Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interventions like randomized controlled trials are infeasible and only observational data are available. Methods based on statistical tests of conditional independence within the Mendelian Randomization framework can detect causality between two observed variables that are each associated with a third instrumental variable. However, these methods for detecting causal relationships between traits (e.g., two gene expression or clinical traits associated with a genetic variant, all observed in the same population) often assume a linear relationship, thereby hindering the discovery of causal gene networks from genomics data.We have developed NLCD, a method for NonLinear Causal Discovery from genomics data based on nonlinear regression modeling and conditional feature importance scoring. NLCD uses these techniques to extend the statistical tests in an existing linear causal discovery method called the Causal Inference Test (CIT). We benchmarked NLCD against current state-of-the-art methods: CIT, Findr, and MRPC. On simulated datasets, NLCD performs comparably to most methods in detecting linear relations (Average AUPRC (Area Under the Precision-Recall Curve) of NLCD = 0.94, CIT = 0.94, Findr = 0.94, and MRPC = 0.99), and outperforms them in detecting nonlinear (sine and sawtooth type) relations between two genes (Average AUPRC of NLCD = 0.76, CIT = 0.60, Findr = 0.56, and MRPC = 0.73). When tested on a nonlinear subset of a yeast genomic dataset to recover known causal relations involving transcription factors, NLCD and CIT performed comparable to each other and slightly better than Findr and MRPC (Average AUPRC of NLCD = 0.82, CIT = 0.81, Findr = 0.71, and MRPC = 0.54). On application to a human genomic dataset, NLCD revealed active causal gene pairs (IRF1 → PSME1 and HLA-C → HLA-T) in the muscle tissue, and clarified the promises and challenges in discovering causal gene networks in tissues under in vivo human settings.
Constitutive models of myocardial mechanics form a cornerstone of personalized cardiac simulations and cardiac digital twins. Researchers traditionally prescribe these models a priori and calibrate them from ex vivo tissue experiments, even though tissue excision alters loading conditions, removes residual stresses, and eliminates important physiological interactions. Multimodal cardiac MRI now provides subject-specific ventricular geometry, deformation, and myocardial microstructure, yet current inverse approaches still rely on predefined constitutive laws. Here we present the first framework to discover constitutive models of passive myocardial mechanics directly from in vivo cardiac imaging data by embedding a constitutive artificial neural network within a nonlinear finite element model of ventricular filling. Using multimodal cardiac MRI that combines ventricular geometry, deformation, and microstructure from a representative healthy individual, the framework identifies sparse, mechanically admissible strain-energy functions without prescribing their form a priori. The best-performing model contains only two fiber- and two sheet-invariant terms, achieves a mean displacement error of 1.62 mm, and reduces the error of the widely used Guccione and Holzapfel models by 34.14% and 26.01%. The discovered models indicate that fiber- and sheet-related anisotropic mechanisms dominate the passive mechanical response during physiological ventricular filling. More broadly, this work establishes a non-invasive strategy for subject-specific constitutive discovery from cardiac imaging data and lays the foundation for personalized cardiac simulations and cardiac digital twins.
Brain age is a global measure that compares structural brain MRI with large reference datasets. Predicted age deviation (PAD) is the deviation between predicted brain age and chronological age, with positive values indicating advanced aging. Identifying blood-based biomarkers that approximate brain PAD could provide an accessible and cost-effective measure of brain health as an alternative to MRI, but no blood-based biomarkers have yet been identified. This study aimed to investigate novel blood-based biomarkers associated with accelerated PAD using an unbiased proteomics approach to discover new biomarkers. This study is a secondary analysis with a cross-sectional case-control design using the LIMBIC-CENC dataset as a discovery approach to understand novel biomarker patterns. Brain age was estimated using brainageR in 137 participants aged ≤40 years with no substantial cognitive deficits or neurological disorders. Cases (n = 76) included individuals with brain age ≥5 years older than chronological age, whereas controls (n = 61) had brain age equal to or younger than chronological age (PAD range: -1.3 to 0; mean = -0.9) and were otherwise matched on demographics and clinical features. Unbiased proteomic profiling of ∼5,400 proteins was performed using the Olink Explore platform. Differential protein expression between groups was assessed using Wilcoxon tests with Benjamini-Hochberg correction. Receiver operating characteristic (ROC) analysis was performed on probabilities derived from generalized linear models (GLMs) to identify optimal protein combinations, prioritizing maximizing both sensitivity and negative predictive value. Olink analyses identified 418 proteins that were significantly different between groups after multiple-comparison correction. Upregulated proteins in participants with PAD≥5 years included: component inhibitor-nuclear factor kappa-b kinase (CHUK), methenyltetrahydrofolate synthetase domain containing (MTHFSD), and epidermal growth factor (EGF), with log2 fold changes of 1.70-1.80. Insulin-like peptide 3 (INSL3) was the most downregulated protein (log2 fold change -2.27). Enriched pathways involved nuclear factor kappa-b (NF-κB), heat-shock protein, and Wingless/Integrated (Wnt) signaling. Models including 6-7 dysregulated proteins (e.g., CHUK and INSL3) achieved AUCs>0.9, with sensitivities >0.90 and specificities >0.70. These discovery-based findings warrant validation in larger cohorts and suggest potential for blood-based protein panel detection of early, clinically silent, pre-pathological accelerated brain aging changes when interventions may be most effective.
Patient serum for anti-HLA antibodies are tested with purified antigens covalently affixed to beads as targets. Serologic nomenclature from the World Health Organization (WHO) is currently used to specify the reactivity of antibodies, although target antigens are defined at a finer allelic level. We investigated whether different reactivity patterns could be present at the allele level of a single WHO serologic antigen. We analyzed 2389 serum samples from 1215 patients waiting for solid-organ transplant by using class II single antigen beads for detection of HLA antibodies. We used principal component analysis to identify unique antibody reactivity patterns against the beads. We particularly focused on patterns within a WHO antigen that suggested the presence of new serologic class II HLA specificities. We used the HLA DQ7 data to illustrate the principle of the discovery methodology. Four assay target beads representing different DQA1 alleles were classified as DQ7. Two principal components accounted for about 98.6%of the DQ7 data variance. The dominant principal component, which accounted for 83.9% of the variance, represented a reaction pattern in which sera reacted equally with all 4 beads. The second principal component (14.7% of the variance) represented sera that reacted selectively with DQA1*03:01, DQB1*03:01. Four of 10 DR and 6 of 7 DQ WHO serologic specificities that were studied showed more than 5% nonconcordant patterns revealed by principal component analysis. Principal component analysis revealed differential reactivity patterns of the alleles of single WHO serological specificity. This method could be a powerful tool for identifying patterns of reactivity, which may represent new serological specificities.
Background: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell cycle progression and an important therapeutic target in cancer treatment. This study aims to identify novel CDK2 inhibitors using an integrated computational approach combining machine learning and structure-based methods. Methods: A computational pipeline was developed incorporating Lipinski's Rule of Five filtering, machine learning (ML)-based activity prediction, molecular docking, and molecular dynamics simulations (MDs). A dataset of CDK2 inhibitors with IC50 values was retrieved from ChEMBL, and molecular fingerprints were generated using PaDEL. A 5-fold stratified cross-validation approach was applied to train multiple classifiers, with the random forest model showing the best performance. Predicted active compounds from the InterBioScreen database were subjected to docking against CDK2 (PDB ID: 2FVD) using PyRx, followed by 100 ns MDS for stability analysis. Results: The random forest classifier achieved an AUC-ROC of 0.90 and an accuracy of 0.84. A total of 187 compounds were predicted as active. Among these, two compounds, STOCK4S-00019 and STOCK4S-00025, demonstrated docking scores comparable to the co-crystallized reference ligand. Molecular dynamics simulations confirmed stable binding, consistent interaction patterns, and favorable conformational behavior throughout the simulation period. Conclusions: The identified compounds, STOCK4S-00019 (hit1) and STOCK4S-00025 (hit2), show strong potential as CDK2 inhibitors. These findings support their further investigation through experimental validation and highlight the effectiveness of integrated computational approaches in anticancer drug discovery.
Hyperuricemia is rising worldwide, with xanthine oxidase (XOD) as a key therapeutic target. This study integrated peptidomics, machine learning, molecular dynamics simulations, and experimental validation to discover XOD-inhibitory peptides from sheep milk. A VHSE-CatBoost model screened milk peptides and identified four candidates: FAWP, GPGGAW, FGER, and YPF. All peptides inhibited XOD dose-dependently, with FAWP showing the strongest activity (IC₅₀ = 12.99 mM). Kinetic analysis showed mixed-type inhibition for FAWP and FGER, and competitive inhibition for GPGGAW and YPF. Thermodynamics demonstrated spontaneous FAWP-XOD binding (ΔG = -6.0 kcal/mol). UV and CD spectra indicated perturbation of aromatic microenvironments and reduced α-helix/β-sheet content in XOD. MD simulations showed that FAWP, GPGGAW, and FGER stably occupy the active site through hydrogen bonding and hydrophobic interactions. Network pharmacology further suggested multi-target effects on inflammation and renal pathways. These sheep milk-derived peptides offer promising natural agents for hyperuricemia management.
Streptomyces spp. is renowned for their capacity to produce structurally diverse secondary metabolites with potent bioactivities, including antimicrobial, antitumor, and antioxidant properties. In the present study, an alkaliphilic strain, designated as Streptomyces S9, was isolated and taxonomically characterised through multilocus sequence analysis (MLSA) using Streptomyces-specific primers. Extracted metabolites exhibited antimicrobial activity against Fusarium sp., Aspergillus sp., Corynespora sp., Bacillus sp., Staphylococcus aureus, Pseudomonas sp., and E. coli. Genomic DNA from strain S9 was subjected to PCR-based screening to detect biosynthetic gene clusters (BGCs) including nonribosomal peptide synthetase (NRPS), type I and type II polyketide synthase (PKS I and PKS II), and monooxygenase genes involved in the synthesis of natural products. Targeted amplification revealed the presence of PKS II and monooxygenase gene fragments, indicating its genetic potential to biosynthesise aromatic polyketides. Bioactive secondary metabolites were extracted from the culture supernatant, fractionated using preparative thin-layer chromatography (TLC), and subsequently analysed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Bioactivity-guided fractionation identified two active fractions (AA2 and AA3) with bactericidal and fungicidal activity. LC-HRMS/MS analysis tentatively indicated two high-molecular-weight metabolites with [M + H]+ ion peaks at m/z 1355 and 1342, whose fragmentation patterns are analogous to glycosylated angucycline-type scaffolds, pending full structural confirmation by NMR spectroscopy.
Deep learning has been increasingly applied to evolutionary genomics as genomic datasets have grown in scale and complexity. However, the literature encompasses heterogeneous biological objectives, modeling assumptions, and evaluation standards, often treated as a unified field despite important conceptual differences. This study presents a systematic review of research published between 2016 and 2025 on the use of deep learning to identify evolutionary patterns in genomic data. Following a structured screening process, 50 studies were selected for qualitative synthesis. The reviewed applications can be organized into three partially overlapping but conceptually distinct domains: (i) population genetic inference, (ii) phylogenetic reconstruction, and (iii) sequence representation learning using DNA and protein language models. In population genetics, deep learning is predominantly employed within simulation-based inference frameworks. In phylogenetics, neural architectures are used to approximate or accelerate tree and model inference under defined conditions. In representation learning, models focus on extracting transferable sequence features for downstream evolutionary or functional analyses. Across domains, deep learning provides flexible modeling of complex genomic inputs. Nevertheless, recurring limitations include challenges in interpretability, sensitivity to training assumptions-particularly under simulation-based settings-heterogeneous evaluation protocols, and substantial computational demands. By organizing the literature using a domain-aligned framework, this review clarifies domain-specific strengths, limitations, and research gaps, providing a structured basis for future methodological development in evolutionary genomics.
Breast cancer is one of the prominent reasons of death in women. HER2 is a promising target to counter breast cancer. In the current research, a structure-based pharmacophore model was generated to map and screen CMNPD, a comprehensive database of marine natural products. The two compounds (CMNPD30448 (hit1) and CMNPD7060 (hit2)) displayed better LibDock scores than the reference co-crystallized ligand. These compounds demonstrated stable molecular dynamics results conducted for 500 ns with stable root mean square deviation (RMSD) at 0.3 nm, stable radius of gyration (Rg) and root mean square fluctuation (RMSF). On ChEMBL compounds, different PaDEL descriptors and various machine learning (ML) and neural network (NN) methods were used. The results showed that PubChem fingerprints with random forest classification model displayed an accuracy of 0.91 and a receiver operating characteristic area under the curve (ROC-AUC) of 0.96. This model further predicted the retrieved compounds as 'active'. The explainable random forest with LIME showed that PubChem fingerprint440 [C(-C)(-O)(=O)], PubChem fingerprint452 [C(-O)(=O)], PubChem fingerprint380 [C(~O)(~O)], PubChem fingerprint566 [O-C-C-N] and PubChem fingerprint712 [C-C(C)-C(C)-C] for hit1 and PubChem fingerprint700 [O-C-C-C-C-C-O-C], PubChem fingerprint380 [C(~O)(~O)], and PubChem fingerprint712 [C-C(C)-C(C)-C] for hit2 have contributed towards plausible inhibitory potential. These findings suggest the two compounds CMNPD30448 and CMNPD7060 might serve as HER2 inhibitors. Further in vitro and in vivo analysis are required before using them.
This observational study characterized obesity care pathways and healthcare resource use in England between 2015 and 2019. Data were obtained from Discover and the Salford Integrated Record (SIR), two databases of linked primary and secondary electronic health records in England. Adults with a body mass index (BMI) ≥ 30 kg/m2 who attended obesity clinics were included over 4 years (January 1, 2015 [SIR] and January 1, 2016 [Discover] to December 31, 2018). Overall, 1698 people living with obesity were included from Discover and 561 from SIR. Most (74.9%-78.6%) received a lifestyle intervention as their first intervention, whereas 6.2%-16.3% received a pharmacological treatment or metabolic/bariatric surgical procedure. Time to first intervention was typically > 6 months, and most individuals remained in their baseline BMI group after 8 months' follow-up. Obesity-related complications resulted in high annual per-person direct healthcare costs, particularly for acute cardiovascular events such as myocardial infarction (Discover: 2285 GBP; SIR: 2194 GBP) and incident stroke (Discover: 3005 GBP; SIR: 1550 GBP). In conclusion, the time to first intervention can be considerable for people living with obesity in England and these individuals may incur high healthcare costs. There is an unmet need for timely and effective weight management in England.
Understanding how cognition unfolds from neurophysiological signals presents a promising direction for cognitive science studies and wearable-enabled human-robot interaction applications. However, uncovering latent neurodynamic geometry and temporal progression remains challenging and underexplored due to the lack of observable temporal organization for annotation and, consequently, the difficulty of training models in a supervised manner. This study proposes a representational learning method for this segmentation problem that shifts the solution away from statistical change-point detection methods and Hidden Markov Models. Our method employs self-supervised learning to discover emergent properties of the underlying temporal organization directly from the neurodynamic data itself. Four objectives are introduced and jointly optimized, including within-stage temporal predictability, boundary contrast, cross-trial alignment, and sparse stage-specific feature weights. Population-based evolutionary search was adopted to explore the multiple-basins-of-attraction landscape, where mutation and crossover govern the convergence process. We validated the framework on EEG recordings collected from participants performing an embodied road-crossing decision-making task, which simulates a typical cognitive processing transition from perceptual assessment to risk evaluation and decision commitment. Results showed that our method achieves an order-of-magnitude improvement in boundary contrast of the discovered stages, indicating that the learning behavior fundamentally changes the working principle from seeking local statistical consistency to capturing higher-order global temporal organization. This inter-stage divergence serves as the driving force for latent regime discovery while preserving local temporal continuity and coherence. Ablation and sensitivity studies demonstrate that the model performance is robust in identifying cross-trial transferable state geometry and handling data variability introduced by subject and stimulus heterogeneity. The reconstructed cognitive stages are also behaviorally plausible, and the dimensions attended by the model are well aligned with the neurophysiological underpinnings governing critical cognitive activities underlying each stage.
Marine medicinal algae represent a valuable reservoir of bioactive metabolites for anticancer drug discovery, yet the efficient identification of target-relevant compounds from chemically complex marine matrices remains challenging. In this study, an integrated cathepsin B-oriented strategy was developed to discover, prioritize, isolate, and validate antitumor metabolites from the brown alga Sargassum polycystum. Affinity ultrafiltration LC-MS was first applied to screen CTSB-binding constituents from the crude extract, followed by molecular docking, molecular dynamics simulation, and gray relational analysis for multidimensional candidate prioritization. Seven CTSB-binding metabolites were characterized, including chlorogenic acid, caffeic acid, cynarin, loliolide, taxifolin, senkyunolide H, and dihydroactinidiolide, with binding degrees of 73.99-85.61% at 2.5 U/mL CTSB. Molecular docking showed predicted binding affinities ranging from -6.3 to -9.4 kcal/mol, compared with -10.2 kcal/mol for the positive control CA-074Me. Integrated computational and biological evaluation identified caffeic acid, cynarin, and taxifolin as the top-ranked candidates. Preparative recovery was then achieved using counter-current chromatography combined with semi-preparative HPLC, and the isolated compounds were structurally identified by LC-MS/MS and NMR. Cellular assays in NCI-H1975 cells suggested that these metabolites reduced CTSB-associated enzymatic activity and intracellular CTSB-related fluorescence signals to different extents, with phenolic acid-type compounds exhibiting comparatively stronger effects. At the extract level, S. polycystum dose-dependently suppressed NCI-H1975 xenograft tumor growth, with inhibition rates of 48.78%, 36.58%, and 22.86% in the high-, middle-, and low-dose groups, respectively, without evident hepatorenal histopathological toxicity. This effect was associated with reduced CTSB, Ki-67, and Bcl-2 staining, increased Bax staining, enhanced apoptosis, and ultrastructural alterations in tumor tissues. Overall, this study provides a practical CTSB-oriented workflow for discovering antitumor metabolites from marine medicinal algae and supports further investigation of S. polycystum as a potential source of anti-NSCLC candidates.
Most drugs target proteins, and proteome-wide genetic analyses in diverse populations could discover potential novel and repurposed targets for improved prevention and treatment of ischemic heart disease (IHD) beyond statin therapy. The purposes of this study were to use cis-acting single nucleotide polymorphisms (cis-pQTLs) identified for plasma proteins in East Asians and Europeans to discover and validate potential drug targets for IHD. We measured plasma levels of 9,520 (Olink/SomaScan: 2,923/7,297) proteins in a case-cohort study of IHD (1,976 incident cases and 2,001 subcohort controls) in statin-free individuals in the prospective China Kadoorie Biobank (CKB). Genome-wide association studies identified 2,895 (Olink/SomaScan: 1,301/1,594) cis-pQTLs for these proteins in CKB. Two-sample Mendelian randomization (MR) and colocalization analyses assessed associations of all available cis-pQTLs for these proteins with IHD in East Asians (n = 29,319 cases), with further replication in Europeans (n = 181,522 cases) and comparison with findings in previous MR studies. In CKB observational analyses, a total of 959 (Olink/SomaScan: 426/533) proteins were associated at false discovery rate-corrected P < 0.05 with IHD after adjusting for major IHD risk factors. Two-sample MR analyses provided genetic support for 54 unique (Olink/SomaScan: 36/28) proteins in IHD etiology. Colocalization analyses confirmed shared gene-protein-IHD associations (posterior probability of hypothesis 4 [PPH4] ≥0.8) for 15 unique (Olink/SomaScan: 10/10) proteins, including 8 lipid-related, 3 inflammation-related, 1 blood pressure-related, and 3 alcohol-related proteins in East Asians. In Europeans, MR analyses of 12 non-alcohol-related proteins showed directionally concordant results for 8 proteins, with 5 having strong colocalization evidence of shared gene-protein-IHD associations (PPH4 ≥0.8), including 4 lipid-related (proprotein convertase subtilisin/kexin type 9, LPA, APOE, cadherin-1) and 1 systolic blood pressure-related (fibroblast growth factor 5) protein. However, 4 proteins showed directionally discordant MR results, including 2 lipid-related (APOA5, SORT1) and 1 inflammation-related (transforming growth factor beta 1) proteins with strong colocalization evidence of shared gene-protein-IHD associations (PPH4 ≥0.8). Comparison with previous MR studies revealed little consistency across studies in the number and identity of target proteins for IHD beyond well-established lipid-related (low-density lipoprotein cholesterol, lipoprotein(a), and triglycerides) or inflammation-related (interleukin-6) protein targets. The findings support a role for lipid-driven chronic inflammation in IHD etiology, and treatment strategies simultaneously targeting multiple lipid and inflammation pathways should be prioritized for further research to improve drug treatment of IHD beyond statin therapy.
Background: Basal cell carcinoma (BCC) of the skin is the most common cancer in humans, and its incidence rises annually. Despite its low death rate, BCC causes significant morbidity because of its destructive nature to local tissues. The aim of this study was to review the state of the BCC research landscape using data published in Scopus from 1972 to 2023. Methods: Using the R package Bibliometrix, we first determined authors, countries, journals, and main topics behind the advancement of BCC research. The Latent Dirichlet Allocation (LDA), a probabilistic topic algorithm, was applied to automatically discover latent (hidden) thematic structures. Additionally, the HJ-Biplot was also chosen to visualize graphical representations of multivariate scientometric and bibliometric data to improve the LDA outcome. Results: Over five decades, we discovered 32 unique themes. BCC research has shifted from studying tumor and immunohistochemically characterization, epidemiology, and surgical/histological clearance to the advancement of diagnostic and imaging techniques, like dermoscopy and reflectance confocal microscopy (RCM). Another exciting BCC research trend has been the discovery of aberrant activation within the Hedgehog signaling. Finally, patient treatment, especially surgery, radiotherapy, topical fluorouracil, and imiquimod, is an object of intense research. Conclusions: By displaying each topic as a group of related words, this in-depth exploration outlined emerging avenues in BCC evidence-based research that will benefit from the development of cutting-edge diagnostic procedures, as well as a deeper knowledge of BCC etiology and genetic underpinnings, the quality of life in BCC patients after surgery, and the application of artificial intelligence.
Halogenases are attractive catalysts to facilitate asymmetric Csp3-halogenation of organic molecules. Traditional halogen insertion reactions employ hazardous reagents and are notoriously unselective, but asymmetric halogenation of drug molecules offers control over pharmacokinetic properties. To date, the toolbox of halogenases to facilitate Csp3-halogenation, and consequently the accessible substrate scope, is limited. Thus, discovering novel enzymatic halogenation reactions has great application potential. One way of achieving this is to screen large libraries of putative halogenases. However, there are currently no suitable (ultra)high-throughput methods available for screening such an immense sequence space to discover halogenases with new substrate profiles. To address this shortcoming, we introduce here a flow cytometry-based assay concept to screen for the intracellular presence of haloalkanes. Our strategy relies on catalytically deficient haloalkane dehalogenases (HLDs), here called HaloTag-like proteins (HTLPs), which can irreversibly trap haloalkanes. For the presented proof-of-concept, we employed the commercialized HaloTag and fused it to the enhanced green fluorescent protein (eGFP), constructing a fluorescent sensor protein that allows semiquantitative detection of haloalkanes at single-cell resolution. We successfully validated our concept by separating mixed populations of Escherichia coli cells that were either exposed or not exposed to six haloalkanes. Moreover, we show that populations can be distinguished based on halohydrin dehalogenase activity. In summary, we provide an assay concept for detecting the intracellular presence of haloalkanes, which has the potential of being applied toward the detection of halogenase activity.
Cardiovascular function is tightly linked to tissue architectures, where the spatial organization of cells, extracellular matrix (ECM), vascular networks, and remodeling processes governs physiological performance and disease progression. Spatial proteomics has, therefore, emerged as a powerful framework for understanding cardiovascular biology and cardiovascular disease mechanisms by revealing spatially organized protein regulation across various physiological and pathological states. In this review, we focus on spatial proteomics strategies most relevant to cardiovascular research and discuss their applications through representative examples. These approaches can be broadly categorized into region-of-interest-based methods, which enable precise characterization of localized cellular heterogeneity, and tissue mapping strategies, which capture spatial organization and biologically relevant region-to-region variability across larger tissue domains. In addition, spatial proteomics platforms differ in their capacity for targeted or untargeted protein analysis, influencing both proteome coverage and their suitability for hypothesis-driven versus discovery-based studies. We evaluate the strengths and limitations of state-of-the-art technologies across 3 key parameters, molecular depth, spatial coverage, and spatial resolution, and discuss how these parameters shape study design and biological understanding. Building on these considerations, we argue that a comprehensive understanding of cardiovascular tissue biology requires spatial proteomics strategies that capture both localized molecular details and spatial organization across large tissue areas, as neither alone is sufficient to explain complex tissue behavior. We propose an integrated workflow in which untargeted, whole-tissue mapping of thousands of proteins is first used to unbiasedly discover spatial patterns and generate hypotheses by identifying candidate regions and proteins of interest, followed by hypothesis testing and validation using high-precision region-of-interest-based proteomics and targeted protein imaging. This sequential framework leverages the complementary strengths of tissue-wide mapping and region-of-interest-based approaches to provide multiscale, mechanistic insights into spatially organized disease processes. Finally, we discuss emerging directions that are poised to expand the scope of spatial proteomics in cardiovascular research.
BackgroundData science, rooted in computer science, statistics, and information science is advancing healthcare research by unlocking "insights" from big data to discover knowledge about patient experiences and answer previously unanswerable questions. The comparatively limited engagement of Canadian nurse researchers in this field, relative to counterparts in other jurisdictions (e.g., United States) served as a key impetus for initiating this data science study.PurposeTo investigate homecare electronic health records (EHRs) of "persons with ALS" (PALS) disease and identify care-related factors that influence their preferences and ability to be safely supported at home.MethodGuided by a nursing informatics' data, information, knowledge, wisdom (DIKW) framework and the knowledge discovery in databases (KDD) methodology, a retrospective, secondary analysis of an integrated dataset (1159 clinical assessments and administrative data) documenting 240 PALS's homecare encounters (April 1, 2009 - July 31, 2019) was conducted. EHR data was analyzed using correlations and logistic regression to model institutionalization risk factors of PALS.ResultsFive significant models were generated that accurately distinguished PALS institutionalization status (at home/not home) while offering comparable predictive performance. The final model featuring six factors offers homecare providers real-time insights into PALS clinical status at the point of care. Four relate to ALS clinical manifestations of disease decline and two assessment outcome measures (MAPLe and CHESS), strongly predicting institutionalization and caregiver burden.ConclusionBig data science offers nurse researchers a transformative way to advance knowledge development that supports high-quality, sustainable healthcare delivery while fulfilling an intended benefit of EHRs.
To discover novel and highly effective pesticides with diverse structures, 16 new methoxyacrylate compounds were designed and synthesized based on linker modification. All target compounds were characterized by 1H NMR, 13C NMR, and HRMS. Preliminary bioassay results indicated that some target compounds exhibited good fungicidal activity against Phakopsora pachyrhizi, Sphaerotheca fuliginea, and Pyricularia oryzae, as well as potent insecticidal activity against Plutella xylostella, Mythimna separata, and Aphis craccivora. Notably, compound 10b showed 100% control efficacy against Phakopsora pachyrhizi at 0.4 mg/L, 100% control efficacy against Sphaerotheca fuliginea at 25 mg/L, and 88.89% control efficacy against Pyricularia oryzae at 100 mg/L. Meanwhile, compound 10b exhibited 69.05% mortality against Aphis craccivora at 4 mg/L and 100% mortality against Mythimna separata at 10 mg/L, demonstrating broad-spectrum activity. These findings may provide valuable insights for the development of novel and highly effective methoxyacrylate derivatives.
Family caregivers of people living with advanced chronic illness experience sustained emotional, physical, and social challenges. However, existing support interventions remain fragmented, frequently individualized, and insufficiently grounded in families' lived experiences. Many focus on a single "primary caregiver," overlooking the relational, dynamic, and shared nature of caregiving within families. Participatory design approaches may help address these gaps, yet structured design thinking frameworks remain underexplored in health research, particularly in family caregiving and primary care contexts. To describe the application of the Double Diamond (DD) framework to the development of HELP-F, a Home-based pErson-centered care to Listen and suPport Family caregiving in advanced chronic illness. A participatory co-design study was conducted following the four phases of the Double Diamond model. The Discover phase involved narrative family and individual interviews with 24 family caregivers providing home-based care to a relative with advanced chronic illness, analyzed using an inductive narrative approach. The Define phase integrated qualitative findings with evidence from a systematic review of home-based caregiver interventions to develop a preliminary logic model grounded in the Gothenburg Centre for Person-Centered Care framework. In the Develop phase, the logic model was refined through its presentation across nine primary care centers and a focus group involving primary care professionals, family representatives, and researchers with recognized expertise in the field. The Deliver phase comprises an ongoing pilot implementation across eight primary care centers to assess feasibility and acceptability. Data were analyzed iteratively to inform each subsequent phase. The process generated a nurse-led intervention structured around four core elements: eliciting family caregiving narratives, co-creating an individualized family care plan, providing longitudinal follow-up, and integrating support within routine primary care. Three design priorities informed the intervention: recognizing the family as the unit of care, strengthening caregivers' preparedness and emotional support, and enabling flexible delivery according to family context. Family caregiver representation was more limited in some later co-design phases, and the pilot phase is ongoing; therefore, effectiveness outcomes are not yet available. This study demonstrates the value of applying a structured design thinking framework to the development of family-centered interventions in primary care. The Double Diamond model offers a transparent and flexible methodological pathway to address the complexity of family caregiving, supporting the co-creation of contextually relevant, practice-oriented interventions. The findings contribute to methodological debates on co-design in health research and support a shift towards family-oriented, primary care-based approaches in advanced chronic illness. NCT07184216; ClinicalTrials.gov.
In this narrative recounting of a Malaysian academic medical department's mystery-seed project, lecturers reflect on the parallels between cultivating plants and supervising students. As the seeds grow, struggle, or fail to germinate, the participants examine how patience, intervention, adaptability, and environmental conditions influence development. Through experimentation and shared reflection, they discover that effective supervision requires recognizing individual needs, responding creatively to setbacks, and providing the conditions in which growth can occur.