BACKGROUND: The outbreak of COVID-19 caused by a novel Coronavirus (termed SARS-CoV-2) has spread to over 140 countries around the world. Currently, reverse transcription quantitative qPCR (RT-qPCR) is used as the gold standard for diagnostics of SARS-CoV-2. However, the positive rate of RT-qPCR assay of pharyngeal swab samples are reported to vary from 30∼60%. More accurate and sensitive methods are urgently needed to support the quality assurance of the RT-qPCR or as an alternative diagnostic approach. METHODS: We established a reverse transcription digital PCR (RT-dPCR) protocol to detect SARS-CoV-2 on 194 clinical pharyngeal swab samples, including 103 suspected patients, 75 close contacts and 16 supposed convalescents. RESULTS: The limit of blanks (LoBs) of the RT-dPCR assays were ∼1.6, ∼1.6 and ∼0.8 copies/reaction for ORF 1ab, N and E genes, respectively. The limit of detection (LoD) was 2 copies/reaction. For the 103 fever suspected patients, the sensitivity of SARS-CoV-2 detection was significantly improved from 28.2% by RT-qPCR to 87.4% by RT-dPCR. For close contacts, the suspect rate was greatly decreased from 21% down to 1%. The overall sensitivity, specificity and diagnostic accuracy of RT-dPCR were 90%, 100% and 93 %, respectively. In addition, quantification of the viral load for convalescents by RT-dPCR showed that a longer observation period was needed in the hospital for elderly patients. CONCLUSION: RT-dPCR could be a confirmatory method for suspected patients diagnosed by RT-qPCR. Furthermore, RT-dPCR was more sensitive and suitable for low viral load specimens from the both patients under isolation and those under observation who may not be exhibiting clinical symptoms.

It is very important to rapidly test the key indicators of water in the field to fully evaluate the quality of the regional water environment. However, a high-resolution measuring device that can generate small currents for low-concentration analytes in water samples is often bulky, complex to operate, and difficult for data sharing. This work introduces a portable multi-channel electrochemical device with a small volume, good interaction, and data-sharing capabilities called PMCED. The PMCED provides an easy-to-operate graphical interactive interface to conveniently set the parameters for cyclic voltammetry or a differential pulse method performed by the four electrode channels. At the same time, the device, with a current sensitivity of 100 nA V⁻¹, was applied to the detection of water samples with high background current and achieved a high-resolution measurement at low current levels. The PMCED uses the Narrow Band Internet of Things (NB-IoT) to meet the needs for uploading data to the cloud in remote areas. The electrochemical signal preprocessing and chemometrics models run in the cloud, and the final results are visualized on a web page, providing a remote access channel for on-site testing results.

Single-molecule sequencing technology, a novel method for gene sequencing, utilizes nano-sized materials to detect electrical and fluorescent signals. Compared to traditional Sanger sequencing and next-generation sequencing technologies, it offers significant advantages, including ultra-long read lengths, rapid sequencing, and the absence of amplification steps, making it widely applicable across various fields. By examining the development and components of single-molecule sequencing technology, it becomes clear that its unique characteristics provide new opportunities for advancing metrological traceability. Notably, its direct detection capabilities offer a novel approach to nucleic acid metrology. This paper provides a detailed overview of library construction, signal generation and detection, and data analysis methods in single-molecule sequencing and discusses its implications for nucleic acid metrology.

Palbociclib is a cyclin-dependent kinase 4/6 inhibitor and a commonly used antitumor drug. Many cancers are susceptible to palbociclib resistance, however, the underlying metabolism mechanism and extent of resistance to palbociclib are unknown. In this study, LC-MS metabolomics was used to investigate the metabolite changes of colorectal cancer SW620 cells that were resistant to palbociclib. The study indicated that there were 76 metabolite expression differences between SW620 cells with palbociclib resistance and the parental SW620 cells involving amino acids, glutathione, ABC transporters, and so on. MetaboAnalyst 6.0 metabolic pathway analysis showed that arginine synthesis, β-alanine metabolism, and purine metabolism were disrupted. These results may provide potential clues to the metabolism mechanism of drug resistance in cancer cells that are resistant to palbociclib. Our study has the potential to contribute to the study of anti-palbociclib resistance.

The exploration of deep-sea microorganisms is transitioning from ex situ laboratory analysis to in situ real-time monitoring. While in situ technologies offer unprecedented access to microbial activities in their natural extreme habitats, they face a critical, yet often overlooked, bottleneck: the absence of a robust metrological framework. This lack of standardized calibration, traceability, and reference materials results in data that are often irreproducible, device-specific, and incomparable across studies, severely undermining scientific discovery and resource assessment. This review provides a systematic analysis of the current landscape of deep-sea microbial detection technologies, categorizing them by their operational principles and critically evaluating their performance, limitations, and metrological readiness. By synthesizing the technological challenges with the principles of metrology, we identify the fundamental gap between advanced sensing capabilities and the lack of in situ measurement standards. To bridge this gap, we propose an innovative “laboratory simulation–in situ detection–remote calibration” trinity calibration system. This framework establishes a complete metrological traceability chain tailored for extreme deep-sea conditions, aiming to transform isolated sensor data into globally comparable, scientifically robust, and industrially actionable information, thereby paving the way for precision deep-sea biology and governance.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of renal cancer, with both incidence and mortality rates on the rise in recent years. This underscores the urgent need for precise diagnostic tools, effective monitoring, and improved management strategies. Advances in tumor sequencing have significantly enhanced our understanding of the molecular biology of ccRCC, leading to the identification of several potential genomic biomarkers. However, no widely accepted tissue or blood biomarker for ccRCC has been incorporated into routine clinical practice. In this study, we integrated transcriptomics and metabolomics analyses using paired cancerous and non-cancerous tissue samples from five patients who underwent radical nephrectomy. Our goal was to systematically investigate the molecular mechanisms underlying ccRCC and identify potential therapeutic targets. We identified ten shared KEGG metabolic pathways between the transcriptome and metabolome, with purine metabolism and glycerophospholipid metabolism emerging as particularly relevant to tumorigenesis and disease progression. Network analysis revealed significant correlations between specific metabolites (e.g., guanosine triphosphate and L-malic acid) and key genes (e.g., MET, SDHC, FH, and SDHB) within the molecular network of ccRCC. Additionally, we identified key enzymes in phospholipid metabolism, such as AK, LPCAT, and LPEAT, as potential therapeutic targets. Inhibiting these pathways could effectively disrupt tumor membrane formation and signal transduction. Our findings not only deepen the understanding of the molecular mechanisms driving ccRCC but also provide valuable insights for early diagnosis, prognosis evaluation, and the development of novel therapeutic strategies. Future work should focus on validating these potential biomarkers and therapeutic targets in larger cohorts and exploring their clinical utility.