Enablers & challenges of tribal women & health system for implementation of screening of non-communicable diseases & common cancers: A mixed-methods study in Palghar district of Maharashtra, India.

Background & objectives: Non-communicable diseases (NCDs) and cancers of breast, oral cavity and cervix contribute to around 5.87 million (60%) deaths in India. Despite this, there is limited evidence on preparedness of the tribal health system in mitigating these conditions. This mixed-methods study aimed at identifying enablers and challenges using a multistakeholder approach for the screening of NCDs and common cancers in a tribal block of Maharashtra, India. Methods: This study was conducted in a tribal community of Dahanu taluka in Palghar district of Maharashtra. A total of nine focus group discussions (FGDs) among tribal women and accredited social health activists (ASHAs), 13 key informant interviews (KIIs) among auxiliary nurse midwives (ANMs) and community health officers (CHO) and facility surveys of five public health facilities were conducted. The FGDs and KIIs were conducted using guides, recorded digitally, transcribed, analyzed and triangulated to identify emerging themes. Results: The tribal women had limited knowledge about NCDs and common cancers. Paucity of health facilities, out-of-pocket expenditure, misconceptions, belief on traditional healers and inability to prioritize health were identified as major challenges. The ASHAs were recognized as a key connecting link between health system and community while provision of culturally appropriate IEC materials and adequate training were recognized as critical enablers by healthcare providers in implementing screening for NCDs and common cancers. Interpretation & conclusions: The study recommends incorporating socioculturally relevant strategies in the tribal population and strengthening health facilities in terms of infrastructure and training with involvement of ASHAs for successful implementation of the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) through health and wellness centres.

Potentiation of etoposide and temozolomide cytotoxicity by curcumin and turmeric force™ in brain tumor cell lines.

We have investigated on the potentiation of etoposide (ETP) and temozolomide (TMZ) cytotoxicity in U-87MG glioblastoma and D283 medulloblastoma cell lines by curcumin (CUR) and turmeric force (TF), a nutraceutical formulation of turmeric, with the objective of assessing the potential for their adjuvant use in brain tumor chemotherapy. While U-87MG cell line was generally resistant to TMZ, IC50 values for CUR and TF were 37.33 and 30.75 µg/ml, respectively. TF is the only agent that demonstrated efficacy at the IC90 level. When CUR or TF was combined with ETP and TMZ, increased chemotherapeutic efficiency in the U-87MG cells was observed. TF is highly cytotoxic to D283 Med cell line compared to curcumin with an IC50 value of 1.55 ug/ml. Although both CUR and TF potentiated ETP and TMZ cytotoxicity, TF is more efficient than CUR in both U-87MG and D283 Med cell lines. Treatment of U-87MG cells with the triple combination of TMZ+ETP+TF induced a high percentage of apoptotic cells. Potential mechanisms that may explain evidence of synergy include down regulation of p10 and p53 mRNAs and increase in BAX/Bcl-2 mRNA ratio. These pre-clinical results suggest that TF may be useful as an adjuvant with ETP and TMZ for brain tumor chemotherapy.

Evaluation of transgenic tobacco plants expressing a bacterial Co-Ni transporter for acquisition of cobalt.

Phytoremediation is a viable strategy for management of toxic wastes in a large area/volume with low concentrations of toxic elemental pollutants. With increased industrial use of cobalt and its alloys, it has become a major metal contaminant in soils and water bodies surrounding these industries and mining sites with adverse effects on the biota. A bacterial Co-Ni permease was cloned from Rhodopseudomonas palustris and introduced into Nicotiana tabacum to explore its potential for phytoremediation and was found to be specific for cobalt and nickel. The transgenic plants accumulated more cobalt and nickel as compared to control, whereas no significant difference in accumulation of other divalent ions was observed. The transgenic plants were evaluated for cobalt content and showed increased acquisition of cobalt (up to 5 times) as compared to control. The plants were also assessed for accumulation of nickel and found to accumulate up to 2 times more nickel than control. At the same initial concentration of cobalt and nickel, transgenic plant preferentially accumulated cobalt as compared to nickel. The present study is perhaps the first attempt to develop transgenic plants expressing heterologous Co transporter with an improved capacity to uptake cobalt.

Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.

It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.

Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic.

BACKGROUND: Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. RESULTS: Accurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve. CONCLUSION: A significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status - amyloidogenic or non-amyloidogenic. The proposed approach achieves acceptable results, comparable to most online predictors. Besides, it compensates the lacuna in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity.

Mutated glutathione S-transferase in combination with reduced glutathione shows a synergistic effect in ameliorating oxidative stress and airway inflammation.

Oxidative stress is implicated in the pathogenesis of asthma, and antioxidant levels are reduced in asthma patients. Previously, glutathione S-transferase (GST) with reduced IgE binding suppressed oxidative stress and modulated airway inflammation to some extent in mice. GST catalyzes the quenching of reactive oxygen species by reduced glutathione (GSH) and the absence of any one of them may limit antioxidative behavior. This study evaluates the effects of mutated (m) GST with GSH in combination and individually in limiting oxidative stress and inflammatory responses in a mouse model. BALB/c mice were immunized and challenged with ovalbumin. The mice were treated with mGST, GSH, mGST + GSH, or alpha-lipoic acid by inhalation and sacrificed to evaluate inflammation and oxidative stress parameters. Treatment with the mGST + GSH combination showed significantly reduced total cell (p<0.01) and eosinophil (p<0.01) counts in BALF compared to other groups. The lung inflammation score was lowest for the mGST + GSH group, along with reduced IL-4 (p<0.01) and OVA-specific IgE compared to the other treatment groups. Oxidative stress as per the lipid peroxidation and 8-isoprostane level in BALF of mGST + GSH mice was reduced significantly compared to the individual antioxidants. In conclusion, mGST in combination with GSH has a synergistic effect in reducing airway inflammation compared to the individual antioxidants and has potential for the treatment of asthma.

Relative axial ligand orientation in bis(imidazole)iron(II) porphyrinates: are "picket fence" derivatives different?

The synthesis of three new bis(imidazole)-ligated iron(II) picket fence porphyrin derivatives, [Fe(TpivPP)(1-RIm) 2] 1-RIm = 1-methyl-, 1-ethyl-, or 1-vinylimidazole) are reported. X-ray structure determinations reveal that the steric requirements of the four alpha,alpha,alpha,alpha-o-pivalamidophenyl groups lead to very restricted rotation of the imidazole ligand on the picket side of the porphyrin plane; the crowding leads to an imidazole plane orientation eclipsing an iron-porphyrin nitrogen bond. An unusual feature for these diamagnetic iron(II) species is that all three derivatives have the two axial ligands with a relative perpendicular orientation; the dihedral angles between the two imidazole planes are 77.2 degrees , 62.4 degrees , and 78.5 degrees . All three derivatives have nearly planar porphyrin cores. Mössbauer spectroscopic characterization shows that all three derivatives have quadrupole splitting constants around 1.00 mm/s at 100K.