Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.40262

PCR-ALERA: A PCR-ELISA analogue but antibody-free DNA detection system

Miles Bonenberger·Roland Krämer
View original source ↗Read source document ↗Export metadata ↓

Abstract

PCR-ALERA is a new concept for DNA detection that combines DNA amplification by polymerase chain reaction (PCR) with an activator-linked enzyme reactivation assay (ALERA). PCR-ALERA resembles the commercial, semi-quantitative PCR-ELISA assay that offers high sensitivity and high-throughput screening in microplates. PCR-ALERA overcomes a major drawback of PCR-ELISA since it does not require the time-consuming handling of expensive antibodies. In the first step of the assay, commercially available modified nucleotides were incorporated during PCR amplification of the target DNA. A selenocysteine containing peptide (the activator) was linked to the amplified DNA by click chemistry. This peptide has been optimized for the highly efficient activation of the latent protease papain by selenolate-triggered disulfide cleavage. The DNA-peptide hybrid was then immoblized in microplate wells and incubated with latent papain. The latter was readily activated and converted a fluorogenic substrate to a fluorescent product with high catalytic turnover. Overall assay time is about 4 hours including PCR.

Explore related subjects

q-bio.QM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Miles Bonenberger, Roland Krämer. 2026-10-29. PCR-ALERA: A PCR-ELISA analogue but antibody-free DNA detection system. https://arxiv.org/abs/2610.40262

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

gpuGEM: GPU-based linear optimisation of genome-scale metabolic models

Summary: Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated optimisation. We present gpuGEM, an open-source Python library for GPU-based optimisation using NVIDIA cuOpt, with size-adaptive settings validated against HiGHS and Gurobi. Across ten models spanning four orders of magnitude, gpuGEM achieved 1.1x to 94x speed-ups over the faster HiGHS method (dual simplex or interior point), and outperformed Gurobi on three models (1.6x to 2.5x). In candidate-gene prioritisation, gpuGEM reduced end-to-end query time from 41 s to 21 s. Availability and Implementation: gpuGEM is released under the MIT licence at https://github.com/Digital-Metabolic-Twin-Centre/gpuGEM and on PyPI (pip install gpugem); release v0.1.3 is archived at https://zenodo.org/records/23422813. It requires an NVIDIA GPU of compute capability 7.0 or newer (Volta onward), Python 3.11 or later, CUDA 12 and cuOpt 26.6 or later. Contact: ronan.mt.fleming@gmail.com Supplementary information: Supplementary data are available as an ancillary file on arXiv.

q-bio.QM↗
arXiv2026

The Verification Wall in Microbial Metabolomics

Computational methods now propose structures for gut microbial metabolites faster than any laboratory can confirm them. We argue that verification, not generation, is the binding constraint, and that better models will not relieve it. Confirming a structure to the field's own standard means having the molecule in hand. Automation can lower what that costs per compound, but the cost stays proportional to the number of structures and never amortizes across them. Two substitutes are on offer and neither removes that requirement. In-silico spectral prediction ranks candidates instead of confirming one, and the strongest current model puts the right structure first 33.5% of the time on a scaffold-split benchmark. Any assignment resting on spectral similarity is, by the field's own definition, made without a reference standard. Microcrystal diffraction does return a structure, but it needs the compound to crystallize out of a trace mixture, which is a different problem rather than an easier one. We sorted 10,645 computationally inferred metabolite structures by what verifying each would take. Between 5% and 37% have no verification route identified under our analysis. That spread reflects where the thresholds are set rather than a measured count of unverifiable compounds, and the inaccessible ones cluster among the most speculative entries. Those figures understate the problem, because the gut makes far more compounds than any database records. HMDB holds only what someone has already thought worth cataloguing, and structure generators are bounded by nothing at all. The claim is easy to disprove. Show a route that confirms structures as fast as they are proposed. Until someone does, two problems stay unsolved. Nobody knows how to spend a verification budget that will always be too small, or what a biologist may conclude from a structure that was never confirmed.

q-bio.QM↗
arXiv2026

Agentic LLM reasoning in pediatric diabetes outcome prediction from multimodal medical records

Large language model (LLM) agents are proposed for clinical prediction but are untested in pediatrics, where cohorts are small and outcomes are uncommon. Using records from 2,489 youth with type 2 diabetes (T2D) and 4,018 with type 1 diabetes (T1D), spanning structured data, note-derived social determinants and, in T1D, continuous glucose monitoring data, we predicted seven T2D outcomes, four also in T1D, at 24 months from 18 months of follow-up. CEDAR (Clinical Evidence-Driven Agent for Risk-prediction), an evidence-citing, self-auditing LLM pipeline, outperformed trained machine learning in both cohorts (pooled area under the receiver operating characteristic curve 0.815 versus 0.656 to 0.700 in T2D and 0.756 versus 0.603 to 0.631 in T1D), most on the sparsely screened outcomes. Only 4 of 70,246 values it cited did not match the chart. Reading only the evidence and reasoning cited by CEDAR, two pediatric endocrinologists reached the same prediction as CEDAR in about nine of ten cases, agreeing with each other on 90.5%.

q-bio.QM↗