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PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2026

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.

Plant diseases destroy 20-40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

convolutional neural networks↗

PubMed1979

Ocular complications following blast transformation in chronic myelogenous leukemia.

A number of ocular problems compromising vision occurred in a patient with chronic myeloid leukemia following blastic transformation. Hemorrhagic retinopathy developed with systemic relapse and resolved with control of systemic disease. Optic nerve involvement occurred with meningeal leukemia and was controlled with intrathecal cytosine arabinoside and methotrexate. Leukemic retinal infiltrates developed despite control of systemic and meningeal disease and were successfully treated with radiation therapy. Finally, bilateral vitreous hemorrhages occurred, severely impairing vision. Leukemic infiltration of the eye may occur with increasing frequency in CML as the survival following bastic transformation improves. Infiltration should be recognized and treated promptly if serious loss of vision is to be avoided. Central nervous system prophylaxis should be considered in patients achieving a complete response following therapy for transformation.

Adult↗

PubMed2026

Epigenetic Reprogramming and Zygotic Genome Activation in Human Preimplantation Development: Mechanisms, Models, and Translational Prospects.

PURPOSE: Early human embryogenesis unfolds through a tightly coupled sequence of events-clearance of maternal transcripts, remodeling of parental chromatin, zygotic genome activation (ZGA), lineage segregation, implantation, and post-implantation patterning-accompanied by epigenetic reprogramming, including X-chromosome dosage compensation around the time of implantation. This review aims to synthesize recent advances in understanding this developmental program and to consider their implications for reproductive medicine. METHODS: I review recent literature on human early embryogenesis, with particular emphasis on findings enabled by single-cell genomics and stem-cell-based embryo modeling, and integrate these insights to identify human-specific features of early development. RESULTS: These approaches have made previously inaccessible aspects of human early embryogenesis experimentally tractable, revealing molecular and epigenetic features that distinguish human development from that of model organisms, including species-specific dynamics of ZGA, maternal transcript clearance, chromatin reprogramming, and X-chromosome dosage compensation. CONCLUSIONS: Advances in single-cell genomics and embryo modeling are transforming our understanding of human early embryogenesis. Building on these insights, while recognizing their current limitations, I propose a vision for improving reproductive medicine, including the potential for next-generation embryo selection strategies.

Journal Article↗

PubMed2026

Spectral Transforms as a Tool to Optimize Digital Phenotyping in Biological Images.

Modern livestock breeding has mastered genotyping. Genome-wide association studies, genomic selection, and SNP arrays enable genetic merit prediction at lower cost. However, phenotyping remains the bottleneck, as manual measurement is slow, expensive, subjective, and unable to capture spatial or temporal trait organization. Digital phenotyping via artificial intelligence could resolve this, but deep learning requires thousands of labelled examples, impractical when phenotyping cost itself limits datasets to hundreds of individuals. This creates a paradox: AI could accelerate phenotyping but requires large numbers of samples to train the models. Here, we demonstrate that integrating computer vision with machine learning offers sample-efficient digital phenotyping using eggshell colour as a model system. Rather than learning features from scratch (deep learning), we engineer physically motivated features via Wavelet transforms that decompose images into multi-scale spatial components. Wavelet features captured 14.2 percentage points more variance (R2 = 0.976 vs. 0.834, p < 0.001) than standard colorimetry, with 50% better sample efficiency (achieving at n = 60 what colorimetry required n = 120). Variance decomposition revealed 77% of discriminative capacity derives from spatial patterns (bands, spots, gradients) invisible to scalar averages. Additionally, we identified "cryptic phenotypes" (3.3%) where spatial patterns contradicted average colour, cases where colorimeters failed but Wavelets succeeded. The underlying principle-that spatial decomposition can recover organizational information lost by scalar averaging-may be applicable to other traits with spatial or temporal structure, such as marbling, dermatitis, or pigmentation rhythms, although whether comparable performance gains would be observed remains to be tested empirically. Hence, for breeding programs implementing genomic selection, computer vision-based digital phenotyping captures complex trait variation without massive training datasets, addressing the bottleneck that increasingly limits genetic progress as genotyping becomes trivial.

Wavelet transform↗

PubMed2026

Environmental Release of Genetically Intervened Microorganisms: Towards a New Narrative.

The deliberate release of genetically engineered microorganisms for environmental applications has remained largely blocked since the early days of recombinant DNA technology, when limited ecological knowledge, lack of success stories and public apprehension shaped a culture of caution and restrictive regulation. Despite profound advances in microbial ecology, synthetic biology and genetic design, current frameworks still rely on outdated assumptions and legacy regulations that equate engineered microbes with inherent danger and demand unrealistic forms of absolute containment. This review examines how laboratory-trained microorganisms exist on a continuum with naturally evolved life, and that their risks are neither categorically different nor greater. Rather than pursuing unachievable containment, governance should shift towards traceability, stewardship and long-term monitoring through genomic barcodes, digital twins and transparent oversight. The vision moves from domination and control to care and partnership recognizing engineered microbes as live amendments capable of restoring degraded ecosystems. Achieving this transformation requires new terminology, phased field-trial frameworks, improved scaling methods, and the integration of epistemological perspectives that emphasize reciprocity and coexistence with nature. Reframing biotechnology in this way could finally unlock the capacity of engineered microorganisms to contribute responsibly and effectively to planetary repair in an era of escalating environmental crises.

Microorganisms, Genetically-Modified↗

PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMed2026

A Foundation Model Based CT Biomarker for Non-Invasive Prediction of Response to Neoadjuvant Immunochemotherapy in Non-Small Cell Lung Cancer.

Predicting pathological complete response (pCR) to neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC) is clinically important yet remains challenging. Here, we introduce a foundation model-derived computed tomography (CT) imaging biomarker established from a multi-center cohort of 702 patients. Specifically, we developed and validated a non-invasive baseline CT-based model for risk stratification of pathological response. To address scanner and protocol heterogeneity, we first built a 3D Vision Mamba-based CT super-resolution model trained on 2494 cases for image standardization. We then fine-tuned a lung cancer-specific CT foundation model from a pretrained 3D model (VoCo) using 6643 chest CT scans. Finally, we constructed a multi-task Swin Transformer that jointly performs risk stratification and segments tumors to generate the imaging biomarker. Across five centers, the model achieved consistently strong generalization (AUC: 0.75-0.87) for pCR prediction. Genomic analysis revealed that the biomarker was independent of tumor mutational burden but significantly associated with TP53 mutations, suggesting an association with a radiogenomic phenotype related to this alteration. Together, these results demonstrate a generalizable and biologically meaningful foundation model-based biomarker for non-invasive risk stratification of pathological response in NSCLC.

Female↗

PubMed2026

Novel insights into retinoblastoma: From oncogenic circuitry to precision diagnosis and eye-preserving therapies.

Retinoblastoma (RB) represents the most common primary intraocular malignancy in childhood and stands as a paradigm for translating molecular oncology into precision clinical management. This review synthesizes the comprehensive evolution in the understanding and treatment of RB. First, we deconstruct the intricate oncogenic circuitry that extends far beyond Knudson's classic "two-hit" RB1 inactivation model, describing non-classical MYCN-driven pathogenesis, multi-layered epigenetic reprogramming (including chromatin, RNA and histone changes), and distinct histological subtypes with defined clinical correlates, such as the favorable-prognosis cavitary RB. Single-cell genomics has elucidated the cellular origin from cone precursor cells and intratumoral heterogeneity. Risk stratification has been refined through well-defined classification systems, from the therapy-guiding International Intraocular Retinoblastoma Classification (IIRC) to the comprehensive American Joint Committee on Cancer Tumor-Node-metastasis (AJCC TNM) staging. Furthermore, the diagnostic paradigm has advanced from conventional anatomical imaging to liquid biopsies, enabling non-invasive molecular staging and monitoring via tumor-derived cell-free DNA analysis. Concurrently, the therapeutic landscape has undergone a radical shift, moving from enucleation and external-beam radiotherapy to an era dominated by local sight-preserving strategies. We provide a critical synthesis of the evidence for intravenous chemotherapy and the transformative role of super-selective intra-arterial chemotherapy (IAC), and describe essential randomized controlled trials, technical innovations, and optimized drug regimens. Finally, we explore emerging targeted molecular therapies and future directions. By integrating cutting-edge molecular insights with robust, high-level clinical evidence, this review offers the framework for achieving patient and eye survival as well as vision preservation in children with Retinoblastoma.

Intra-arterial chemotherapy↗

PubMed2026

Proteomics as a theranostic compass in BCR::ABL1-negative myeloproliferative neoplasms: Integrating biomarker discovery with therapeutic stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal haematopoietic stem cell disorders with marked heterogeneity in clinical phenotype, disease trajectory, and therapeutic response. Genomic stratification by driver and cooperating mutations only partially accounts for this variability, leaving gaps in predicting thrombotic risk, fibrotic progression, leukaemic transformation, and treatment benefit. Proteomics bridges this gap by providing function-proximal readouts of protein abundance, post-translational modifications, pathway activity, and intercellular signalling that genomics and transcriptomics cannot capture, positioning it as a theranostic platform in which the same molecular readouts simultaneously inform diagnostic stratification and therapeutic decision-making. We propose a five-stage translational framework spanning from discovery-scale mass spectrometry and affinity-based plasma profiling to targeted validation, multicentre standardisation, and machine learning-integrated clinical panels. Proteomic evidence is synthesised across the following four disease axes: clonal fitness in haematopoietic stem and progenitor cells; bone marrow microenvironmental remodelling and fibrosis; chronic inflammation and thrombosis; and leukaemic transformation. We further describe how phosphoproteomics reveals resistance mechanisms to JAK inhibitors, including AXL-MAPK bypass and PP2A-autophagy-mediated tolerance, and how protein-level biomarkers (BCL2-BCL-XL, RAS-ERK, CAMK2G, and ROCK1/2) can guide individualised therapeutic selection. Affinity-based platforms (Olink PEA and SomaScan) and spatially resolved technologies (CODEX and single-cell proteomics) complement discovery proteomics. At present, however, this evidence base is constrained by small and heterogeneous cohorts, limited cross-platform reproducibility, and a scarcity of independent external validation for candidate protein panels. Realising this vision will require multicentre standardisation, analytically validated panel assays, and prospective clinical studies that translate molecular findings into decision-grade tools for patients with MPNs.

Humans↗

PubMed2025

Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function.

In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.

Molecular Biology↗

PubMed1978

The development of the parachute reaction: a visuo-vestibular response.

The development of the parachute reaction as a postural response has been tested under various optic stimuli in normal and statomotorically retarded infants. Complete reaction consists in symmetric extension of the arms with extension and spreading of the fingers on quick approach to visual surface. Using a glass plate with three different sized stimulus patterns the parachute response is scored as complete or incomplete. The complete reaction to large visual surface develops significantly earlier than to small visual surface. The response also requires simultaneously a visual and a vestibular sensory input. The developmental course of the parachute reaction is statistically significant linear correlated in double logarithmic transformation to the stimulus pattern and to the age indicating a complex biological maturating process. When the whole retinal area was stimulated by moving visual scenes similar to normal environmental conditions the reaction starts with about 4 months of age and is fully developed from 9 months onwards. The time of quickest development is around 6.3 months of age, a period where locomotor abilities shift from horizontal postiion to active verticalisation of the body. In statomotorically retarded infants the response develops significantly later according to their active motor behaviour. Brain integration and computing centers of sensory afferent input could be multilevel from reticular formation to cerebral cortex, wherein the latter, probable the parietal lobe should play the major role. It is concluded that the parachute reaction results from a combined visuovestibular mechanism of interaction in connection with sufficient kinesthetic experience in visuo-motor behaviour.

Age Factors↗