Objectives: CRISPR-Cas13a system-based nucleic acid detection methods are reported to have rapid and sensitive DNA detection. However, the screening strategy for crRNAs that enables CRISPR-Cas13a single-base resolution DNA detection of human pathogens remains unclear.Methods: A combined rational design and target mutation-anchoring CRISPR RNA (crRNA) screening strategy was proposed.Results: A set of crRNAs was found to enable the CRISPR-Cas13 system to dramatically distinguish fluroquinolone resistance mutations in clinically isolated Mycobacterium tuberculosis strains from the highly homologous wild type, with a signal ratio ranging from 8.29 to 38.22 in different mutation sites. For the evaluation of clinical performance using genomic DNA from clinically isolated M. tuberculosis, the specificity and sensitivity were 100 and 91.4%, respectively, compared with culture-based phenotypic assays.Conclusion: These results demonstrated that the CRISPR-Cas13a system has potential for use in single nucleotide polymorphism (SNP) detection after tuning crRNAs. We believe this crRNA screening strategy will be used extensively for early drug resistance monitoring and guidance for clinical treatment.

As of September 26, 2022, the COVID-19 pandemic caused by SARS-CoV-2 had led to more than 611 million infections and 6.5 million deaths globally.¹ Owing to the continual evolution and adaptation of the virus, variants of SARS-CoV-2 with greater transmissibility and immune escape have persistently emerged,^2,3 posing significant challenges for mitigation through public health and social measures (PHSMs) and vaccination.

Background: Although COVID-19 vaccines and their booster regimens protect against symptomatic infections and severe outcomes, there is limited evidence about their protection against asymptomatic and symptomatic infections in real-world settings, particularly when considering that the majority of SARS-CoV-2 Omicron infections were asymptomatic. We aimed to assess the effectiveness of the booster dose of inactivated vaccines in mainland China, i.e., Sinopharm (BBIBP-CorV) and Sinovac (CoronaVac), against Omicron infection in an Omicron BA.5 seeded epidemic.Methods: Based on an infection-naive but highly vaccinated population in Urumqi, China, the study cohort comprised all 37,628 adults who had a contact history with individuals having SARS-CoV-2 infections, i.e., close contacts, between August 1 and September 7, 2022. To actively detect SARS-CoV-2 infections, RT-PCR tests were performed by local authorities on a daily basis for all close contacts, and a testing-positive status was considered a laboratory-confirmed outcome. The cohort of close contacts was matched at a ratio of 1:5 with the fully vaccinated (i.e., 2 doses) and booster vaccinated groups (i.e., 3 doses) according to sex, age strata, calendar date, and contact settings. Multivariate conditional logistic regression models were adopted to estimate the marginal effectiveness of the booster dose against Omicron BA.5 infection after adjusting for confounding variables. Subgroup analyses were performed to assess vaccine effectiveness (VE) in different strata of sex, age, the time lag from the last vaccine dose to exposure, and the vaccination status of the source case. Kaplan–Meier curves were employed to visualize the follow-up process and testing outcomes among different subgroups of the matched cohort.Findings: Before matching, 37,099 adult close contacts were eligible for cohort enrolment. After matching, the 2-dose and 3-dose groups included 3317 and 16,051 contacts, and the proportions with Omicron infections were 1.03% and 0.62% among contacts in the 2-dose and 3-dose groups, respectively. We estimated that the adjusted effectiveness of the inactivated booster vaccine versus 2 doses against Omicron infection was 35.5% (95% CI 2.0, 57.5). The booster dose provided a higher level of protection, with an effectiveness of 60.2% (95% CI 22.8, 79.5) for 15–180 days after vaccination, but this VE decreased to 35.0% (95% CI 2.8, 56.5) after 180 days. Evidence for the protection of the booster dose was detected among young adults aged 18–39 years, but was not detected for those aged 40 years or older.Interpretation: The receipt of the inactivated vaccine booster dose was associated with a significantly lower Omicron infection risk, and our findings confirmed the vaccine effectiveness (VE) of booster doses against Omicron BA.5 variants. Given the rapid evolution of SARS-CoV-2, we highlight the importance of continuously monitoring the protective performance of vaccines against the genetic variants of SARS-CoV-2, regardless of existing vaccine coverage.

Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions.

Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data.

Recent studies have shown that incessant job stress could eventually result in sleep dysfunction (SD), and most importantly, the essential role dopamine receptor D2 (DRD2) gene polymorphisms play in the psychopathological mechanism of SD. The Effort-Reward Imbalance scale and the Pittsburgh Sleep Quality Index were both used to access SD and job stress (JS). A significant negative correlation was observed between the sDA levels and SD subscale scores (sleep efficiency, daytime dysfunction). The findings revealed that high levels of JS were linked to a higher SD score (OR = 2.13, 95% CI: 1.46–3.12). Likewise, the homozygous A1A1 genotype of DRD2 rs1800497 was more likely to be associated with SD (OR = 2.90, 95% CI: 1.75–4.82). Compared to participants with low JS and heterozygous A1A2/A2A2 genotype, those with both high JS and homozygous A1A1 genotype had a higher SD score (OR = 5.40, 95% CI: 2.89–10.11). The A1 allele of the DRD2 rs1800497 polymorphism also enhances the likelihood of SD when undergoing JS. Besides, subjects with low JS and the homozygous A1A1 genotype also showed an increased possibility for sleep dysfunction (OR = 2.05, 95% CI: 1.03–4.11). Our results suggest that the DA system may interrelate with JS to affect sleep.