACT

BACKGROUND AND AIM: Extranodal NK/T-cell lymphoma (ENKTL) is a rare and aggressive subtype of non-Hodgkin's lymphoma that most commonly affects the nasal cavity and nasopharynx. The lung is a rare site for ENKTL involvement, and its clinical behavior and prognostic factors are not well understood. This study aimed to analyze survival outcomes and identify prognostic factors in patients with primary lung ENKTL.

METHODS: A retrospective analysis was conducted using data from 20 cases of primary lung ENKTL, including four patients who were treated at Peking University Third Hospital in Beijing and 16 patients were extracted from the Surveillance, Epidemiology, and End Results Program database. Clinical characteristics, treatment modalities, and survival data were collected and analyzed using Kaplan-Meier and Cox regression models to identify potential prognostic factors.

RESULTS: The study cohort included 13 male (65%) and 7 female (35%) patients with a median age of 57 years. Sex was a significant predictor of survival (P = 0.030), with female patients having lower survival rates. Other factors, including age, race, and disease stage, were not significantly associated with survival. Most patients received chemotherapy (45%) or a combination of chemotherapy and radiotherapy (5%), but treatment data were incomplete for 40% of the cohort. The median overall survival was poor, reflecting the aggressive nature of primary lung ENKTL.

CONCLUSIONS: Primary lung ENKTL is a rare, aggressive malignancy with limited available data. In this cohort, sex was a significant prognostic factor, while other demographic and clinical variables did not show significant associations with survival. Future research should focus on understanding the molecular and immunological drivers of this disease, with an emphasis on discovering novel therapeutic approaches. Large-scale multicenter studies are needed to improve diagnostic and treatment strategies for primary lung ENKTL.

PMID:39926277 | PMC:PMC11802421 | DOI:10.3389/fonc.2025.1496735

Front Plant Sci. 2025 Jan 24;15:1455156. doi: 10.3389/fpls.2024.1455156. eCollection 2024.

ABSTRACT

In our study, we investigated the effects of Expando, a commercial biostimulant derived from seaweed and yeast extracts, on the secondary metabolism of Lady cot and Orange prima apricot cultivars. Notably, treatments with or 5.0 L/ha of Expando improved fruit uniformity and harvests synchronization, providing agronomic benefits. Expando positively influenced the biosynthesis of essential bioactive compounds such as polyphenols, flavonoids, proanthocyanidins, and anthocyanins in both apricot pulp and peel, as validated by HPLC-ESI-MS/MS analysis. These metabolic enhancements translated into significantly increased total antioxidant activity, particularly evident in the peel samples. Principal Component Analysis (PCA) revealed distinct effects of the 5.0 and 4.0 L/ha treatments, distinguishing them from lower doses and the control group. Our findings emphasize the potential of Expando to enhance the phytochemical profile of apricot fruits, positioning biostimulants as pivotal tools for improving fruit quality and sustainability in agriculture. Expando offers a sustainable and eco-friendly approach to enhancing crop yield and nutritional value, representing a significant step towards more resilient and environmentally conscious farming practices. Further research is needed to explore its broader implications and optimize application strategies for commercial orchards.

PMID:39925374 | PMC:PMC11802282 | DOI:10.3389/fpls.2024.1455156