Chemphyschem. 2025 Aug 4:e2500094. doi: 10.1002/cphc.202500094. Online ahead of print.

ABSTRACT

The generalized many-body expansion for building density matrices (GMBE-DM), truncated at the one-body level and combined with a purification scheme, is applied to rank protein-ligand binding affinities across two cyclin-dependent kinase 2 (CDK2) datasets and one Janus kinase 1 (JAK1) dataset, totaling 28 ligands. This quantum fragmentation-based method achieves strong correlation with experimental binding free energies (R2 = 0.84), while requiring less than 5 min per complex without extensive parallelization, making it highly efficient for rapid drug screening and lead prioritization. In addition, our physics-informed, machine learning-corrected dispersion potential (D3-ML) demonstrates even stronger ranking performance (R2 = 0.87), effectively capturing binding trends through favorable cancelation of non-dispersion, solvation, and entropic contributions, emphasizing the central role of dispersion interactions in protein-ligand binding. With sub-second runtime per complex, D3-ML offers exceptional speed and accuracy, making it ideally suited for high-throughput virtual screening. By comparison, the deep learning model Sfcnn shows lower transferability across datasets (R2 = 0.57), highlighting the limitations of broadly trained neural networks in chemically diverse systems. Together, these results establish GMBE-DM and D3-ML as robust and scalable tools for protein-ligand affinity ranking, with D3-ML emerging as a particularly promising candidate for large-scale applications in drug discovery.

PMID:40758915 | DOI:10.1002/cphc.202500094

Phytopathology. 2025 Aug 4. doi: 10.1094/PHYTO-05-25-0187-R. Online ahead of print.

ABSTRACT

Measuring individual components of pathogen reproduction is key to understanding mechanisms underlying rate-reducing quantitative resistance (QR). Simulation models predict that lesion expansion plays a key role in seasonal epidemics of foliar diseases, but measuring lesion growth with sufficient precision and scale to test these predictions under field conditions has remained impractical. We used deep learning-based image analysis to track 6889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars across two field seasons, enabling 27,218 precise and objective measurements of lesion growth in the field. Lesion appearance traits reflecting specific interactions between particular host and pathogen genotypes were consistently associated with lesion growth, whereas overall effects of host genotype and environment were modest. Both host cultivar and cultivar-by-environment interaction effects on lesion growth were highly significant and moderately heritable (h2 ≥ 0.40). After excluding a single outlier cultivar, a strong and statistically significant association between lesion growth and overall QR was found. Lesion expansion appears to be an important component of QR to STB in most-but not all-wheat cultivars, underscoring its potential as a selection target. By facilitating the dissection of individual resistance components, our approach can support more targeted, knowledge-based breeding for durable QR.

PMID:40758903 | DOI:10.1094/PHYTO-05-25-0187-R

PLoS One. 2025 Aug 4;20(8):e0328507. doi: 10.1371/journal.pone.0328507. eCollection 2025.

ABSTRACT

Mural image recognition plays a critical role in the digital preservation of cultural heritage; however, it faces cross-cultural and multi-period style generalization challenges, compounded by limited sample sizes and intricate details, such as losses caused by natural weathering of mural surfaces and complex artistic patterns.This paper proposes a deep learning model based on DenseNet201-FPN, incorporating a Bidirectional Convolutional Block Attention Module (Bi-CBAM), dynamic focal distillation loss, and convex regularization. First, a lightweight Feature Pyramid Network (FPN) is embedded into DenseNet201 to fuse multi-scale texture features (28 × 28 × 256, 14 × 14 × 512, 7 × 7 × 1024). Second, a bidirectional LSTM-driven attention module iteratively optimizes channel and spatial weights, enhancing detail perception for low-frequency categories. Third, a dynamic temperature distillation strategy (T = 3 → 1) balances supervision from teacher models (ResNeXt101) and ground truth, improving the F1-score of rare classes by 6.1%. Experimental results on a self-constructed mural dataset (2,000 images,26 subcategories.) demonstrate 87.9% accuracy (+3.7% over DenseNet201) and real-time inference on edge devices (63ms/frame at 8.1W on Jetson TX2). This study provides a cost-effective solution for large-scale mural digitization in resource-constrained environments.

PMID:40758742 | DOI:10.1371/journal.pone.0328507

PLOS Digit Health. 2025 Aug 4;4(8):e0000939. doi: 10.1371/journal.pdig.0000939. eCollection 2025 Aug.

ABSTRACT

The potential of deep learning to predict renal obstruction using kidney ultrasound images has been demonstrated. However, these image-based classifiers have incorporated information using only single-visit ultrasounds. Here, we developed machine learning (ML) models incorporating ultrasounds from multiple clinic visits for hydronephrosis to generate a hydronephrosis severity index score to discriminate patients into high versus low risk for needing pyeloplasty and compare these against models trained with single clinic visit data. We included patients followed for hydronephrosis from three institutions. The outcome of interest was low risk versus high risk of obstructive hydronephrosis requiring pyeloplasty. The model was trained on data from Toronto, ON and validated on an internal holdout set, and tested on an internal prospective set and two external institutions. We developed models trained with single ultrasound (single-visit) and multi-visit models using average prediction, convolutional pooling, long-short term memory and temporal shift models. We compared model performance by area under the receiver-operator-characteristic (AUROC) and area under the precision-recall-curve (AUPRC). A total of 794 patients were included (603 SickKids, 102 Stanford, and 89 CHOP) with a pyeloplasty rate of 12%, 5%, and 67%, respectively. There was no significant difference in developing single-visit US models using the first ultrasound vs. the latest ultrasound. Comparing single-visit vs. multi-visit models, all multi-visit models fail to produce AUROC or AUPRC significantly greater than single-visit models. We developed ML models for hydronephrosis that incorporate multi-visit inference across multiple institutions but did not demonstrate superiority over single-visit inference. These results imply that the single-visit models would be sufficient in aiding accurate risk stratification from single, early ultrasound images.

PMID:40758672 | DOI:10.1371/journal.pdig.0000939

J Vis Exp. 2025 Jul 18;(221). doi: 10.3791/68664.

ABSTRACT

Cardiovascular magnetic resonance (CMR) cine sequences serve as the cornerstone imaging technique for evaluating dynamic left atrial (LA) function in myocarditis patients. By capturing three-dimensional motion characteristics throughout the cardiac cycle with high temporal resolution, this modality provides critical data for analyzing myocardial contractile coordination and wall motion abnormalities. Key technological innovations, such as dynamic modeling and strain-encoded imaging, enable quantitative assessment of early-stage LA systolic-diastolic dysfunction in myocarditis. However, the primary challenges in cine sequence segmentation involve dynamic artifacts and spatiotemporal continuity modeling of thin-walled structures. Traditional threshold-based segmentation methods demonstrate limited consistency in dynamic sequences due to their inability to capture motion patterns. Deep learning approaches utilizing three-dimensional fully convolutional network (3D-FCN) achieved superior accuracy through three strategic enhancements: (1) Spatiotemporal feature fusion: This employed 3D convolutional kernels to simultaneously extract spatial structures and temporal dimensional features, thereby reducing motion blurring effects. (2) Dynamic skip connections: Incorporated within encoder-decoder architectures, these connections strengthened deformation correlation modeling across different cardiac phases through cross-temporal feature propagation. (3) Lightweight design: By utilizing patch-wise processing and depthwise separable convolutions, computational efficiency was optimized for real-time processing of large-scale four-dimensional datasets. The 3D-FCN achieved a Dice coefficient of 0.921 for LA segmentation, representing a 12.3% improvement over conventional methods. This design reduced the LA ejection fraction prediction error from 8.7% to 3.2%. The segmentation results directly facilitated the calculation of quantitative metrics, including LA volume-time curves and strain rates. These metrics supported the clinical diagnosis of myocarditis-associated atrial mechanical dysfunction.

PMID:40758568 | DOI:10.3791/68664

IEEE Trans Pattern Anal Mach Intell. 2025 Aug 4;PP. doi: 10.1109/TPAMI.2025.3590979. Online ahead of print.

ABSTRACT

Person re-identification (ReID) is to identify the same person across non-overlapping camera views. After a decade of development, the methods based on deep networks have achieved high performance on benchmarks and become mainstream. In applications, the features of gallery images extracted by deep learning-based methods are stored to speed up the query process and protect the sensitive information contained in the images. Unfortunately, it is demonstrated that turning the images into features cannot properly protect privacy, as these features could be reversed to the corresponding images, revealing the sensitive information they contain. Therefore, for preventing privacy leakage, recent methods learn their features against some feature reversal methods, and most conventional reversal methods focus on minimizing the difference between a reconstruction and its original image. However, there could be many reasonable reconstruction results from a single feature, and the conventional reversal methods will inevitably generate reconstruction results that lie in a different distribution from one of the original images, which cannot properly assess the private information for learning to protect and thus hamper the privacy-protected feature learning. To mitigate this problem, we enforce the reconstructions to follow the same distribution as the original images by the generative adversarial network (GAN). We operate this GAN-based feature reversal module accompanied by the conventional ReID feature extraction module and form a novel GAN-based feature privacy-protected person ReID model, which is expected to protect feature privacy so as against reversal attack and maintain ReID utility. We demonstrate that optimizing ReID model to accommodate privacy protection faces a double adversarial objective and is thus challenging. As a remedy, we design a novel two-step training and lazy update strategy that alternatively optimizes the feature extraction module and stabilizes the update process of the GAN-based feature reversal module. To evaluate the efficiency of the model in balancing its ReID utility and feature privacy protection, we introduce a novel metric called utility-reversibility ratio (URR). Compared with existing privacy-protected feature extraction models, the proposed method achieves a better balance between privacy protection and person ReID performance. Extensive experiments validate that our model can effectively protect feature privacy at a tiny accuracy cost, and validate the effectiveness of our model with the emerging diffusion model.

PMID:40758524 | DOI:10.1109/TPAMI.2025.3590979

IEEE Trans Pattern Anal Mach Intell. 2025 Aug 4;PP. doi: 10.1109/TPAMI.2025.3593987. Online ahead of print.

ABSTRACT

Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expensive computational requirements. This challenge has recently reached another scale with the emergence of large language models (LLMs). In order to reduce both their memory footprint and latency, a promising technique is quantization. It consists in converting floating point representations to low bit-width fixed point representations, usually by assuming a uniform mapping onto a regular grid. This process, referred to in the literature as uniform quantization, may however be ill-suited as most DNN weights and activations follow a bell-shaped distribution. This is even worse on LLMs whose weight distributions are known to exhibit large, high impact, outlier values. In this work, we propose an improvement over the most commonly adopted way to tackle this limitation in deep learning models quantization, namely, non-uniform quantization. NUPES leverages automorphisms to preserve the scalar multiplications. Such transformations are derived from power functions. However, the optimization of the exponent parameter and weight values remains a challenging and novel problem which could not be solved with previous post training optimization techniques which only learn to round up or down weight values in order to preserve the predictive function. We circumvent this limitation with a new paradigm: learning new quantized weights over the entire quantized space. Similarly, we enable the optimization of the power exponent, i.e. the optimization of the quantization operator itself during training by alleviating all the numerical instabilities. The resulting predictive function is compatible with integer-only low-bit inference. We show the ability of the method to achieve state-of-the-art compression rates in both, data-free and data-driven configurations. Our empirical benchmarks highlight the ability of NUPES to circumvent the limitations of previous post-training quantization techniques on transformers and large language models in particular.

PMID:40758517 | DOI:10.1109/TPAMI.2025.3593987

IEEE Trans Med Imaging. 2025 Aug 4;PP. doi: 10.1109/TMI.2025.3595111. Online ahead of print.

ABSTRACT

Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on labeled data, demanding extensive human annotation efforts. In this study, we present a novel human annotation-free method by leveraging pre-trained Vision-Language Models (VLMs). Without human annotation, pseudo-labels of the training set are obtained by utilizing the zero-shot inference capabilities of VLM, which may contain a lot of noise due to the domain gap between the pre-training and target datasets. To address this issue, we introduce VLM-CPL, a novel approach that contains two noisy label filtering techniques with a semi-supervised learning strategy. Specifically, we first obtain prompt-based pseudo-labels with uncertainty estimation by zero-shot inference with the VLM using multiple augmented views of an input. Then, by leveraging the feature representation ability of VLM, we obtain feature-based pseudo-labels via sample clustering in the feature space. Prompt-feature consensus is introduced to select reliable samples based on the consensus between the two types of pseudo-labels. We further propose High-confidence Cross Supervision by to learn from samples with reliable pseudo-labels and the remaining unlabeled samples. Additionally, we present an innovative open-set prompting strategy that filters irrelevant patches from whole slides to enhance the quality of selected patches. Experimental results on five public pathological image datasets for patch-level and slide-level classification showed that our method substantially outperformed zero-shot classification by VLMs, and was superior to existing noisy label learning methods. The code is publicly available at https://github.com/HiLab-git/VLM-CPL.

PMID:40758498 | DOI:10.1109/TMI.2025.3595111

IEEE Trans Med Imaging. 2025 Aug 4;PP. doi: 10.1109/TMI.2025.3594724. Online ahead of print.

ABSTRACT

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability.

PMID:40758497 | DOI:10.1109/TMI.2025.3594724

IEEE J Biomed Health Inform. 2025 Aug 4;PP. doi: 10.1109/JBHI.2025.3595101. Online ahead of print.

ABSTRACT

Spatial transcriptomics (ST), known as an expensive medical examination, plays an important role in analyzing the spatial heterogeneity of tumors. When considering the correlation between tissue morphological patterns and gene profiles, predicting corresponding gene expression from pathology image obtained from affordable biopsies is regarded as an instantaneous and cost-effective alternative. However, accurately modeling the complex and nonlinear relationship between histological features and gene expression remains challenging. Existing deep learning models often struggle to generalize on limited ST datasets due to their large and overparameterized architectures. The primary advantage of kernel adaptive filtering (KAF) lies in its ability to transform a challenging nonlinear problem arising in the original space into a linear regression problem in the higher-dimensional feature space via kernel methods. Therefore, this paper proposes a framework called KAFSTExp, which utilizes the state-ofthe-art pathology foundation model UNI to encode image feature vectors, and then introduces the kernel least mean square algorithm with Nystrom approximation to predict the ¨ normalized transcript counts of specific genes. Extensive experiments show that KAFSTExp significantly improves prediction accuracy while reducing computational cost and training time. KAFSTExp demonstrates consistent performance gains across multiple ST datasets, achieving relative improvements in PCC ranging from 1.24% to 94.23%, with an average increase of 19.80% over the best-performing non-KAF methods. External validation and further clinical analysis confirm the generalization performance and clinical application value of the proposed KAFSTExp.

PMID:40758493 | DOI:10.1109/JBHI.2025.3595101

Proc Natl Acad Sci U S A. 2025 Aug 12;122(32):e2423437122. doi: 10.1073/pnas.2423437122. Epub 2025 Aug 4.

ABSTRACT

During development, groups of cells generate shape by coordinating their mechanical properties through an interplay of self-organization and prepatterning. Hydra displays a striking planar pattern of actin fibers at the organism scale, and mechanics influence the morphogenesis of biological structures during its prepatterned regeneration. However, how mechanics participate in the formation of an ordered pattern from a totally disordered state remains unknown. To study this, we used cellular aggregates formed from dissociated Hydra cells, which initially lose all actin polarity yet regenerate a long-range actin pattern. We showed quantitatively that the actin meshwork evolves from a disordered symmetric state to an ordered state in which rotational symmetry is broken, and translation symmetry is partially broken, with the nematic and smectic order parameters increasing over days. During the first hours, the actin meshwork displayed spatial heterogeneity in the nematic order parameter, and ordered domains separated by line defects progressively grew and fused. This suggests that local cell-cell interactions drive the transition from disorder to order. To understand the mechanism of ordering, we perturbed the tissue's physical constraints. We showed that while topology and geometry do not have a direct effect, anisotropic stretch biases the emerging orientation of the actin meshwork within hours. Surprisingly, although a Wnt head organizer is expected to play a role in the actin ordering, the stretch-associated alignment happened without the prior formation of a head organizer. This demonstrates the role of tissue mechanics in the alignment of the actin fibers during the disorder-to-order transition.

PMID:40758890 | DOI:10.1073/pnas.2423437122

Proc Natl Acad Sci U S A. 2025 Aug 12;122(32):e2505217122. doi: 10.1073/pnas.2505217122. Epub 2025 Aug 4.

ABSTRACT

Ribosome-associated quality control (RQC) is a pivotal biological process that governs the fidelity of messenger RNA (mRNA) homeostasis and protein synthesis. Defects in RQC are implicated in cellular dysfunction and proteotoxicity, but their impact on aging remains elusive. Here, we show that Pelota, the ribosome rescue factor, promotes longevity and protects against age-related pathological phenotypes in multiple metazoan species. By performing a targeted genetic screen, we find that Pelota is indispensable for longevity in the nematode Caenorhabditis elegans. We show that Pelota mitigates premature senescence in cultured human cells, muscle aging in mice, and neuropathology in cellular and organoid models of Alzheimer's disease. Mechanistically, we demonstrate that Pelota maintains autophagy-mediated proteostasis, by preventing the hyperactivation of mechanistic target of rapamycin signaling. Overall, our work highlights the conserved functional significance of RQC, regulated by Pelota, in extending lifespan and protecting diverse species against age-associated disease phenotypes.

PMID:40758887 | DOI:10.1073/pnas.2505217122

PLoS Pathog. 2025 Aug 4;21(8):e1013371. doi: 10.1371/journal.ppat.1013371. Online ahead of print.

ABSTRACT

The zoonotic transmission of bat coronaviruses poses a threat to human health. However, the diversity of bat-borne coronaviruses remains poorly characterized in many geographical areas. Here, we recovered eight coronavirus genomes by performing a metagenomic analysis of fecal samples from hundreds of individual bats captured in Spain, a country with high bat diversity. Three of these genomes corresponded to potentially novel coronavirus species belonging to the alphacoronavirus genus. Phylogenetic analyses revealed that some of these viruses are closely related to coronaviruses previously described in bats from other countries, suggesting a shared viral reservoir worldwide. Using viral pseudotypes, we investigated the receptor usage of the identified viruses and found that one of them can use human ACE2, albeit with lower affinity than SARS-CoV-2. However, the receptor usage of the other viruses remains unknown. This study broadens our understanding of coronavirus diversity and identifies research priorities for the prevention of zoonotic viral outbreaks.

PMID:40758759 | DOI:10.1371/journal.ppat.1013371

J Vis Exp. 2025 Jul 18;(221). doi: 10.3791/66840.

ABSTRACT

Enhancers are DNA regions that regulate gene expression. Mutations within enhancers can result in abnormal gene regulation leading to disease. Therefore, identifying enhancers that regulate gene activity in specific tissues is crucial for understanding the genetic basis of disease. However, enhancers are difficult to identify as they do not encode proteins. While numerous enhancer repositories and identification tools are available, the complexity of these tools can present a challenge for biologists. To facilitate biologists in using these resources, we present a biologist-friendly protocol (https://github.com/Ramialison-Lab/EnhancerWorkflow) which leverages existing web-based genomics data such as H3K4me1 and H3K27ac histone marks and chromatin conformation analysis (Hi-C) data to discover enhancers associated with a gene of interest (GoI) in a target tissue where the enhancer is active. This protocol is entirely web-based and does not require programming skills from end-users. We demonstrated the utility of this approach by characterising candidate enhancers regulating TBX5, a gene critical for heart development. This protocol facilitates the identification of enhancers associated with this gene in the left ventricle.

PMID:40758622 | DOI:10.3791/66840

Biochem Biophys Rep. 2025 Jul 25;43:102175. doi: 10.1016/j.bbrep.2025.102175. eCollection 2025 Sep.

ABSTRACT

Candida glabrata is well known to be the second most common cause of invasive candidiasis (IC) within immunocompromised and hospitalized patients, after Candida albicans. Candida species adhere to host cells and implanted medical devices by means of cell wall proteins (CWP), of which the moonlight proteins have recently been described and are of particular importance because they have been identified in response to various virulence and/or pathogenic factors. Among the identified CWP moonlights, fructose-bisphosphate aldolase (Fba1) and pyruvate kinase (Pk) have been observed to confer immune protection against C. albicans and C. glabrata in a mouse model. In other pathogens, these proteins have been used as therapeutic targets. As the treatment of IC has been based on four main drug classes for decades, the Candida species has developed resistance mechanisms. In addition, C. glabrata has an innate resistance to the antifungal drugs, which makes the treatment of IC by this pathogen difficult. It is essential to have new formulations that allow new treatments of patients affected by this pathogen, so new targets with antifungal activity is of primary necessity. For this purpose, in this study we propose the moonlight CWPs Fba1 and Pk as novel candidates for drug targets. Using structural modeling, virtual database analysis, in vitro susceptibility tests, and enzymatic activity assays, we propose the use of new chemical molecules as potential antifungals against C. glabrata. In this sense, we chose to evaluate three chemical molecules (FE1, FE2 and FE3), whose chemical structure gives them the possible molecular leadership against Fba1 and Pk. Through the susceptibility experiments, our data showed that of the three molecules evaluated, FE1 was the best ligand against C. glabrata. We also found that Fba1 and Pk of C. glabrata had the characteristics of therapeutic targets against IC. In the present work, considering a group of tools in silico and experiments in vitro it was possible to identify the best candidate molecule as a possible antifungal for the treatment of IC caused by C. glabrata.

PMID:40756780 | PMC:PMC12318263 | DOI:10.1016/j.bbrep.2025.102175

Theranostics. 2025 Jun 23;15(15):7653-7676. doi: 10.7150/thno.112608. eCollection 2025.

ABSTRACT

Background: The complexity of biological systems and misconceptions about neuroprotection have hindered the development of neuroprotective drugs for ischemic stroke. This study aims to identify new neuroprotective agents by integrating ischemic stroke transcriptomics with neuronal protection data using a Multidimensional Data-Driven Computational Drug Repositioning strategy (MDCDR). Methods: Three microarray datasets related to ischemic stroke (GSE16561, GSE58294, and GSE22255) were obtained from the GEO dataset and pre - processed to analyze differentially expressed genes (DEGs). The Connectivity Map (CMap) database was used to predict potential drugs. A neuroprotection activity prediction model was constructed by combining six molecular fingerprints with three machine learning algorithms (Random Forest RF, Support Vector Machine SVM, Gradient Boosting Decision Tree GBDT) to screen for potential neuroprotective agents. The efficacy of the screened compounds was evaluated through in vitro experiments on SH-SY5Y cells treated with oxygen-glucose deprivation/reperfusion (OGD/R) and in vivo experiments on middle cerebral artery occlusion/reperfusion (MCAO/R) rat models. Multiple experimental techniques (such as RNA sequencing, DARTS, CETSA, etc.) were used to explore their potential mechanisms of action. Results: The MDCDR strategy screened out 19 potential neuroprotective agents, among which sulbutiamine (SUL) stood out. SUL significantly increased the survival rate, reduced neurological deficit scores, and decreased neuronal loss in MCAO/R rat models, and inhibited cell death in OGD/R - induced cell models. Mechanistic studies revealed that SUL inhibited pyruvate dehydrogenase kinase 2 (PDK2), enhanced mitochondrial function, reduced reactive oxygen species (ROS) levels, thereby suppressing the MAPK signaling pathway and reducing neuronal apoptosis. Silencing PDK2 abolished the protective effect of SUL on OGD/R - treated SH - SY5Y cells. Conclusion: This study successfully developed the MDCDR strategy for screening neuroprotective agents for ischemic stroke. SUL was identified as a promising neuroprotective agent, and PDK2 was a crucial target. This research provides new directions and a theoretical basis for the development of neuroprotective agents against ischemic stroke.

PMID:40756350 | PMC:PMC12316036 | DOI:10.7150/thno.112608

J R Soc N Z. 2024 Oct 6;55(6):2422-2439. doi: 10.1080/03036758.2024.2406824. eCollection 2025.

ABSTRACT

Pharmacogenetics is the study of genetic variants in genes which may impact on the outcome of drug treatment, either through safety considerations (occurrence of adverse drug reactions or therapeutic failure) or altered drug pharmacokinetics. This paper provides a brief history of pharmacogenetics research in the Aotearoa/New Zealand context, and a commentary on our current state. Factors that have limited translation of pharmacogenetic knowledge into ou r healthcare system are considered, and possible solutions to these are proposed. Pharmacogenetic knowledge has long been invoked as a way to improve the safety and success of drug prescribing, but (with some notable exceptions) it has largely failed to achieve this promise. Several barriers to clinical implementation need to be overcome to ensure that pharmacogenetics becomes a key component of precision health for all people in Aotearoa/New Zealand.

PMID:40756875 | PMC:PMC12315163 | DOI:10.1080/03036758.2024.2406824

J R Soc N Z. 2024 Nov 6;55(6):2506-2512. doi: 10.1080/03036758.2024.2392804. eCollection 2025.

ABSTRACT

Genomic sequencing is a transformative technology, and its integration and implementation in the practice of medicine requires systemwide change. Genomic technology is already influencing many areas of medicine: rare disease, reproductive health, cancer, pharmacogenetics and infectious disease. Ensuring genomic tests are available and are adopted in an effective and efficient way requires forethought and simultaneous change across multiple areas of health. Beyond the technical requirements of sequencing and bioinformatics capacity, planning will need to address data integration and interpretation, workforce capacity and capability, public acceptability, government engagement and ethical and legislative issues. To ensure all these considerations are managed, many states or nations have developed policy frameworks or strategies. Implementation science enables us to understand the factors that support adoption of new technologies. The lessons learnt from other jurisdictions who have implemented genomic medicine programmes clearly support the need for a coordinated whole of systems approach. New Zealand should heed these lessons. New Zealand needs a genomic medicine strategy.

PMID:40756825 | PMC:PMC12315147 | DOI:10.1080/03036758.2024.2392804

Genet Med Open. 2025 Jun 30;3:103441. doi: 10.1016/j.gimo.2025.103441. eCollection 2025.

ABSTRACT

PURPOSE: We aimed to identify themes and knowledge gaps about the current state of cardiovascular genetic testing implementation from the nongenetics clinician's perspective.

METHODS: Cardiovascular genetics is an emerging subspecialty for which no formal training exists, and its implementation is a complex endeavor. Therefore, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review, which involves broad inclusion criteria and is agnostic to the quality of evidence, we conducted a scoping review methodology.

RESULTS: PubMed and Scopus searches identified 32 original research articles documenting limited implementation by nongenetics professionals in familial hypercholesterolemia (n = 10), cardiovascular pharmacogenomics (n = 10), cardiomyopathy/arrhythmia (n = 5), and congenital heart defects (n = 1). Common barriers included education and cost, whereas facilitators included multidisciplinary collaboration and adopting technology to assist with case identification. The perspective of payers and public policymakers was largely missing from this literature.

CONCLUSION: Cardiovascular genetics implementation by nongenetics professionals is still in its early stages. Based on our results, we recommend prioritizing implementation research on topics related to clinician education, health economics, technology, and collaborative models in consultation with payers and policymakers. Informed by barriers and facilitators, we offer suggestions to clinicians and researchers implementing genetic medicine in cardiology clinics.

PMID:40756326 | PMC:PMC12314394 | DOI:10.1016/j.gimo.2025.103441

Lancet Respir Med. 2025 Aug;13(8):698-708. doi: 10.1016/S2213-2600(25)00089-X. Epub 2025 Jun 16.

ABSTRACT

BACKGROUND: Despite increasing recognition of bronchiectasis worldwide, there are no multicountry data characterising bronchiectasis in children. We aimed to describe clinical features, comparing inter-country and regional variations, and describe indices of overall quality-of-care standards assessed against international consensus statements for children and young people with bronchiectasis.

METHODS: Child-BEAR-Net is an international collaborative paediatric bronchiectasis network across several continents. Using our International Paediatric Bronchiectasis Registry data from secondary and tertiary hospitals across eight countries, we conducted a multicentre, cross-sectional cohort study of all patients in the registry younger than 18 years diagnosed with bronchiectasis. Data were grouped into four geographical regions: Australia, South Africa, Greece-Italy-Spain, and Albania-Türkiye-Ukraine. Patients with cystic fibrosis or a history of heart or lung transplantation were excluded. We assessed baseline clinical characteristics, causes, treatments, and quality-of-care indicators, and compared findings across regions. Data were analysed using descriptive statistics and non-parametric tests for between-group comparisons.

FINDINGS: Between June 1, 2020, and Feb 9, 2024, 408 patients were enrolled (median age at diagnosis 6·0 years [IQR 3·2-9·0]; 229 (56%) male and 179 (44%) female patients). The most common underlying causes were post-infection (127 [31%]), primary and secondary immunodeficiencies (79 [19%]), and known genetic disorders (55 [13%]). Common comorbidities included asthma (70 [17%]), otorhinolaryngeal disorders (58 [14%]), and congenital major airway malformation (51 [13%]). In the previous 12 months, 106 (38%) had at least three exacerbations and 89 (49%) required hospitalisation at least once. 107 (27%) of 400 reported daily sputum. Lung function was normal in 133 (59%) of 227 patients but with considerable between-group differences (median forced vital capacity Z score ranged from -0·12 [-0·95 to 0·65] in Australia to -1·54 [-3·39 to -0·04] in South Africa). We found marked inter-group differences in lower airway bacteria (Haemophilus influenzae in 56 [70%] of 80 patients in Australia to three [16%] of 19 in Albania-Türkiye-Ukraine; Pseudomonas aeruginosa in eight [24%] of 34 in South Africa to one [5%] in Albania-Türkiye-Ukraine), treatment (long-term azithromycin for 47 [50%] of 94 in Greece-Italy-Spain to 15 [19%] of 79 in Albania-Türkiye-Ukraine; and inhaled corticosteroids for 48 [61%] in Albania-Türkiye-Ukraine to 28 [22%] of 126 in Australia), and radiographic markers (cystic bronchiectasis in 49 [45%] of 109 in South Africa to three [2%] of 126 in Australia [p<0·0001]). In quality-of-care standard markers, the recommended panel of investigations was done in 66-95% of patients; only 78 (47%) of 167 saw a paediatric physiotherapist in the previous 12 months.

INTERPRETATION: Our study presents the first internationally derived paediatric registry data highlighting geographical variations in cause, lung function, bacteriology, and treatment in children and young people with bronchiectasis, as well as the need to improve quality care.

FUNDING: None.

PMID:40757932 | DOI:10.1016/S2213-2600(25)00089-X