J Chem Phys. 2025 May 21;162(19):194104. doi: 10.1063/5.0256659.

ABSTRACT

Reaction-diffusion equations are commonly used to model a diverse array of complex systems, including biological, chemical, and physical processes. Typically, these models are phenomenological, requiring the fitting of parameters to experimental data. In the present work, we introduce a novel formalism to construct reaction-diffusion models that is grounded in the principle of maximum entropy. This new formalism aims to incorporate various types of experimental data, including ensemble currents, distributions at different points in time, or moments of such. To this end, we expand the framework of Schrödinger bridges and maximum caliber problems to nonlinear interacting systems. We illustrate the usefulness of the proposed approach by modeling the evolution of (i) a morphogen across the fin of a zebrafish and (ii) the population of two varieties of toads in Poland, so as to match the experimental data.

PMID:40377200 | DOI:10.1063/5.0256659

Elife. 2025 May 16;12:RP89862. doi: 10.7554/eLife.89862.

ABSTRACT

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health vs IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

PMID:40377187 | DOI:10.7554/eLife.89862

Ann N Y Acad Sci. 2025 May 16. doi: 10.1111/nyas.15373. Online ahead of print.

ABSTRACT

Recursion, the neuro-computational operation of nesting a signal or pattern within itself, lies at the structural basis of language. Classically considered absent in the vocal repertoires of nonhuman animals, whether recursion evolved step-by-step or saltationally in humans is among the most fervent debates in cognitive science since Chomsky's seminal work on syntax in the 1950s. The recent discovery of self-embedded vocal motifs in wild (nonhuman) great apes-Bornean male orangutans' long calls-lends initial but important support to the notion that recursion, or at least temporal recursion, is not uniquely human among hominids and that its evolution was based on shared ancestry. Building on these findings, we test four necessary predictions for a gradual evolutionary scenario in wild Sumatran female orangutans' alarm calls, the longest known combinations of consonant-like and vowel-like calls among great apes (excepting humans). From the data, we propose third-order self-embedded isochrony: three hierarchical levels of nested isochronous combinatoric units, with each level exhibiting unique variation dynamics and information content relative to context. Our findings confirm that recursive operations underpin great ape call combinatorics, operations that likely evolved gradually in the human lineage as vocal sequences became longer and more intricate.

PMID:40376956 | DOI:10.1111/nyas.15373

Discov Oncol. 2025 May 16;16(1):786. doi: 10.1007/s12672-025-02502-6.

ABSTRACT

BACKGROUND: Drug-induced second tumors (DIST) refer to new primary cancers that develop during or after the treatment of an initial cancer due to the long-term effects of medications. As a severe long-term adverse event, DIST has gained widespread attention globally in recent years. With the increasing prevalence of cancer treatments and the prolonged survival of patients, drug-induced second tumors have become more prominent and pose a significant public health challenge. However, most existing studies have focused on individual drugs or small patient cohorts, lacking large-scale, real-world data evaluations. Particularly, the potential second-tumor risk of new drugs remains underexplored.

OBJECTIVE: This study aims to systematically assess the adverse event signals between drugs and second tumors using the U.S. FDA Adverse Event Reporting System (FAERS) database, employing disproportionality analysis (DPA) methods. It particularly focuses on uncovering drugs that have not clearly labeled second-tumor risks.

METHODS: Data from the FDA Adverse Event Reporting System (FAERS), covering reports from its inception to the third quarter of 2024, was retrieved. After data standardization, four disproportionality methods were used: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). These methods assessed the correlation between azacitidine and adverse drug events (ADEs). Additionally, the Weibull Shape Parameter (WSP) was used to analyze the characteristic patterns of time-to-onset curves. Newly discovered signals were verified against FDA drug labels to confirm their novelty. The Weibull analysis was conducted to examine the temporal aspects of adverse event occurrences.

RESULTS: Since 2004, drug-induced tumor events have been increasing annually, with a total of 7597 drug-related tumor adverse events recorded. A total of 250 drugs were identified as having potential risk signals. High-incidence populations were primarily aged between 65 and 85 years, with a higher proportion of individuals with a body weight ≥ 90 kg. The most frequent occurrence was observed in patients with Chronic Myeloid Leukemia (13.36%). Among the top 5 drugs with the highest number of reported drug-induced second tumor adverse events, IMATINIB (906 reports), RUXOLITINIB (554 reports), PALBOCICLIB (552 reports), OCTREOTIDE (399 reports), and DOXORUBICIN (380 reports) were identified. Among these, PALBOCICLIB, OCTREOTIDE, and DOXORUBICIN are drugs for which the risk of drug-induced second tumors is not explicitly mentioned in their labels. A total of 76 drugs were identified through four disproportionality algorithms (ROR, PRR, MGPS, BCPNN), with a minimum time to drug-induced tumor occurrence of 5 years, exhibiting an early failure-type curve.

CONCLUSION: This study, based on large-scale real-world data, reveals the potential associations between drugs and second tumors, especially highlighting the risks of some new drugs. The findings provide valuable insights for drug safety monitoring and have significant public health implications. By uncovering previously unrecognized potential risks, this research lays the groundwork for further advancements in pharmacovigilance.

PMID:40377769 | DOI:10.1007/s12672-025-02502-6

Tissue Barriers. 2025 May 16:2503523. doi: 10.1080/21688370.2025.2503523. Online ahead of print.

ABSTRACT

Vascular endothelial barrier disruption is a critical determinant of morbidity and mortality in sepsis. Whole blood represents a key source of paracrine signaling molecules inducing vascular endothelial barrier disruption in sepsis. This study analyzes whole-genome transcriptome data from sepsis patients' whole blood available in the NCBI GEO database to identify paracrine mediators of vascular endothelial barrier dysfunction, uncovering novel insights that may guide drug repositioning strategies. This study identifies the regulated expression of paracrine signaling molecules TFPI, MMP9, PROS1, JAG1, S1PR1, and S1PR5 which either disrupt or protect vascular endothelial barrier function in sepsis and could serve as potential targets for repositioning existing drugs. Specifically, TFPI (barrier protective), MMP9 (barrier destructive), PROS1 (barrier protective), and JAG1 (barrier destructive) are upregulated, while S1PR1 (barrier protective) and S1PR5 (barrier protective) are downregulated. Our observations highlight the importance of considering both protective and disruptive mediators in the development of therapeutic strategies to restore endothelial barrier integrity in septic patients. Identifying TFPI, MMP9, PROS1, JAG1, S1PR1, and S1PR5 as druggable paracrine regulators of vascular endothelial barrier function in sepsis could pave the way for precision medicine approaches, enabling personalized treatments that target specific mediators of endothelial barrier disruption to improve patient outcomes in sepsis.

PMID:40376886 | DOI:10.1080/21688370.2025.2503523

Recenti Prog Med. 2025 May;116(5):310-321. doi: 10.1701/4495.44951.

ABSTRACT

INTRODUCTION: Duchenne muscular dystrophy (DMD) is a rare disease that causes a progressive loss of muscle function in males, presenting at the age of two years, and involving respiratory and heart function starting from teenage years. This retrospective observational study has identified patients potentially affected by DMD and described their utilization of healthcare resources and direct healthcare costs charged to the Italian National Healthcare Service (INHS).

METHODS: From the Foundation Ricerca e Salute (ReS), through a specific algorithm based only on administrative healthcare data of 5.4 million inhabitants in 2021 (index date), male patients aged <30 years potentially affected by DMD were identified. Comorbidities at baseline, and utilization of healthcare resources and direct healthcare costs charged to the INHS during the year following index date, were described.

RESULTS: In 2021, 120 male patients aged <30 years were identified as potentially affected with DMD (2.2/100,000 inhabitants; 16.1/100,000 males aged <30 years). Chronic airway disease and cardiomyopathy were found in 19.2% and 15.0% of patients, respectively. During follow-up: 41.7% of patients were treated with deflazacort, 2.5% with ataluren and about one third with cardiac drugs; 29.2% and 42.5% were admitted to overnight and day hospitalization, respectively, mainly due to neurological, cardiac, and respiratory diseases; 12.5% accessed the emergency department, mainly for traumatisms and fractures; 70.8% received local outpatient specialist care, half of which were specialist visits, and about 15% cardiac diagnostics. On average, the per capita annual total cost charged to the -INHS was € 6713; ataluren accounted for more than half of this expenditure. After having excluded the dispensation of ataluren during follow-up, the mean per capita total cost was € 2548, more than half of which due to hospitalizations.

CONCLUSIONS: This study of administrative data has identified patients potentially affected by DMD, a rare disease, from a large sample of INHS beneficiaries, and assessed their healthcare pathway. This is useful for regulatory purposes and for improved access to emerging innovative therapies.

PMID:40376903 | DOI:10.1701/4495.44951

Fam Process. 2025 Jun;64(2):e70041. doi: 10.1111/famp.70041.

ABSTRACT

Parents caring for children with rare diseases are more impaired regarding health-related quality of life (HRQoL) and mental health than healthy controls and norm data. To address the research gap in psychological care for these parents, this study evaluates the effectiveness of two family-based interventions. The children affected by rare disease and their families network (CARE-FAM-NET) study is a multicenter randomized controlled 2 × 2 factorial trial for affected families with children (0-21 years). This paper focuses on evaluating the impact of two interventions, one face-to-face (CARE-FAM) and one online (WEP-CARE), on the HRQoL and mental health of parents. One thousand, one hundred sixty-eight parents participated: TAU = 291, CARE-FAM = 296, WEP-CARE = 300, and CARE-FAM + WEP-CARE combined = 281. Data were collected at four time points over a period of 18 months using standardized questionnaires. The results had to be interpreted exploratively. The results indicate that there are no clinically relevant differences in the parents' HRQoL and mental health between the treatment groups. However, time-dependent differences in the intervention effects for WEP-CARE were observed. Although the results did not show clear relevant differences between conditions, trends in improvement in HrQoL and mental health were identified. CARE-FAM shows a greater reduction in parental distress and WEP-CARE shows a greater distortion of distress, particularly at T3 and T4. Given the exploratory nature of this study, it highlights the urgent need for further confirmatory research in this area.

PMID:40375458 | DOI:10.1111/famp.70041

Front Pharmacol. 2025 May 1;16:1559399. doi: 10.3389/fphar.2025.1559399. eCollection 2025.

ABSTRACT

BACKGROUND: Pharmacogenomic (PGx) variants can significantly impact drug response, but limited data exists on their prevalence in Middle Eastern populations. This study aimed to investigate the inheritance of certain markers in candidate pharmacogenes among healthy Saudis.

METHODS: DNA samples from 95 unrelated healthy Saudi participants were genotyped using the Affymetrix Axiom Precision Medicine Diversity Array. Thirty-eight variants in 15 pharmacogenes were analyzed based on their clinical relevance and lack of previous reporting in Saudi populations.

RESULTS: Twenty-six of the 37 tested markers were undetected in the cohort. The selected variants in six genes [DPYD (rs1801268), CACNA1S (rs772226819), EGFR (rs121434568), RYR1 (rs193922816), CYP2B6 (rs3826711), and MT-RNR1 (rs267606617, rs267606618, rs267606619)] were found to be non-existing among Saudis. In contrast, 11 variants and alleles in nine pharmacogenes were detected at varying frequencies. Notable findings included high frequencies of variants in ATIC [rs4673993, minor allele frequency (MAF) = 0.71)] and SLC19A1 (rs1051266, MAF = 0.48) affecting methotrexate efficacy. Three alleles were identified in CYP3A4, including a common (CYP3A4 rs2242480) and two rare alleles (*3 and *22). Another three markers [rs16969968 in CHRNA5, rs11881222 in IFNL3 (IL28B), and SLCO1B1*14] were found to be highly distributed among the participants (MAF = 0.35, 0.30, and 0.14, respectively). Conversely, three rare markers: CYP2A6*2, NAT2*14, and rs115545701 in CFTR, were identified at low-frequency levels (MAF = 0.021, 0.011, 0.005, respectively). Statistically significant differences in allele frequencies were observed for eight variants between Saudi and African populations, five variants compared to East Asians, and two variants compared to Europeans.

CONCLUSION: This study provides novel insights into the distribution of clinically relevant PGx variants in the Saudi population. The findings have implications for personalizing treatments for various conditions, including rheumatoid arthritis, cystic fibrosis, and hepatitis C. These data contribute to the development of population-specific PGx testing panels and treatment guidelines.

PMID:40376268 | PMC:PMC12078325 | DOI:10.3389/fphar.2025.1559399

Pharmacogenomics. 2025 May 16:1-7. doi: 10.1080/14622416.2025.2502316. Online ahead of print.

ABSTRACT

AIMS: To study the availability, perceived necessity, barriers, and preferred formats for pharmacogenomics (PGx) education disseminated to healthcare professionals by professional societies.

MATERIALS & METHODS: A web-based survey of professional organizations affiliated with the Inter-Society Coordinating Committee for Practitioner Education in Genomics (ISCC-PEG), a U.S.-based initiative coordinated by the National Human Genome Research Institute, targeted representatives who could reflect their organization's educational stance.

RESULTS: Of the 34 unique responses analyzed, most organizations provided general and genomic education (94.1% and 82.4%, respectively), and 70.6% offered PGx-specific education. Most (61.8%) indicated they either needed major additions to the education they provide or had no PGx education resources. Key barriers included a lack of PGx focus within organizations (78.1%) and challenges in maintaining an up-to-date curriculum (75.0%). Preferred educational formats were live webinars (84.4%), hybrid courses (78.1%), and self-study modules (78.1%).

CONCLUSIONS: Our study identifies gaps in PGx education across professional organizations and underscores the need for resources to advance clinician competence in PGx. While some PGx education is available, many organizations require additional resources and support. Enhancing PGx education through targeted initiatives by organizations like ISCC-PEG may improve clinician competence and the integration of PGx into clinical practice.

PMID:40375817 | DOI:10.1080/14622416.2025.2502316

J Respir Biol Transl Med. 2025 Mar;2(1):10002. doi: 10.70322/jrbtm.2025.10002. Epub 2025 Mar 24.

ABSTRACT

Pulmonary arterial hypertension (PAH) is a progressive, lethal, and incurable disease of the pulmonary vasculature. A previous genome-wide association study (GWAS) with Affymetrix microarray analysis data exhibited elevated histidine triad nucleotide-binding protein 3 (HINT3) in the lung samples of PAH compared to control subjects (failed donors, FD) and the positive correlations of HINT3 with deubiquitinase USP11 and B-cell lymphoma 2 (BCL2). In this study, we aim to investigate the roles and interplay of USP11 and HINT3 in the apoptosis resistance of PAH. The levels of USP11 and HINT3 were increased in the lungs of idiopathic PAH (IPAH) patients and Hypoxia/Sugen-treated mice. USP11 and HINT3 interacted physically, as shown by co-immunoprecipitation (co-IP) assay in human pulmonary arterial endothelial cells (HPAECs). HINT3 was degraded by polyubiquitination, which was reversed by USP11. Furthermore, HINT3 interacted with the anti-apoptotic mediator, BCL2. Overexpression of USP11 increased BCL2 content, congruent to elevated lung tissue levels seen in IPAH patients and Hypoxia/Sugen-treated mice. Conversely, the knockdown of HINT3 function led to a depletion of BCL2. Thus, we conclude that USP11 stabilizes HINT3 activation, which contributes to endothelial apoptosis-resistance of pulmonary arterial endothelial cells in PAH. This can potentially be a novel therapeutic target for ubiquitination modulators for PAH.

PMID:40376595 | PMC:PMC12080269 | DOI:10.70322/jrbtm.2025.10002

Front Pharmacol. 2025 May 1;16:1559399. doi: 10.3389/fphar.2025.1559399. eCollection 2025.

ABSTRACT

BACKGROUND: Pharmacogenomic (PGx) variants can significantly impact drug response, but limited data exists on their prevalence in Middle Eastern populations. This study aimed to investigate the inheritance of certain markers in candidate pharmacogenes among healthy Saudis.

METHODS: DNA samples from 95 unrelated healthy Saudi participants were genotyped using the Affymetrix Axiom Precision Medicine Diversity Array. Thirty-eight variants in 15 pharmacogenes were analyzed based on their clinical relevance and lack of previous reporting in Saudi populations.

RESULTS: Twenty-six of the 37 tested markers were undetected in the cohort. The selected variants in six genes [DPYD (rs1801268), CACNA1S (rs772226819), EGFR (rs121434568), RYR1 (rs193922816), CYP2B6 (rs3826711), and MT-RNR1 (rs267606617, rs267606618, rs267606619)] were found to be non-existing among Saudis. In contrast, 11 variants and alleles in nine pharmacogenes were detected at varying frequencies. Notable findings included high frequencies of variants in ATIC [rs4673993, minor allele frequency (MAF) = 0.71)] and SLC19A1 (rs1051266, MAF = 0.48) affecting methotrexate efficacy. Three alleles were identified in CYP3A4, including a common (CYP3A4 rs2242480) and two rare alleles (*3 and *22). Another three markers [rs16969968 in CHRNA5, rs11881222 in IFNL3 (IL28B), and SLCO1B1*14] were found to be highly distributed among the participants (MAF = 0.35, 0.30, and 0.14, respectively). Conversely, three rare markers: CYP2A6*2, NAT2*14, and rs115545701 in CFTR, were identified at low-frequency levels (MAF = 0.021, 0.011, 0.005, respectively). Statistically significant differences in allele frequencies were observed for eight variants between Saudi and African populations, five variants compared to East Asians, and two variants compared to Europeans.

CONCLUSION: This study provides novel insights into the distribution of clinically relevant PGx variants in the Saudi population. The findings have implications for personalizing treatments for various conditions, including rheumatoid arthritis, cystic fibrosis, and hepatitis C. These data contribute to the development of population-specific PGx testing panels and treatment guidelines.

PMID:40376268 | PMC:PMC12078325 | DOI:10.3389/fphar.2025.1559399

Small. 2025 May 16:e2502328. doi: 10.1002/smll.202502328. Online ahead of print.

ABSTRACT

Drug co-assemblies have attracted extensive attention due to their advantages of easy preparation, adjustable performance and drug component co-delivery. However, the lack of a clear and reasonable co-assembly strategy has hindered the wide application and promotion of drug-co assembly. This paper introduces a deep learning-based sonosensitizer-drug interaction (SDI) model to predict the particle size of the drug mixture. To analyze the factors influencing the particle size after mixing, the graph neural network is employed to capture the atomic, bond, and structural features of the molecules. A multi-scale cross-attention mechanism is designed to integrate the feature representations of different scale substructures of the two drugs, which not only improves prediction accuracy but also allows for the analysis of the impact of molecular structures on the predictions. Ablation experiments evaluate the impact of molecular properties, and comparisons with other machine and deep learning methods show superiority, achieving 90.00% precision, 96.00% recall, and 91.67% F1-score. Furthermore, the SDI predicts the co-assembly of the chemotherapy drug methotrexate (MET) and the sonosensitizer emodin (EMO) to form the nanomedicine NanoME. This prediction is further validated through experiments, demonstrating that NanoME can be used for fluorescence imaging of liver cancer and sonodynamic/chemotherapy anticancer therapy.

PMID:40376918 | DOI:10.1002/smll.202502328

Digit Health. 2025 May 14;11:20552076251341092. doi: 10.1177/20552076251341092. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Early and accurate detection of COVID-19 and pneumonia through medical imaging is critical for effective patient management. This study aims to develop a robust framework that integrates synthetic image augmentation with advanced deep learning (DL) models to address dataset imbalance, improve diagnostic accuracy, and enhance trust in artificial intelligence (AI)-driven diagnoses through Explainable AI (XAI) techniques.

METHODS: The proposed framework benchmarks state-of-the-art models (InceptionV3, DenseNet, ResNet) for initial performance evaluation. Synthetic images are generated using Feature Interpolation through Linear Mapping and principal component analysis to enrich dataset diversity and balance class distribution. YOLOv8 and InceptionV3 models, fine-tuned via transfer learning, are trained on the augmented dataset. Grad-CAM is used for model explainability, while large language models (LLMs) support visualization analysis to enhance interpretability.

RESULTS: YOLOv8 achieved superior performance with 97% accuracy, precision, recall, and F1-score, outperforming benchmark models. Synthetic data generation effectively reduced class imbalance and improved recall for underrepresented classes. Comparative analysis demonstrated significant advancements over existing methodologies. XAI visualizations (Grad-CAM heatmaps) highlighted anatomically plausible focus areas aligned with clinical markers of COVID-19 and pneumonia, thereby validating the model's decision-making process.

CONCLUSION: The integration of synthetic data generation, advanced DL, and XAI significantly enhances the detection of COVID-19 and pneumonia while fostering trust in AI systems. YOLOv8's high accuracy, coupled with interpretable Grad-CAM visualizations and LLM-driven analysis, promotes transparency crucial for clinical adoption. Future research will focus on developing a clinically viable, human-in-the-loop diagnostic workflow, further optimizing performance through the integration of transformer-based language models to improve interpretability and decision-making.

PMID:40376574 | PMC:PMC12078974 | DOI:10.1177/20552076251341092

Digit Health. 2025 May 14;11:20552076251333195. doi: 10.1177/20552076251333195. eCollection 2025 Jan-Dec.

ABSTRACT

In recent times, appropriate diagnosis of brain tumour is a crucial task in medical system. Therefore, identification of a potential brain tumour is challenging owing to the complex behaviour and structure of the human brain. To address this issue, a deep learning-driven framework consisting of four pre-trained models viz DenseNet169, VGG-19, Xception, and EfficientNetV2B2 is developed to classify potential brain tumours from medical resonance images. At first, the deep learning models are trained and fine-tuned on the training dataset, obtained validation scores of trained models are considered as model-wise weights. Then, trained models are subsequently evaluated on the test dataset to generate model-specific predictions. In the weight-aware decision module, the class-bucket of a probable output class is updated with the weights of deep models when their predictions match the class. Finally, the bucket with the highest aggregated value is selected as the final output class for the input image. A novel weight-aware decision mechanism is a key feature of this framework, which effectively deals tie situations in multi-class classification compared to conventional majority-based techniques. The developed framework has obtained promising results of 98.7%, 97.52%, and 94.94% accuracy on three different datasets. The entire framework is seamlessly integrated into an end-to-end web-application for user convenience. The source code, dataset and other particulars are publicly released at https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app [Rishik Sai Santhosh, "Brain Tumour Image Classification Application," https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app] for academic, research and other non-commercial usage.

PMID:40376570 | PMC:PMC12078957 | DOI:10.1177/20552076251333195

Digit Health. 2025 May 14;11:20552076241310060. doi: 10.1177/20552076241310060. eCollection 2025 Jan-Dec.

ABSTRACT

Diagnosis and treatment of endometrial diseases are crucial for women's health. Over the past decade, ultrasound has emerged as a non-invasive, safe, and cost-effective imaging tool, significantly contributing to endometrial disease diagnosis and generating extensive datasets. The introduction of artificial intelligence has enabled the application of machine learning and deep learning to extract valuable information from these datasets, enhancing ultrasound diagnostic capabilities. This paper reviews the progress of artificial intelligence in ultrasound image analysis for endometrial diseases, focusing on applications in diagnosis, decision support, and prognosis analysis. We also summarize current research challenges and propose potential solutions and future directions to advance ultrasound artificial intelligence technology in endometrial disease diagnosis, ultimately improving women's health through digital tools.

PMID:40376569 | PMC:PMC12078975 | DOI:10.1177/20552076241310060

Biophys Rev. 2025 Mar 18;17(2):335-345. doi: 10.1007/s12551-025-01301-1. eCollection 2025 Apr.

ABSTRACT

Despite its long history and widespread use, conventional bright-field optical microscopy has received recent attention as an excellent option to perform accurate, label-free, imaging of biological objects. As with any imaging system, bright-field produces an ill-defined representation of the specimen, in this case characterized by intertwined phase and amplitude in image formation, invisibility of phase objects at exact focus, and both positive and negative contrast present in images. These drawbacks have prevented the application of bright-field to the accurate imaging of unlabeled specimens. To address these challenges, a variety of methods using hardware, software or both have been developed, with the goal of providing solutions to the inverse imaging problem set in bright-field. We revise the main operating principles and characteristics of bright-field microscopy, followed by a discussion of the solutions (and potential limitations) to reconstruction in two dimensions (2D). We focus on methods based on conventional optics, including defocusing microscopy, transport of intensity, ptychography and deconvolution. Advances to achieving three-dimensional (3D) bright-field imaging are presented, including methods that exploit multi-view reconstruction, physical modeling, deep learning and conventional digital image processing. Among these techniques, optical sectioning in bright-field microscopy (OSBM) constitutes a direct approach that captures z-image stacks using a standard microscope and applies digital filters in the spatial domain, yielding inverse-imaging solutions in 3D. Finally, additional techniques that expand the capabilities of bright-field are discussed. Label-free, inverse imaging in conventional optical microscopy thus emerges as a powerful biophysical tool for accurate 2D and 3D imaging of biological samples.

PMID:40376420 | PMC:PMC12075049 | DOI:10.1007/s12551-025-01301-1

Prostate Cancer. 2025 May 8;2025:2019841. doi: 10.1155/proc/2019841. eCollection 2025.

ABSTRACT

Introduction: Prostate cancer is the second most common cancer among men worldwide. This cancer has become extremely noticeable due to the increase of prostate cancer in Iranian men in recent years due to the lack of marriage and sexual intercourse, as well as the abuse of hormones in sports without any standards. Methods: The histopathology images from a treatment center to diagnose prostate cancer are used with the help of deep learning methods, considering the two characteristics of Tile and Grad-CAM. The approach of this research is to present a prostate cancer diagnosis model to achieve proper performance from histopathology images with the help of a developed deep learning method based on the manifold model. Results: Similarly, in addition to the diagnosis of prostate cancer, a study on the methods of preventing this disease was investigated in literature reviews, and finally, after simulation, prostate cancer presentation factors were determined. Conclusions: The simulation results indicated that the proposed method has a performance advantage over the other state-of-the-art methods, and the accuracy of this method is up to 97.41%.

PMID:40376132 | PMC:PMC12081159 | DOI:10.1155/proc/2019841

Front Physiol. 2025 May 1;16:1581699. doi: 10.3389/fphys.2025.1581699. eCollection 2025.

ABSTRACT

Freezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson's Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. The methodology combines CNNs for spatial feature extraction, BiLSTM networks for temporal modeling, and an attention mechanism to enhance interpretability and focus on critical gait features. The approach leverages multimodal datasets, including tDCS FOG, DeFOG, Daily Living, and Hantao's Multimodal, to ensure robustness and generalizability. The proposed model deals with sensor noise, inter-subject variability, and data imbalance through comprehensive preprocessing techniques such as sensor fusion, normalization, and data augmentation. The proposed model achieved an average accuracy of 92.5%, F1-score of 89.3%, and AUC of 0.91, outperforming state-of-the-art methods. Post-training quantization and pruning enabled deployment on edge devices such as Raspberry Pi and Coral TPU, achieving inference latency under 350 ms. Ablation studies show the critical contribution of key architectural components to the model's effectiveness. Optimized to be deployed real-time, it is a potentially promising solution that can help correctly detect FoG, thereby achieving better clinical monitoring and improving patients' outcomes in a controlled as well as real world.

PMID:40376117 | PMC:PMC12079673 | DOI:10.3389/fphys.2025.1581699

bioRxiv [Preprint]. 2025 Apr 18:2025.04.13.648574. doi: 10.1101/2025.04.13.648574.

ABSTRACT

Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.

PMID:40376092 | PMC:PMC12080944 | DOI:10.1101/2025.04.13.648574

Front Immunol. 2025 May 1;16:1513589. doi: 10.3389/fimmu.2025.1513589. eCollection 2025.

ABSTRACT

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) exhibits higher hypoxia level than most solid tumors, and the presence of intratumoral hypoxia is associated with a poor prognosis. However, the identification of hypoxia levels based on pathological images, and the mechanisms regulating ferroptosis resistance, remain to be elucidated. The objective of this study was to construct a deep learning model to evaluate the hypoxia characteristics of PDAC and to explore the role of Sulfide quinone oxidoreductase (SQOR) in hypoxia-mediated ferroptosis resistance.

METHODS: Multi-omics data were integrated to analyze the correlation between hypoxia score of PDAC, SQOR expression and prognosis, and ferroptosis resistance level. A deep learning model of Whole Slide Images (WSIs) were constructed to predict the hypoxia level of patients. In vitro hypoxia cell models, SQOR knockdown experiments and nude mouse xenograft models were used to verify the regulatory function of SQOR on ferroptosis.

RESULTS: PDAC exhibited significantly higher hypoxia levels than normal tissues, correlating with reduced overall survival in patients. In slide level, our deep learning model can effectively identify PDAC hypoxia levels with good performance. SQOR was upregulated in tumor tissues and positively associated with both hypoxia score and ferroptosis resistance. SQOR promotes the malignant progression of PDAC in hypoxic environment by enhancing the resistance of tumor cells to ferroptosis. SQOR knockdown resulted in decreased cell viability, decreased migration ability and increased MDA level under hypoxic Ersatin induced conditions. Furthermore, SQOR inhibitor in combination with ferroptosis inducer has the potential to inhibit tumor growth in vivo in a synergistic manner.

DISCUSSION: This study has established a hypoxia detection model of PDAC based on WSIs, providing a new tool for clinical evaluation. The study revealed a new mechanism of SQOR mediating ferroptosis resistance under hypoxia and provided a basis for targeted therapy.

PMID:40375994 | PMC:PMC12078260 | DOI:10.3389/fimmu.2025.1513589