1000 drape prep stick blue towels drapes  (Bovie Medical Corporation)

Journal: iScience

Article Title: Genome-wide mapping of the Galleria mellonella transcription start sites during fungal infection and treatment

doi: 10.1016/j.isci.2026.117675

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Article Snippet: MGIEasy RNA Directional Library Prep Set , MGI Tech , 1000006386.

Techniques: Suction Filtration, Saline, Sensitivity, Qubit, Sequencing, Genome Wide, Infection, Software

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Differentially expressed small RNAs in patients with CRC and HCs (A) Volcano plots showing the differential expression of miRNAs, tsRNAs, and rsRNAs between serum samples from patients with CRC and HCs. Red and blue dots indicate significantly upregulated and downregulated sRNAs, respectively, defined by a subclass-specific Benjamini-Hochberg-adjusted p value (FDR) < 0.05 and |log 2 (fold change)| > 1, whereas gray dots indicate nonsignificant transcripts. The four selected candidates are highlighted. (B) Screening identified 20 sRNAs that met the biomarker-selection criteria. A heatmap of these sRNAs in 10 CRC and 10 HC samples showed separation between the two groups. Expression values were Z score normalized after log10 transformation of transcripts per million values. (C) Rank plots of the 200 most abundant sRNAs ordered by log2(fold change), with the 20 candidate sRNAs indicated. (D) The 20 candidates were evaluated using conventional two-step RT-qPCR in an independent screening cohort comprising 30 patients with CRC and 30 HCs. Four sRNAs were significantly increased in CRC: hsa-miR-223-3p ( p < 0.0001), tsRNA-Asp-GTC-1 ( p < 0.0001), rsRNA-28S-12 ( p < 0.0001), and hsa-let-7f-5p ( p = 0.0012). (E) ROC curve analysis of the four selected sRNAs for distinguishing patients with CRC from HCs. The AUCs were 0.8778 (95% CI, 0.7925–0.9631) for hsa-miR-223-3p, 0.8633 (95% CI, 0.7730–0.9536) for tsRNA-Asp-GTC-1, 0.9350 (95% CI, 0.8731–0.9969) for rsRNA-28S-12, and 0.7422 (95% CI, 0.6163–0.8682) for hsa-let-7f-5p. Comparisons in (D) are presented as the mean ± SEM, with each point representing one participant. Comparisons in (D) were performed using two-tailed unpaired student’s t tests. ROC AUCs are reported with their corresponding 95% confidence intervals. Asterisks denote the following significance thresholds: p ≥ 0.05, ns, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001.

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Quantitative Proteomics, Blood/Plasma/Serum Samples, Screening, Biomarker Discovery, Selection, Expressing, Transformation Assay, Quantitative RT-PCR, Analysis, Two Tailed Test

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Overview of the OM-sqPCR platform The OM-sqPCR platform is an integrated system for multiplex small RNA quantification, enabling simultaneous reverse transcription and real-time quantitative PCR of multiple small RNA markers within a single closed reaction tube. Algorithm-guided optimization of primer-probe combinations markedly improves quantification efficiency, assay reproducibility, and multiplex compatibility. (A) The OM-sqPCR system employs optimized enzyme cocktails—including reverse transcriptase, hot-start DNA polymerase, and uracil-DNA glycosylase (UDG)—together with precisely tuned buffer compositions and temperature control tailored for short RNA templates. (B) Algorithms calculate primer-probe parameters, including sequence length, GC content, secondary structure stability, dimerization potential, and annealing temperature, to ensure target-specific amplification while minimizing cross-interference among multiple sRNAs. (C) The OM-sqPCR workflow comprises two coordinated steps: (i) algorithm-guided design and validation of primer-probe sets, (ii) one-tube integration of reverse transcription and qPCR reactions. The primer-probe sets include four sequences: ① stemloop primer; ② forward primer; ③ probe; ④ reverse primer. Sequence ① enables reverse transcription, while sequences ②–④ facilitate real-time fluorescence quantitative PCR. Compared with conventional two-step RT-qPCR, OM-sqPCR rapidly identifies optimal primer-probe configurations, eliminates iterative optimization, and performs all amplification steps in a single tube—thereby reducing assay time and cost (< US$1 per test) while streamlining workflows and improving data stability. This figure was created using BioRender under agreement no. WQ2A4QH3S7.

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Multiplex Assay, Reverse Transcription, Real-time Polymerase Chain Reaction, Control, Sequencing, Stability, Amplification, Biomarker Discovery, Fluorescence, Quantitative RT-PCR

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Screening and performance evaluation of one-step reaction chemistry in the OM-sqPCR detection system (A) Workflow comparison between the one-step OM-sqPCR and conventional two-step RT-qPCR. OM-sqPCR integrates reverse transcription and real-time qPCR within a single closed tube, eliminating the need for intermediate handling and reducing total assay time. (B) Four commercial RT-qPCR reagents were selected to detect 10-fold serially diluted standards of hsa-miR-16-5p and hsa-let-7g-5p (0.1–10,000 fM). (C) Screening of optimized one-step RT-qPCR reaction mixtures. Four optimized one-step RT-qPCR reaction mixtures (mix 1–4) were evaluated using 10-fold serial dilutions (0.1–1,000 fM) of four representative circulating small RNA biomarkers—hsa-miR-223-3p, rsRNA-28S-12, tsRNA-Asp-GTC-1, and hsa-let-7f-5p. Amplification performance was assessed by ΔCt values across concentration gradients, where a ΔCt of approximately 3.3 per log 10 dilution represents theoretical amplification efficiency. (D) Representative fluorescence amplification curves comparing one-step and two-step RT-qPCR systems. (E) Standard curves generated for four sRNA targets using one-step single-channel RT-qPCR, showing strong linearity (R 2 > 0.99) across six orders of magnitude. (F) Agarose gel electrophoresis (4%) of PCR products from the one-step single-channel assay. Lane M, 20 bp DNA ladder; Lane 1, hsa-miR-223-3p; Lane 2, rsRNA-28S-12; Lane 3, tsRNA-Asp-GTC-1; Lane 4, hsa-let-7f-5p. (G) Correlation analysis between OM-sqPCR and traditional two-step RT-qPCR results across matched samples, showing high concordance (Pearson’s r = 0.95). Data are presented as the mean ± SEM in (B), (C), and (E).

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Screening, Comparison, Quantitative RT-PCR, Reverse Transcription, Amplification, Concentration Assay, Fluorescence, Generated, Agarose Gel Electrophoresis, Analysis

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Analytical performance of the OM-sqPCR platform (A) Development of the algorithm-guided assay-design framework. A parameter library for sRNA RT-qPCR was established, encompassing stem-loop motifs, primer-probe length ranges, melting-temperature windows, Gibbs free-energy (ΔG) thresholds, forward-primer 5′-extension lengths, and reverse-primer gap distances. Experimental feedback was used to refine the parameter network and identify high-performing primer–probe combinations. A scoring algorithm then ranked multiplex configurations by predicted amplification efficiency, specificity, and spectral compatibility. (B) Real-time amplification curves for four representative sRNAs measured by OM-sqPCR across concentrations from 100 aM to 100 pM. (C) Reproducibility analysis of serum samples with high, medium, and low RNA abundance. (D) Sanger sequencing confirmed the identities of the four amplicons. (E) Interoperator and interinstrument reproducibility of rsRNA-28S-12 measurements across 27 replicate measurements (nine measurements for each operator-instrument configuration). (F) Comparison of OM-sqPCR and conventional two-step RT-qPCR measurements in the same 40 samples. No significant between-method differences were detected for hsa-miR-223-3p ( p = 0.9992), tsRNA-Asp-GTC-1 ( p > 0.9999), rsRNA-28S-12 ( p = 0.9525), or hsa-let-7f-5p ( p > 0.9999). (G) OM-sqPCR measurements in patients with CRC ( n = 15) and non-CRC controls ( n = 25). Concentrations were higher in CRC for hsa-miR-223-3p ( p < 0.0001), tsRNA-Asp-GTC-1 ( p = 0.0002), rsRNA-28S-12 ( p = 0.0007), and hsa-let-7f-5p ( p = 0.0004). The coefficient of variation was calculated as CV (%) = 100 × SD/mean (E). Data in (F) and (G) are presented as the mean ± SEM. Comparisons used two-tailed paired student’s t tests in (F) and two-tailed unpaired student’s t tests in (G). Asterisks denote the following significance thresholds: p ≥ 0.05, ns, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001.

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Quantitative RT-PCR, Multiplex Assay, Amplification, Analysis, Blood/Plasma/Serum Samples, DNA Sequencing, Comparison, Two Tailed Test

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Diagnostic performance of the four-sRNA biomarker panel in the training cohort (A) Serum concentrations of hsa-miR-223-3p, rsRNA-28S-12, tsRNA-Asp-GTC-1, and hsa-let-7f-5p in HC ( n = 59), CRP ( n = 49), and CRC ( n = 101) groups. Tukey-adjusted p values for HC versus CRP, HC versus CRC, and CRP versus CRC, respectively, were 0.8399, < 0.0001, and < 0.0001 for hsa-miR-223-3p; 0.8653, < 0.0001, and < 0.0001 for rsRNA-28S-12; 0.9358, 0.0003, and 0.0002 for tsRNA-Asp-GTC-1; and > 0.9999, < 0.0001, and < 0.0001 for hsa-let-7f-5p. (B) ROC analysis for distinguishing CRC from HC. AUCs were 0.9114 (95% CI, 0.8656–0.9572) for hsa-miR-223-3p, 0.9142 (95% CI, 0.8674–0.9611) for rsRNA-28S-12, 0.8397 (95% CI, 0.7761–0.9033) for tsRNA-Asp-GTC-1, 0.8856 (95% CI, 0.8334–0.9377) for hsa-let-7f-5p, and 0.9261 (95% CI, 0.8782–0.9739) for the combined panel. (C) ROC analysis for distinguishing CRC from CRP. AUCs were 0.8917 (95% CI, 0.8417–0.9417), 0.8828 (95% CI, 0.8305–0.9351), 0.9147 (95% CI, 0.8690–0.9604), 0.8909 (95% CI, 0.8411–0.9406), and 0.9447 (95% CI, 0.9109–0.9786), respectively. (D) ROC analysis of four machine-learning models in the training cohort. AUCs were 0.9659 (95% CI, 0.9432–0.9886) for SVM-Linear, 0.9630 (95% CI, 0.9383–0.9878) for LogReg-L2, 0.9518 (95% CI, 0.9238–0.9797) for LDA-lsqr, and 0.9506 (95% CI, 0.9229–0.9782) for random forest. (E) SHAP-based ranking of the four sRNA features from the SVM-Linear model. (F) Confusion matrix for the internal test set (non-CRC, n = 45; CRC, n = 30) at the prespecified classification threshold of 0.4603. The resulting sensitivity was 90.0% and specificity was 95.6%. Data in (A) are presented as the mean ± SEM. Group comparisons in (A) used one-way ANOVA followed by Tukey’s multiple-comparisons test. ROC AUCs are reported with 95% confidence intervals. Asterisks denote the following Tukey-adjusted significance thresholds: adjusted p ≥ 0.05, ns, ∗adjusted p < 0.05, ∗∗adjusted p < 0.01, ∗∗∗adjusted p < 0.001, ∗∗∗∗adjusted p < 0.0001.

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Diagnostic Assay, Biomarker Discovery, Analysis, Sensitivity

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Blinded multicenter validation of the SVM-Linear classifier at a prespecified probability threshold of 0.4603 (A) Predicted probabilities for HC, CRP, and CRC groups in three independent validation cohorts—Nanjing (NJ: HC, n = 52; CRP, n = 44; CRC, n = 58), Lianyungang (LYG: HC, n = 62; CRP, n = 24; CRC, n = 50), and Zhejiang (ZJ: HC, n = 39; CRP, n = 24; CRC, n = 48)—and in the combined multicenter cohort. Tukey-adjusted p values for HC versus CRP, HC versus CRC, and CRP versus CRC, respectively, were 0.8796, < 0.0001, and < 0.0001 in NJ; 0.9911, < 0.0001, and < 0.0001 in LYG; 0.9994, < 0.0001, and < 0.0001 in ZJ; and 0.9866, < 0.0001, and < 0.0001 in the combined cohort. (B) Predicted probabilities in HC ( n = 153), CRP ( n = 92), and CRC groups stratified by pathological stage (stage I, n = 31; stage II, n = 47; stage III, n = 61; stage IV, n = 17). Each CRC stage differed from HC and CRP (all adjusted p < 0.0001), whereas HC and CRP did not differ (adjusted p > 0.9999). (C) ROC analysis comparing the sRNA classifier with serum CEA for distinguishing CRC from non-CRC. AUCs for the classifier and CEA, respectively, were 0.9696 (95% CI, 0.9425–0.9968) and 0.5869 (95% CI, 0.4733–0.7006) in NJ; 0.9807 (95% CI, 0.9638–0.9976) and 0.6207 (95% CI, 0.5012–0.7401) in LYG; 0.9431 (95% CI, 0.9034–0.9829) and 0.5137 (95% CI, 0.3972–0.6301) in ZJ; and 0.9685 (95% CI, 0.9539–0.9830) and 0.5786 (95% CI, 0.5127–0.6444) in the combined cohort. (D) Confusion matrices for each cohort and the combined analysis. Sensitivity and specificity were 89.7% and 94.8% in NJ, 86.0% and 94.2% in LYG, 81.3% and 88.9% in ZJ, and 85.9% and 93.1% in the combined cohort, respectively. Data in (A) and (B) are presented as the mean ± SEM. Comparisons in (A) and (B) used one-way ANOVA followed by Tukey’s multiple-comparisons test. ROC AUCs are reported with 95% confidence intervals. Asterisks denote the following Tukey-adjusted significance thresholds: adjusted p ≥ 0.05, ns; ∗adjusted p < 0.05, ∗∗adjusted p < 0.01, ∗∗∗adjusted p < 0.001, ∗∗∗∗adjusted p < 0.0001.

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Biomarker Discovery, Analysis, Sensitivity

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

Figure Lengend Snippet: Scalability validation of OM-sqPCR in RCC and THCA (A) Absolute quantification of four sRNAs in serum from HCs; ( n = 40) and patients with RCC ( n = 40). All four markers were increased in RCC: hsa-miR-122-5p ( p < 0.0001), hsa-miR-155-5p ( p < 0.0001), hsa-miR-210-3p ( p < 0.0001), and hsa-miR-21-5p ( p < 0.0001). (B) Relative quantification of three sRNAs in serum from HC ( n = 40) and patients with THCA ( n = 40), normalized to U6. hsa-miR-222-3p and hsa-miR-451a were increased in THCA (both p < 0.0001), whereas hsa-miR-146a-5p was decreased ( p = 0.0002). (C) ROC analysis for distinguishing RCC from HC. AUCs were 0.9938 (95% CI, 0.9830–1.0000) for hsa-miR-210-3p, 0.9594 (95% CI, 0.9171–1.0000) for hsa-miR-21-5p, 0.9550 (95% CI, 0.9135–0.9965) for hsa-miR-122-5p, 0.7978 (95% CI, 0.6922–0.9035) for hsa-miR-155-5p, and 0.9938 (95% CI, 0.9835–1.0000) for the combined panel. (D) ROC analysis for distinguishing THCA from HC. AUCs were 0.9791 (95% CI, 0.9490–1.0000) for hsa-miR-222-3p, 0.9297 (95% CI, 0.8718–0.9875) for hsa-miR-451a, 0.7169 (95% CI, 0.6058–0.8280) for hsa-miR-146a-5p, and 0.9819 (95% CI, 0.9554–1.0000) for the combined panel. Data in (A) and (B) are presented as the mean ± SEM. Between-group comparisons used two-tailed unpaired student’s t tests. The combined RCC and THCA panels were fitted separately using L2-regularized binary logistic regression (scikit-learn LogisticRegression). Features were Z score standardized before fitting (C = 1.0; solver = “lbfgs”; maximum iterations = 1,000), and positive-class probabilities were used for ROC analysis. ROC AUCs are reported with 95% confidence intervals. Asterisks denote the following significance thresholds: p ≥ 0.05, ns, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001.

Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Biomarker Discovery, Quantitative Proteomics, Analysis, Two Tailed Test

Journal: iScience

Article Title: OM-sqPCR enables one-step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classification

doi: 10.1016/j.isci.2026.117540

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Article Snippet: VAHTS Small RNA Library Prep Kit , Vazyme , NR811.

Techniques: Sequencing, Gene Expression, Software

Journal: iScience

Article Title: Gapmers targeting conserved SARS-CoV-2 single-stranded regions reduce viral load and reshape inflammatory macrophage responses

doi: 10.1016/j.isci.2026.117639

Figure Lengend Snippet:

Article Snippet: NucleoMag NGS Clean-up and Size Select Beads , Macherey-Nagel , 744970.50.

Techniques: Virus, Variant Assay, Recombinant, Reverse Transcription, Diagnostic Assay, Luminex, Ligation, Multiplex Assay, Generated, Software

Journal: iScience

Article Title: Gapmers targeting conserved SARS-CoV-2 single-stranded regions reduce viral load and reshape inflammatory macrophage responses

doi: 10.1016/j.isci.2026.117639

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Article Snippet: End Prep library kit , NEB , E7546.

Techniques: Virus, Variant Assay, Recombinant, Reverse Transcription, Diagnostic Assay, Luminex, Ligation, Multiplex Assay, Generated, Software