4 research outputs found
Leather-like Mycelium Material: Future Prospects for Sustainable Innovation in India
No full textLeather production poses significant environmental challenges and questions about sustainability. The harmful chemicals, overdrawn water, polluted air and water resources, and contribution to the destruction of forests, are some of the main issues in leather production, and have fueled the search for sustainable alternatives. This paper provides a comprehensive analysis of the current status and future prospects of LMM (leather-like mycelium material) in the Indian market.
The study begun by elucidating the environmental impact of traditional leather and the need for sustainable innovation. The paper then delves into the production of LMM, exploring the cultivation of fungus mycelium and its conversion into a viable leather alternative. Authors highlight its potential through its versatility, and suitability as an emergent solution.
Furthermore, the paper analyzes the current regulatory environmentand consumer attitudes towards LMM. Authors identify potentialchallenges and opportunities by considering market acceptance,technological advancements, and collaborative efforts. Case studiesprovide practical insights into its current applications at global leveland Indian initiatives reference to mycelium products.
In conclusion, with India’s focus on sustainable circular economic practices, LMM holds great promise for a more eco-friendly and socially responsible leather industry. The paper concludes with strategic recommendations for stakeholders, policymakers, and industry players to facilitate the adoption and growth of LMM inthe dynamic Indian market
Kaplan-Meier estimation of the ATP-BCa dataset and the uncensored/censored time histograms.
No full text(a) Kaplan-Meier estimation with 95% confidence interval. Note that the y-axis starts from 0.95; (b) Uncensored (blue) and censored time (yellow) histogram of the participants in the ATP-BCa dataset; (c) Rescaling, and showing only the uncensored time histogram of the ATP-BCa dataset.</p
Rare pathogenic variants in WNK3 cause X-linked intellectual disability
No full textThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordData availability: All data are available upon request. The sequence variants in WNK3 (NM_004656.3) reported in the paper have been deposited in ClinVar database. Their respective accession numbers (SCV002107163 to SCV002107168) are indicated in Tables 1 and S1.Purpose
WNK3 kinase (PRKWNK3) has been implicated in the development and function of the brain via its regulation of the cation-chloride cotransporters, but the role of WNK3 in human development is unknown.
Method
We ascertained exome or genome sequences of individuals with rare familial or sporadic forms of intellectual disability (ID).
Results
We identified a total of 6 different maternally-inherited, hemizygous, 3 loss-of-function or 3 pathogenic missense variants (p.Pro204Arg, p.Leu300Ser, p.Glu607Val) in WNK3 in 14 male individuals from 6 unrelated families. Affected individuals had identifier with variable presence of epilepsy and structural brain defects. WNK3 variants cosegregated with the disease in 3 different families with multiple affected individuals. This included 1 large family previously diagnosed with X-linked Prieto syndrome. WNK3 pathogenic missense variants localize to the catalytic domain and impede the inhibitory phosphorylation of the neuronal-specific chloride cotransporter KCC2 at threonine 1007, a site critically regulated during the development of synaptic inhibition.
Conclusion
Pathogenic WNK3 variants cause a rare form of human X-linked identifier with variable epilepsy and structural brain abnormalities and implicate impaired phospho-regulation of KCC2 as a pathogenic mechanism.Estonian Research CouncilNational Natural Science Foundation of ChinaRoyal SocietySouth Carolina Department of Disabilities and Special Needs (SCDDSN)National Institute of Neurological Disorders and Stroke (NINDS
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data
No full textBackground: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease.
Methods
Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interestnamely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trialENTHUSE M1in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone.
Findings
50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·394·62, p<0·0001; reference model: 2·56, 1·853·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker.
Interpretation
Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.European Union within the ERC grant LatentCauses supported the work of C.F and I.K. German Research Foundation (DFG) within the Collaborative Research Centre 1243, subproject A17 awarded to C.F. German Federal Ministry of Education and Research (BMBF) through the Research Consortium e:AtheroMED (Systems medicine of myocardial infarction and stroke) under the auspices of the e:Med Programme (grant # 01ZX1313C) supported the work of D.P.A., P.D., C.F., C.K., I.K., N.K., M.L., H.S. and J.F.S. at the Institute of Computational Biology. NIH Grants RR025747-01, MH086633 and 1UL1TR001111, and NSF Grants SES-1357666, DMS-14-07655 and BCS0826844 supported the work of C.H., J.I., E.L., Y.W., H.Y., H.Z. and J.Z. NSFC Grant Nos. 61332013, 61572139 supported the work of X.L, Y.L, Y.Z., and S.Z. National Natural Science Foundation of China grants [Nos. 61422309, 61379092] was awarded to S.Z. The Patrick C. Walsh Prostate Research Fund and the Johns Hopkins Individualized Health Initiative supported the work of R.Y.C., D.D., Y.D., Z.J., K.R., Z.W. and Y.Z. FCT Ph.D. Grant SFRH/BD/80925/2011 was awarded to S.C. Clinical Persona Inc., East Palo Alto, CA supported the work of L.B. and D.K. The Finnish Cultural Foundation and the Drug Research Doctoral Programme (DRDP) at the University of Turku supported T.D.L. The National Research Foundation Singapore and the Singapore Ministry of Education, under its Research Centres of Excellence initiative, supported the work of J.G. and K.T. A grant from the Russian Science Foundation 14-24-00155 was awarded to M.D.K. A*MIDEX grant (no. ANR-11-IDEX-0001-02) was awarded to P.J.B. NSERC supported the work of R.G. The Israeli Centers of Research Excellence (I-CORE) program (Center No. 4/11) supported the work of E.G. Academy of Finland (grants 292611, 269862, 272437, 279163, 295504), National Cancer Institute (16X064), and Cancer Society of Finland supported the work of T.A. Academy of Finland (grant 268531) supported the work of T.
