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PubMed2026

Application of Three-Dimensionally Printed Surgical Guides in Precise Sacral Tumor Excision and Defect Reconstruction.

OBJECTIVE: Precise resection of sacral tumors remains technically demanding due to their deep anatomical location and close proximity to critical neurovascular structures. Conventional freehand techniques often result in suboptimal resection margins, excessive blood loss, and compromised lumbopelvic stability. This study evaluated whether patient-specific three-dimensional (3D)-printed guiding templates improve surgical accuracy and perioperative outcomes in sacral tumor resection and reconstruction. METHODS: Nineteen patients undergoing en bloc sacral tumor resection (S1-S3 involvement) with spinopelvic reconstruction (2006-2020) were retrospectively analyzed. Patients were divided into a 3D-printing group (n = 10) and a conventional freehand group (n = 9). In the 3D-printing group, computer-aided design and 3D-printed templates were used for osteotomy, screw placement, and defect reconstruction. Perioperative metrics, surgical accuracy, and complications were compared between groups using Welch's t-test and the Hodges-Lehmann method; oncologic events during follow-up were recorded descriptively. RESULTS: The 3D-printing group demonstrated significantly shorter operative time (456.5 ± 62.36 vs. 574.44 ± 114.58 min, p = 0.012), reduced blood loss (4081.40 ± 838.99 vs. 5090.0 ± 1059.67 mL, p = 0.034), and fewer fluoroscopic exposures (4.2 ± 0.79 vs. 10.0 ± 1.58, p < 0.001) compared with the conventional group. Osteotomy accuracy was also superior in the 3D-printing group, with significantly lower angular deviation (3.33° ± 0.45° vs. 6.79° ± 2.16°, p = 0.0012). Postoperative complication rates were comparable (30% vs. 44.4%, p = 0.649), but hospital stay was significantly shorter in the 3D-printing group (10.7 ± 2.71 vs. 18.11 ± 4.01 days, p < 0.001). CONCLUSION: Patient-specific 3D-printed guiding templates enhance precision in sacral tumor excision and reconstruction, improving surgical efficiency and perioperative safety. This computer-assisted, template-guided approach represents a valuable advancement for complex sacral oncologic surgery.

Humans↗

PubMed2026

A hybrid effectiveness-implementation trial to integrate precision skin cancer risk feedback in federally qualified health centers.

BACKGROUND: Skin cancers are the most common type of cancer in the United States, occur in all segments of the population, and are preventable. Our previous research with primary care patients' demonstrated interest in and efficacy of a precision prevention intervention providing feedback on MC1R risk level (higher versus average) in combination with prevention education materials relative to a standard educational intervention. Our current study is a hybrid type 1 effectiveness-implementation trial deployed at six federally-qualified health centers. This paper presents the study protocol. METHODS: A community advisory panel will guide development of study materials and measures. Staff training at each clinic will be completed in-person. Patients will be approached and screened in-person. Those completing genetic testing and the baseline survey will be randomized to the precision versus standard intervention for each risk level with a target sample size of 286 for each combination. Primary outcomes of effectiveness, assessed at 6 and 12 months, include a tanning score (5 items assessing intentional and unintentional tanning), number of sunburns, conduct of a skin self-examination, and electronic health record documentation of clinician-patient communication about skin cancer prevention. Effectiveness comparisons will focus on the precision relative to the standard intervention among higher risk participants. Implementation data will be collected to identify barriers and facilitators. RESULTS: Effectiveness and implementation outcomes will be evaluated following study completion. CONCLUSIONS: Results will guide subsequent scale-up of the precision intervention, including modifications of the intervention as well as methods for implementation. CLINICAL TRIALS IDENTIFIER: NCT07222995.

Humans↗

PubMed2026

Precision targeting of teacher burnout using network-informed ecological momentary interventions.

Teacher well-being affects classroom functioning and workforce stability, yet generic digital programs rarely use person-specific affect dynamics to select support. This cluster-randomised trial evaluated whether micro-interventions selected from high expected influence (EI) nodes in teachers' contemporaneous affect networks produced larger changes in burnout-related EI and everyday happiness than content-matched random allocation. The objectives were to estimate allocation effects on changes in estimated network summaries and happiness, evaluate network change as a statistical mediator, examine personality moderation, and benchmark simpler allocation rules. A two-arm cluster randomised platform trial was conducted in 84 public schools across four urban districts in H Province. After a 14 day baseline of ecological momentary assessment (EMA), person specific partial correlation networks were estimated for happiness, exhaustion, detachment, efficacy and rumination. An optimisation engine prioritised three brief micro-intervention types per teacher according to baseline EI, while the active control received the same library without network information. EMA continued for 8 weeks; Bayesian multilevel models, permutation-based mediation, and benchmarking analyses were applied. EI-based targeting produced larger reductions in the composite EI-change index than active control (mean difference 0.11, 95% credible interval 0.08 to 0.14) and higher week 7 EMA happiness (4.4 points on a 0 to 100 scale, 95% credible interval 2.7 to 6.0), with a positive arm by week slope difference of 0.62 points per week (95% credible interval 0.39 to 0.85). Model-based mediation estimates were consistent with approximately one half of the happiness difference being statistically associated with change in the composite EI-change index (average conditional mediation estimate 3.5 points, 95% credible interval 2.0 to 5.2). Benchmarking showed smaller gains under severity, threshold, or group-level centrality rules. Effects were stronger among teachers higher in conscientiousness. The findings indicate that integrating EMA, network modelling, and EI-driven optimisation yields measurable gains beyond content-matched exposure, providing a proof of concept for district-scale precision mental health that requires prospective implementation testing. Replication in additional regions, expanded node sets, and longer follow up are warranted to assess durability and generalisability.

Female↗

PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗

PubMed2026

Targeted Nanoparticle Delivery CRISPR/Cas9: overcoming biological barriers, enhancing stability, and improving therapeutic precision.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has emerged as a promising gene-editing platform for genetic disorders; however, its in vivo application remains limited by low delivery efficiency and biological barriers. Many CRISPR payloads fail to reach target sites due to extracellular degradation, immune clearance, and intracellular trafficking limitations. This review examines the interplay between biological barriers and nanoparticle engineering strategies for CRISPR/Cas9 delivery. A barrier-oriented engineering approach is proposed as a central framework, encompassing ligand-based surface modification for enhanced targeting and uptake, improved circulation stability via PEGylation and biomimetic coatings, and optimized payload release through endosomal escape strategies. Stimulus-responsive nanoparticle systems further enable spatiotemporal control over payload release. Nuclear targeting strategies, including optimization of nuclear localization signals (NLS) and exploitation of endogenous trafficking pathways, are highlighted as key factors for improving genome-level editing efficiency. Despite these advances, major challenges-including limited intracellular delivery efficiency, insufficient targeting precision, and safety concerns-continue to hinder clinical translation. Future directions highlight artificial intelligence-driven nanoparticle design, personalized delivery systems, and next-generation CRISPR platforms. Overall, an integrated, barrier-oriented engineering strategy is essential for advancing CRISPR/Cas9 delivery toward clinical applications, ultimately advancing global good health and well-being.

CRISPR/Cas9↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Retrosigmoid craniotomy surgical guide: The way forward for precise exposure of the transverse-sigmoid sinuses.

INTRODUCTION: The retrosigmoid craniotomy is the workhorse approach to the cerebellopontine angle. Accurate localisation of the transverse-sigmoid junction (TSJ) is key for optimised exposure and cerebellar retraction. Various methods, both anatomical and navigational, have been used but with suboptimal results. We utilised a 3D-printed retrosigmoid surgical guide in an attempt to overcome this and report our early outcomes and experiences in the design, production and utilisation of the guide. METHODS: This is a prospective cohort study of the patients with retrosigmoid craniotomies performed using the surgical guides. Patient demographics and diagnoses, along with the accuracy of the planned burrhole and craniotomy, need for craniotomy extension, presence of venous sinus injury, set-up time, and cost were reported. RESULTS: There were ten cerebellopontine angle cases in which the surgical guides were utilised, three petrous meningiomas, two trigeminal neuralgias, two metastasis, and three other tumours. The planned burrhole and craniotomy were precise in all cases with accurate exposure of the TSJ and no requirement for craniotomy extension. The mean set up time was 3.9 min, and the mean cost of the surgical guides was USD 470.90. One elderly patient had an intraoperative transverse sinus injury related to adherent dura that was planned for exposure. CONCLUSION: The 3D-printed surgical guide is a potential solution to the rapid, precise and consistent identification of the TSJ when performing a retrosigmoid craniotomy. We present our early experience and discuss nuances in the designing, production, and intraoperative phases to optimise the precision of this guide. We suggest two methods to avoid sinus injury in elderly patients: either to plan the craniotomy to the edge of the sinus, or to plan sinus exposure but to use burr drills rather than the osteotome, as in our case, to expose the sinus.

Humans↗

PubMed2026

Engineered MXene-based nanozyme platform: NIR-II photothermal and dual enzyme-mimetic potentiated chemodynamic synergy for precision tumor eradication.

The antioxidant defense barrier in the tumor microenvironment, particularly glutathione (GSH), considerably restricts the therapeutic efficacy of chemodynamic therapy (CDT). Moreover, CDT generally exhibits relatively mild therapeutic efficacy owing to its intrinsic reaction kinetics, making it difficult to achieve complete tumor eradication within a short time. To address these issues, we construct a functionalized nanotherapeutic platform, Nb2CTx@Ru-PEG2000-FA (NCRPF), for tumor photothermal ablation and enhanced CDT resulting from GSH depletion. NCRPF possesses three key advantages: 1. Efficient near-infrared II photothermal conversion (η = 42.08%), raising the tumor temperature above 45 °C within 90 s for rapid ablation; 2. Dual peroxidase-like and glutathione peroxidase-like activities, simultaneously depleting GSH and generating a burst of ·OH to eliminate residual tumors; 3. Targeted tumor accumulation with 2.9-fold higher efficiency than passive diffusion. Both in vitro and in vivo results confirm that this combined strategy achieves complete tumor eradication with favorable biosafety. Collectively, the NCRPF nanotherapeutic system provides a powerful new paradigm with high translational potential for the complete eradication of breast cancer.

Animals↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

Comparative Effectiveness of Pharmacogenomics for Treatment of Depression.

PURPOSE/BACKGROUND: Pharmacogenomics (PGx), or the use of genetic information to assess drug-gene interactions, is an important step toward precision medicine. It is unclear if clinician use of PGx yields better outcomes for their patients. This study compared the effectiveness of combinatorial PGx-guided plus guideline-informed treatment (PGx+GIT) with guideline-informed treatment (GIT) alone to improve well-being in individuals with major depressive disorder. METHODS/PROCEDURES: Eligible participants (N=201) were randomized to PGx+GIT or GIT alone. PGx was measured with the proprietary GeneSight combinatorial test. PGx+GIT participant clinicians received test results within 2 business days to inform decisions about medication changes. Participants completed the World Health Organization Well-Being Index (WHO-5), Patient Health Questionnaire (PHQ-9), and PROMIS Profile physical functioning and social roles and activity domains every 2 weeks for 2 months and then every 2 months for the remaining 10 months. Monthly medication changes operationalized as necessary clinical adjustments were tracked with the medication recommendation tracking form. FINDINGS/RESULTS: Both groups improved average well-being over the 12-month study period (model-based change in WHO-5 per log (week) [95% CI]: 4.1 [3.3, 5.0] PGx+GIT and 4.8 [4.0, 5.5] GIT). PGx+GIT did not result in superior improvement in well-being (model-based difference [95% CI]: -0.6 [-1.8, 0.5], P =0.270), or any secondary outcomes. The effect of randomized treatment on well-being was not moderated by depression severity, number of previous failed medications for major depressive disorder, or presence of a comorbid condition. IMPLICATIONS/CONCLUSIONS: These data suggest PGx+GIT was not superior to GIT alone, possibly due to a ceiling effect of GIT, or PGx did not yield better results.

Humans↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Exploring precision risk in pediatric vesicoureteral reflux: Innate immune gene variations and reflux outcomes in the RIVUR cohort.

INTRODUCTION: Children with vesicoureteral reflux (VUR) are at increased risk for morbidity from recurrent urinary tract infections (UTIs), yet the factors influencing spontaneous VUR resolution remain poorly defined. This study evaluates whether genetic variations in key urinary innate immune effectors (DEFA1A3, DMBT1, and RNASE7) influences VUR resolution and interacts with prophylaxis to alter clinical response. METHODS: We conducted a secondary analysis of 303 RIVUR participants with available DEFA1A3 and DMBT1 copy number variation (CNV) data and RNASE7 rs1263872 genotype. Primary outcomes were (1) VUR improvement (decrease in grade) and (2) VUR resolution at study exit. Multivariable logistic regression models included genotype, treatment, and their interactions, adjusting for age, sex, baseline grade (high vs low), laterality, bowel/bladder dysfunction, and any UTI. Internal validation used 2000-sample bootstrap with bias-corrected and accelerated confidence intervals and influence diagnostics. RESULTS: Clinical covariates did not significantly predict VUR improvement. Children with DEFA1A3 CNV >5 had higher odds of improvement (OR 2.36, 95% CI 1.12-4.96, p = 0.023), an effect that remained significant in bootstrap analyses. High-grade VUR was associated with lower odds of resolution (OR 0.34, 95% CI 0.12-0.94, p = 0.038). A significant interaction was observed between prophylaxis and high DMBT1 copy number for VUR resolution (interaction OR 2.99, 95% CI 1.11-8.04, p = 0.031); no interaction was seen for improvement. RNASE7 rs1263872 was not associated with either outcome. CONCLUSION: Innate immune gene variation may contribute to heterogeneity in VUR outcomes. High DEFA1A3 copy number was associated with reflux improvement and a DMBT1-prophylaxis interaction was associated with reflux resolution. The results of this study is hypothesis-generating and prompt further evaluation to assess whether a subset of children may experience structural benefit from prophylaxis or have a more favorable natural history based on their innate immune genotype.

Humans↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗

PubMed2026

Long-term microbiome and clinical effects of a microbiome-guided personalized diet versus low-FODMAP diet in irritable bowel syndrome: A 12-month follow-up randomized controlled trial.

Dietary therapy is central to irritable bowel syndrome (IBS) management, yet the long-term durability of the low-FODMAP diet (LFD), and of microbiome-guided personalization, remains unclear. We assessed the long-term clinical and gut-microbiome effects of a microbiome-guided personalized diet (PD) compared with a standard LFD in adults meeting Rome IV criteria for IBS. In this multicenter, open-label randomized controlled trial with blinded outcome assessment, participants who completed a 6-week dietary intervention (PD or LFD) were followed at 6 and 12 months without further dietary intervention. Outcomes included the IBS Severity Scoring System (IBS-SSS), IBS Quality of Life (IBS-QOL), and the Hospital Anxiety and Depression Scale (HADS); gut microbiota were profiled by 16S rRNA sequencing. Longitudinal changes were evaluated using linear mixed-effects models, responder analyses, PERMANOVA, and PERMDISP. Both diets reduced IBS-SSS at 6 weeks. PD maintained symptom improvement at 6 and 12 months (-82.0 and -78.3 points from baseline), whereas LFD benefits regressed by 12 months (+29.3 points; between-group p = 0.001). At 12 months, IBS-SSS responder rates were higher with PD than LFD (62.5% vs 34.5%; absolute risk difference +28.0%, 95% CI 4.2-47.7; Fisher p = 0.029), and IBS-QOL, HADS-anxiety, and HADS-depression showed more favourable trajectories with PD. PD was associated with sustained Shannon alpha-diversity gains (+0.488 at 6 weeks; +0.205 at 12 months; both p < 0.01). A modest between-group beta-diversity difference at 6 months (R2 = 0.035; p = 0.011) was not significant at 12 months. This hypothesis-generating follow-up suggests more durable benefit with PD; larger trials powered for long-term clinical and microbiome outcomes are warranted.

Humans↗