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PubMed2026

Using the OPTIMAL Theory to Optimize Aerodynamics in Respiratory Training for Healthy Adults and Individuals With Parkinson's Disease.

BACKGROUND: The OPTIMAL (Optimizing Performance Through Intrinsic Motivation and Attention for Learning) theory is a motor learning framework proposing that optimizing intrinsic motivation enhances motor performance and learning. The theory identifies three key components-Enhanced Expectancies (EE), Autonomy Support (AS) and External Focus of Attention (EF)-which facilitate more efficient, goal-directed movement. These components have been shown to improve motor outcomes in limb-based tasks; however, their application to respiratory training, particularly in clinical contexts such as voice and swallowing therapy in patients with Parkinson's disease (pwPD), has not yet been systematically explored. AIMS: This study aimed to investigate whether implementing OPTIMAL theory strategies during a respiratory muscle strength training (RMST) task improves immediate respiratory motor performance in healthy adults and pwPD. Additionally, we aimed to examine the effects of these strategies on motivation and cognitive engagement. METHODS: This quasi-randomized, single-session trial included 47 participants: Healthy CONTROL (n = 17), Healthy OPTIMAL (n = 16) and PD OPTIMAL (n = 14). Healthy participants were quasi-randomly assigned to either intervention or control conditions, whereas pwPD completed the intervention only. All participants completed a single respiratory session that included baseline, practice and retention phases. Outcome measures included peak expiratory flow, cough peak expiratory flow, cognitive engagement (EEG-based Cognitive Engagement Index) and self-administered motivation questionnaire. OUTCOMES AND RESULTS: Exhalation force improved from baseline to retention in the Healthy OPTIMAL group (baseline: M = 296 L/min; retention: M = 338 L/min; p < 0.001) and the PD OPTIMAL group (baseline: M = 315 L/min; retention: M = 370 L/min; p < 0.0001), but not in the Healthy CONTROL group (p > 0.05). No significant changes in cough strength were observed in any group. No correlations were found between cognitive engagement and exhalation force or motivation scores. However, motivation increased more in the Healthy OPTIMAL group (Questionnaire 1: M = 57.2; Questionnaire 2: M = 60.7) and the PD OPTIMAL group (Questionnaire 1: M = 60.1; Questionnaire 2: M = 62.8) than in the Healthy CONTROL group (Questionnaire 1: M = 61.1; Questionnaire 2: M = 62.5). CONCLUSIONS AND IMPLICATIONS: Implementing the OPTIMAL theory enhances immediate respiratory motor performance in both healthy participants and pwPD. OPTIMAL theory has clinical value in voice and swallowing therapy, although further research is needed to establish long-term efficacy and clinical impact. WHAT THIS PAPER ADDS: What is already known on the subject Motivation is a critical factor in rehabilitation. The OPTIMAL theory has been shown to improve both motivation and motor performance in limb-based tasks. Its impact on respiratory training, however, has not been previously examined. What this paper adds to the existing knowledge This study shows that applying OPTIMAL strategies during a respiratory muscle strength training task significantly improved peak expiratory flow in both healthy adults and people with Parkinson's disease. What are the potential or clinical implications of this work? Integrating the OPTIMAL theory principles into respiratory therapy may enhance motor outcomes, supporting voice, swallowing and cough rehabilitation.

Humans↗

PubMed2025

Optimizing participant and community engagement in cancer genomic sequencing research.

PURPOSE: We describe strategies implemented across research centers of the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network to optimize engagement of participants and communities in cancer genomics research. We also present consensus definitions of engagement and engagement optimization, informed by our shared experiences in the Network. METHODS: Key informant interviews and a document review identified engagement and optimization strategies across PE-CGS research centers. Findings were synthesized using qualitative content analysis. Consensus on definitions of engagement and optimization were developed through iterative review by PE-CGS members. RESULTS: PE-CGS research centers adopted tailored strategies based on community needs and scientific gaps. Engagement strategies included community-based efforts (eg, advisory boards and newsletters) and participant-focused approaches (eg, enhanced informed consent and decision support tools). Optimization strategies leveraged scientific methods (eg, randomized controlled trials and surveys) to evaluate engagement. Engagement was described as the sustained and meaningful interactions between researchers, participants, and communities. Optimization was described as the application of scientific methods to refine and improve engagement and research processes and outcomes. CONCLUSION: Engagement and optimization strategies have informed research planning, conduct, and dissemination across PE-CGS. These approaches and definitions provide a foundation for developing evidence-based practices to strengthen participant and community involvement in cancer genomics research.

Humans↗

PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗

PubMed2026

Dispositional optimism and open-label placebo responses in hair cortisol concentrations and psychological distress-A randomized controlled trial.

Open-label placebo (OLP) treatments show beneficial effects on various health-related outcomes, but studies investigating OLP effects on physiological measures remain scarce. This randomized controlled trial examined the effect of a 4-week OLP intervention on psychological distress and hair cortisol concentrations (HCC) in 202 healthy university students preparing for mandatory oral exams and whether dispositional optimism moderates the OLP effects. Participants were randomly assigned to an OLP or control group. Psychological distress was repeatedly assessed via negative affect, test anxiety, and subjective stress. HCC was measured before and within the intervention. Treatment expectations were additionally examined in interaction with optimism. Results show that OLPs significantly reduced psychological distress and HCC compared to the controls. Optimism moderated the OLP effect on HCC, with less optimistic individuals demonstrating the strongest reduction, independent of expectation. Optimism did not moderate OLP effects in psychological distress. However, the OLP effect on psychological distress depended on the three-way interaction of group, optimism, and expectation. The results suggest that OLPs alleviate the psychophysiological impact of a real-life stressor and indicate that optimism and expectation differently shape psychological and physiological OLP responses. These findings are discussed within the framework of the interactionist perspective.

Humans↗

PubMed2026

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

Sequence Alignment↗

PubMed2026

Sequence optimization targeting mRNA stability enhances monoclonal antibody titers in CHO cells.

This study presents a DNA sequence optimization approach that integrates mRNA stability as a tunable design parameter to enhance monoclonal antibody expression in Chinese hamster ovary (CHO) cells. A comprehensive combinatorial library of synonymous coding-sequence variants of an IgG1 light chain was integrated as single copies at a defined genomic locus in CHO cells with identical regulatory elements. Steady-state mRNA abundance, quantified by deep sequencing of gDNA and mRNA, served as a proxy for mRNA stability. These data were used to train a machine learning model that predicts mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer. This abundance predictor, together with established translational metrics, was incorporated into a genetic algorithm for multi-objective codon optimization. As proof-of-concept, we optimized sequences encoding Trastuzumab to either maximize or minimize the abundance criterion and obtained benchmark sequences from two commercial providers. Using targeted integration, we generated CHO cell lines and measured protein titer and cell-specific productivity. Sequences optimized for high abundance significantly increased intracellular mRNA levels (+41%), protein titer (+59%), and cell-specific productivity (+85%) relative to low-abundance designs, while viable cell densities remained comparable. Compared to commercial benchmarks, high-abundance sequences achieved significantly higher titer (+70%) and cell-specific productivity (+98%). These findings establish mRNA stability as a practical and complementary design parameter for codon optimization in monoclonal antibody production, with potential applicability to other proteins and expression systems.

CHO↗

PubMed2026

Impact of transfection optimization on adeno-associated virus purification performance and vector quality.

Recombinant adeno-associated virus (rAAV) has shown great promise as a viral vector for gene therapy. However, efficient manufacture of high-quality rAAV to meet clinical demands remains challenging. Here, we optimized rAAV production via a design of experiments (DoE) approach to evaluate the effects of five transfection parameters on vector genome titer, full particle ratio (FE ratio), and viral protein (VP) stoichiometry. The DoE model showed that no single set of transfection conditions could maximize all three responses simultaneously. Subsequently, the materials from DoE-optimized transfections were purified by affinity chromatography followed by anion exchange chromatography (AEX). We observed that AEX recovery declined when capsid loading was high, a situation caused by the combination of high titer with low FE ratio, reducing some of the gains in titer achieved by transfection optimization. Furthermore, AEX had limited capacity to enrich full particles from materials with a very low initial FE ratio. Finally, materials from DoE-optimized transfections showed improved transduction efficiency, which was associated with the ratios of VP1 and VP2 to total VP in the capsid. Overall, this study demonstrates that transfection-derived quality attributes affect purification outcomes and overall vector quality. Our findings highlight the necessity of strategically balancing multiple quality attributes during transfection optimization and provide insights for integrated upstream transfection and downstream purification development.

Adeno-associated virus↗

PubMed2025

Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants.

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Humans↗

PubMed2026

Yoga MAT: A factorial randomized study using the Multiphase Optimization Strategy to develop a multicomponent yoga intervention for people with chronic pain taking medications for opioid use disorder.

BACKGROUND: People taking medications for opioid use disorder (MOUD) commonly experience chronic pain. Yoga interventions show promise for decreasing pain-related disability in other populations. More time spent in yoga practice may improve pain-related outcomes. METHODS: The Multiphase Optimization Strategy (MOST) provided the framework for developing an optimized yoga intervention package. In a 2x2x2x2 factorial experiment, we evaluated four candidate intervention components which, when added to a weekly yoga class, might increase yoga engagement. The primary outcome was minutes per week of yoga practice (classes and other yoga practice) over the 12-week intervention period. We sought to determine which combination of intervention components was associated with the most yoga practice for people with chronic pain taking buprenorphine or methadone as MOUD. RESULTS: We enrolled 192 adults. There was a significant main effect for Component "B" (having two private sessions with a yoga teachers; IRR = 1.10, 90%CI 1.02; 1.18), and a synergistic interaction between Components "B" and "D" (D was financial incentives for attending class; IRR = 1.11, 90%CI 1.02; 1.19). This combination of these two components (without other potential components) was associated with the second highest model-predicted mean minutes of yoga per week (157.1min; 90% CI = 120.1-194.0) which was only 4min less than the combination including all four components. CONCLUSIONS: We identified a combination of intervention components as the optimized intervention. A next step will be to test the effect of this optimized intervention on pain and substance use outcomes in a randomized controlled clinical trial.

Humans↗

PubMed2025

Optimized GMP-grade production of non-viral Sleeping Beauty-generated CARCIK cells for enhanced fitness and clinical scalability.

BACKGROUND: Strict adherence to GMP guidelines and regulatory compliance is crucial when transitioning from research to clinical-grade production of ATMPs like CAR T cells. The success of CAR T cell therapy in treating hematological malignancies highlights the need for closed or automated systems to ensure quality and efficacy. Recent evidence also suggests that ex vivo culture conditions can significantly impact CAR T cell functionality. METHODS: We present our optimized methodology for expanding Sleeping Beauty transposon-engineered Chimeric Antigen Receptor-Cytokine-Induced Killer (CARCIK) cells using G-Rex devices and evaluate its impact on CARCIK cell phenotype and T cell fitness. RESULTS: Building on our previously validated protocol, we introduced key simplifications to optimize the CARCIK differentiation process. Delaying the nucleofection step eliminated the need for feeder cells while maintaining efficient CAR expression and high cell viability. Transitioning from T-flasks to G-Rex bioreactors reduced operator hands-on time from 21 to 28 days to 14-17 days and resulted in a less differentiated CARCIK cell product. Metabolic and transcriptional analyses showed that the novel protocol improves CARCIK cell fitness and in vivo efficacy against B-cell lymphoma. The novel method was validated in Good Manufacturing Practices (GMP) conditions at our two Cell Factories and yielded enough numbers of CARCIK-CD19 cells for clinical use. CONCLUSIONS: Optimizing non-viral CARCIK cell production using G-Rex bioreactors and refined timing adjustments has streamlined the workflow, enhanced cell fitness, and resulted in a highly effective therapeutic product with demonstrated in vivo efficacy in mice. These improvements reduced manipulation and contamination risks, while optimizing logistics and space efficiency, facilitating allogeneic CARCIK generation for a current phase I/II clinical trial (NCT05869279) in patients with R/R CD19 + non-Hodgkin Lymphoma (B-cell NHL) and Chronic Lymphocytic Leukemia (CLL), confirming the approach's scalability and clinical potential.

Humans↗

PubMed2025

Heterologous expression and optimization of the antimicrobial peptide acidocin 4356 in Komagataella phaffii to target Pseudomonas aeruginosa.

Multidrug-resistant (MDR) pathogens, particularly Pseudomonas aeruginosa, pose a serious global health threat due to their increasing prevalence and limited therapeutic options. Antimicrobial peptides (AMPs) offer promising alternatives to traditional antibiotics, yet their large-scale application remains constrained by high production costs and technical challenges. This research sought to develop a yeast-based system for the cost-efficient synthesis of acidocin 4356 (ACD), an antimicrobial peptide proven effective against P. aeruginosa. A codon-optimized ACD gene was cloned into the pPICZα-A expression vector and integrated into the Komagataella phaffii (formerly Pichia pastoris) GS115 genome. Colony PCR confirmed successful integration, and specific transformants demonstrated expression of the 6 × His-ECS-rACD fusion protein, as verified by SDS-PAGE and dot blot analysis. After Ni-NTA chromatography and enterokinase digestion, rACD was found at ~ 20 kDa instead of 8.3 kDa, suggesting oligomerization or post-translational modifications. Response surface methodology determined the optimal temperature, pH, and methanol concentration for peptide synthesis. Under optimal circumstances (21 °C, pH 6.24, and 1.089% methanol), rACD synthesis increased by 34.12% over baseline conditions (30 °C, pH 6, 1% methanol). AlphaFold structural modeling identified three α-helices in high-confidence regions, implicated in bacterial membrane disruption. Antimicrobial assays demonstrated potent rACD activity against P. aeruginosa, yielding a 58.29% reduction in growth at 150 µg/mL and MIC50 and MIC90 values of 143.04 and 320.64 µg/mL, respectively. These findings underscore K. phaffii as a robust platform for AMP production and highlight rACD's therapeutic potential as an effective agent against MDR P. aeruginosa, warranting further investigation into its clinical and industrial applications. KEY POINTS: • Developing a novel K. phaffii strain for heterologous expression supports efficient rACD peptide production. • Optimized conditions boosted expression yield by 34.12% above the reference fermentation settings. • Recombinant acidocin suppressed Pseudomonas aeruginosa growth by 58%, indicating anti-MDR activity.

Pseudomonas aeruginosa↗

PubMed2026

Optimized AAV5-RPGR ORF15 Gene Therapy Rescues Photoreceptor Structure and Function in X-Linked Retinitis Pigmentosa Mouse Model.

PURPOSE: To develop and evaluate an rAAV5-based gene therapy vector expressing an optimized human RPGR ORF15 transgene (rAAV5-RPGR) for the treatment of X-linked retinitis pigmentosa caused by RPGR mutations, addressing the challenges of cloning the unstable wild-type ORF15 sequence. DESIGN: This was a prospective experimental study. SUBJECTS: This was an animal study. METHODS: An optimized RPGR ORF15 sequence was designed to eliminate problematic secondary structures and cryptic splice sites. In vitro expression was validated in HEK 293T and photoreceptor-like 661 W cells. A complete Rpgr knockout mouse model (Rpgr-knockout [KO]) was generated and characterized phenotypically. Therapeutic efficacy was assessed in Rpgr-KO mice via subretinal injection of rAAV5-RPGR at low (1 × 10⁹ vg/eye), medium (3 × 10⁹ vg/eye), or high (1 × 10¹⁰ vg/eye) doses. Structural and functional outcomes were evaluated at 12- and 14-month postinjection. Short-term safety was assessed in rabbits 1 month after subretinal injection. MAIN OUTCOME MEASURES: Level of RPGR protein expression and Protein isoform profile (elimination of truncated isoforms), Cellular localization of transgene expression and Dose-dependence of expression, outer nuclear layer thickness, and electroretinography parameters. RESULTS: (1) The optimized vector increased RPGR protein expression 3.3-fold in vitro compared to wild-type and eliminated truncated isoforms. (2) Subretinal delivery of rAAV5-RPGR in mice demonstrated dose-dependent transgene expression localized correctly to photoreceptor inner segments. (3) In Rpgr-KO mice, high-dose treatment significantly preserved outer nuclear layer thickness at the injection site (42% greater than controls at 14 months, P < .01) and central retina (P < .05), reduced aberrant rhodopsin mislocalization (P < .01), and partially restored retinal function. ERG showed significantly improved scotopic a-wave (≥100 vs <90 µV in controls at 10 cd·s/m²) and photopic b-wave amplitudes (49-66 vs 31-46 µV at 30 cd·s/m²) in treated mice. (4) No vector-related toxicity was observed in rabbits. CONCLUSIONS: rAAV5-RPGR mediated efficiently, targeted expression of optimized RPGR-ORF15, significantly preserved photoreceptor structure and function in a severe X-linked retinitis pigmentosa mouse model, and demonstrated a favorable safety profile. This study provides preclinical proof-of-concept for RPGR-targeted gene replacement therapy.

Animals↗

PubMed2026

Optimization of a niosomal formulation for Quercetin delivery: Comparative effects on growth performance, antioxidant and immune responses, and disease resistance against Streptococcus iniae in rainbow trout (Oncorhynchus mykiss).

Quercetin (QUR) is a flavonoid with antibacterial and antioxidant properties that has been studied for its effects on fish health and the immune system. Due to the low bioavailability of QUR, the present study was primarily aimed at developing a niosomal formulation of QUR to enhance its bioavailability and therapeutic properties. The performance of niosomal QUR was then compared with that of its free form by evaluating their effects on the growth and immune system of rainbow trout, Oncorhynchus mykiss. For this purpose, an optimal QUR-containing niosome was formulated by testing different proportions of cholesterol, surfactant, and stabilizer, and the niosomes were then added to the fish diet. A QUR-free diet and a diet containing QUR-free niosomes (QFN) were considered control groups. Niosomes prepared at low hydrophilic-lipophilic balance (HLB) values showed higher QUR entrapment efficiency (QUR-EE%) (P < 0.01). An increase in the molar ratio of cholesterol to surfactant significantly reduced QUR-EE% (P < 0.05). A surfactant/cholesterol/stabilizer ratio of 1:1:0.1 increased QUR-EE% (P < 0.05). The particle size of the niosomes was also affected by HLB and the surfactant/cholesterol/stabilizer ratio. Low HLB values resulted in smaller particle sizes (P < 0.01). Furthermore, a surfactant/cholesterol/stabilizer ratio of 1:1:0.1 resulted in the smallest niosomal particle size (P < 0.05). The presence of diacetyl phosphate as the stabilizer in the formulation improved the niosomal zeta potential to -40.55 mV. The optimal niosomal formulation (HLB = 6.6; surfactant/cholesterol/stabilizer ratio of 1:1:0.1) remained stable at 2 °C over 10 days of storage, as the niosomal QUR content and size did not show significant changes during this period (P > 0.01). After preparation of the diets containing the optimal niosomal QUR, fish were fed the experimental diets for two months. QUR, in both niosomal and free forms, resulted in better growth performance than the QUR-free diets (P < 0.01). There were no significant differences in growth performance between the free and niosomal forms of QUR; however, 500 mg/kg QUR showed significantly better growth performance than 250 mg/kg QUR (P < 0.01). Although both the niosomal and free forms of QUR enhanced immune and antioxidant components in the fish, the effects of the niosomal form were more significant (P < 0.01). In addition, QUR in both free and niosomal forms reduced fish mortality after challenge with Streptococcus iniae. In conclusion, the results of this study suggest that the niosomal form of QUR has strong potential for improving the immune system of fish and increasing resistance to S. iniae infection.

Animals↗

PubMed1999

An optimal algorithm for automatic genotype elimination.

In an effort to accelerate likelihood computations on pedigrees, Lange and Goradia defined a genotype-elimination algorithm that aims to identify those genotypes that need not be considered during the likelihood computation. For pedigrees without loops, they showed that their algorithm was optimal, in the sense that it identified all genotypes that lead to a Mendelian inconsistency. Their algorithm, however, is not optimal for pedigrees with loops, which continue to pose daunting computational challenges. We present here a simple extension of the Lange-Goradia algorithm that we prove is optimal on pedigrees with loops, and we give examples of how our new algorithm can be used to detect genotyping errors. We also introduce a more efficient and faster algorithm for carrying out the fundamental step in the Lange-Goradia algorithm-namely, genotype elimination within a nuclear family. Finally, we improve a common algorithm for computing the likelihood of a pedigree with multiple loops. This algorithm breaks each loop by duplicating a person in that loop and then carrying out a separate likelihood calculation for each vector of possible genotypes of the loop breakers. This algorithm, however, does unnecessary computations when the loop-breaker vector is inconsistent. In this paper we present a new recursive loop breaker-elimination algorithm that solves this problem and illustrate its effectiveness on a pedigree with six loops.

Algorithms↗

PubMed2025

MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.

MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods. RESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications. AVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.

Circulating Tumor DNA↗

PubMed2026

CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.

MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes. RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction. AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2010

AAV's anatomy: roadmap for optimizing vectors for translational success.

Adeno-Associated Virus based vectors (rAAV) are advantageous for human gene therapy due to low inflammatory responses, lack of toxicity, natural persistence, and ability to transencapsidate the genome allowing large variations in vector biology and tropism. Over sixty clinical trials have been conducted using rAAV serotype 2 for gene delivery with a number demonstrating success in immunoprivileged sites, including the retina and the CNS. Furthermore, an increasing number of trials have been initiated utilizing other serotypes of AAV to exploit vector tropism, trafficking, and expression efficiency. While these trials have demonstrated success in safety with emerging success in clinical outcomes, one benefit has been identification of issues associated with vector administration in humans (e.g. the role of pre-existing antibody responses, loss of transgene expression in non-immunoprivileged sites, and low transgene expression levels). For these reasons, several strategies are being used to optimize rAAV vectors, ranging from addition of exogenous agents for immune evasion to optimization of the transgene cassette for enhanced therapeutic output. By far, the vast majority of approaches have focused on genetic manipulation of the viral capsid. These methods include rational mutagenesis, engineering of targeting peptides, generation of chimeric particles, library and directed evolution approaches, as well as immune evasion modifications. Overall, these modifications have created a new repertoire of AAV vectors with improved targeting, transgene expression, and immune evasion. Continued work in these areas should synergize strategies to improve capsids and transgene cassettes that will eventually lead to optimized vectors ideally suited for translational success.

Cystic Fibrosis↗

PubMed2026

Optimized Hot Phenol-Based RNA Extraction from Mycobacteria: A Robust Approach for Reliable Gene Expression Analysis.

Mycobacterium tuberculosis (Mtb) remains a major global health threat, underscoring the need for reliable transcriptomic studies to understand its biology and drug resistance mechanisms. Such analyses depend on obtaining high-quality, high-yield RNA. Although several RNA extraction methods are available, many require expensive reagents, large culture volumes, or specialized equipment, limiting their suitability for large-scale studies, particularly in resource-constrained settings. Here, an optimized Hot Phenol based RNA extraction method specifically tailored for mycobacteria is presented. The method uses minimal culture volume and commonly available reagents to consistently yield high-quality RNA suitable for high-throughput transcriptomic applications. RNA quantity and integrity were assessed by gel electrophoresis and RNA integrity analysis (RIN), and its suitability for downstream applications was confirmed by qPCR and Qubit 4. To benchmark the performance of the optimized method, a parallel RNA extraction using TRIzol and RNeasy under identical experimental conditions was carried out, including the same Mycobacterium species, culture volume, growth phase (logarithmic and stationary), and lysis conditions. This allowed a direct comparison of yield, quality, feasibility, and cost. The optimized Hot Phenol method demonstrated comparable or improved RNA yield and quality while significantly reducing reagent cost and dependence on specialized equipment. Owing to its efficiency, reproducibility, and affordability, this protocol provides a practical alternative for large-scale gene expression and transcriptomic studies in Mtb and other mycobacterial species.

RNA, Bacterial↗