Out of 120 patients presented to us in a period of 3months, 31 patients had increased symptoms when compared to pre-lockdown status. 20 out of 31 patients had low vitamin D3 levels. 14 patients also developed symptoms of fibromyalgia.

There might be many reasons for increased pain during lockdown, but we focussed specially only on vitamin D3 because of its association with increased symptoms of COVID-19. This is a gentle reminder to test for vitamin D3 levels and supplement if found deficient.

The online version contains supplementary material available at 10.1007/s43465-021-00376-8.

The online version contains supplementary material available at 10.1007/s43465-021-00376-8.This paper mitigates the gap in the Indian context about the non-consideration of vertical and horizontal intra-industry trade (IIT) distinctly in testing empirical hypotheses about industry-level determinants of IIT. Our study indicates that failure to segregate vertical and horizontal IIT from the total IIT possibly leads to potential bias in econometric results. Drawing on annual multilateral trade data encompassing two and half decades of the liberalization period, we find India’s IIT outpaced the growth of inter-industry trade over the years and its contribution mainly came from six manufacturing industry groups whose export baskets had been loaded with low vertically differentiated goods. However, horizontal and high vertical IIT have gained some momentum since the end of the last decade. Given the fractional nature of our dependent variable, we initially estimate a (random effects) Tobit model followed by the Exponential Regression of Fractional Response model. The robust econometric findings show that product differentiation has a positive impact only on total IIT. Whereas vertical and horizontal IIT are promoted in industries with concentrated and competitive market structures, respectively. The prevalence of concentrated market structure indicates that (large) Indian firms sustain import competition by specializing in low vertically differentiated goods, as they efficiently adjust to resource reallocation.The importance of access to intellectual property rights (IPR) protected subject-matter in two crucial areas – public health, and educational and cultural engagement – has been extensively demonstrated during the COVID-19 pandemic. Although they involve separate legal areas, patent and copyright, the common thread linking the two is intellectual property’s difficult relationship with access in the public interest. This paper examines the tensions caused by access barriers, the tools used to reduce them and their effectiveness. It is clear that the access barriers magnified by COVID-19 are not restricted to narrow or specific contexts but are widespread. They are created by, and are a feature of, our existing IPR frameworks. Open movements provide limited remedies because they are not designed to, nor can adequately address the wide range of access barriers necessary to promote the public interest. Existing legislative mechanisms designed to remove access barriers similarly fail to effectively remedy access needs. Akt inhibitor These existing options are premised on the assumption that there is a singular “public” motivated by homogenous “interests”, which fails to reflect the plurality and cross-border reality of the public(s) interest(s) underpinning the welfare goals of IPR. We conclude that a systemic re-evaluation is required and call for positive and equitable legal measures protective of the public(s) interest(s) to be built within IPR frameworks that also address non-IPR barriers. The current pandemic and development of a “new normal” provides a crucial opportunity to comprehensively consider the public(s) interest(s), not just during a global health crisis, but on an ongoing basis.Introduction The effect of dexamethasone in the initial phase of infection by SARS-CoV-2 and its influence on COVID-19 is not well defined. We describe clinical-radiological characteristics, the cytokine storm parameters, and the clinical evolution of a series of patients treated with dexamethasone in the disease’s initial phase.Method A study of 8 patients who received dexamethasone before the development of COVID-19. We evaluate clinical variables, imaging tests, cytokine release parameters, treatment used and patient evolution.Results All patients received a 6 mg/day dose with a mean duration of 4.5 days before admission. High resolution computed tomography (HRCT) revealed that most of them presented a severe extension; most patients had a slightly elevated level of cytokine release parameters. Three patients required high-flow oxygen therapy due to respiratory failure; none required orotracheal intubation or died.Conclusion Dexamethasone in the early stages of SARS-CoV-2 infection appears to be associated with severe COVID-19.Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. We introduce Mi3-GPU (“mee-three”, for MCMC Inverse Ising Inference) software for solving the inverse Ising problem for protein-sequence datasets with few analytic approximations, by parallel Markov-Chain Monte-Carlo sampling on GPUs. We also provide tools for analysis and preparation of protein-family Multiple Sequence Alignments (MSAs) to account for finite-sampling issues, which are a major source of error or bias in inverse Ising inference. Our method is “generative” in the sense that the inferred model can be used to generate synthetic MSAs whose mutational statistics (marginals) can be verified to match the dataset MSA statistics up to the limits imposed by the effects of finite sampling. Our GPU implementation enables the construction of models which reproduce the covariation patterns of the observed MSA with a precision that is not possible with more approximate methods. The main components of our method are a GPU-optimized algorithm to greatly accelerate MCMC sampling, combined with a multi-step Quasi-Newton parameter-update scheme using a “Zwanzig reweighting” technique. We demonstrate the ability of this software to produce generative models on typical protein family datasets for sequence lengths L ~ 300 with 21 residue types with tens of millions of inferred parameters in short running times.