Socio-economics: a propensity of self-medication among OPD patients of a teaching hospital

Background: Use of over the counter (OTC) drug is very much common in India. Not only medical professionals or educated urban population but also it is common in rural area and low educated person. This study was done to assess the extent of knowledge and practices of OTC drugs among OPD patients of a tertiary care hospital.Methods: A questionnaire based study was conducted among 1680 Medicine OPD (outpatient department) patients.Results: Among the participants, 62% patients taking OTC drugs at least once in their life. Most common symptoms for self-medication were fever (89%), cough and cold (78%), headache (67%), pain (53%), diarrhea (10%), vomiting (10%), indigestion (20%). Antipyretics (65%), analgesics (35%) among NSAIDs which were common used as OTC drugs. Others drugs were antacids (50%), cough and cold preparations (50%), Vitamins (30%), antiemetics (5%).Conclusions: Awareness is necessary to use drugs rationally with proper prescription and know about harmful effects of OTC drugs

Class IA PI3Kinase Regulatory Subunit, p85α, Mediates Mast Cell Development through Regulation of Growth and Survival Related Genes

Stem cell factor (SCF) mediated KIT receptor activation plays a pivotal role in mast cell growth, maturation and survival. However, the signaling events downstream from KIT are poorly understood. Mast cells express multiple regulatory subunits of class 1A PI3Kinase (PI3K) including p85α, p85β, p50α, and p55α. While it is known that PI3K plays an essential role in mast cells; the precise mechanism by which these regulatory subunits impact specific mast cell functions including growth, survival and cycling are not known. We show that loss of p85α impairs the growth, survival and cycling of mast cell progenitors (MCp). To delineate the molecular mechanism (s) by which p85α regulates mast cell growth, survival and cycling, we performed microarray analyses to compare the gene expression profile of MCps derived from WT and p85α-deficient mice in response to SCF stimulation. We identified 151 unique genes exhibiting altered expression in p85α-deficient cells in response to SCF stimulation compared to WT cells. Functional categorization based on DAVID bioinformatics tool and Ingenuity Pathway Analysis (IPA) software relates the altered genes due to lack of p85α to transcription, cell cycle, cell survival, cell adhesion, cell differentiation, and signal transduction. Our results suggest that p85α is involved in mast cell development through regulation of expression of growth, survival and cell cycle related genes

Restoration of IFNγR Subunit Assembly, IFNγ Signaling and Parasite Clearance in Leishmania donovani Infected Macrophages: Role of Membrane Cholesterol

Despite the presence of significant levels of systemic Interferon gamma (IFNγ), the host protective cytokine, Kala-azar patients display high parasite load with downregulated IFNγ signaling in Leishmania donovani (LD) infected macrophages (LD-MØs); the cause of such aberrant phenomenon is unknown. Here we reveal for the first time the mechanistic basis of impaired IFNγ signaling in parasitized murine macrophages. Our study clearly shows that in LD-MØs IFNγ receptor (IFNγR) expression and their ligand-affinity remained unaltered. The intracellular parasites did not pose any generalized defect in LD-MØs as IL-10 mediated signal transducer and activator of transcription 3 (STAT3) phosphorylation remained unaltered with respect to normal. Previously, we showed that LD-MØs are more fluid than normal MØs due to quenching of membrane cholesterol. The decreased rigidity in LD-MØs was not due to parasite derived lipophosphoglycan (LPG) because purified LPG failed to alter fluidity in normal MØs. IFNγR subunit 1 (IFNγR1) and subunit 2 (IFNγR2) colocalize in raft upon IFNγ stimulation of normal MØs, but this was absent in LD-MØs. Oddly enough, such association of IFNγR1 and IFNγR2 could be restored upon liposomal delivery of cholesterol as evident from the fluorescence resonance energy transfer (FRET) experiment and co-immunoprecipitation studies. Furthermore, liposomal cholesterol treatment together with IFNγ allowed reassociation of signaling assembly (phospho-JAK1, JAK2 and STAT1) in LD-MØs, appropriate signaling, and subsequent parasite killing. This effect was cholesterol specific because cholesterol analogue 4-cholestene-3-one failed to restore the response. The presence of cholesterol binding motifs [(L/V)-X1–5-Y-X1–5-(R/K)] in the transmembrane domain of IFNγR1 was also noted. The interaction of peptides representing this motif of IFNγR1 was studied with cholesterol-liposome and analogue-liposome with difference of two orders of magnitude in respective affinity (KD: 4.27×10−9 M versus 2.69×10−7 M). These observations reinforce the importance of cholesterol in the regulation of function of IFNγR1 proteins. This study clearly demonstrates that during its intracellular life-cycle LD perturbs IFNγR1 and IFNγR2 assembly and subsequent ligand driven signaling by quenching MØ membrane cholesterol

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings