Radiation damage by extensive local water ionization from two-step electron-transfer-mediated decay of solvated ions.

Biomolecular radiation damage is largely mediated by radicals and low-energy electrons formed by water ionization rather than by direct ionization of biomolecules. It was speculated that such an extensive, localized water ionization can be caused by ultrafast processes following excitation by core-level ionization of hydrated metal ions. In this model, ions relax via a cascade of local Auger-Meitner and, importantly, non-local charge- and energy-transfer processes involving the water environment. Here, we experimentally and theoretically show that, for solvated paradigmatic intermediate-mass Al3+ ions, electronic relaxation involves two sequential solute-solvent electron transfer-mediated decay processes. The electron transfer-mediated decay steps correspond to sequential relaxation from Al5+ to Al3+ accompanied by formation of four ionized water molecules and two low-energy electrons. Such charge multiplication and the generated highly reactive species are expected to initiate cascades of radical reactions.

Breast cancer stage prediction: a computational approach guided by transcriptome analysis.

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.

ERBB3-induced furin promotes the progression and metastasis of ovarian cancer via the IGF1R/STAT3 signaling axis.

High-grade serous carcinoma, accounts for up to 70% of all ovarian cases. Furin, a proprotein convertase, is highly expressed in high-grade serous carcinoma of ovarian cancer patients, and its expression is even higher in tumor omentum than in normal omentum, the preferred site of ovarian cancer metastasis. The proteolytic actions of this cellular endoprotease help the maturation of several important precursors of protein substrates and its levels increase the risk of several cancer. We show that furin activates the IGF1R/STAT3 signaling axis in ovarian cancer cells. Conversely, furin knockdown downregulated IGF1R-ß and p-STAT3 (Tyr705) expression. Further, silencing furin reduced tumor cell migration and invasion in vitro and tumor growth and metastasis in vivo. Collectively, our findings show that furin can be an effective therapeutic target for ovarian cancer prevention or treatment.

Optimization of antioxidant efficacy of a deflavored and decolorized rosemary extract: effect of carnosol content on the oxidative stability of paprika colored beef patties.

Considering the significance of natural antioxidants to preserve meat, the present study was undertaken to evaluate the efficacy of a deflavored and decolorised extract of rosemary (StabilRose™) for the production and preservation of naturally colored fresh meat. Oxidative rancidity of meat and color degradation of paprika oleoresin were exploited as model systems and compared with butylated hydroxyanisole (BHA). The results showed similar efficacy for 3% carnosic acid extract and BHA, with further enhancement in efficacy with respect to the carnosic acid content. A synergetic antioxidant effect of carnosol on carnosic acid content was also noticed to an extent of 1:1 (w/w) ratio, and further increase in carnosol content showed no improvement in the antioxidant efficacy. Finally, stabilized paprika and optimized rosemary extract containing carnosic acid and carnosol in 1:1 (w/w) ratio was successfully applied to produce naturally colored meat suitable for storage at 4 ± 1 °C.

Cytopathological image analysis using deep-learning networks in microfluidic microscopy.

Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening.

Each year, about 7-8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate.

Framework for morphometric classification of cells in imaging flow cytometry.

Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis.

Safety and anti-ulcerogenic activity of a novel polyphenol-rich extract of clove buds (Syzygium aromaticum L).

Despite the various reports on the pharmacology of Clove bud [Syzygium aromaticum]-derived essential oil and its major component eugenol, systematic information on the bioactivity of clove polyphenols is very limited. Clove buds being one of the richest sources of dietary polyphenols with many traditional medicinal uses, the present contribution attempted to derive their standardized polyphenol-rich extracts as a water soluble free flowing powder (Clovinol) suitable for functional food applications, without the issues of its characteristic pungency and aroma. The extract was characterized by electrospray ionization-time of flight mass spectrometry (ESI-TOF-MS), and investigated for in vivo antioxidant, anti-inflammatory and anti-ulcerogenic activities. Clovinol showed significant antioxidant and anti-inflammatory effects as measured by cellular antioxidant levels, and the ability to inhibit carrageenan-induced paw swelling in mice. Further investigations revealed its significant anti-ulcerogenic activity (>97% inhibition of ethanol-induced stomach ulcers in Wistar rats when orally administered at 100 mg per kg b.w.) and up regulation of in vivo antioxidants such as superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT). Clovinol also reduced the extent of lipid peroxidation among ulcer induced rats, indicating its usefulness in ameliorating oxidative stress and improving gastrointestinal health, especially upon chronic alcohol consumption. The extract was also shown to be safe and suitable for further investigations and development upon acute toxicity studies at 5 g per kg body weight and 28 days of repeated dose toxicity studies at 2.5 g per kg b.w.

Hydroxylation catalysis by mononuclear and dinuclear iron oxo catalysts: a methane monooxygenase model system versus the Fenton reagent Fe(IV)O(H2O)5(2+).

Hydroxylation of aliphatic C-H bonds is a chemically and biologically important reaction, which is catalyzed by the oxidoiron group FeO(2+) in both mononuclear (heme and nonheme) and dinuclear complexes. We investigate the similarities and dissimilarities of the action of the FeO(2+) group in these two configurations, using the Fenton-type reagent [FeO(2+) in a water solution, FeO(H(2)O)(5)(2+)] and a model system for the methane monooxygenase (MMO) enzyme as representatives. The high-valent iron oxo intermediate MMOH(Q) (compound Q) is regarded as the active species in methane oxidation. We show that the electronic structure of compound Q can be understood as a dimer of two Fe(IV)O(2+) units. This implies that the insights from the past years in the oxidative action of this ubiquitous moiety in oxidation catalysis can be applied immediately to MMOH(Q). Electronically the dinuclear system is not fundamentally different from the mononuclear system. However, there is an important difference of MMOH(Q) from FeO(H(2)O)(5)(2+): the largest contribution to the transition state (TS) barrier in the case of MMOH(Q) is not the activation strain (which is in this case the energy for the C-H bond lengthening to the TS value), but it is the steric hindrance of the incoming CH(4) with the ligands representing glutamate residues. The importance of the steric factor in the dinuclear system suggests that it may be exploited, through variation in the ligand framework, to build a synthetic oxidation catalyst with the desired selectivity for the methane substrate.