FEMS Microbiol Lett. 2025 Jan 31:fnaf017. doi: 10.1093/femsle/fnaf017. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa has increasing clinical relevance and commonly occupies the cystic fibrosis (CF) airways. Its ability to colonize and persist in diverse niches is attributed to its large accessory genome, where prophages represent a common feature and may contribute to its fitness and persistence. We focused on the CF airways niche and used 197 longitudinal isolates from 12 patients persistently infected by P. aeruginosa. We computationally predicted intact prophages for each longitudinal group and scored their long-term persistence. We then confirmed prophage inducibility and mapped their location in the host chromosome with lysate sequencing. Using comparative genomics, we evaluated prophage genomic diversity, long-term persistence and level of genomic maintenance. Our findings support previous findings that most P. aeruginosa genomes harbour prophages some of which can self-induce, and that a common CF-treating antibiotic, ciprofloxacin, can induce prophages. Induced prophage genomes displayed high diversity and even genomic novelty. Finally, all induced prophages persisted long-term with their genomes avoiding gene loss and degradation over four years of host replication in the stressful CF airways niche. This and our detection of phage genes which contribute to host competitiveness and adaptation, lends support to our hypothesis that the vast majority of prophages detected as intact and inducible in this study facilitated their host fitness and persistence.

PMID:39890605 | DOI:10.1093/femsle/fnaf017

J Cyst Fibros. 2025 Jan 30:S1569-1993(25)00001-3. doi: 10.1016/j.jcf.2025.01.001. Online ahead of print.

ABSTRACT

BACKGROUND: CFTR modulator (CFTR-M) therapy has led to improved clinical outcomes amongst people with cystic fibrosis (PwCF) eligible for these therapies. However, there is limited data on their impact on the basic life needs and financial concerns of PwCF.

METHODS: We used data from the Wellness in the Modulator Era (Well-ME) survey, which includes data from 900 PwCF both taking and not taking CFTR-M. We examined self-reported financial well-being over time and changes associated with school or work, financial planning, and costs of living. Descriptive statistics were used to analyze responses.

RESULTS: Most respondents reported no change in financial well-being, but 13 % identified a positive change and 16 % reported a negative change. Positive changes in basic life needs included fewer missed work and school days, while negative changes included medical out-of-pocket costs. Worries about financial problems were reported in 35 % of all respondents and were more common in PwCF who never took CFTR-M or had been taking one and then stopped, in PwCF with lower lung function, and in PwCF with Medicaid insurance.

CONCLUSIONS: These results indicate that for most PwCF, CFTR-M have not affected their basic life needs, and a substantial proportion of PwCF continue to experience financial stress and concerns. Many respondents' financial concerns focused on medical costs and insurance. These data underscore the continued need for CF care teams to address PwCF's financial stress and ability to meet basic life needs, even in the era of improved physical health outcomes due to CFTR-M therapy.

PMID:39890522 | DOI:10.1016/j.jcf.2025.01.001

Eur J Med Res. 2025 Jan 31;30(1):61. doi: 10.1186/s40001-025-02339-3.

ABSTRACT

Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall survival (OS) and progression-free survival (PFS) as key measures to evaluate patients. However, predicting survival using traditional methods such as the Kaplan-Meier estimator and the Cox Proportional Hazards (CPH) model has faced many challenges and inaccuracies. Recently, advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled significant improvements in survival prediction for glioma patients by integrating multimodal data such as imaging, clinical parameters and molecular biomarkers. This study highlights the comparative effectiveness of imaging-based, non-imaging and combined AI models. Imaging models excel at identifying tumour-specific features through radiomics, achieving high predictive accuracy. Non-imaging approaches also excel in utilising clinical and genetic data to provide complementary insights, whilst combined methods integrate multiple data modalities and have the greatest potential for accurate survival prediction. Limitations include data heterogeneity, interpretability challenges and computational demands, particularly in resource-limited settings. Solutions such as federated learning, lightweight AI models and explainable AI frameworks are proposed to overcome these barriers. Ultimately, the integration of advanced AI techniques promises to transform glioma management by enabling personalised treatment strategies and improved prognostic accuracy.

PMID:39891313 | DOI:10.1186/s40001-025-02339-3

BMC Biomed Eng. 2025 Feb 1;7(1):2. doi: 10.1186/s42490-025-00088-2.

ABSTRACT

AIM: The aim of this study is to apply a novel hybrid framework incorporating a Vision Transformer (ViT) and bidirectional long short-term memory (Bi-LSTM) model for classifying physical activity intensity (PAI) in adults using gravity-based acceleration. Additionally, it further investigates how PAI and temporal window (TW) impacts the model' s accuracy.

METHOD: This research used the Capture-24 dataset, consisting of raw accelerometer data from 151 participants aged 18 to 91. Gravity-based acceleration was utilised to generate images encoding various PAIs. These images were subsequently analysed using the ViT-BiLSTM model, with results presented in confusion matrices and compared with baseline models. The model's robustness was evaluated through temporal stability testing and examination of accuracy and loss curves.

RESULT: The ViT-BiLSTM model excelled in PAI classification task, achieving an overall accuracy of 98.5% ± 1.48% across five TWs-98.7% for 1s, 98.1% for 5s, 98.2% for 10s, 99% for 15s, and 98.65% for 30s of TW. The model consistently exhibited superior accuracy in predicting sedentary (98.9% ± 1%) compared to light physical activity (98.2% ± 2%) and moderate-to-vigorous physical activity (98.2% ± 3%). ANOVA showed no significant accuracy variation across PAIs (F = 2.18, p = 0.13) and TW (F = 0.52, p = 0.72). Accuracy and loss curves show the model consistently improves its performance across epochs, demonstrating its excellent robustness.

CONCLUSION: This study demonstrates the ViT-BiLSTM model's efficacy in classifying PAI using gravity-based acceleration, with performance remaining consistent across diverse TWs and intensities. However, PAI and TW could result in slight variations in the model's performance. Future research should concern and investigate the impact of gravity-based acceleration on PAI thresholds, which may influence model's robustness and reliability.

PMID:39891283 | DOI:10.1186/s42490-025-00088-2

Reprod Biol Endocrinol. 2025 Jan 31;23(1):16. doi: 10.1186/s12958-025-01351-w.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency and reliability in clinical settings during its development and validation process. We present a methodology for developing and validating an AI model across multiple datasets to demonstrate reliable performance in evaluating blastocyst-stage embryos.

METHODS: This multicenter analysis utilizes time-lapse images, pregnancy outcomes, and morphologic annotations from embryos collected at 10 IVF clinics across 9 countries between 2018 and 2022. The four-step methodology for developing and evaluating the AI model include: (I) curating annotated datasets that represent the intended clinical use case; (II) developing and optimizing the AI model; (III) evaluating the AI's performance by assessing its discriminative power and associations with pregnancy probability across variable data; and (IV) ensuring interpretability and explainability by correlating AI scores with relevant morphologic features of embryo quality. Three datasets were used: the training and validation dataset (n = 16,935 embryos), the blind test dataset (n = 1,708 embryos; 3 clinics), and the independent dataset (n = 7,445 embryos; 7 clinics) derived from previously unseen clinic cohorts.

RESULTS: The AI was designed as a deep learning classifier ranking embryos by score according to their likelihood of clinical pregnancy. Higher AI score brackets were associated with increased fetal heartbeat (FH) likelihood across all evaluated datasets, showing a trend of increasing odds ratios (OR). The highest OR was observed in the top G4 bracket (test dataset G4 score ≥ 7.5: OR 3.84; independent dataset G4 score ≥ 7.5: OR 4.01), while the lowest was in the G1 bracket (test dataset G1 score < 4.0: OR 0.40; independent dataset G1 score < 4.0: OR 0.45). AI score brackets G2, G3, and G4 displayed OR values above 1.0 (P < 0.05), indicating linear associations with FH likelihood. Average AI scores were consistently higher for FH-positive than for FH-negative embryos within each age subgroup. Positive correlations were also observed between AI scores and key morphologic parameters used to predict embryo quality.

CONCLUSIONS: Strong AI performance across multiple datasets demonstrates the value of our four-step methodology in developing and validating the AI as a reliable adjunct to embryo evaluation.

PMID:39891250 | DOI:10.1186/s12958-025-01351-w

BMC Med Inform Decis Mak. 2025 Jan 31;25(1):48. doi: 10.1186/s12911-025-02889-w.

ABSTRACT

This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing network width augmentation to enhance efficiency. The proposed model addresses potential biases associated with skin conditions, particularly in individuals with darker skin tones or excessive hair, by incorporating feature fusion to assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet's effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, were employed to enhance the interpretability of the model's decisions. Comparative analysis was performed with three pre-existing deep learning networks-EfficientNet, MobileNet, and Darknet. The results demonstrate SWNet's superiority, achieving an accuracy of 99.86% and an F1 score of 99.95%, underscoring its efficacy in gradient propagation and feature capture across various levels. This research highlights the significant potential of SWNet in advancing skin cancer detection and classification, providing a robust tool for accurate and early diagnosis. The integration of feature fusion enhances accuracy and mitigates biases associated with hair and skin tones. The outcomes of this study contribute to improved patient outcomes and healthcare practices, showcasing SWNet's exceptional capabilities in skin cancer detection and classification.

PMID:39891245 | DOI:10.1186/s12911-025-02889-w

BMC Pulm Med. 2025 Jan 31;25(1):52. doi: 10.1186/s12890-025-03522-2.

ABSTRACT

BACKGROUND: Pulmonary fibrosis is characterized by the destruction of normal lung tissue and then replacement by abnormal fibrous tissue, leading to an overall decrease in gas exchange function. The effective treatment for pulmonary fibrosis remains unknown. The upstream pathogenesis of pulmonary fibrosis may involve cellular senescence of the lung tissue. Previously, a new gene therapy technology using Box A of the HMGB1 plasmid (Box A) was used to reverse cellular senescence and cure liver fibrosis in aged rats.

METHODS: Here, we show that Box A is a promising medicine for the treatment of lung fibrosis. In a bleomycin-induced pulmonary fibrosis model in the male Wistar rats, Student's t-test and one-way ANOVA were used to compare groups of samples.

RESULTS: Box A effectively lowered fibrous tissue deposits (from 18.74 ± 0.62 to 3.45 ± 1.19%) and senescent cells (from 3.74 ± 0.40% to 0.89 ± 0.18%) to levels comparable to those of the negative control group. Moreover, after eight weeks, Box A also increased the production of the surfactant protein C (from 3.60 ± 1.68% to 6.82 ± 0.65%).

CONCLUSIONS: Our results demonstrate that Box A is a promising therapeutic approach for pulmonary fibrosis and other senescence-promoted fibrotic lesions.

PMID:39891078 | DOI:10.1186/s12890-025-03522-2

J Biotechnol. 2025 Jan 29:S0168-1656(25)00025-2. doi: 10.1016/j.jbiotec.2025.01.019. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the most common chronic liver disease worldwide, affecting more than 30 percent of adults. The most severe form of MASLD, metabolic dysfunction-associated steatohepatitis (MASH), is characterized by necrotizing inflammation and rapid fibrosis progression, often leading to cirrhosis and hepatocellular carcinoma. Currently, only Resmetirom is approved for the treatment of MASH one of the main reasons is the absence of representative in vivo or in vitro models for MASH. To address this challenge, we developed a high-throughput 3D spheroid model consisting of human hepatocellular carcinoma cells (HepG2) and human hepatic stellate cells (LX-2) on microwell arrays. This model, induced with free fatty acids (FFA) to simulate steatosis and fibrosis, enables the assessment of efficacy and mechanisms for potential anti-MASH drugs. Our findings demonstrate that this in vitro spheroid model replicates key pathological features of human MASLD, including steatosis, oxidative stress, and fibrosis. Upon validation, we selected pirfenidone (PFD) and yinfenidone (AC-003), which are commonly used to treat idiopathic pulmonary fibrosis (IPF), to test their anti-MASH efficacy. Treatment with these drugs showed that they could regulate lipid synthesis and metabolism genes, reduce lipid accumulation, oxidative stress, and fibrosis levels. This 3D spheroid model represents a straightforward and efficient tool for screening anti-MASH drugs and investigating the molecular mechanisms of drug action.

PMID:39889902 | DOI:10.1016/j.jbiotec.2025.01.019

BMC Cancer. 2025 Jan 31;25(1):182. doi: 10.1186/s12885-025-13580-8.

ABSTRACT

BACKGROUND: Small-molecule compounds that even partially restore the GTPase activity of RASG12V can be used in anticancer therapy. Until now, attempts to obtain such compounds have failed. Compounds with this ability have been defined in our research.

METHODS: The compounds were initially identified through virtual screening, and their optimal binding conformation in the RAS SW-II pocket was determined using the flexible docking technique. Efficacy was verified based on the IC50 determination, GTPase activity, as well as the AKT and ERK phospho WB assays.

RESULTS: The IC50 of the tested compounds was significantly lower against cells with the RASG12V mutation than against selected types of normal cells. The molecular mechanism of action of these compounds was proposed - minimization of the negative impact of the V12 sidechain on GTP hydrolysis of RASG12V. The work also indicates that the model of action of RAS mutants in cell lines is incomplete. The analysed cell line (SW-480) with RAS mutations does not always show increased ERK and AKT activity.

CONCLUSIONS: We have demonstrated molecules that partially restore the GTPase activity of RASG12V. Their mechanism of action is well explained based on current RAS mutant conformation and mechanistic models. These molecules inhibit the RAS-AKT pathway and show higher cytotoxicity against cancer cells with the RASG12V mutation (SW-480 cell line). However, SW-480 cells can switch into the subline proliferating independently of AKT phosphorylation and show partial resistance to the molecules described in this article.

PMID:39891136 | DOI:10.1186/s12885-025-13580-8

Sci Rep. 2025 Jan 31;15(1):3946. doi: 10.1038/s41598-025-87867-y.

ABSTRACT

This paper focuses on the elective surgical scheduling problem with multi-resource constraints, including material resources, such as operating rooms (ORs) and non-operating room (NOR) beds, and human resources (i.e., surgeons, anesthesiologists, and nurses). The objective of multi-resource constrained elective surgical scheduling (MESS) is to simultaneously minimize the average recovery completion time for all patients, the average overtime for medical staffs, and the total medical cost. This problem can be formulated as a mixed integer linear multi-objective optimization model, and the honey badger algorithm based on the Nash equilibrium (HBA-NE) is developed for the MESS. Experimental studies were carried out to test the performance of the proposed approach, and the performance of the proposed surgical scheduling scheme was validated. Finally, to narrow the gap between the optimal surgical scheduling solution and actual hospital operations, digital twin (DT) technology is adopted to build a physical-virtual hospital surgery simulation model. The experimental results show that by introducing a digital twin, the physical and virtual spaces of the smart hospital can be integrated to visually simulate and verify surgical processes.

PMID:39890977 | DOI:10.1038/s41598-025-87867-y

Inflamm Res. 2025 Jan 31;74(1):31. doi: 10.1007/s00011-025-01996-8.

ABSTRACT

OBJECTIVE AND DESIGN: Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of inflammatory muscle disorders of unknown etiology. It is postulated that mitochondrial dysfunction and protein aggregation in skeletal muscle contribute to myofiber degeneration. However, molecular pathways that lead to protein aggregation in skeletal muscle are not well defined.

SUBJECTS: Here we have isolated membrane-bound organelles (e.g., nuclei, mitochondria, sarcoplasmic/endoplasmic reticulum, Golgi apparatus, and plasma membrane) from muscle biopsies of normal (n = 3) and muscle disease patients (n = 11). Of the myopathy group, 10 patients displayed mitochondrial abnormalities (IIM (n = 9); mitochondrial myopathy (n = 1)), and one IIM patient did not show mitochondrial abnormalities (polymyositis).

METHODS: Global proteomic analysis was performed using an Orbitrap Fusion mass spectrometer. Upon unsupervised clustering, normal and mitochondrial myopathy muscle samples clustered separately from IIM samples.

RESULTS: We have confirmed previously known protein alterations in IIM and identified several new ones. For example, we found differential expression of (i) nuclear proteins that control cell division, transcription, RNA regulation, and stability, (ii) ER and Golgi proteins involved in protein folding, degradation, and protein trafficking in the cytosol, and (iii) mitochondrial proteins involved in energy production/metabolism and alterations in cytoskeletal and contractile machinery of the muscle.

CONCLUSIONS: Our data demonstrates that molecular alterations are not limited to protein aggregations in the cytosol (inclusions) and occur in nuclear, mitochondrial, and membrane compartments of IIM skeletal muscle.

PMID:39890639 | DOI:10.1007/s00011-025-01996-8

FEMS Microbiol Lett. 2025 Jan 31:fnaf017. doi: 10.1093/femsle/fnaf017. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa has increasing clinical relevance and commonly occupies the cystic fibrosis (CF) airways. Its ability to colonize and persist in diverse niches is attributed to its large accessory genome, where prophages represent a common feature and may contribute to its fitness and persistence. We focused on the CF airways niche and used 197 longitudinal isolates from 12 patients persistently infected by P. aeruginosa. We computationally predicted intact prophages for each longitudinal group and scored their long-term persistence. We then confirmed prophage inducibility and mapped their location in the host chromosome with lysate sequencing. Using comparative genomics, we evaluated prophage genomic diversity, long-term persistence and level of genomic maintenance. Our findings support previous findings that most P. aeruginosa genomes harbour prophages some of which can self-induce, and that a common CF-treating antibiotic, ciprofloxacin, can induce prophages. Induced prophage genomes displayed high diversity and even genomic novelty. Finally, all induced prophages persisted long-term with their genomes avoiding gene loss and degradation over four years of host replication in the stressful CF airways niche. This and our detection of phage genes which contribute to host competitiveness and adaptation, lends support to our hypothesis that the vast majority of prophages detected as intact and inducible in this study facilitated their host fitness and persistence.

PMID:39890605 | DOI:10.1093/femsle/fnaf017

J Dairy Sci. 2025 Jan 29:S0022-0302(25)00046-3. doi: 10.3168/jds.2024-25812. Online ahead of print.

ABSTRACT

Increasing production and environmental challenges in dairy cattle means that selecting for resilience is becoming more important. This study explored whether data on cows that exit before completing their lactation and those that die during lactation can be used to predict resilience. To identify predictors of resilience, exiting the herd by 60, 120, 180, and 240 d were defined as traits. Additional traits were defined, by including all the cows that died during the entire lactation to the cows that exited at different times up to 240 d of lactation. For all traits, cows that exited the herd or died were coded as 1, otherwise as 0 at the end of the lactation. We used performance and exit data of Holstein (H) and Jersey (J) cows that calved between 1998 and 2023. The data were analyzed using a multi-trait sire model to estimate heritability and correlations with milk yield (MY), somatic cell count (SCC), calving interval (CIN), and selected type traits. The results showed that the proportion of cows that exited by 60 d was 2%, increasing by about 2% every 2 mo until exit by 240 d. The trend over the years in the proportion of exits, taking Exit 180d and Exit 180 + death as an example, showed an undesirable increase from 5.6% in 2000 to 9.4% in 2022. Heritability of all exit traits was low, increasing from below 1% for exit by 60 to 2.8% for exit by 240 d + all deaths over the lactation. The genetic correlation of early exit (i.e., 60 or 120 d) with first test-day MY was positive (unfavorable) and higher at the beginning (0.4), decreasing over time to be favorable in J (-0.2) and near zero in H (0.1) by the end of the lactation. On the other hand, the genetic correlation of exit with first test-day SCC became stronger (favorable) at the end of the lactation (0.3 to 0.4). Exit at any time during the lactation had the strongest genetic correlation with CIN (i.e., fertility). The genetic correlation of exit traits with body condition score (BCS) and angularity showed that the likelihood of cow exit, especially up to 180 d, was higher for thin and more angular cows. The genetic correlation estimates imply that cows with high potential for MY, poor fertility, poor BCS, and high scores for angularity are more likely to exit early due to metabolic stress. The change in genetic correlation between exit and MY early from unfavorable to favorable in J is due to more culling for milk and less for fertility and udder health is leading to an undesirable genetic trend for exit by 180 d as well as exit by 180 d + all death. However, the increasing phenotypic trend of exit rates in both breeds suggests a need for close monitoring. The selective use of exit data can help to develop genetic evaluations for resilience and health traits and validate and complement data collected to improve health and welfare during the transition period.

PMID:39890077 | DOI:10.3168/jds.2024-25812

Cell Rep Med. 2025 Jan 28:101931. doi: 10.1016/j.xcrm.2025.101931. Online ahead of print.

ABSTRACT

Chimeric antigen receptor (CAR) T cell therapy is a promising immunotherapy against cancer. Although there is a growing interest in other cell types, a comparison of CAR immune effector cells in challenging solid tumor contexts is lacking. Here, we compare mouse and human NKG2D-CAR-expressing T cells, natural killer (NK) cells, and macrophages against glioblastoma, the most aggressive primary brain tumor. Invitro we show that T cell cancer killing is CAR dependent, whereas intrinsic cytotoxicity overrules CAR dependence for NK cells, and CAR macrophages reduce glioma cells in co-culture assays. In orthotopic immunocompetent glioma mouse models, systemically administered CAR T cells demonstrate superior accumulation in the tumor, and each immune cell type induces distinct changes in the tumor microenvironment. An otherwise low therapeutic efficacy is significantly enhanced by co-expression of pro-inflammatory cytokines in all CAR immune effector cells, underscoring the necessity for multifaceted cell engineering strategies to overcome the immunosuppressive solid tumor microenvironment.

PMID:39889712 | DOI:10.1016/j.xcrm.2025.101931

Cell Rep Med. 2025 Jan 27:101935. doi: 10.1016/j.xcrm.2025.101935. Online ahead of print.

ABSTRACT

Chronic hepatic injury and inflammation from various causes can lead to fibrosis and cirrhosis, potentially predisposing to hepatocellular carcinoma. The molecular mechanisms underlying fibrosis and its progression remain incompletely understood. Using a proteo-transcriptomics approach, we analyze liver and plasma samples from 330 individuals, including 40 healthy individuals and 290 patients with histologically characterized fibrosis due to chronic viral infection, alcohol consumption, or metabolic dysfunction-associated steatotic liver disease. Our findings reveal dysregulated pathways related to extracellular matrix, immune response, inflammation, and metabolism in advanced fibrosis. We also identify 132 circulating proteins associated with advanced fibrosis, with neurofascin and growth differentiation factor 15 demonstrating superior predictive performance for advanced fibrosis(area under the receiver operating characteristic curve [AUROC] 0.89 [95% confidence interval (CI) 0.81-0.97]) compared to the fibrosis-4 model (AUROC 0.85 [95% CI 0.78-0.93]). These findings provide insights into fibrosis pathogenesis and highlight the potential for more accurate non-invasive diagnosis.

PMID:39889710 | DOI:10.1016/j.xcrm.2025.101935

Semin Cell Dev Biol. 2025 Jan 30;167:22-39. doi: 10.1016/j.semcdb.2025.01.002. Online ahead of print.

ABSTRACT

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

PMID:39889540 | DOI:10.1016/j.semcdb.2025.01.002

Phytomedicine. 2025 Jan 20;138:156412. doi: 10.1016/j.phymed.2025.156412. Online ahead of print.

ABSTRACT

BACKGROUND: Vascular dementia (VaD), a significant cognitive disorder, is caused by reduced cerebral blood flow. Unraveling the metabolic heterogeneity and reprogramming in VaD is essential for understanding its molecular pathology and developing targeted therapies. However, the in situ metabolic regulation within the specific brain regions affected by VaD has not been thoroughly investigated, and the therapeutic mechanisms of Erigeron breviscapus injection (EBI), a traditional Chinese medicine, require further elucidation.

PURPOSE: To investigate the region-specific metabolic alterations in a VaD rat model, explore the therapeutic effects of EBI at a microregional level, identify the key metabolic pathways and metabolites involved in VaD, and elucidate how EBI modulates these pathways to exert its therapeutic effects.

METHODS: Air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), a novel technique, was employed to investigate the metabolic changes in the brain microregions. We used a bilateral common carotid artery occlusion model to induce VaD in rats. Network analysis and network pharmacology were used to assess the local metabolic effects of the EBI treatment (3.6 mL/kg/day for 2 weeks).

RESULTS: The EBI treatment significantly ameliorated the neurological deficits in VaD rats. AFADESI-MSI revealed 31 key metabolites with significant alterations in the VaD model, particularly within the pathways related to neurotransmitter metabolism, redox homeostasis, and osmoregulation. The metabolic disturbances were primarily observed in the striatum (ST), pyriform cortex (PCT), hippocampus (HP), and other critical brain regions. The EBI treatment effectively reversed these metabolic imbalances, especially in neurotransmitter metabolism, suggesting its potential in mitigating VaD-related cognitive decline.

CONCLUSION: Our findings not only shed light on the molecular underpinnings of VaD but also highlight the potential of EBI as a therapeutic agent in neurodegenerative disorders. Moreover, this study demonstrates the power of advanced mass spectrometry imaging techniques in phytomedicine, offering new insights into the spatial metabolic changes induced by botanical treatments.

PMID:39889490 | DOI:10.1016/j.phymed.2025.156412

J Neural Eng. 2025 Jan 31. doi: 10.1088/1741-2552/adb0f2. Online ahead of print.

ABSTRACT

Abstract-Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio (SNR) and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.

APPROACH: This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning. Specifically, this study enhanced SSVEP features using Global template alignment (GTA) and Discriminant Spatial Pattern (DSP), and then designed a Compacted Temporal-Spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.

MAIN RESULTS: The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, deep learning methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset.The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.

SIGNIFICANCE: This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.

PMID:39889306 | DOI:10.1088/1741-2552/adb0f2

J Opt Soc Am A Opt Image Sci Vis. 2024 Jul 1;41(7):1426-1434. doi: 10.1364/JOSAA.523092.

ABSTRACT

Coded aperture compressive temporal imaging (CACTI) is a recently developed imaging technique based on the theory of compressed sensing. It uses an optical imaging system to sample a high-speed dynamic scene (a set of consecutive video frames), integrates the sampled data in time according to masks (sensing matrix), and thus obtains compressive measurements. Considerable effort has been devoted to the sampling strategy and the ill-posed inverse process of reconstructing a three-dimensional (3D) high-speed dynamic scene from two-dimensional (2D) compressive measurements. The importance of the reconstruction algorithm and the optimization mask is evident. In this paper, a flexible, efficient, and superior quality Landweber iterative method is proposed for video reconstruction through jointly learning the optimal binary mask strategy, relaxation strategy, and regularization strategy. To solve the sparse representation problem in iteration, multiple denoisers are introduced to obtain more regularization prior information. By combining the mathematical structure of the Landweber iterative reconstruction method with deep learning, the challenging parameter selection procedure is successfully tackled. Extensive experimental results demonstrate the superiority of the proposed method.

PMID:39889132 | DOI:10.1364/JOSAA.523092

J Opt Soc Am A Opt Image Sci Vis. 2024 Nov 1;41(11):2263-2276. doi: 10.1364/JOSAA.533551.

ABSTRACT

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIC method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIC has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIC method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module ECA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIC methods in practical experiments. Moreover, our model outperforms traditional DIC methods in cases of large displacement scenarios.

PMID:39889089 | DOI:10.1364/JOSAA.533551