tion of the targeted messenger RNA (mRNA) almost completely. 19,25 This regulation ensures accuracy and robustness by repressing the expression of mRNAs that linger from previous cell states or the products of leaky transcription. 25

Objectives Investigate the feasibility of saliva sampling as a noninvasive and safer tool to detect SARS-CoV-2 and to compare its reproducibility and sensitivity with nasopharyngeal swab samples (NPS). The use of sample pools was also investigated. Methods 2107 paired samples were collected from asymptomatic health care and office workers in Mexico City. Sixty of these samples were also analyzed in two other independent laboratories for concordance analysis. Sample processing and analysis of virus genetic material were performed according to standard protocols described elsewhere. Pooling analysis was performed by analyzing the saliva pool and the individual pool components. Results The concordance between NPS and saliva results was 95.2% (Kappa: 0.727, p = 0.0001) and 97.9% without considering inconclusive results (Kappa: 0.852, p = 0.0001). Saliva had a lower number of inconclusive results than NPS (0.9% vs 1.9%). Furthermore, saliva shows a significantly higher concentration of both total RNA and viral copies than NPS. Comparison of our results with those of the other two laboratories shows 100% and 97% concordance. Saliva samples are stable without the use of any preservative, a positive SARS-CoV-2 sample can be detected 5, 10, and 15 days after collection when the sample is stored at 4 °C. Conclusions Our results indicate that saliva is as effective as NPS for the identification of SARS-CoV-2-infected asymptomatic patients, sample pooling facilitates the analysis of a larger number of samples with the benefit of cost reduction.

Breast cancer is one of the leading causes of mortality in women worldwide, and neoadjuvant chemotherapy has emerged as an option for the management of locally advanced breast cancer. Extensive efforts have been made to identify new molecular markers to predict the response to neoadjuvant chemotherapy. Transcripts that do not encode proteins, termed long noncoding RNAs (lncRNAs), have been shown to display abnormal expression profiles in different types of cancer, but their role as biomarkers in response to neoadjuvant chemotherapy has not been extensively studied. Herein, lncRNA expression was profiled using RNA sequencing in biopsies from patients who subsequently showed either response or no response to treatment. The GATA3-AS1 Q10 transcript was overexpressed in the nonresponder group and was the most stable feature when performing selection in multiple random forest models. GATA3-AS1 was experimentally validated by RT-qPCR Q11 in an extended group of 68 patients. Expression analysis confirmed that GATA3-AS1 is overexpressed primarily in patients who were nonresponsive to neoadjuvant chemotherapy, with a sensitivity of 92.9%, a specificity of 75.0%, and an area under the curve of approximately 0.90, as measured by receiver operating characteristic curve analysis. The statistical model was based on luminal B-like patients and adjusted by menopausal status and phenotype (odds ratio, 37.49; 95% CI, 6.74e208.42; P Z 0.001); GATA3-AS1 was established as an independent predictor of response. Thus, lncRNA GATA3-AS1 is proposed as a potential predictive biomarker of nonresponse to neoadjuvant chemotherapy. Q12

SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.