BioTechniques

While cycle threshold (Ct) values from quantitative PCR (qPCR) serve as the gold-standard indicators of target abundance, their clinical interpretation is frequently confounded by inherent variability across diverse assay designs, reagents, and instrumentation. In this study, we present a data-driven consensus framework for Ct evaluation that uses large-scale, multi-assay amplification data to establish reference patterns of normal Ct behavior. Based on a total of 41,770 amplification curves collected from four routine diagnostic assays across two PCR platforms, we evaluated machine learning models across three experimental scenarios: within-platform validation, cross-assay generalization, and cross-platform transfer. Extreme gradient boosting (XGBoost) achieved the most accurate and stable predictions under data-sufficient, within-platform conditions with a mean absolute error (MAE) of 0.0419, while pooled multi-assay training improved cross-assay robustness compared with single-assay models. Model performance was further assessed using a deviation-based metric to quantify differences between predicted and instrument-reported Ct values, allowing efficient identification of anomalous amplification curves in large datasets. Notably, direct application across platforms without recalibration led to a substantial decline in performance, with a MAE of 2.62, showing platform-dependent variability. These findings indicate strong stability under within-platform and cross-assay conditions, with scalability contingent upon appropriate cross-platform calibration.

Loop-mediated isothermal amplification (LAMP) is a cost-effective and portable assay technique for performing nucleic acid-based diagnostics in the field whose adoption is hindered by design and reproducibility issues. This is due to a complex primer design process that fine-tunes parameters across 6-8 binding regions. The likelihood of assay success depends on satisfying thermodynamic and secondary structure constraints while maintaining target specificity and avoiding overlaps between multiple primers. Software such as the NEB(R) LAMP Primer Design Tool, PREMIER Biosoft LAMP Designer, Primer3, PCR Signature Erosion Tool (PSET), and PrimerExplorer enable automation of this task for researchers. However, in our experience, these programs can sometimes yield inconsistent results in laboratory testing. Here, we approached the issue by comparing and training multiple machine learning (ML) models on primer sets targeting various organisms from working assays and failing ones to determine significant features and improve predictions prior to ordering primer sets. A literature review produced an initial list of primer sets (n=116), which were then filtered down based on reference template availability to discern their FIP/BIP components (F2/F1c and B1c/B2). The final training set (n=109) included sequence and thermodynamic features derived from primers collected from the review (n=74) and those designed in-house with PSET (n=35). Failing assays were difficult to obtain from the publications, so we provided our own (n=23). Using WEKA Experimenter, models were created based on decision tree and Bayesian learning algorithms using an experimental scheme that performed a parameter grid search, seeded replicates, feature selection, and cross-validation while avoiding data-leakage and outputting logs for model comparison, feature analysis, and overfit assessment. Notably, thermodynamic features associated with the F1c and B1c primers consistently appeared in the top ranks according to consensus between information gain, class-correlation, and model-based feature ranking. For classification, the NaiveBayes algorithm had a TP and TN rate of 0.90 ({+/-} 0.02) and 0.73 ({+/-} 0.05) while achieving Cohens kappa coefficient and F-score values of 0.61 ({+/-} 0.06) and 0.91 ({+/-} 0.01). This work highlights how a practical model was built from a small, imbalanced training set incorporating negative research results, of which more are needed to improve generalization and refine parameters critical to assay success.

Polysome profiling is a powerful technique used to analyze the association of mRNA with ribosomes, providing insights into the translational status of a cell. It relies on the separation of ribosome-bound mRNAs through sucrose density gradient centrifugation, where the number of ribosomes on an mRNA correlates with its sedimentation rate. While numerous studies have successfully applied this method to cell line and mouse tissue, application to the human postmortem brain remains scarce due to challenges related to sample quality and low concentration of recoverable material. To overcome these challenges, we: O_LIImplemented a protocol specifically optimized for low-concentration human post-mortem brain tissue. C_LIO_LIImplemented a gradient-maker-free method to manually prepare sucrose gradients with tunable sensitivity for low-input samples. C_LIO_LIAdapted the human brain tissue protocol for neuronal cell lines and mouse brain with minimal modification. C_LI

Mimiviral polymerase X, mvPolX, is a repair polymerase that is involved in base excision repair (BER) and carries out the gap-filling function in double-stranded DNA (dsDNA). We demonstrate a sensitive and sequence-specific DNA detection method using this polymerase. mvPolX begins polymerizing DNA from the 3 end of a gap, displacing the downstream nucleotides without exonuclease activity. Our detection method is built on this activity of mvPolX. We designed a probe molecule consisting of a partial dsDNA with a 3 over-hang region complementary to the target DNA to be detected. The probe has a fluorophore-quencher (FAM-BHQ1) tag to facilitate detection upon strand removal. Binding of the probe to the complementary target forms a dsDNA with a single nucleotide gap in one strand. mvPolX binds this gap region and begins polymerisation eventually displacing the quencher strand leading to an increase in fluorescence. Proof-of-concept has been established using a synthetic 19 bp target DNA sequence. The method is specific and did not show any strand displacement when a single or double mismatched nucleotide at the 3 end of the target DNA was used. To demonstrate this molecular assay, we used M13 phage as our target. Asymmetric PCR (aPCR) was used to obtain single-stranded target DNA (158 bases) from M13 genomic DNA, which was directly used in the assay as target. The combination of aPCR and mvPolX assay can detect as low as 10 copies of genomic DNA. The enzymatic reaction is fast, requiring only 15 min of incubation with mvPolX at 30 {degrees}C. We have further demonstrated the efficiency of the assay in presence of multiple non-target DNA by detecting the target DNA from M13 phage spiked lakewater samples.

Quantitative PCR (qPCR) depends on reliable quantification cycle (Cq) estimation from amplification curves, which are not always well-behaved. We developed qPCR Guru to provide a complete analysis pipeline including data quality assessment, relative quantification, standard-curve diagnostics, and dual-method Cq evaluation. The latter compares the conventional instrument-derived threshold ("Reported") Cq versus the full-curve five-parameter logistic (5PL) second-derivative-maximum ("Fit") Cq and automatically flags curve-shape abnormalities and disagreement between the two estimates. On high-expressing targets (mRNA and microRNA), the two methods showed strong convergence, confirming general-purpose performance. On low-expressing targets, such as serum microRNA, baseline artifacts and biphasic amplification result in threshold miscalls that standard instrument analysis does not flag. Fit Cq restored replicate-concordant values where Reported Cq split the technical replicates by 17-20 cycles, recovered MIQE-compliant amplification efficiencies lost to biphasic miscalls (from 74% to 102% and 387% to 98%), and lowered within-group variability by 48% and 68% in feline and bovine samples, respectively. Together, these results demonstrate that full-curve estimation, with integrated curve-level diagnostics, strengthens qPCR analyses against threshold miscalls. ARTICLE HIGHLIGHTSO_LIqPCR Guru is a free, browser-based platform that provides a complete analysis pipeline and facilitates side-by-side comparisons of an instruments threshold (Reported) Cq and a full-curve (Fit) Cq, from the five-parameter logistic fitting with second-derivative-maximum (SDM/cpD2). C_LIO_LIFor every well the application automatically flags curve-shape abnormalities and disagreement between the two Cq estimates. C_LIO_LIOn clean, high-expressing mRNA and microRNA targets, the two estimators (Reported Cq and Fit Cq) were strongly concordant and produced equivalent relative quantification with comparable precision. C_LIO_LIIn low-expressing serum microRNA, baseline artifacts and biphasic amplification produced threshold Cq miscalls of up to [~]20 cycles and were detected by curve-shape flags and/or method disagreement. C_LIO_LIThe full-curve Cq estimate recovered replicate-concordant values, restored MIQE-compliant amplification efficiencies, and reduced within-group variability in serum microRNA. C_LI

Fluorescence-based CRISPR diagnostic assays have become a popular platform for nucleic acid detection due to their programmability, configurability, specificity, and compatibility with standard laboratory equipment. However, reported enzymatic kinetic rates and limits of detection for CRISPR trans-cleavage assays vary by several orders of magnitude across the literature. This variation in performance parameters is coupled with and exacerbated by inconsistent calibration, incomplete correction of measurement biases, and nonstandardized or incomplete data-analysis procedures. We present an experimental protocol and quantitative analysis framework for fluorescence-based enzyme assays using routine laboratory instrumentation, including thermocyclers and fluorescence microplate readers. Building on previous studies of CRISPR enzyme kinetics and fluorescence calibration, we describe procedures for flat-field and background correction; comprehensive fluorescence calibration including correction for inner-filter-effect; quantification and implications of reporter degradation; extraction of Michaelis-Menten kinetic parameters; and determination of assay limits of detection. We provide step-by-step experimental guidelines and open-source Python implementations for each stage of the workflow. Using representative Cas12 trans-cleavage datasets, we demonstrate that explicit fluorescence calibration and correction procedures substantially reduce systematic bias in measured kinetic rates and improve consistency between experiments. Our framework aims to establish standardized practices for quantitative fluorescence-based CRISPR assays and provides researchers with practical tools for reproducible kinetic characterization and rational assay design.

Volatile organic compounds (VOCs) in human breath have been explored as non-invasive biomarkers for disease, including respiratory infections and cancer, yet none are clinically validated. A major barrier is the difficulty of identifying and controlling confounding factors that affect volatile exhaled breath composition. A critical and overlooked confounder is the oral microbiome, which produces VOCs that can obscure the trace volatiles originating from the lower airways. To investigate this, we conducted an intervention study on sixteen healthy volunteers, using real-time breath analysis, which demonstrates that oral microbiota rapidly alter exhaled VOC profiles following a low-dose (0.5 g) oral glucose administration. Acetoin levels respond promptly to glucose, confirming its oral microbial origin. However, pathogenic bacteria resulting from respiratory infections can also produce acetoin, underscoring the challenge of distinguishing sources of breath VOCs. Similarly, other volatiles, such as acetic acid and ethanol, are also influenced by small glucose doses, complicating their use as biomarkers in non-targeted volatilomic studies. Recognising the metabolic context of each volatile is essential to distinguish infection signals from physiological background. Beyond serving as a cautionary note for exhaled breath research, these results may encourage the oral health and dentistry communities to adopt breathomics analytical tools for rapid chairside diagnostics, transforming respiratory confounders into clinical opportunities for dental care.

Background Thalassemia is one of the most common monogenic disorders worldwide, current screening strategies combining hematological testing with molecular assays still carry a risk of missed diagnoses and undesirable efficiency, particularly for complex structural variants and rare mutations. Methods In this prospective double-blind, multicenter cohort study of 3,842 participants (3,362 pregnant women and 480 male partners), we conducted a head-to-head comparison to systematically evaluate the incremental clinical value and detection performance of single-molecule nanopore sequencing in thalassemia (SMITH) against conventional hematological testing and next-generation sequencing (NGS). Findings The overall concordance rate between NGS and SMITH was 98.6% (3789/3842). The discrepant cases (n=53) were directly attributed to the superior detection capabilities of SMITH, which successfully identified complex structural rearrangements-including 45 -globin gene triplications and four HK alleles-that were missed by NGS. Furthermore, SMITH accurately detected four rare variants (c.134_135insT/, c.-22(C>T)/, {beta}N/{beta}c.316-290delinsAGGGCAATAATTT and {beta}3.5 kb deletion/{beta}N ) and resolved ten trans and three cis configurations within the globin gene allele. Clinically, these technical advantages translated to a 9.3% (5/54) increase in the detection rate of high-risk prenatal couples, effectively preventing one birth affected by moderate-to-severe thalassemia. Additionally, SMITH corrected a diagnostic discrepancy in one case (HK vs. -3.7), sparing the couple from an unnecessary invasive procedure. Interpretation Our findings demonstrate that SMITH provides a powerful platform for resolving globin gene rearrangements, detecting rare variants, and enabling direct haplotype phasing. By effectively eliminating diagnostic blind spots, SMITH is expected to become an optimal method for thalassemia prevention programs. Funding This study was supported by Chinese National Natural Science Foundation Projects 81760037 and 82271894.

A multi-omic approach utilizing a single biospecimen is important to avoid intra-sample heterogeneity associated with testing multiple omic single-samples, and for more efficient use of small volumes of precious biopsies (<30 mg). This is especially true for the microanatomy of post-mortem human brain samples. Using post-mortem human brain biospecimens from the NIH NeuroBioBank, a penta-omic sequential extraction method is described, Simultaneous Metabolomic, Proteomic, Lipidomic - DNA, RNA Extraction (SiMPL-DREx). Each sequential omic extract was compared to those obtained by the gold standard single omic method. Preserving RIN is critical for brain and tissue banks, as it is a primary measure of tissue quality. For all five omic extracts, the tissue integrity numbers and omic profiles did not significantly differ from those obtained by the respective omic gold standard method. Unlike past multi-omic studies, this study quantified the relative solvent percentages and upstream losses for both the organic and aqueous phases, confirming an omics loss of under 5%.

The human small intestine (SI) plays a central role in nutrient processing, host-microbe interactions, and immune regulation, yet remains poorly characterized due to the lack of minimally disruptive sampling methods. Here, we present a protocol for deploying, recovering, and analyzing samples collected using an ingestible device that enables multi-region, lumen-targeted SI sampling during normal digestion. The device incorporates a ~30-cm collapsible tube wound into pH- or time-responsive layers that sequentially unfurl in situ, typically capturing three spatially ordered samples with high yield and reliable retrieval. This protocol outlines study design, participant handling, device recovery, contamination control, and standardized workflows for analyses, including cell quantification, culturomics, sequencing, and metabolomics. We further describe benchmarking approaches for evaluating spatial resolution and strategies for assay prioritization when sample volume is limiting. By reducing participant burden and facilitating integration with stool, saliva, and clinical metadata, this approach enables longitudinal and large-cohort studies linking SI microbial ecology and host physiology to human health.

Acidocalcisomes are evolutionarily conserved acidic organelles that are rich in cations and inorganic phosphate, primarily polyphosphates. In kinetoplastid parasites, acidocalcisomes and their polyphosphate content are essential for osmoregulation and environmental adaptation during host switching. In this organelle, polyphosphate is synthesised and transported to the lumen by the vacuolar transporter chaperone (VTC) complex. Interestingly, unlike yeast VTC, which has five components, only two have been observed in kinetoplastids: Vtc1, which contains only a transmembrane domain and Vtc4, which, in addition to a transmembrane domain, also consists of SPX and catalytic domains. In this study, we used proximity-dependent biotinylation (BioID) in Leishmania tarentolae to identify proteins located close to the VTC complex. The complex was found near several known acidocalcisomal proteins, including membrane-bound pyrophosphatase (mPPase), vacuolar-type H-ATPase (V-H+-ATPase), Ca{superscript 2}-transporting P-type ATPase (Ca2+-ATPase), zinc transporter (ZnT), and palmitoyl acyltransferase 2 (PAT2). Importantly, this approach revealed three novel VTC binding partners (VBPs) that colocalise and interact with the complex in acidocalcisomes, as confirmed by confocal microscopy, pulldown assays, and AlphaFold3 structural predictions. Together, our results expand the acidocalcisome interactome and suggest that the newly identified VBPs may contribute to the structural organisation and regulatory function of the VTC complex in phosphate homeostasis of kinetoplastid parasites. Author summaryProtozoan parasites such as Leishmania and Trypanosoma cause serious diseases affecting millions of people worldwide. To better understand how these parasites survive environmental changes during transmission between hosts, we studied a specialised organelle called the acidocalcisome, which stores polyphosphates and helps regulate stress responses. In this work, we used the non-pathogenic Leishmania tarentolae as a safe and cost-effective model that shares key cellular features with disease-causing species. Using a combination of CRISPR-Cas9 genome editing, proximity-based labelling (BioID), confocal microscopy, pulldown assays and AlphaFold3 structure prediction, we investigated the vacuolar transporter chaperone (VTC) complex, which synthesises and transports polyphosphate into the acidocalcisome lumen. Proximity proteomics identified several known proteins located near the VTC complex, and importantly, led us to discover three novel proteins that interact with it. These findings open new directions for exploring the organisation and regulation of the VTC complex in protozoan parasites. By revealing novel protein interactions, our study contributes to a deeper understanding of parasite biology and may help identify therapeutic targets for treating neglected tropical diseases.

The 16S rRNA gene is the most widely used genetic marker for microbial community profiling, but its limited sequence divergence often prevents species-level identification. The RNA polymerase {beta}-subunit gene (rpoB) offers higher sequence variability, single-copy occurrence, and stronger phylogenetic consistency, yet its adoption in metataxonomic studies has been constrained by the lack of universal primer sets. Here, we present a novel universal primer pair that amplifies an [~]1,800 bp rpoB region (rpoB_MV) compatible with long-read sequencing platforms. In silico evaluation across 17683 bacterial reference genomes demonstrated high universality, with over 86% of genomes predicted to amplify. Compared with full-length and partial 16S rRNA gene markers, the rpoB_MV amplicon exhibited significantly greater inter-species sequence divergence and improved phylogenetic concordance with core-genome trees. Sequencing of two complementary mock communities confirmed superior species-level identification accuracy, with misclassification rates below 0.01% and no reads assigned to unresolved species clusters. These results establish rpoB_MV as a robust alternative to 16S rRNA gene-based profiling for high-resolution metataxonomic applications. IMPORTANCEMicrobial community studies increasingly require species-level resolution because species within the same genus can differ substantially in pathogenicity, ecological function, and metabolic capacity. Current 16S rRNA gene-based methods frequently fail to distinguish closely related species, collapsing biologically distinct organisms into the same taxonomic assignment and obscuring community differences that matter for clinical diagnostics, food safety, and environmental monitoring. The rpoB_MV primer pair presented here overcomes this limitation by targeting a longer, more variable region of the rpoB gene, enabling accurate species-level identification across diverse bacterial phyla. Combined with advances in long-read sequencing, this approach provides researchers with a practical tool to resolve microbial communities at the species-level.

Milk is a challenging matrix to extract sufficient microbial DNA from for downstream analysis. This study assessed fourteen DNA extraction protocols for their DNA outputs. An adaptation of the QIAGEN DNeasy PowerSoil kit, which increased initial sample volume and maintained all volume of lysate following bead beating proved most effective.

Short-read amplicon sequencing is widely used for fungal surveys but can limit taxonomic resolution. Long-read sequencing enables recovery of the full internal transcribed spacer (ITS) region and may improve ecological and taxonomic inference. Here, we conducted a paired comparison of Illumina ITS2 and PacBio HiFi full-length ITS sequencing using identical DNA extracts from built-environmental air and surface samples (n = 68) collected across homes, a dormitory, and laboratories. Both datasets were taxonomically assigned using the same algorithm and reference database. We performed paired statistics, in-silico ITS2 trimming of long-read sequences, and cross-platform mapping at multiple identity thresholds. Full-length ITS provided higher taxonomic resolution, assigning a greater fraction of ASVs at the family (98% vs. 88%) and species (42% vs. 32%) ranks than ITS2 (paired Wilcoxon q = 0.002). Alpha-diversity comparisons showed similar Shannon diversity across pipelines, whereas richness metrics were consistently higher for full-length ITS. Beta-diversity analyses indicated broadly comparable community-level patterns, although full-length ITS revealed stronger sample-type- and location-associated structure (PERMANOVA R{superscript 2} [&ge;] 0.06, p = 0.0001). In-silico ITS2 trimming reduced these differences, indicating that amplicon length is a major contributor to enhanced taxonomic resolution and ecological inference. Cross-platform mapping further showed extensive one-to-many relationships between ITS2 and full-length ITS ASVs, consistent with increased sequence resolution in long-read data. Together, these results show that ITS2 sequencing provides robust community-level profiling, while full-length ITS enables improved richness estimates and finer ecological and taxonomic resolution. This paired, bias-aware framework provides a practical template for selecting fungal amplicon sequencing strategies in built-environment mycobiome studies. ImportanceFungal communities in built environments influence indoor air quality and human exposure, yet their characterization depends strongly on sequencing strategy. This study provides a controlled, paired comparison of short-read ITS2 and long-read full-length ITS sequencing, showing that differences in amplicon length substantially contribute to variation in taxonomic resolution and ecological inference. While both approaches yield comparable community-level patterns, full-length ITS improves richness estimates, species-level assignment, and environmental discrimination by resolving sequence variation collapsed in ITS2 surveys. By integrating paired diversity analyses, in-silico ITS2 trimming, and cross-platform ASV mapping, this work offers a bias-aware framework for evaluating fungal amplicon pipelines. Importantly, improved species-level resolution enables functional interpretation of indoor fungi, for example the identification of taxa associated with pathogenic traits, allergen production, or toxin synthesis, supporting the development of more informative exposure metrics and targeted assays relevant to human health in built environments.

Luminescence is a powerful method for detecting trace analytes and monitoring biological processes. However, most bioluminescence reagents, including luciferase and its substrates, are sensitive to temperature, limiting their useable shelf lives, and resulting in inconsistent performance. Enhancing the stability of these reagents could improve data quality, simplify workflows, and address cold chain storage issues. In this study, we demonstrate the application of the platform stabilization technology, capillary-assisted vitrification (CAV), as a tool to stabilize different luciferases and their substrates, and the application of the stabilized reagents in both in vitro and in vivo bioluminescent assays. We demonstrate that CAV-stabilized reagents can be stored and shipped ambiently, maintain consistent performance over time, and are suitable for use in cell viability quantification, tumor monitoring, in vivo imaging, microbial detection, and immunoassays. Additionally, different reagents can be co-formulated to make ready-to-use assay kits that can also be shipped and stored ambiently. Our results demonstrate that CAV stabilization is a viable alternative to traditional storage methods, with broad potential to improve bioluminescence workflows.

Assessing reproducibility across different molecular profiling studies is a persistent methodological challenge (Zhang et al., 2009; Sweeney et al., 2017; Ioannidis, 2005). Differences in platform technology, cohort composition, analytical pipelines, and feature definitions often make it difficult to interpret cross-study comparisons based solely on gene-identity overlap. In this study, we conducted a retrospective computational analysis of seven publicly available analytical datasets (including alternative analytical pipelines applied to the same cohort) derived from five biologically independent peripheral blood transcriptomic and DNA methylation cohorts, comprising 3,487 samples (1,824 Parkinsons disease cases and 1,663 controls). Reproducibility was evaluated using gene-identity overlap, enrichment-based comparisons, and a permutation-based framework to assess directional consistency of effect estimates across datasets. We also tested the robustness of results by varying false discovery rate thresholds and applying alternative probe-to-gene collapsing strategies. All analyses were performed using reproducible workflows implemented in R and Python with fixed random seeds. Across independent cohorts, gene-identity overlap was generally limited, with enrichment ratios close to one, especially when datasets were generated using different platforms. In several datasets, limited numbers of statistically significant features further constrained overlap-based comparisons. In contrast, directional consistency showed greater stability. High levels of directional consistency were observed across independent cohort comparisons when restricted to overlapping statistically significant features and remained stable across statistical thresholds (90.0% at FDR < 0.05 and 82.8% at FDR < 0.10). When evaluated across the full shared gene universe without conditioning on statistical significance, directional consistency was substantially lower ([~]30 to 32%) but remained significantly above permutation-based null expectations. Permutation testing confirmed that the observed directional consistency exceeded what would be expected by chance. A combined analysis including methodological replicates (n [&ge;] 3 datasets) showed 98.3% directional consistency; however, this estimate includes non-independent analytical pipelines applied to the same cohort and reflects analytical stability rather than independent biological replication. Rather than introducing a new statistical method, this study examines how commonly used reproducibility metrics behave under crossstudy heterogeneity and identifies their practical limitations and appropriate use boundaries.

Digital Pathology (DP) is a fast-emerging branch of pathology focused on digitizing pathology data. A key challenge of DP usage for pathology laboratories, especially mid- to small-sized clinical labs, are the upfront costs associated with instrumentation and the logistical challenges of implementation. In the current project, we built an end-to-end DP solution using low-cost, open-source components that is user-friendly at a small scale. We repurposed readily available microscopy components in a pathology lab to assemble a fully functional DP pipeline for translational research applications. We tested multiple low-cost complementary metal-oxide semiconductor (CMOS) cameras in this project and chose a user-friendly Canon camera for image acquisition. An open-source DP server solution, OMERO v.5.6.4, was used as the image management system (IMS) to host and serve the WSIs on an Ubuntu 22.04 operating system. The server-hosted WSI images were evaluated remotely and asynchronously by multiple pathologists physically situated in Albuquerque, NM; Salt Lake City, UT; and Palo Alto, CA. Each pathologist assessed the quality of the WSI pipeline, image quality, and WSI interaction experience using a 23-question survey. Overall, the custom, low-cost WSI pipeline was noted to be a robust and user-friendly experience by the pathologists. The current DP setup is unlikely to be useful as a commercial, scalable DP pipeline for large-scale clinical applications. However, it demonstrates the feasibility of creating customized, small-scale DP solutions (at a low price point) for asynchronous translational pathology research applications. Additionally, building customized DP pipelines provides excellent educational opportunities for pathology residents to gain in-depth knowledge of the various technical elements of a DP workflow. In summary, we have established a low-cost, end-to-end WSI DP pipeline useful for spatiotemporally asynchronous translational pathology research, in an academic setting.

This protocol details the extraction of high-molecular-weight genomic DNA from grapevine tissues (wild and cultivated Vitis spp., including pathogen-infected samples) and the subsequent preparation of Illumina(R) whole-genome sequencing libraries using bead-bound Tn5 transposase. It is designed to overcome challenges from polyphenolic compounds and secondary metabolites in wild plants, providing a cost-effective workflow for large-scale population genomics. It includes recipes for buffers, incubation times, critical notes, and troubleshooting tips to maximize yield and library quality. Although designed for the grapevine DNA, this protocol is potentially applicable to other similar wild plant species HighlightsO_LIOptimized CTAB-PTB DNA extraction protocol for field-collected wild plant tissues. C_LIO_LIEffective removal of polyphenols and secondary metabolites associated with DNA using PTB. C_LIO_LICost-effective Illumina DNA Prep library preparation using bead-bound Tn5 transposase (Tagmentation). C_LIO_LIScalable workflow suitable for large-scale population genomics in Vitis species. C_LIO_LIValidated method for high-molecular-weight DNA and high-quality sequencing data. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/713680v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@b637d4org.highwire.dtl.DTLVardef@10b563aorg.highwire.dtl.DTLVardef@14a32caorg.highwire.dtl.DTLVardef@4c9577_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundNanopore sequencing offers a cost-effective and portable platform for microbiome analysis, but amplicon-based approaches remain limited by higher sequencing error rates and a lack of workflows tailored to mixed domain ribosomal RNA profiling. While short-read technologies dominate microbial community analysis, their portability and flexibility are constrained. There is therefore a need for robust pipelines designed specifically for cross-domain Nanopore amplicon data. ResultsWe introduce the Nanopore sequencing-based Amplicon Pipeline (NAP; https://github.com/Luke-B-Jones/NAP), an open-source workflow optimised for flexible mixed domain primer sets such as 515Y/926R. NAP performs adaptive quality filtering, chimera removal, centroid generation, BLAST-based taxonomic classification, hierarchical consensus correction, and domain-aware post-processing, outputting decontaminated abundance tables suitable for downstream analysis. Initial validation against two complementary commercial mock communities showed that NAP achieved strong genus-level performance across both low complexity logarithmic and more compositionally complex gut mock communities. Detection was most reliable above ca. 1% relative abundance, and replicate outputs showed strong agreement with expected composition under Bray-Curtis, Jaccard, agreement-plot, and Bland-Altman analyses. Benchmarking of NAPs internal filtering modes showed that the default adaptive setting provided the most robust balance of read quality, retained depth, and downstream taxonomic fidelity across heterogeneous inputs. Direct comparison against QIIME2 and Kraken2/Bracken further showed that NAP most accurately preserved expected community structure, with markedly fewer false positive assignments at genus level and substantially stronger species-level behaviour under the tested conditions. Species-level assignments were informative for some taxa, but remained less robust than genus-level outputs with the default V4-V5 amplicon. ConclusionsNAP provides a robust and flexible workflow for cross-domain Nanopore amplicon profiling, with strongest performance at genus level and competitive species-level behaviour for well resolved taxa. Although analysis of field-derived data was not assessed here, NAP compatibility with portable Nanopore sequencing supports accurate mixed domain microbiome profiling under the tested conditions.

Cystatin-C is an important renal function biomarker, and conventional quantification requires centralized laboratory analyzers, which limits timely testing in primary care and resource-limited settings. To address this need, we developed and validated a simple, rapid, and quantitative smartphone-based (SP) lateral flow immunoassay (LFIA) for measuring serum Cystatin-C. The SP-LFIA platform consists of a colorimetric LFIA strip and a custom SP reader with uniform LED illumination and macro lens for image capture. Quantitative image analysis of the colorimetric signal is performed by a dedicated application using a pre-defined third order polynomial calibration model. Following systematic optimization, the assay demonstrated a wide quantitative range of 0.32-8.00 mg/L, with a limit of detection of 0.15 mg/L. Analytical validation conducted according to CLSI guidelines showed excellent precision, with intra- and inter-assay coefficients of variation below 10%, and no significant interference from bilirubin, triglycerides, hemoglobin, or rheumatoid factor. Accelerated stability testing confirmed robust strip performance after storage at 50 {degrees}C for 28 days. Method comparison using 100 clinical serum samples showed high agreement with a commercial PETIA reference method (R{superscript 2} = 0.993) and minimal bias. These results indicate that the developed smartphone-based LFIA provides a reliable, cost-effective, and practical tool for point-of-care Cystatin-C monitoring.