LitCovid in 2022: an information resource for the COVID-19 literature.

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

Comparing the effects of three neonicotinoids on embryogenesis of the South African clawed frog Xenopus laevis.

Neonicotinoids (NEOs) are widely used insecticides that are ubiquitous in agricultural use. Since NEOs are found in natural waters as well as in tap water and human urine in regions where NEOs are widely used, NEOs pose a potential hazard to non-target organisms such as animals and humans. Some of the commonly detected NEOs are imidacloprid (IMD), thiamethoxam (TMX), and its metabolite clothianidin (CLO). Although previously published scientific information, including an assessment of the environmental risks, particularly for bees, had resulted in a ban on the outdoor use of these three NEOs in the EU - their use is now only permitted in closed greenhouses - these NEOs continue to be used in agriculture in many other parts of the world. Therefore, a detailed study and comparison of the effects of NEOs on the embryonic development of non-target organisms is needed to further define the risk profiles. Embryos of the South African clawed frog Xenopus laevis, a well-established aquatic model, were exposed to different concentrations of IMD, TMX, or CLO (0.1-100 mg/L) to study and compare the possible effects of a single contaminant in natural water bodies on early embryogenesis. The results included a reduced body length, a smaller orbital space, impaired cranial cartilage and nerves, and an altered heart structure and function. At the molecular level, NEO exposure partially resulted in an altered expression of tissue-specific factors, which are involved in eye, cranial placode, and heart development. Our results suggest that the NEOs studied negatively affect the embryonic development of the non-target organism X. laevis. Since pesticides, especially NEOs, pollute the environment worldwide, it is suggested that they are strictly controlled and monitored in the areas where they are used. In addition, the question arises as to whether pesticide metabolites also pose a risk to the environment and need to be investigated further so that they can be taken into account when registering ingredients.

Comprehensive Analysis of a tRNA-Derived Small RNA in Colorectal Cancer.

Colorectal cancer often presents as a highly variable disease with myriad forms that are at times difficult to detect in early screenings with sufficient accuracy, for which novel diagnostic methods are an attractive and valuable area of improvement. To improve colorectal cancer diagnosis and prognosis, new biomarkers that can be assembled into a diagnostic panel must be identified, and tRNA-derived small RNAs (tsRNAs) are a particularly interesting and increasingly visible new class of molecules to examine. In this study, small RNA-seq data were profiled for the expression of 104 human tsRNAs in tumor tissue and adjacent normal tissue samples, and a diagnostic model was built based on four differentially expressed tsRNAs: tRF-22-WB86Q3P92, tRF-22-WE8SPOX52, tRF-22-WE8S68L52, tRF-18-8R1546D2. Furthermore, the diagnostic model was validated by two independent validation datasets (AUC was 0.97 and 0.99), and a LASSO model was applied to develop a seven-tsRNA-based risk score model for colorectal cancer prognosis. Finally, a tsRNA-mRNA interaction network was established according to potential mRNA targets predicted by bioinformatic methods. In conclusion, the results suggest that abnormal expression of tsRNA in colorectal cancer may have a functional effect on tumor action and moreover, that some of the tsRNAs identified in this study with diagnostic and prognostic potential could be of clinical significance.

Prediction of human drug-drug interactions from time-dependent inactivation of CYP3A4 in primary hepatocytes using a population-based simulator.

Time-dependent inactivation (TDI) of human cytochromes P450 3A4 (CYP3A4) is a major cause of clinical drug-drug interactions (DDIs). Human liver microsomes (HLM) are commonly used as an enzyme source for evaluating the inhibition of CYP3A4 by new chemical entities. The inhibition data can then be extrapolated to assess the risk of human DDIs. Using this approach, under- and overpredictions of in vivo DDIs have been observed. In the present study, human hepatocytes were used as an alternative to HLM. Hepatocytes incorporate the effects of other mechanisms of drug metabolism and disposition (i.e., phase II enzymes and transporters) that may modulate the effects of TDI on clinical DDIs. The in vitro potency (K(I) and k(inact)) of five known CYP3A4 TDI drugs (clarithromycin, diltiazem, erythromycin, verapamil, and troleandomycin) was determined in HLM (pooled, n = 20) and hepatocytes from two donors (D1 and D2), and the results were extrapolated to predict in vivo DDIs using a Simcyp population trial-based simulator. Compared with observed DDIs, the predictions derived from HLM appeared to be overestimated. The predictions based on TDI measured in hepatocytes were better correlated with the DDIs (n = 37) observed in vivo (R(2) = 0.601 for D1 and 0.740 for D2) than those from HLM (R(2) = 0.451). In addition, with the use of hepatocytes a greater proportion of the predictions were within a 2-fold range of the clinical DDIs compared with using HLM. These results suggest that DDI predictions from CYP3A4 TDI kinetics in hepatocytes could provide an alternative approach to balance HLM-based predictions that can sometimes substantially overestimate DDIs and possibly lead to erroneous conclusions about clinical risks.

Modeling, prediction, and in vitro in vivo correlation of CYP3A4 induction.

CYP3A4 induction is not generally considered to be a concern for safety; however, serious therapeutic failures can occur with drugs whose exposure is lower as a result of more rapid metabolic clearance due to induction. Despite the potential therapeutic consequences of induction, little progress has been made in quantitative predictions of CYP3A4 induction-mediated drug-drug interactions (DDIs) from in vitro data. In the present study, predictive models have been developed to facilitate extrapolation of CYP3A4 induction measured in vitro to human clinical DDIs. The following parameters were incorporated into the DDI predictions: 1) EC(50) and E(max) of CYP3A4 induction in primary hepatocytes; 2) fractions unbound of the inducers in human plasma (f(u, p)) and hepatocytes (f(u, hept)); 3) relevant clinical in vivo concentrations of the inducers ([Ind](max, ss)); and 4) fractions of the victim drugs cleared by CYP3A4 (f(m, CYP3A4)). The values for [Ind](max, ss) and f(m, CYP3A4) were obtained from clinical reports of CYP3A4 induction and inhibition, respectively. Exposure differences of the affected drugs in the presence and absence of the six individual inducers (bosentan, carbamazepine, dexamethasone, efavirenz, phenobarbital, and rifampicin) were predicted from the in vitro data and then correlated with those reported clinically (n = 103). The best correlation was observed (R(2) = 0.624 and 0.578 from two hepatocyte donors) when f(u, p) and f(u, hept) were included in the predictions. Factors that could cause over- or underpredictions (potential outliers) of the DDIs were also analyzed. Collectively, these predictive models could add value to the assessment of risks associated with CYP3A4 induction-based DDIs by enabling their determination in the early stages of drug development.