ACS Appl Mater Interfaces. 2025 Mar 20. doi: 10.1021/acsami.4c19494. Online ahead of print.

ABSTRACT

The emergence of inverse design approaches leveraging generative models offers a promising avenue for thermoelectric material design. However, these models heavily depend on diverse training data, and current thermoelectric data sets are limited, primarily encompassing group IV-VI materials operating within moderate temperature ranges. This constraint poses a significant challenge in the pursuit of materials with high thermoelectric figure of merit (zT) through generative modeling. Our study introduces an inverse design model tailored for the constrained thermoelectric materials data set. By augmenting the data with 2000 entries from the experimental literature and incorporating a generative model featuring a diversity loss function and residual network (ResNet) architecture to enhance complexity, our approach has been trained to systematically generate high-zT thermoelectric materials across various temperature ranges. Under predefined high-zT criteria, our deep generative model successfully predicted 100 doped materials with zT values exceeding 1.0. Furthermore, this research analyzes density of states (DOS) plots for the generated materials, identifying 25 unreported previously potential thermoelectric candidates in the material database. Notably, we experimentally validated the synthesis of Mg3.1Sb0.5Bi1.497Te0.003, a representative thermoelectric material from the Mg3(Sb, Bi)2 family suitable for room temperature applications. This validation underscores the efficacy of our model in exploring and discovering novel thermoelectric materials.

PMID:40110715 | DOI:10.1021/acsami.4c19494

Med Phys. 2025 Mar 20. doi: 10.1002/mp.17752. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning (DL)-based systems have not yet been broadly implemented in clinical practice, in part due to unknown robustness across multiple imaging protocols.

PURPOSE: To this end, we aim to evaluate the performance of several previously developed DL-based models, which were trained to distinguish idiopathic pulmonary fibrosis (IPF) from non-IPF among interstitial lung disease (ILD) patients, under standardized reference CT imaging protocols. In this study, we utilized CT scans from non-IPF ILD subjects, acquired using various imaging protocols, to assess the model performance.

METHODS: Three DL-based models, including one 2D and two 3D models, have been previously developed to classify ILD patients into IPF or non-IPF based on chest CT scans. These models were trained on CT image data from 389 IPF and 700 non-IPF ILD patients, retrospectively, obtained from five multicenter studies. For some patients, multiple CT scans were acquired (e.g., one at inhalation and one at exhalation) and/or reconstructed (e.g., thin slice and/or thick slice). Thus, for each patient, one CT image dataset was selected to be used in the construction of the classification model, so the parameters of that data set serve as the reference conditions. In one non-IPF ILD study, due to its specific study protocol, many patients had multiple CT image data sets that were acquired under both prone and supine positions and/or reconstructed under different imaging parameters. Therefore, to assess the robustness of the previously developed models under different (e.g., non-reference) imaging protocols, we identified 343 subjects from this study who had CT data from both the reference condition (used in model construction) and non-reference conditions (e.g., evaluation conditions), which we used in this model evaluation analysis. We reported the specificities from three model under the non-reference conditions. Generalized linear mixed effects model (GLMM) was utilized to identify the significant CT technical and clinical parameters that were associated with getting inconsistent diagnostic results between reference and evaluation conditions. Selected parameters include effective tube current-time product (known as "effective mAs"), reconstruction kernels, slice thickness, patient orientation (prone or supine), CT scanner model, and clinical diagnosis. Limitations include the retrospective nature of this study.

RESULTS: For all three DL models, the overall specificity of the previously trained IPF diagnosis model decreased (p < 0.05 for two out of three models). GLMM further suggests that for at least one out of three models, mean effective mAs across the scan is the key factor that leads to the decrease in model predictive performance (p < 0.001); the difference of mean effective mAs between the reference and evaluation conditions (p = 0.03) and slice thickness (3 mm; p = 0.03) are flagged as significant factors for one out of three models; other factors are not statistically significant (p > 0.05).

CONCLUSION: Preliminary findings demonstrated the lack of robustness of IPF diagnosis model when the DL-based model is applied to CT series collected under different imaging protocols, which indicated that care should be taken as to the acquisition and reconstruction conditions used when developing and deploying DL models into clinical practice.

PMID:40111345 | DOI:10.1002/mp.17752

Tree Physiol. 2025 Mar 3;45(3):tpaf016. doi: 10.1093/treephys/tpaf016.

ABSTRACT

Ink disease caused by the hemibiotrophic root pathogen Phytophthora cinnamomi (Pc) is devastating for the European chestnut (Castanea sativa), unlike Asian chestnuts and interspecific hybrids, which are resistant to Pc. The role that hormone responses play for Pc resistance remains little understood, especially regarding the temporal regulation of hormone responses. We explored the relationship between changes in tree health and physiology and alterations in leaf and root phytohormones and primary and secondary metabolites during compatible and incompatible Castanea spp.-Pc interactions. Susceptible (S) C. sativa and resistant (R) C. sativa × C. crenata ramets were inoculated with Pc in roots and assessed for disease progression, leaf physiology and biochemistry at 1, 3, 5 and 8 days after inoculation (d.a.i.). In S chestnuts, Pc increasingly deteriorated the leaf physiological functioning by decreasing leaf CO2 assimilation, stomatal conductance, transpiration rate, chlorophylls content and the maximum quantum yield of the photosystem II over time, triggering aerial symptoms (leaf wilting and chlorosis) 8 d.a.i. Pc had little impact on the leaf physiological functioning of R chestnuts, which remained asymptomatic. In roots of S chestnuts, Pc transiently induced jasmonates signaling (5 d.a.i.) while impairing root carbohydrates metabolism over time. In leaves, a transient antioxidant burst (5 d.a.i.) followed by abscisic acid (ABA) accumulation (8 d.a.i.) was observed. R chestnuts responded to Pc by up-regulating root salicylic acid (SA) at early (1 d.a.i.) and late (8 d.a.i.) infection stages, in an antagonistic crosstalk with root ABA. Overall, the results pinpoint an important role of SA in mediating the resistant response of chestnuts to Pc, but also show that the specific hormone pathways induced by Pc are genotype dependent. The study also highlights that the dynamic nature of hormone responses over time must be considered when elucidating hormone-regulated responses to Pc.

PMID:40111229 | DOI:10.1093/treephys/tpaf016

Eur J Immunol. 2025 Mar;55(3):e202451388. doi: 10.1002/eji.202451388.

ABSTRACT

Epigenetic modification plays a crucial role in establishing the transcriptional program that governs the differentiation of CD8+ effector T cells. However, the mechanisms by which this process is regulated at an early stage, prior to the expression of master transcription factors, are not yet fully understood. In this study, we have identified PDCD5 as an activation-induced molecule that is necessary for the proper differentiation and expansion of antigen-specific CD8+ effector T cells in a mouse model of chronic viral infection. The genetic deletion of Pdcd5 resulted in impaired differentiation and function of effector T cells, while T-cell activation, metabolic reprogramming, and the differentiation of memory/exhausted T cells were largely unaffected. At the molecular level, we observed reduced chromatin accessibility and transcriptional activity of Tbx21 and its regulated genes in Pdcd5-/- CD8+ T cells. We further identified that PRDM9 facilitates the H3K4me3 modification of genes associated with the effector phenotype in CD8+ T cells. The interaction between PDCD5 and PRDM9 promotes the nuclear translocation and lysine methyltransferase activity of PRDM9. Collectively, these findings highlight the crucial role of the PDCD5/PRDM9 axis in epigenetic reprogramming during the early stages of fate determination for effector CD8+ T cell fate.

PMID:40111008 | DOI:10.1002/eji.202451388

Appl Environ Microbiol. 2025 Mar 20:e0212324. doi: 10.1128/aem.02123-24. Online ahead of print.

ABSTRACT

Lignocellulose is a renewable resource for the production of a diverse array of platform chemicals, including the biofuel isoprenol. Although this carbon stream provides a rich source of sugars, other organic compounds, such as acetate, can be used by microbial hosts. Here, we examined the growth and isoprenol production in a Pseudomonas putida strain pre-tolerized ("PT") background where its native isoprenol catabolism pathway is deleted, using glucose and acetate as carbon sources. We found that PT displays impaired growth in minimal medium containing acetate and often fails to grow in glucose-acetate medium. Using a mutant recovery-based approach, we generated tolerized strains that overcame these limitations, achieving fast growth and isoprenol production in the mixed carbon feed. Changes in the glucose and acetate assimilation routes, including an upregulation in PP_0154 (SpcC, succinyl-CoA:acetate CoA-transferase) and differential expression of the gluconate assimilation pathways, were key for higher isoprenol titers in the tolerized strains, whereas a different set of mechanisms were likely enabling tolerance phenotypes in media containing acetate. Among these, a coproporphyrinogen-III oxidase (HemN) was upregulated across all tolerized strains and in one isolate required for acetate tolerance. Utilizing a defined glucose and acetate mixture ratio reflective of lignocellulosic feedstocks for isoprenol production in P. putida allowed us to obtain insights into the dynamics and challenges unique to dual carbon source utilization that are obscured when studied separately. Together, this enabled the development of a P. putida bioconversion chassis able to use a more complex carbon stream to produce isoprenol.IMPORTANCEAcetate is a relatively abundant component of many lignocellulosic carbon streams and has the potential to be used together with sugars, especially in microbes with versatile catabolism such as P. putida. However, the use of mixed carbon streams necessitates additional optimization. Furthermore, the use of P. putida for the production of the biofuel target, isoprenol, requires the use of engineered strains that have additional growth and production constraints when cultivated in acetate and glucose mixtures. In this study, we generate acetate-tolerant P. putida strains that overcome these challenges and examine their ability to produce isoprenol. We show that acetate tolerance and isoprenol production, although independent phenotypes, can both be optimized in a given P. putida strain. Using proteomics and whole genome sequencing, we examine the molecular basis of both phenotypes and show that tolerance to acetate can occur via alternate routes and result in different impacts on isoprenol production.

PMID:40110994 | DOI:10.1128/aem.02123-24

Front Res Metr Anal. 2025 Mar 5;10:1509502. doi: 10.3389/frma.2025.1509502. eCollection 2025.

ABSTRACT

OBJECTIVE: Literature-based Discovery (LBD) identifies new knowledge by leveraging existing literature. It exploits interconnecting implicit relationships to build bridges between isolated sets of non-interacting literatures. It has been used to facilitate drug repurposing, new drug discovery, and study adverse event reactions. Within the last decade, LBD systems have transitioned from using statistical methods to exploring deep learning (DL) to analyze semantic spaces between non-interacting literatures. Recent works explore knowledge graphs (KG) to represent explicit relationships. These works envision LBD as a knowledge graph completion (KGC) task and use DL to generate implicit relationships. However, these systems require the researcher to have domain-expert knowledge when submitting relevant queries for novel hypothesis discovery.

METHODS: Our method explores a novel approach to identify all implicit hypotheses given the researcher's search query and expedites the knowledge discovery process. We revise the KGC task as the task of predicting interconnecting vertex embeddings within the graph. We train our model using a similarity learning objective and compare our model's predictions against all known vertices within the graph to determine the likelihood of an implicit relationship (i.e., connecting edge). We also explore three approaches to represent edge connections between vertices within the KG: average, concatenation, and Hadamard. Lastly, we explore an approach to induce inductive biases and expedite model convergence (i.e., input representation scaling).

RESULTS: We evaluate our method by replicating five known discoveries within the Hallmark of Cancer (HOC) datasets and compare our method to two existing works. Our results show no significant difference in reported ranks and model convergence rate when comparing scaling our input representations and not using this method. Comparing our method to previous works, we found our method achieves optimal performance on two of five datasets and achieves comparable performance on the remaining datasets. We further analyze our results using statistical significance testing to demonstrate the efficacy of our method.

CONCLUSION: We found our similarity-based learning objective predicts linking vertex embeddings for single relationship closed discovery replication. Our method also provides a ranked list of linking vertices between a set of inputs. This approach reduces researcher burden and allows further exploration of generated hypotheses.

PMID:40110121 | PMC:PMC11920161 | DOI:10.3389/frma.2025.1509502

Juntendo Med J. 2025 Jan 17;71(1):26-31. doi: 10.14789/ejmj.JMJ24-0026-P. eCollection 2025.

ABSTRACT

Stroke remains a leading cause of mortality and morbidity globally, posing significant challenges to healthcare systems due to its impact on Activities of Daily Living, Quality of Life, and healthcare costs. Current treatments primarily focus on acute management through thrombolytic therapy and thrombectomy, but only a limited number of patients benefit, underscoring the need for effective therapies to aid chronic stroke recovery. Despite ongoing clinical trials, cell therapy faces substantial logistical and cost-related hurdles, limiting its widespread adoption. Strategies to minimalize post-stroke sequelae emphasize preventing cerebral infarction deterioration, utilizing predictive scoring systems for focused treatment, and exploring drug repositioning. The complex interplay within the Neurovascular Unit and Oligovascular Niche highlights the role of various cell types and neurotrophic factors in stroke pathophysiology and recovery phases. Notably, microglia and astrocytes exhibit dual phenotypes ─ either inflammatory or protective ─ depending on the environment, influencing neural damage or repair processes post-stroke. Mitochondrial therapy emerges as a promising avenue, leveraging the organelles' ability to migrate between cells and mitigate inflammatory responses. Studies suggest that mitochondria transferred from astrocytes or other sources could transform inflammatory astrocytes into protective ones, thereby promoting white matter integrity and potentially reducing dementia progression associated with stroke sequelae. In conclusion, addressing stroke's multifaceted challenges requires innovative therapeutic approaches targeting inflammatory mechanisms and enhancing neuroprotection. Early detection and intervention, coupled with advancements in mitochondrial therapy and understanding intercellular interactions, hold promise for improving stroke outcomes and reducing long-term neurological complications.

PMID:40109397 | PMC:PMC11915467 | DOI:10.14789/ejmj.JMJ24-0026-P

Adv Healthc Mater. 2025 Mar 20:e2404117. doi: 10.1002/adhm.202404117. Online ahead of print.

ABSTRACT

Ovarian cancer (OC) poses a significant clinical challenge due to its high recurrence rates and resistance to standard therapies, particularly in advanced stages where recurrence is common, and treatment is predominantly palliative. Personalized treatments, while effective in other cancers, remain underutilized in OC due to a lack of reliable biomarkers predicting clinical outcomes. Accordingly, precision medicine approaches are limited, with PARP inhibitors showing efficacy only in specific genetic contexts. Drug repurposing offers a promising, rapidly translatable strategy by leveraging existing pharmacological data to identify new treatments for OC. Patient-derived polyclonal spheroids, isolated from ascites fluid closely mimic the clinical behavior of OC, providing a valuable model for drug testing. Using these spheroids, a high-throughput drug screening pipeline capable of evaluating both cytotoxicity and anti-migratory properties of a diverse drug library, including FDA-approved, investigational, and newly approved compounds is developed. The findings highlight the importance of 3D culture systems, revealing a poor correlation between drug efficacy in traditional 2D models and more clinically relevant 3D spheroids. This approach has expedited the identification of promising candidates, such as rapamycin, which demonstrated limited activity as a monotherapy but synergized effectively with standard treatments like cisplatin and paclitaxel in vitro. In combination with platinum-based therapy, Rapamycin led to significant in vitro cytotoxicity and a marked reduction in tumor burden in a syngeneic in vivo model. This proof-of-concept study underscores the potential of drug repurposing to rapidly advance new treatments into clinical trials for OC, offering renewed hope for patients with advanced disease.

PMID:40109101 | DOI:10.1002/adhm.202404117

Inflammopharmacology. 2025 Mar 19. doi: 10.1007/s10787-025-01695-0. Online ahead of print.

ABSTRACT

Sporadic Alzheimer's disease is a progressive neurodegenerative disorder affecting the central nervous system. Its main two hallmarks are extracellular deposition of aggregated amyloid beta resulting in senile plaques and intracellular hyperphosphorylated tau proteins forming neuro-fibrillary tangles. As those processes are promoted by the glycogen synthase kinase-3 enzyme, GSK3 inhibitors may be of therapeutic value in SAD. GSK3 is also inhibited by the action of insulin on insulin signaling. Insulin receptor desensitization in the brain is hypothesized to cause inhibition of insulin signaling pathway that ultimately causes cognitive deficits seen in SAD. In extant research, induction of cognitive impairment is achieved by intracerebroventricular injection of streptozotocin-a diabetogenic compound that causes desensitization to insulin receptors in the brain leading to the appearance of most of the SAD signs and symptoms. Valproic acid -a histone deacetylase inhibitor and anti-epileptic drug-has been recently studied in the management of SAD as a possible GSK3 inhibitor. Accordingly, the aim of the present study is to explore the role of multiple VPA doses on the downstream effects of the insulin signaling pathway in ICV STZ-injected mice and suggest a possible mechanism of VPA action. ICV STZ-injected mice showed deficiency in short- and long-term memory as well as increased anxiety, as established by open field test, Modified Y-maze, Morris water maze, and elevated plus maze neurobehavioral tests.

PMID:40108007 | DOI:10.1007/s10787-025-01695-0

Sci Rep. 2025 Mar 19;15(1):9516. doi: 10.1038/s41598-025-92192-5.

ABSTRACT

In recent years, there has been a growing field of research focused on comprehending complexity in relation to web platform accessibility. It has shown that it is quite difficult to accurately assess and identify web accessibility concerns while taking multifaceted factors into account. It is imperative to prioritize multi-dimensional characteristics as they facilitate the integration of many aspects into the assessment process, which is a critical component in enhancing the accessibility evaluation process. Although many existing solutions with varying degrees of computational success have been proposed by scholars, they are confined to (1) following a certain set of rules of a specific guideline; (2) limited evaluation properties; (3) disregard for user criteria; and (4) complex functional properties or architectural design. To address these problems, we present in this work a straightforward yet precise model that assesses webpage accessibility by taking into account common features of the structural and visual elements of webpages that are part of the HTML Document Object Model (DOM) structure. In order to predict a webpage's accessibility status, we implemented three distinct algorithms to analyze web features/objects considering both semantic and non-semantic aspects. We performed experimental work to validate 20 university webpages in Hungary through our developed tool. The computed result of the developed tool was assessed by comparing the result with a user study where we performed user testing that included 40 users' 80 reviews on the same 20 university webpages in Hungary. Additionally, we compared our developed tool with other scientific models (that already exist) and existing ten open-source commercial automated testing tools considering several functional characteristics or properties. This two-phase assessment result shows that the developed tool has several advanced properties and the potential to predict the accessibility issues of the tested webpages.

PMID:40108199 | DOI:10.1038/s41598-025-92192-5

Precis Clin Med. 2025 Feb 22;8(1):pbaf004. doi: 10.1093/pcmedi/pbaf004. eCollection 2025 Mar.

ABSTRACT

Pharmacogenomics, therapeutic drug monitoring, and the assessments of hepatic and renal function have made significant contributions to the advancement of individualized medicine. However, their lack of direct correlation with protein abundance/non-genetic factors, target drug concentration, and drug metabolism/excretion significantly limits their application in precision drug therapy. The primary task of precision medicine is to accurately determine drug dosage, which depends on a precise assessment of the ability to handle drugs in vivo, and drug metabolizing enzymes and transporters are critical determinants of drug disposition in the body. Therefore, accurately evaluating the functions of these enzymes and transporters is key to assessing the capacity to handle drugs and predicting drug concentrations in target organs. Recent advancements in the evaluation of enzyme and transporter functions using exogenous probes and endogenous biomarkers show promise in advancing personalized medicine. This article aims to provide a comprehensive overview of the latest research on markers used for the functional evaluation of drug-metabolizing enzymes and transporters. It also explores the application of marker omics in systematically assessing their functions, thereby laying a foundation for advancing precision pharmacotherapy.

PMID:40110576 | PMC:PMC11920622 | DOI:10.1093/pcmedi/pbaf004

Front Med (Lausanne). 2025 Mar 5;12:1476685. doi: 10.3389/fmed.2025.1476685. eCollection 2025.

ABSTRACT

The incidence of skin cancer continues to rise due to increased sun exposure and tanning habits, requiring early detection and treatment for favorable outcomes. Skin biopsy is an important diagnostic tool in dermatology and pathology, as it provides a valuable understanding of various skin diseases. Proper handling of skin biopsy specimens is vital to ensure accurate histopathological assessment. Still, the use of light microscopy and immunofluorescence provides a comprehensive approach to evaluating skin biopsy specimens, with each contributing unique information to aid in accurate diagnosis and management. This review highlights the evolution of skin biopsy practices, from traditional techniques to advanced methods incorporating artificial intelligence (AI) and convolutional neural networks. AI technologies enhance diagnostic accuracy and efficiency, aiding in the rapid analysis of skin lesions and biopsies. Despite challenges such as the need for extensively annotated datasets and ethical considerations, AI shows promise in dermatological diagnostics. The future of skin biopsy lies in minimally invasive techniques, liquid biopsies, and integrated pharmacogenomics for personalized medicine.

PMID:40109731 | PMC:PMC11919677 | DOI:10.3389/fmed.2025.1476685

Pharmacogenomics. 2025 Mar 20:1-7. doi: 10.1080/14622416.2025.2479409. Online ahead of print.

ABSTRACT

AIM: Providers can use combinatorial pharmacogenomic panels to aid psychiatric medication prescribing. Results are typically documented in static documents which list the genotype and predicted phenotype (interpretation). However, genotype-to-phenotype translations can differ between laboratories and change as scientific consensuses evolves. Here, we describe the implications of reinterpreting phenotype after combinatorial psychiatric pharmacogenomic testing in a real-world setting.

PATIENTS AND METHODS: 143 patients underwent testing from 2014 to 2021. Reported genotypes and phenotypes were compared to 2024 Clinical Pharmacogenetics Implementation Consortium definitions. Chi-square tests and logistic regression were used to examine the differences in phenotype frequencies before and after reinterpretation and examine the association with time since testing.

RESULTS: Eighty-one patients (57%) required at least one updated interpretation. CYP2C19 interpretations changed for 44/143 patients (31%), followed by CYP2D6 (29%), CYP2B6 (3%), and CYP2C9 (1%). Reinterpretation reduced the number of CYP2D6 ultrarapid and poor metabolizers (p = 0.005), which has implications for antidepressant prescribing. Likelihood of a patient having a reinterpreted phenotype was not associated with time since reporting (p = 0.71).

CONCLUSIONS: Reported phenotypes from combinatorial PGx testing often do not align with current standardized definitions, even from tests performed recently. Health systems should establish procedures to standardize and periodically update pharmacogenomic interpretations.

PMID:40109143 | DOI:10.1080/14622416.2025.2479409

Hum Cell. 2025 Mar 20;38(3):73. doi: 10.1007/s13577-025-01192-0.

ABSTRACT

The reciprocal fate decision of mesenchymal stem cells (MSCs) to either bone or adipocytes is determined by Wnt-related signaling and the glucagon-like peptide-1 receptor (GLP-1R). Azoramide, an ER stress alleviator, was reported to have an antidiabetic effect. In this study, we investigated the function of azoramide in regulating the lineage determination of MSCs for either adipogenic or osteogenic differentiation. Microcomputed tomography and histological analysis on bone morphogenetic protein (BMP)2-induced parietal periosteum bone formation assays, C3H10T1/2 and mouse bone marrow MSC-derived bone formation and adipogenesis assays, and specific staining for bone tissue and lipid droplets were used to evaluate the role of azoramide on the lineage determination of MSC differentiation. Cells were harvested for Western blot and quantitative real-time polymerase chain reaction (PCR), and immunofluorescence staining was used to explore the potential mechanism of azoramide for regulating MSC differentiation. Based on MSC-derived bone formation assays both in vivo and in vitro, azoramide treatment displayed a cell fate determining ability in favor of adipogenesis over osteogenesis. Further mechanistic characterizations disclosed that both the GLP-1R agonist peptide exendin-4 (Ex-4) and GLP-1R small interfering (si)RNA abrogated azoramide dual effects. Moreover, cAMP-protein kinase A (PKA)-mediated nuclear β-catenin activity was responsible for the negative function of azoramide on bone formation in favor of adipogenesis. These data provide the first evidence to show that azoramide may serve as an inhibitor against GLP-1R in MSC lineage determination.

PMID:40108027 | DOI:10.1007/s13577-025-01192-0

Int J Popul Data Sci. 2025 Feb 20;8(5):2924. doi: 10.23889/ijpds.v8i5.2924. eCollection 2023.

ABSTRACT

INTRODUCTION: Cystic fibrosis (CF) heterozygotes (also known as 'carriers') are people who have one mutated copy of the CFTR gene. Research into the health risks of CF carriers has been limited by a lack of large cohorts tested for CF carrier status, but routine clinical testing identifies CF carriers in the population. Such test records additionally contain large amounts of clinical information, making them a valuable research resource to not only identify CF carriers in the population but also to provide additional data not found elsewhere.

METHODS: Following governance approvals, we adapted 30 years worth of CF genetic testing records generated by the All-Wales Medical Genomics Service (AWMGS) and submitted them to the SAIL Databank for anonymised linkage.

RESULTS: Unexpected obstacles meant that a minimum amount of clinical information could be annotated ahead of linkage. The raw data were highly heterogeneous due to the records' longitudinal collection and clinical origins, making standardisation difficult. Moreover, the presence of unique identifiers in the clinical data violated the separation principle, requiring manual annotation to produce a cleaned dataset. Explicit identification of patients or their relatives throughout the records complicated split file anonymisation.

CONCLUSION: Extracting useful information from historical clinical genetic test records is a significant challenge with technical and governance aspects. The mixing of unique identifiers with clinical data in heterogeneous, unstructured free text combined with a lack of automated tools meant that manual annotation was required to adhere to the separation principle. As such, only a minimum of the available clinical data was annotatable within the project timeline and mutually exclusive access to the identifiable and pseudonymised data meant that annotations could not later be validated. Future efforts to link clinical genetic test records for research must consider these challenges in their approach.

PMID:40110575 | PMC:PMC11922013 | DOI:10.23889/ijpds.v8i5.2924

Technol Health Care. 2024 Dec 16:9287329241291334. doi: 10.1177/09287329241291334. Online ahead of print.

ABSTRACT

BackgroundThe complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.ObjectivesA novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets.MethodsWe determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data.ResultsOur method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders.ConclusionsThis innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.

PMID:40110612 | DOI:10.1177/09287329241291334

Front Behav Neurosci. 2025 Mar 5;19:1549666. doi: 10.3389/fnbeh.2025.1549666. eCollection 2025.

ABSTRACT

Social hierarchy refers to the set of social ranks in a group of animals where individuals can gain priority access to resources through repeated social interactions. Key mechanisms involved in this process include conflict, social negotiation, prior experience, and physical advantages. The establishment and maintenance of social hierarchies not only promote group stability and well-being but also shape individual social behaviors by fostering cooperation and reducing conflict. Existing research indicates that social hierarchy is closely associated with immune responses, neural regulation, metabolic processes, and endocrine functions. These physiological systems collectively modulate an individual's sensitivity to stress and influence adaptive responses, thereby playing a critical role in the development of psychiatric disorders such as depression and anxiety. This review summarizes the primary behavioral methods used to assess social dominance in mice, evaluates their applicability and limitations, and discusses potential improvements. Additionally, it explores the underlying neural mechanisms associated with these methods to deepen our understanding of their biological basis. By critically assessing existing methodologies and proposing refinements, this study aims to provide a systematic reference framework and methodological guidance for future research, facilitating a more comprehensive exploration of the neural mechanisms underlying social behavior. The role of sex differences in social hierarchy formation remains underexplored. Most studies focus predominantly on males, while the distinct social strategies and physiological mechanisms of females are currently overlooked. Future studies should place greater emphasis on evaluating social hierarchy in female mice to achieve a more comprehensive understanding of sex-specific social behaviors and their impact on group structure and individual health. Advances in automated tracking technologies may help address this gap by improving behavioral assessments in female mice. Future research may also benefit from integrating physiological data (e.g., hormone levels) to gain deeper insights into the relationships between social status, stress regulation, and mental health. Additionally, developments in artificial intelligence and deep learning could enhance individual recognition and behavioral analysis, potentially reducing reliance on chemical markers or implanted devices.

PMID:40110389 | PMC:PMC11920152 | DOI:10.3389/fnbeh.2025.1549666

Front Plant Sci. 2025 Mar 5;16:1538051. doi: 10.3389/fpls.2025.1538051. eCollection 2025.

ABSTRACT

The removal of non-tobacco related materials (NTRMs) is crucial for improving tobacco product quality and consumer safety. Traditional NTRM detection methods are labor-intensive and inefficient. This study proposes a novel approach for real-time NTRM detection using hyperspectral imaging (HSI) and an enhanced YOLOv8 model, named Dual-branch-YOLO-Tobacco (DBY-Tobacco). We created a dataset of 1,000 images containing 4,203 NTRMs by using a hyperspectral camera, SpectraEye (SEL-24), with a spectral range of 400-900 nm. To improve processing efficiency of HSIs data, three characteristic wavelengths (580nm, 680nm, and 850nm) were extracted by analyzing the weighted coefficients of the principal components. Then the pseudo color image fusion and decorrelation contrast stretch methods were applied for image enhancement. The DBY-Tobacco model features a dual-branch backbone network and a BiFPN-Efficient-Lighting-Feature-Pyramid-Network (BELFPN) module for effective feature fusion. Experimental results demonstrate that the DBY-Tobacco model achieves high performance metrics, including an F1 score of 89.7%, mAP@50 of 92.8%, mAP@50-95 of 73.7%, and a processing speed of 151 FPS, making it suitable for real-time applications in dynamic production environments. The study highlights the potential of combining HSI with advanced deep learning techniques for improving tobacco product quality and safety. Future work will focus on addressing limitations such as stripe noise in HSI and expanding the detection to other types of NTRMs. The dataset and code are available at: https://github.com/Ikaros-sc/DBY-Tobacco.

PMID:40110354 | PMC:PMC11921890 | DOI:10.3389/fpls.2025.1538051

Eur Heart J Digit Health. 2025 Jan 28;6(2):270-284. doi: 10.1093/ehjdh/ztaf005. eCollection 2025 Mar.

ABSTRACT

Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.

PMID:40110224 | PMC:PMC11914731 | DOI:10.1093/ehjdh/ztaf005

Eur Heart J Digit Health. 2025 Feb 25;6(2):170-179. doi: 10.1093/ehjdh/ztae088. eCollection 2025 Mar.

ABSTRACT

AIMS: Sudden cardiac arrest (SCA) is a commonly fatal event that often occurs without prior indications. To improve outcomes and enable preventative strategies, the electrocardiogram (ECG) in conjunction with deep learning was explored as a potential screening tool.

METHODS AND RESULTS: A publicly available data set containing 10 s of 12-lead ECGs from individuals who did and did not have an SCA, information about time from ECG to arrest, and age and sex was utilized for analysis to individually predict SCA or not using deep convolution neural network models. The base model that included age and sex, ECGs within 1 day prior to arrest, and data sampled from windows of 720 ms around the R-waves from 221 individuals with SCA and 1046 controls had an area under the receiver operating characteristic curve of 0.77. With sensitivity set at 95%, base model specificity was 31%, which is not clinically applicable. Gradient-weighted class activation mapping showed that the model mostly relied on the QRS complex to make predictions. However, models with ECGs recorded between 1 day to 1 month and 1 month to 1 year prior to arrest demonstrated predictive capabilities.

CONCLUSION: Deep learning models processing ECG data are a promising means of screening for SCA, and this method explains differences in SCAs due to age and sex. Model performance improved when ECGs were nearer in time to SCAs, although ECG data up to a year prior had predictive value. Sudden cardiac arrest prediction was more dependent upon QRS complex data compared to other ECG segments.

PMID:40110219 | PMC:PMC11914729 | DOI:10.1093/ehjdh/ztae088