Antimicrobial activity of seed, pomace and leaf extracts of sea buckthorn (Hippophae rhamnoides L.) against foodborne and food spoilage pathogens

The present study was conducted to evaluate the total phenolic content (TPC) and antibacterial properties of crude extracts of sea buckthorn (Hippophae rhamnoides L.) pomace, seeds and leaves against 17 foodborne pathogens. The methanolic extract of leaves exhibited high total phenolic content (278.80 mg GAE/g extract) and had low minimum inhibitory concentration (MIC) value of 125 μg/ml against Listeria monocytogenes. Salmonella typhimurium strain was found to be resistant against all tested extracts. The antilisterial activity of the methanolic extract of leaves was tested on carrots. Bacterial enumeration was significantly reduced by 0.15 to 0.31, 0.26 to 1.72 and 0.59 to 4.10 log cfu/g after 0 to 60 min exposure when treated with 125, 2500 and 5000 μg/ml extract, respectively. Thus, in addition to its use as a functional food ingredient, leaves extract from sea buckthorn (SBT) can possibly be used as a biosanitizer in food industries.Key words: Antimicrobial activity, Hippophae, Listeria monocytogenes, natural sanitizer, seabuckthorn

Morphometric analysis of Dactylorhiza hatagirea (D. Don), a critically endangered orchid in cold desert Ladakh region of India

The morphometric study was conducted during 2009 to 2010. About 28 morphological characters were measured under 13 natural populations of Dactylorhiza hatagirea. Geographic variation in morphology reflects phenotypic responses to environmental gradients and evolutionary history of populations and species. At points, beside its broad geographic range (Nubra, Suru and Indus valley) characterization of Dactylorhiza phenotype is normally accomplished by use of morphological descriptors, hence as a first step, phenotype collection and its morphometric analysis was assessed. However, plant height, leaf length, lowermost leaf length, length of second leaf from base and mean length from lowest bract to the top of inflorescence are presented to account for the remarkable variation in morphological characters. Tirith population showed more values of this trait while Skurru showed less value. From this, it is concluded that Tirith showed great morphometric variation as compared to other population. Multivariate morphometric techniques, principal component analysis (PCA), multidimensional scaling (MDS) and cluster analysis were used to determine whether these populations can be reliably morphologically similar or dissimilar. The first two principal components encompass more than 75% variation among population. The results of PCA and MDS analysis were comparable to the cluster analysis, which shows considerable phenotypic variation in morphological and horticultural traits that can be utilized in its genetic improvement. To support this study, further constructive information were provided on the status of the populations of D. hatagirea which may increase the conservation value of this site and resolve the suitable areas with taxonomic and nomenclatural controversies.Keywords: Morphological characters, principal component analysis (PCA), multidimensional scaling (MDS), plant height, leaf length, leaf widt

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C.T in the 59 untranslated region (UTR), 3037C.T in nsp3, 14408C.T in the RNA-dependent RNA polymerase (RdRp), and 23403A.G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C.T), P323L (14408C.T), and Q57H (25563G.T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of .0.4 and a frequency of .30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.</p

Preclinical formulation for the pharmacokinetics and efficacy of GBO-006, a selective polo like kinase 2 (PLK2) inhibitor for the treatment of triple negative breast cancer

GBO-006 was shown to be a highly specific and selective PLK2 inhibitor that promoted mitotic arrest in various cancer cell lines, subsequently resulting in their apoptotic death. Intraperitoneal alternate day dosing of GBO-006 using 100 % DMSO as formulation showed significant tumor regression in xenograft models, demonstrating proof of concept of PLK2 inhibition in vivo. These studies necessitated the development of a suitable and GRAS (generally considered as safe) preformulation for pharmacokinetic and efficacy studies. GBO-006 possesses challenging physicochemical and biopharmaceutical properties like poor solubility in aqueous media, low permeability and a crystalline nature. Different methods like cosolvency, complexation and micellar solubilization were employed to improve the solubility of GBO-006. A strategy of co-solvency is used to solubilize the GBO-006 up to 10 mg/mL. A formulation with 20 % DMSO, 40 % PEG 400, 30 % of 100 mM citrate buffer (pH 3.0) and 10 % solutol displayed clear solution without any visual precipitation of the drug even after 2 weeks of storage. GBO-006 showed moderate clearance in rat and high systemic clearance in mouse and dog. It showed poor oral bioavailability across all species. Intraperitoneal dosing of GBO-006 demonstrated the linear exposure. GBO-006 showed significant inhibition of tumor progression

Triphala inhibits both in vitro and in vivo xenograft growth of pancreatic tumor cells by inducing apoptosis

<p>Abstract</p> <p>Background</p> <p>Triphala is commonly used in Ayurvedic medicine to treat variety of diseases; however its mechanism of action remains unexplored. This study elucidates the molecular mechanism of Triphala against human pancreatic cancer in the cellular and in vivo model.</p> <p>Methods</p> <p>Growth-inhibitory effects of Triphala were evaluated in Capan-2, BxPC-3 and HPDE-6 cells by Sulphoradamine-B assay. Apoptosis was determined by cell death assay and western blotting. Triphala was administered orally to nude mice implanted with Capan-2 xenograft. Tumors were analyzed by immunohistochemistry and western blotting.</p> <p>Results</p> <p>Exposure of Capan-2 cells to the aqueous extract of Triphala for 24 h resulted in the significant decrease in the survival of cells in a dose-dependent manner with an IC50 of about 50 μg/ml. Triphala-mediated reduced cell survival correlated with induction of apoptosis, which was associated with reactive oxygen species (ROS) generation. Triphala-induced apoptosis was linked with phosphorylation of p53 at Ser-15 and ERK at Thr-202/Tyr-204 in Capan-2 cells. Above mentioned effects were significantly blocked when the cells were pretreated with an antioxidant N-acetylcysteine (NAC), suggesting the involvement of ROS generation. Pretreatment of cells with pifithrin-α or U0126, specific inhibitors of p53 or MEK-1/2, significantly attenuated Triphala-induced apoptosis. Moreover, NAC or U0126 pretreatment significantly attenuated Triphala-induced p53 transcriptional activity. Similarly, Triphala induced apoptosis in another pancreatic cancer cell line BxPC-3 by activating ERK. On the other hand, Triphala failed to induce apoptosis or activate ERK or p53 in normal human pancreatic ductal epithelial (HPDE-6) cells. Further, oral administration of 50 mg/kg or 100 mg/kg Triphala in PBS, 5 days/week significantly suppressed the growth of Capan-2 pancreatic tumor-xenograft. Reduced tumor-growth in Triphala fed mice was due to increased apoptosis in the tumors cells, which was associated with increased activation of p53 and ERK.</p> <p>Conclusion</p> <p>Our preclinical studies demonstrate that Triphala is effective in inhibiting the growth of human pancreatic cancer cells in both cellular and in vivo model. Our data also suggests that the growth inhibitory effects of Triphala is mediated by the activation of ERK and p53 and shows potential for the treatment and/or prevention of human pancreatic cancer.</p

Regulation of inflammation in Japanese encephalitis

Uncontrolled inflammatory response of the central nervous system is a hallmark of severe Japanese encephalitis (JE). Although inflammation is necessary to mount an efficient immune response against virus infections, exacerbated inflammatory response is often detrimental. In this context, cells of the monocytic lineage appear to be important forces driving JE pathogenesis

Characterizing Prostate Cancer Risk Through Multi-Ancestry Genome-Wide Discovery of 187 Novel Risk Variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages