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PubMed2026

The environmental impact of diagnosis and therapy in obstructive sleep Apnea: A systematic review.

Healthcare contributes significantly to global greenhouse gas (GHG) emissions, yet the environmental impact of sleep medicine, particularly the diagnosis and therapy of obstructive sleep apnea (OSA), remains poorly characterized. We systematically searched PubMed, Scopus, and Embase (2015-2025) for studies on OSA care reporting environmental metrics (carbon footprint, energy use, resource consumption) or healthcare resource utilization. Supplementary searches identified additional non-peer-reviewed sustainability-focused studies that have been presented at conferences. Of 19 primary peer-reviewed studies on OSA care and utilization, only one reported environmental metrics (telemedicine CO2 savings related to reduction in travel-related emissions). Supplementary sources revealed that OSA care has a measurable carbon footprint driven by disposable equipment, device electricity, and travel and that OSA diagnostics create significant solid waste with opportunities for waste reduction through the use of reusable equipment. This review shows that while the environmental impact of sleep medicine has been rarely studied to this date, available evidence suggests significant opportunities for sustainability through virtual care, home testing, and equipment optimization. Future research should incorporate environmental impact into the assessment of clinical pathways.

Humans↗
PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗
PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗
PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗
PubMed2026

Global Patterns of Net Ecosystem Exchange in peatlands: A Systematic Review and Meta-analysis of Drivers Across Land Use and Environmental Gradients.

Peatlands play an essential role in the global carbon cycle, storing approximately one-third of the world's soil carbon despite covering less than 3% of the land surface. Peatland degradation from anthropogenic activities and climate change can convert peatlands from net carbon sinks to sources by altering carbon cycling. Net Ecosystem Exchange (NEE), the balance between CO2 uptake and emission, is a critical indicator for assessing peatland condition and restoration efforts. We conducted a systematic quantitative literature review to investigate global patterns of NEE in peatlands and identify key environmental and anthropogenic drivers of CO2 flux variability. Annual NEE values from 120 globally distributed sites reported in peer-reviewed literature were analyzed in relation to climatic zone, land use, vegetation type, peatland condition, and water table depth. Our synthesis revealed significant geographic gaps, with peatland NEE studies substantially underrepresented in the Tropics, Africa, and Oceania. Agricultural peatlands emitted significantly more CO2 than sites under natural land uses or peat extraction, while degraded peatlands were significantly greater net CO2 sources than intact and restored systems. Restored peatlands remained net CO2 sources on average, emphasizing the importance of long-term monitoring and adaptive management following restoration interventions. Water table depth significantly affected NEE variability, with CO2 emissions increasing approximately 7.2 gCO2-C m-2yr-1 for every centimeter of water table drawdown. A substantial variability in measurement methods, data processing software, and protocols highlighted the critical need for methodological standardization. Our findings provide evidence-based targets for peatland conservation and restoration monitoring as nature-based climate solutions.

Ecosystem↗
PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

Variability and ozone formation potential of ambient non-methane hydrocarbons in a tropical semi-arid atmosphere of northwest India.

We present first-time measurements of twenty-six ambient non-methane hydrocarbons (NMHCs; C2-C8), including isoprene (C5H8), at a semi-arid site in northwest India (Ajmer; 26.45°N, 74.64°E), during January 2022-December 2023. Ambient samples were analyzed using a thermal desorption gas chromatography system equipped with dual flame ionization detectors. Daily total NMHC levels ranged from 6 ppbV to >100 ppbV. Most NMHCs, except isoprene, toluene, ethylbenzene, m-xylene and o-xylene, exhibited the highest levels in winter and the lowest in the monsoon. In contrast, others were highest in the pre-monsoon and toluene was highest during monsoon. These variations reflect the combined influence of emissions, chemistry and meteorology. Toluene and o-xylene were the dominant NMHCs (25 %-67 %). Correlation analyses indicated major contributions from liquefied petroleum gas (LPG) and vehicular emissions, with additional influence from urban and oil and natural gas activities. Compared with other Indian sites, NMHCs levels at Ajmer were 2-5 times lower than Ahmedabad and Udaipur and 10 times lower than Delhi. The total ozone formation potential was highest in the monsoon (about 175 ppbV) and lowest in the post-monsoon (about 55 ppbV), with dominance of o-xylene (24 %-33 %) and toluene (8 %-37 %). Propylene-equivalent concentrations were highest in the pre-monsoon (30.23 ppb C) and lowest in the post-monsoon (7.28 ppb C). Similarly, OH reactivity was highest in the pre-monsoon (19.58 s⁻¹) and lowest in the post-monsoon (4.71 s⁻¹), dominated by benzene and toluene. These findings emphasize the importance of NMHC chemistry in a climatically sensitive region and highlight the need for their continuous monitoring.

India↗
PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2026

Viscoelastic-Assisted Patient Interface Docking: A Technical Optimization in LenSx Femtosecond Laser-Assisted Cataract Surgery.

PURPOSE: To evaluate the efficacy of viscoelastic-assisted patient interface docking in LenSx (Alcon Laboratories, Inc) femtosecond laser-assisted cataract surgery (FLACS). METHODS: This was a randomized controlled trial. Patients undergoing FLACS from January to August 2025 at Aier Eye Hospital of Wuhan University were randomized via a random number table to receive balanced salt solution (BSS) or visoeleastic as the patient interface docking medium. The primary outcome was docking efficiency, measured by one-time docking success rate, the number of docking attempts, and mean docking time. Secondary outcomes included surgical safety (subconjunctival hemorrhage, capsulotomy completeness/tear rate), laser treatment duration (anterior capsulotomy time, nucleus pretreatment time, total laser emission time), and patient comfort (post-laser pain sensation). RESULTS: A total of 100 patients were enrolled, 50 in each group. Suction loss occurred in 7 patients (14%) in the BSS group and 1 patient (2%) in the viscoelastic group; the one-time docking success rate was significantly higher in the viscoelastic group (98%) than in the BSS group (86%) (chi-square = 3.93, P < .05). The viscoelastic group also had fewer mean docking attempts (1.02 &#xb1; 0.14) than the BSS group (1.16 &#xb1; 0.42), showing a significant difference (t = 2.23, P < .05). The viscoelastic group exhibited significantly shorter mean docking time (44.66 &#xb1; 4.47 seconds) compared to the BSS group (48.62 &#xb1; 3.11 seconds) (t = 2.17, P < .05). No significant differences were observed between the groups in subconjunctival hemorrhage, capsulotomy completeness/tear rate, anterior capsulotomy time, nucleus pretreatment time, total femtosecond laser emission time, or patient-reported pain sensation (all P > .05). CONCLUSIONS: Viscoelastic-assisted patient interface docking in FLACS effectively elevates one-time docking success rate, reduces docking attempts, and shortens docking time.

Humans↗
PubMed2026

Linked-color imaging with computer-aided detection and the proximal adenoma miss rate: a randomized tandem trial.

BACKGROUND AND AIMS: Linked-color imaging (LCI) aids the detection and characterization of lesions. Computer-aided detection (CADe) systems have been introduced to improve lesion detection during colonoscopy. Although several studies have been reported regarding LCI, few have investigated the combination of LCI and CADe. This study aimed to evaluate the efficacy of LCI with CADe colonoscopy compared to conventional white-light colonoscopy. METHODS: A single-center, randomized tandem trial was conducted. Participants referred for first-time colonoscopy after fecal immunochemical test (FIT)-positive, asymptomatic screening, or surveillance colonoscopy were randomized (1:1) to undergo CADe-assisted colonoscopy of LCI or white-light imaging (WLI) in the right side of the colon. The primary outcome was adenoma miss rate (AMR) in the right side of the colon. Secondary outcomes included polyp miss rate (PMR), diminutive adenoma miss rate (dAMR), sessile serrated lesion miss rate (SSLMR), advanced adenoma miss rate, advanced neoplasia miss rate, flat-type lesion miss rate (FMR), and the differences in miss rates based on expertise. RESULTS: Among 232 randomized participants, 209 were analyzed (LCI/CADe: 102; WLI: 107). AMR (WLI: 39% vs LCI/CADe: 20%; P = .001), PMR (42% vs 18%; P < .001), and dAMR (42% vs 21%; P = .003) were significantly lower in the LCI/CADe arm, particularly among experts. SSLMR (46% vs 0%), advanced AMR (30% vs 0%), advanced neoplasia miss rate (25% vs 0%), and FMR (27% vs 5.6%) were lower in LCI/CADe, although without statistical significance. CONCLUSIONS: Compared to conventional colonoscopy, LCI with CADe colonoscopy resulted in a statistically significant decrease, especially in AMR. (UMIN 000050685).

Humans↗
PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5&#x202f;kcal/mol, Wogonin (-9.3&#x202f;kcal/mol) and Xanthohumol (-8.1&#x202f;kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗
PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans↗
PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗
PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗
PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗
PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (&#x3a8;) represents one of the most abundant and conserved RNA modifications. &#x3a8; provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of &#x3a8; sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel &#x3a8; site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA &#x3a8;-site prediction. The &#x3a8; modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA &#x3a8;-site prediction. Meta-PseU offers a new framework for robust &#x3a8;-site identification by using long sequences.

Pseudouridine↗
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&#x2009;=&#x2009;10) and a conventional freehand group (n&#x2009;=&#x2009;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&#x2009;&#xb1;&#x2009;62.36 vs. 574.44&#x2009;&#xb1;&#x2009;114.58&#x2009;min, p&#x2009;=&#x2009;0.012), reduced blood loss (4081.40&#x2009;&#xb1;&#x2009;838.99 vs. 5090.0&#x2009;&#xb1;&#x2009;1059.67&#x2009;mL, p&#x2009;=&#x2009;0.034), and fewer fluoroscopic exposures (4.2&#x2009;&#xb1;&#x2009;0.79 vs. 10.0&#x2009;&#xb1;&#x2009;1.58, p&#x2009;<&#x2009;0.001) compared with the conventional group. Osteotomy accuracy was also superior in the 3D-printing group, with significantly lower angular deviation (3.33&#xb0;&#x2009;&#xb1;&#x2009;0.45&#xb0; vs. 6.79&#xb0;&#x2009;&#xb1;&#x2009;2.16&#xb0;, p&#x2009;=&#x2009;0.0012). Postoperative complication rates were comparable (30% vs. 44.4%, p&#x2009;=&#x2009;0.649), but hospital stay was significantly shorter in the 3D-printing group (10.7&#x2009;&#xb1;&#x2009;2.71 vs. 18.11&#x2009;&#xb1;&#x2009;4.01&#x2009;days, p&#x2009;<&#x2009;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↗