Pharmacological and pharmacognostical valuation of Canna indica leaves extract by quantifying safety profile and neuroprotective potential

The current study primarily focused on the pharmacognostical and phytochemical screening of Canna indica and further analyzing the leaves extract for toxicological profile and neuroprotective potential. The microscopic, dry powder properties of the leaf material and phytochemical, physicochemical analysis was evaluated for pharmacognostical assessment. Dry leaves of C. indica were extracted using methanol and then further studied for both in vitro and in vivo toxicological study. The acute toxicity was measured by estimating the antioxidant defense system and anatomical impairment in the rat's organs. Also, the neuroprotective activity of the plant extract was assessed using anticholinesterase enzymatic inhibitory assay. The extract was found to be hemocompatible and showed absences of induction of behavioural changes. Likewise, no changes were seen on the anatomical structure of the rat’s organs. The methanolic extract portrayed a significant upsurge in the reduced glutathione level and showed a comparable acetylcholinesterase inhibition in a dosedependent manner with an IC50 value of 14.53 μg/mL compared to the standard Donepezil with an IC50 value of 13.31 μg/mL. C. indica has compelling pharmacognostical characteristics, good safety reports, and significant antioxidant as well as the neuroprotective potential that shows great potential for its further in-depth research for pharmacological use.     

Inhibitory monoclonal antibody targeting ADAM17 expressed on cancer cells

ADAM17 is upregulated in many cancers and in turn activates signaling pathways, including EGFR/ErbB, as well as those underlying resistance to targeted anti-EGFR therapies. Due to its central role in oncogenic pathways and drug resistance mechanisms, specific and efficacious monoclonal antibodies against ADAM17 could be useful for a broad patient population with solid tumors. Hence, we describe here an inhibitory anti-ADAM17 monoclonal antibody, named D8P1C1, that preferentially recognizes ADAM17 on cancer cells. D8P1C1 inhibits the catalytic activity of ADAM17 in a fluorescence-based peptide cleavage assay, as well as the proliferation of a range of cancer cell lines, including breast, ovarian, glioma, colon and the lung adenocarcinoma. In mouse models of triple-negative breast cancer and ovarian cancer, treatment with the mAb results in 78% and 45% tumor growth inhibition, respectively. Negative staining electron microscopy analysis of the ADAM17 ectodomain in complex with D8P1C1 reveals that the mAb binds the ADAM17 protease domain, consistent with its ability to inhibit the ADAM17 catalytic activity. Collectively, our results demonstrate the therapeutic potential of the D8P1C1 mAb to treat solid tumors.     

Natural variation in biogenesis efficiency of individual Arabidopsis thaliana microRNAs

Like protein-coding genes, loci that produce microRNAs (miRNAs) are generally considered to be under purifying selection, consistent with miRNA polymorphisms being able to cause disease. Nevertheless, it has been hypothesized that variation in miRNA genes may contribute to phenotypic diversity. Here we demonstrate that a naturally occurring polymorphism in the MIR164A gene affects leaf shape and shoot architecture in Arabidopsis thaliana, with the effects being modified by additional loci in the genome. A single base pair substitution in the miRNA complementary sequence alters the predicted stability of the miRNA:miRNA(?) duplex. It thereby greatly reduces miRNA accumulation, probably because it interferes with precursor processing. We demonstrate that this is not a rare exception and that natural strains of Arabidopsis thaliana harbor dozens of similar polymorphisms that affect processing of a wide range of miRNA precursors. Our results suggest that natural variation in miRNA biogenesis resulting from cis mutations is a common contributor to phenotypic variation in plants.     

Quantitative proteomics of heavy metal stress responses in Sydney rock oysters

Currently, there are few predictive biomarkers in key biomonitoring species, such as oysters, that can detect heavy metal pollution in coastal waterways. Several attributes make oysters superior to other organisms for positive biomonitoring of heavy metal pollution. In particular, they are filter feeders with a high capacity for bioaccumulation. In this study, we used two proteomics approaches, namely label-free shotgun proteomics based on SDS-PAGE gel separation and gas phase fractionation, to investigate the heavy metal stress responses of Sydney rock oysters. Protein samples were prepared from haemolymph of oysters exposed to 100 μg/L of PbCl(2), CuCl(2), or ZnCl(2) for 4 days in closed aquaria. Peptides were identified using a Bivalvia protein sequence database, due to the unavailability of a complete oyster genome sequence. Statistical analysis revealed 56 potential biomarker proteins, as well as several protein biosynthetic pathways to be greatly impacted by metal stress. These have the potential to be incorporated into bioassays for prevention and monitoring of heavy metal pollution in Australian oyster beds. The study confirms that proteomic analysis of biomonitoring species is a promising approach for assessing the effects of environmental pollution, and our experiments have provided insights into the molecular mechanisms underlying oyster stress responses.     

Molecular docking of phenolic compounds and screening of antioxidant and antidiabetic potential of Moringa oleifera ethanolic leaves extract from Qassim region,Saudi Arabia

IntroductionOxidative stress is crucial in diabetic pathophysiology, hence the prerequisite of ingesting naturally derived antioxidants as a remedial target. This study investigates the naturally occurring antioxidant and antidiabetic potential of Moringa oleifera ethanolic leaves extract.MethodsMoringa oleifera leaves were macerated (MOLE) by using 70% ethanol. Physiochemical and phytochemical examinations of MOLE was assayed using standard methods. The antioxidant activity was analyzed by DPPH (1, 1-diphenyl-2-picrylhydrazil) radical scavenging assay. In vitro antidiabetic was analyzed by pancreatic α-amylase enzyme inhibitory assay. The molecular docking was performed using AutoDock Vina v1.1.2 in PyRx 30.8.ResultsEthanolic extraction of MOLE by maceration technique, 14 % yield. Loss on drying, foreign organic matters and total ash value of OLE showed 0.27 w/w, 0.8 % and 19 %, respectively. Phytochemical test on MOLE confirmed starch, carbohydrate, flavonoid, gum, glycoside, saponin, tannin, and phenol presences. The total phenolic and flavonoid contents of MOLE are 260 mg GAE/g and 755 mg RUE/g of extract. MOLE (IC 50 55.6 ± 0.18 µg/mL) showed functional DPPH scavenging assay comparable to ascorbic acid (IC 50 46.71 ± 0.24 µg/mL). In the alpha-amylase inhibitory activity, Acarbose showed an IC 50 value of 19.45 ± 0.26 µg/mL, while MOLE portrayed an IC 50 value of 27.54 ± 0.07 µg/mL. Docking studies revealed that most phenolic compounds found within MOLE have minimum docking scores and high binding affinity against Human pancreatic alpha-amylase.ConclusionsThe invitro and docking results suggest that MOLE has been a viable natural bioactive source and might be a great potential source for future antidiabetic medicine.     

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO₂ Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO₂) waveforms 5 and 30 minutes in the future using only prior SpO₂ values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO₂ waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.     

Maintenance of telomeres in SV40-transformed pre-immortal and immortal human fibroblasts

Shortening of telomeres has been hypothesized to contribute to cellular senescence and may play a role in carcinogenesis of human cells. Furthermore, activation of telomerase has frequently been demonstrated in tumor-derived and in vitro immortalized cells. In this study, we have assessed these phenomena during the life span of simian virus 40 (SV40)-transformed preimmortal and immortal human fibroblasts. We observed progressive reduction in telomere length in preimmortal transformed cells with extended proliferative capacity, with the most dramatic shortening at late passage. Telomere lengths became stabilized (or increased) in immortal fibroblasts accompanied, in one case, by the activation of telomerase. However, an independent immortal cell line that displayed stable telomeres did not have detectable telomerase activity. Furthermore, we found significant telomerase activity in two preimmortal derivatives. Our results provide further evidence for maintenance of telomeres in immortalized human fibroblasts, but they suggest a lack of causal relationship between telomerase activation and immortalization. © 1996 Wiley-Liss, Inc.     

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.