The global biosensors market is rapidly growing, driven by increasing demand for quick, affordable, and portable diagnostic tools across sectors, including healthcare, environmental monitoring, food safety, and biomedical research. The key to biosensor function is the biological recognition element (BRE), which determines the specificity, sensitivity, and reliability of the device. Traditional BREs, such as antibodies and enzymes, face significant limitations, including instability, high costs, and variability. Phage display technology offers a strong alternative, providing durable, stable, and highly specific peptides as BREs. However, it requires effective amplification and purification methods to produce high-quality peptide libraries. This study examines key factors affecting the amplification of filamentous M13 bacteriophage, highlighting the negative impact of high multiplicity of infection (MOI) caused by superinfection exclusion and reduced phage adsorption efficiency. Our findings indicate that the bacterial growth phase is the most important determinant of M13 amplification efficiency. Furthermore, post-infection PEG/NaCl precipitation followed by high-speed centrifugation significantly outperforms traditional filtration methods in purifying phages, maximizing recovery and viability. These findings present an optimized, reproducible, and scalable approach to M13 phage amplification, improving the effectiveness of phage display for developing advanced biorecognition elements. Ultimately, this research provides a foundational framework for more efficient biosensing and therapeutic applications, filling critical gaps in the current biosensor development landscape.

Robust transient knockdown strategies are critical to oligonucleotide therapeutic development, yet ineffective and variable RNA delivery across experimental models continues to limit target validation. Efficient delivery of nucleic acids into acute myeloid leukemia (AML) cells is particularly challenging due to their immature, suspension phenotype. Here, we performed a head-to-head benchmarking comparison of commonly used chemical transfection reagents (Lipofectamine 3000, Lipofectamine 2000, RNAiMAX, INTERFERin) alongside a physical electroporation approach (Lonza nucleofection) across four genetically distinct AML cell lines (THP-1, OCI-AML3, MV4-11, MOLM-14). To establish recommended RNA delivery strategies, we used small-interfering RNA (siRNA) and quantified functional knockdown by RT-qPCR, with protein-level validation and paired assessment of post-transfection viability. Lipid-based formulations were most effective in more differentiated AML cell lines (for example, THP-1 and OCI-AML3), whereas more immature lines (MV4-11 and MOLM-14) were poorly responsive to chemical transfection but efficiently transfected by electroporation. A short serum-free incubation period enhanced lipid-mediated delivery in permissive lines and produced measurable gains in more resistant models. Transfection-associated cytotoxicity was strongly method-dependent, with lipid-based reagents producing minimal to modest viability losses and nucleofection causing substantially greater short-term reductions in viable cell numbers. Based on this systematic comparison, most chemical reagents supported efficient delivery in THP-1 and, to a lesser extent, OCI-AML3, while MV4-11 and MOLM-14 demonstrated strict dependence on electroporation for meaningful intracellular uptake. Together, our results define a concise, qPCR-guided workflow, validated at the protein level, that provides a replicable, decision-oriented framework for selecting efficient and fit-for-purpose short RNA delivery strategies in AML cell lines.

Several Golden Gate-based DNA assembly techniques have been developed previously with different limitations including the requirement for domestication of sequences with internal sites for the type IIS restriction enzymes used, insertion of persistent scars in assembled DNA and the need for multiple assembly vectors and overhang sequences. We developed UniClo, which overcomes all these problems. Sequences with internal type IIS sites can be assembled, it allows fully scarless hierarchical assembly and requires only three assembly vectors and two universal overhang sequences. This is achieved by three key elements: (i) Recombinant methylases are used in vitro to methylate, and thus inactivate, any sites in fragments to be assembled for the type IIS restriction enzyme used in the assembly as well as the outer sites of the assembly vectors. (ii) A CRISPR-dCas9 molecule is used to protect the type IIS restriction enzyme sites required for the assembly from methylation, thus preserving the activity of these sites. (iii) A set of engineered vectors is used to trim overhangs that would otherwise generate scars. Here, we present a detailed protocol for performing DNA assembly using UniClo and describe the methylation-protection of the fragments to be assembled, methylation of the scarless vectors, the assembly reaction, and analysis of the final assembled DNA molecule. UniClo offers substantial flexibility in the assembly design and enables the assembly of any DNA molecule regardless of its sequence, nature and application.

Identifying disease-associated single-nucleotide polymorphisms (SNPs) is fundamental to understanding complex disease genetics, yet genome-wide association studies (GWAS) remain costly and data-intensive. Network-based approaches provide a complementary strategy by exploiting linkage disequilibrium (LD) structure- and disease-relatedness to prioritize candidate variants. We present DisSNPNet, a heterogeneous network-based framework that integrates chromosome-specific SNP LD networks derived from 1000 Genomes Project Phase 1 and Phase 3 data, a MeSH-based disease similarity network, and known disease–SNP associations from CAUSALdb. Random walk with restart was applied to rank SNPs for each disease. Predictive performance was evaluated using disease-wise 3-fold cross-validation with AUROC and AUPR. Biological plausibility was assessed by querying top-ranked SNPs in GWAS resources and by disease-specific KEGG pathway enrichment. A chromosome-matched random baseline was constructed to contextualize external GWAS evidence. DisSNPNet consistently outperformed SNP-only LD networks, with heterogeneous networks yielding higher AUROC and AUPR across chromosomes. Strong LD networks (r2 ≥ 0.8) improved precision, particularly in imbalanced settings. Top-ranked SNPs showed significantly greater GWAS evidence than random expectation across all chromosomes, indicating nonrandom enrichment. Disease-specific pathway enrichment revealed biologically coherent mechanisms across immune, metabolic, cardiovascular, and structural diseases. DisSNPNet provides a robust and interpretable framework for prioritizing disease-associated SNPs. While not a substitute for GWAS, it offers a scalable, evidence-supported approach for SNP prioritization and hypothesis generation, complementing experimental and population-based studies.

Systematic reviews (SRs) are essential for evidence-based practice but remain labor-intensive, especially during abstract screening. This study evaluates whether multiple large language models (multi-LLMs) collaboration can improve the efficiency and reduce costs for abstract screening. Abstract screening was framed as a question-answering (QA) task using cost-effective LLMs. Three multi-LLM collaboration strategies were evaluated, including majority voting by averaging opinions of peers, multi-agent debate for answer refinement, and LLM-based adjudication against answers of individual QA baselines. These strategies were evaluated on 28 SRs of the CLEF eHealth 2019 technology-assisted review benchmark using standard performance metrics such as mean average precision (MAP) and work saved over sampling at 95% recall (work saved over sampling WSS@95%). Multi-LLM collaboration significantly outperformed QA baselines. Majority voting was overall the best strategy, achieving the highest MAP 0.462 and 0.341 on subsets of SRs about clinical intervention and diagnostic technology assessment, respectively, with WSS@95% 0.606 and 0.680, enabling in theory up to 68% workload reduction at 95% recall of all relevant studies. Multi-agent debate improved weaker models most. Our own adjudicator-as-a-ranker method was the second strongest approach, surpassing adjudicator-as-a-judge, but at a significantly higher cost than majority voting and debating. Multi-LLM collaboration substantially improves abstract screening efficiency, and the success lies in model diversity. Making the best use of diversity, majority voting stands out in terms of both excellent performance and low cost compared to adjudication. Despite context-dependent gains and diminishing model diversity, multi-agent debate is still a cost-effective strategy and a potential direction of further research.

Latent factor models are valuable in bioinformatics for accounting for unmeasured variation alongside observed covariates. Yet many methods struggle to separate known effects from latent structure and to handle losses beyond standard regression. We present a unified framework that augments row and column predictors with a low-rank latent component, jointly modeling measured effects and residual variation. To remove ambiguity in estimating observed and latent effects, we impose a carefully designed set of orthogonality constraints on the coefficient and latent factor matrices, relative to the spans of the predictor matrices. These constraints ensure identifiability, yield a decomposition in which the latent term captures only variation unexplained by the covariates, and improve interpretability. An efficient algorithm handles general non-quadratic losses via surrogates with monotone descent. Each iteration updates the latent term by truncated singular value decomposition of a doubly projected residual and refines coefficients by projections. The number of latent factors is selected by applying an elbow rule to a degrees-of-freedom-adjusted information criterion. A parametric bootstrap provides valid inference on feature-outcome associations under the regularized low-rank structure. Applied to real pharmacogenomic data, the method recovers biologically coherent gene-drug associations missed by standard factor models, such as the EGFR-inhibitor link, highlights novel candidates with plausible mechanisms, and reveals gene programs aligned with compound modes of action, including a latent unfolded-protein-response module affecting drug sensitivity. These results support the framework’s utility for precision oncology, yielding stronger biomarkers for patient stratification and deeper insight into drug resistance mechanisms.

High-throughput sequencing generates vast data, often containing low-quality bases, chimeras, and artifacts that can mislead taxonomic classification and diversity assessments. Divisive amplicon denoising algorithm 2 (DADA2) enhances taxonomic resolution by excluding low-quality bases and optimizing amplicon sequence variant inference. Proper truncation reduces computational load while maintaining key hypervariable regions for accurate classification. In this study, we examine the effect of various truncation lengths during the DADA2 analysis in ensuring statistical robustness and improving the reliability of microbial community profiling in ecological and environmental studies. Truncation of read length from 175 to 185 bp improves the quality read recovery rate, and preserves microbial diversity in the V4 hypervariable region of the Illumina paired-end reads. Incorporating the optimal truncation length strategy optimizes read recovery and preserves the richness and evenness of microbial communities.

Pacak–Zhuang syndrome is an emerging pseudohypoxic disorder that causes defined but varied manifestations of neuroendocrine tumours with or without polycythemia or exclusively polycythemia. This disease is caused by mutations in the EPAS1 gene, which encodes for one of three hypoxia-inducible factor (HIF) α subunits, HIF2α. As new mutations in this gene are observed in individuals exhibiting the manifestations of Pacak–Zhuang syndrome, there is a need to distinguish bona-fide disease causing mutations from benign mutations, which could have a valuable impact on the direction of patient care. We recently showed that reductions in the affinity of prolyl-hydroxylase 2 (PHD2) for HIF2α due to mutations are at the root of the mechanism underlying Pacak–Zhuang syndrome. The determination of affinity was accomplished using microscale thermophoresis (MST). Here, we describe a detailed protocol for the assessment of binding affinities between HIF2α peptides or the entire oxygen-dependent degradation domains of HIFα proteins and PHD2 using MST and propose that this method can be used to assess the potential pathogenicity of novel mutations in HIF2α.

Quantitative loop-mediated isothermal amplification (qLAMP) is a gene expression quantification method that has gained popularity in recent years, particularly in disease identification, including during the recent SARS-CoV-2 pandemic. Unlike conventional quantitative PCR (qPCR), qLAMP features reaction kinetics that may diverge from sigmoidal expectation, and may not include ROX dye in commercial kits. Determining cycle threshold (Ct or Cq) values through automatic thresholding may therefore produce inaccurate results, and the nature of these thresholds complicates comparability between studies and softwares. We introduce a new method for transforming sigmoidal amplification curves into inflection cycle threshold curves (iCt) to address issues with auto thresholds and analysis of qLAMP. This method is implemented as a set of R functions named LAMPrey, suitable for analysis of both qPCR and qLAMP reactions performed in the two most commonly used real-time thermocyclers. We simulate qLAMP amplification differences, demonstrate that iCt and Ct methods perform equivalently for conventional qPCR with an Illumina library quantitation kit, and show that iCt values outperform Ct and the sigmoid curve-fitting metric FDM for quantifying 2416 qLAMP reactions in of zebrafish embryos. All scripts developed for this article are available at https://github.com/dodged13/LAMPrey.

DNA end resection is critical for DNA double-strand break repair via homologous recombination (HR) and replication-coupled repair. Traditional approaches for detecting DNA end resection in cells include fluorescence imaging for replication protein A foci, 5-bromo-2'-deoxyuridine (BrdU) labeling followed by anti-BrdU staining under native conditions to detect ssDNA, and quantitative PCR to detect single-stranded DNA (ssDNA) using the ER-AsiSI U2OS cell system. Here, we comprehensively examined a proximity ligation assay (PLA)-based approach, named ssDNA-PLA, to detect protein-ssDNA interaction by combining BrdU genome-wide DNA labeling with PLA using anti-BrdU and antibody against proteins of interest. We showed that the ssDNA-PLA method is a robust and reliable approach to detect proteins interacting with ssDNA in cells in response to DNA damage induced by various agents and replication stress, including known ssDNA-binding proteins replication protein A, RAD51, and BLM. This approach can be used for studying the proximity of proteins to ssDNA that play roles in DNA end resection and HR repair. Keywords DNA damage repair, DNA end resection, PLA, ssDNA, RPA.