Expanding the Repertoire of Modified Vaccinia Ankara-Based Vaccine Vectors via Genetic Complementation Strategies

nkara (MVA) is a safe, highly attenuated orthopoxvirus that is being developed as a recombinant vaccine vector for immunization against a number of infectious diseases and cancers. However, the expression by MVA vectors of large numbers of poxvirus antigens, which display immunodominance over vectored antigens-of-interest for the priming of T cell responses, and the induction of vector-neutralizing antibodies, which curtail the efficacy of subsequent booster immunizations, remain as significant impediments to the overall utility of such vaccines. Thus, genetic approaches that enable the derivation of MVA vectors that are antigenically less complex may allow for rational improvement of MVA-based vaccines. during infection, and that the processes governing the generation of antiviral antibody responses are more readily saturated by viral antigen than are those that elicit CD8+ T cell responses. deletion, enables the generation of novel replication-defective MVA mutants and expands the repertoire of genetic viral variants that can now be explored as improved vaccine vectors

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Standardization of Phala Varthi an Ayurvedic Rectal Suppository

Suppositories are solid dosage forms intended for insertion into body cavities or orifices, where they melt or dissolved to exert either local or systemic effect. Phalavarti is an Ayurvedic way of application of suppositories in rectal ailments as discussed in Samhitas. Phalavarthi is one of VatiKalpana, It is a simple homemade medicament which can be prepared with easily available ingredients, It can be used in various types of GIT disorders like Udavarta, Anaha, Adhmana, Arsas, Gulma, Udara, Vibanda, which are some of the very common problems faced by general population due to irregular dietary habits and sedentary life style leading to Mandagni. The impaired digestion is main cause for various types of systemic and metabolic disorders. In such conditions Vatanulomana is main line of management. Apart from above specified conditions, Phalavarthi preparations can also be used in Panchakarma ayoga conditions like Virechana, Niruha, Anuvasana and Uttara vasthi. Despite the fact that various types of Phalavarthi preparations have been mentioned in Ayurveda texts, they are not in regular use due to less attention and focus received on this subject. There fore the present scientific study has been taken up to overcome the practical difficulties in both preparation and application of Phala vartis with a scientific approach. Phalavarthi is a very unique concept which is far superior to modern concept of rectal suppositories, it can be administered to children, old persons and all those who cannot swallow drugs easily. Hence keeping this advantage in mind, an attempt has been made to prepare and administer Phalavarthi in GIT disorders following the guidelines of Acharya Charaka and also keeping in view of modern principles of preparation of suppositories

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Not AvailableThis document includes the achievements of various interventions of KVKs in zone V in tribal dominated districts to impart skill among tribal youth and to facilitate establishment of enterprise units for ensuring enhanced income and livelihood securityNot Availabl