OBJECTIVES : This study translates the Pain Catastrophizing Scale (PCS) into Hindi and examines the psychometric properties of the translated version (Hindi PCS [Hi-PCS]) in patients with chronic low back pain (CLBP). METHODS : Forward and backward translations were performed from English to Hindi according to standard methodology. A final version was evaluated by a committee of clinical experts and Hi-PCS was then pilot-tested in 10 patients with CLBP. Cross-cultural validation of the resulting adapted Hi-PCS was done by administering Hi-PCS at baseline to 100 patients with CLBP (≥ 12 weeks pain) who were able to read and write in Hindi, and re-administering Hi-PCS after 3 days. Construct validity was assessed using factor analysis. Psychometric properties including internal consistency; test-retest reliability; and convergent validity with pain severity, functional disability, and health-related quality of life (HRQoL) were also assessed. RESULTS : Principal component analysis observed a three-factor structure, which explained 58% of the variance. Confirmatory factor analysis elicited the best fit as judged by the model fit indices. Hi-PCS as a whole was deemed to be internally consistent (Cronbach's α = 0.76). Intraclass correlation coefficient for the Hi-PCS is 0.923 (95% CI: 0.875-0.953). Hi-PCS was moderately correlated with pain intensity (r = 0.651) and functional disability (r = 0.352), and negatively correlated with QoL (r = -0.380). CONCLUSIONS : PCS translation and cross-cultural adaptation to Hindi demonstrated good factor structure along adequate psychometric properties and could be recommended for use in CLBP research in India.
The continuous increase in pathogenic viruses and the intensive laboratory research for development of novel antiviral therapies often poses challenge in terms of cost and time efficient drug design. This accelerates research for alternate drug candidates and contributes to recent rise in research of antiviral peptides against many of the viruses. With limited information regarding these peptides and their activity, modifying the existing peptide backbone or developing a novel peptide is very time consuming and a tedious process. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful for wet lab scientist to screen potential antiviral candidates of interest and expedite the initial stage of peptide drug development. To our knowledge this is the first ever use of GAN models for antiviral peptides across the viral spectrum. In this study, we develop PandoraGAN that utilizes GAN to design bio active antiviral peptides. Available antiviral peptide data was manually curated for preparing highly active peptides data set to include peptides with lower IC50 values. We further validated the generated sequences comparing the physico-chemical properties of generated antiviral peptides with manually curated highly active training data. Antiviral sequences generated by PandoraGAN are available on PandoraGAN server.
Physicochemical n‐Grams Tool (PnGT) is an open‐source standalone software for calculating physicochemical descriptors of protein. PnGT was developed using the Python scripting language and developed the user interface using Tkinter. The software currently calculates 33 physicochemical descriptors along with the sequence length for the given protein primary sequence. The descriptor generated by this tool can be directly utilized as the feature vector for the development of proteomics statistical or machine learning predictive model.
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