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"Multiplex RT-PCR for SARS-CoV-2 variant surveillance in resource-limited settings: an in-house validation study in Cuba"

Background SARS-CoV-2 genomic surveillance is vital for public health, but whole-genome sequencing (WGS) remains costly and inaccessible in many resource-limited settings. We developed and validated a multiplex real-time RT-PCR assay for rapid, economical detection of key mutations associated with variants of interest (VOI) and concern (VOC). Methodology Two multiplex mixes (M1, M2) targeting eight mutations in the ORF1a and Spike genes were designed. Analytical validation included sensitivity, specificity, reproducibility, and limit of detection (LoD) using WHO international standards and a respiratory pathogen panel. In parallel, an in silico analysis evaluated oligonucleotide efficacy against 10.4 million SARS-CoV-2 genomes from GISAID/NCBI, assessing inclusivity, target-site secondary structure (RNAalifold), and hybridization energy (Primer3Plus). Results The assay demonstrated 100% clinical sensitivity among samples with valid RT-PCR results (41/42 samples yielded interpretable results, with one inhibited sample excluded from sensitivity calculation), a LoD of 5.7 log10 IU/mL, and 100% analytical specificity against 32 non-SARS-CoV-2 respiratory pathogens. Six out of eight oligonucleotide sets showed >96% inclusivity; two sets exhibited reduced inclusivity (94.03%, 90.14%) and structural features potentially affecting binding against emerging variants. The assay enables direct identification of major VOCs (Alpha, Beta, Gamma, Delta, Omicron) and indirect detection of multiple VOIs (P.2, Epsilon, Kappa, Eta, Iota, Lambda). Conclusion This standardized multiplex assay provides a rapid, sensitive, and low-cost alternative for SARS-CoV-2 variant surveillance in Cuba and similar settings. The integration of experimental and in silico validation offers a robust, adaptable framework to sustain diagnostic accuracy amid viral evolution, optimizing the allocation of scarce sequencing resources.

Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems

arXiv:2606.12806v1 Announce Type: cross Abstract: Short-term load forecasting is essential for reliable energy management, but practical deployment on edge devices requires models that remain accurate under limited memory, finite measurement budgets, and hardware noise. This work proposes a hardware-efficient Quantum Reservoir Computing (QRC) framework for energy load forecasting, where a fixed quantum reservoir transforms temporal input windows into high-dimensional features and only a classical Elastic Net readout is trained. To reduce deployment cost, the trained readout is compressed using post-training fixed-point quantization at bit widths from 8 to 2 bits. The framework is evaluated on the Tetouan and Spain energy load datasets under exact statevector simulation, 512-shot finite sampling, and realistic hardware-noise models from IBM FakeTorino and IBM FakeMarrakesh. Results show that 6-bit readout precision preserves full-precision forecasting performance while reducing readout memory by 81.2%. Below this point, degradation becomes dataset dependent, with Tetouan showing stronger sensitivity and Spain degrading more gradually. Hardware-noise validation further shows that the trained readout transfers to noisy reservoir states without retraining. These findings support quantized QRC as a resource-aware forecasting approach for near-term quantum time-series applications.