The study includes 102 confirmed cases of carcinoma breast with and without metastasis and 25 healthy non-pregnant females. They were evaluated for blood levels of Ferritin, GSH, LDH, ALP, GGT and Hb before and 21 days after mastectomy. A significant increase (p<0.001) was observed in ferritin, LDH and GSH levels in cancer patients without metastasis in comparison to normal control subjects. Patients with metastasis had further elevated (p<0.001) levels of Ferritin, ALP and GGT as compared to non-metastatic patients. Mastectomy in both the cases i.e. with and without metastasis resulted in non-significant decrease in all the biochemical parameters suggesting that longer follow up could confirm post surgery decrease in the biochemical parameters. The results of the study suggest cost effective, usefulness of Ferritin, ALP, GGT and GSH/Hb ratio in differentiating breast cancer patients with and without metastasis which can be assayed in smaller laboratories.

Objectives: Endometrial cancer incidence and mortality are rising in the US. Disease recurrence has been shown to have a significant impact on mortality. However, to date, there are no accurate and validated prediction models that would discriminate which individual patients are likely to recur. Reliably predicting recurrence would be of benefit for treatment decisions following surgery. We present an integrated model constructed with comprehensive clinical, pathological and molecular features designed to discriminate risk of recurrence for patients with endometrioid endometrial adenocarcinoma. Subjects and methods: A cohort of endometrioid endometrial cancer patients treated at our institution was assembled. Clinical characteristics were extracted from patient charts. Primary tumors from these patients were obtained and total tissue RNA extracted for RNA sequencing. A prediction model was designed containing both clinical characteristics and molecular profiling of the tumors. The same analysis was carried out with data derived from The Cancer Genome Atlas for replication and external validation. Results: Prediction models derived from our institutional data predicted recurrence with high accuracy as evidenced by areas under the curve approaching 1. Similar trends were observed in the analysis of TCGA data. Further, a scoring system for risk of recurrence was devised that showed specificities as high as 81% and negative predictive value as high as 90%. Lastly, we identify specific molecular characteristics of patient tumors that may contribute to the process of disease recurrence. Conclusion: By constructing a comprehensive model, we are able to reliably predict recurrence in endometrioid endometrial cancer. We devised a clinically useful scoring system and thresholds to discriminate risk of recurrence. Finally, the data presented here open a window to understanding the mechanisms of recurrence in endometrial cancer.

We compared nearly 1400 hand-hygiene-related events observed by an automated system and by human observations. The records differed for 38% of these events. Two likely explanations for the inconsistencies were the distance between the observer and the event and the busyness of the clinic.

Agricultural sustainability is a vital parameter to be ascertained locally and globally if food security is to be achieved and maintained. Agricultural sustainability is the combined product of social, economic and ecological sustainability. It is also a function of temporal and spatial variations, a fact which indicates that area-specific sustainability indices need to be designed. We present here an Agricultural Sustainability Index (ASI) for rural eastern India and use it to calculate the ASI for 150 farms for three decades over a 60-year period, viz., 1950-1960, 1980-1990 and 2000-2010 for a representative Indian village of Gangapur (25°83 0 N, 85°65 0 E). The ASI was calculated using 30 variables, 10 each of social, economic and ecological sustainability. An extensive questionnairebased survey was carried out to collect the relevant data. Our study reveals that over a 60-year period, ASI values do not show a statistically significant change. We conclude that the agricultural practices of the region have maintained sustainability so far although the scope for improvement in several broad areas identified by us is immense. Increased ecological literacy and better implementation of government policies, aiming at health, education and better scientist-farmer interactions, must target improved ASI values in coming decades.

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.