Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Developement of health risk rating scale for indoor airborne fungal exposure.

This paper aims to quantify airborne fungal load in air-conditioned rooms and develop a health risk rating scale for different indoor environments. Five sampling locations in Kolkata frequented by a heterogeneous human population, containing various types of fungal growth-promoting substances (FGPS) like old documents, food items, waste hair, etc. were chosen as sampling locations where an Andersen Two-Stage Cascade Impactor was ran using Rose Bengal agar and Potato Dextrose agar media plates. Total spore load (CFU/m3), species diversity, species dominance, human exposure time, susceptible age and FGPS were considered the risk factors for this study. A risk rating scale was developed after evaluating the relative importance of these different factors in relation to human health. The most dominant genera were Aspergillus, followed by Penicillium. Maximum CFU was observed at library, followed by computer room.

A new species of Polypedates Tschudi, 1838 (Amphibia: Anura: Rhacophoridae) from West Bengal State, Eastern India.

A new species of frog belonging to the genus Polypedates Tschudi is described from the state of West Bengal, Eastern India. A mid-sized frog, SVL ranges from 47.9-53.6 mm in males and 72.0 mm in the single female. The species is diagnosable in showing the following suite of characters: digits lack webbing, inner and outer metacarpal tubercles present; no dermal fold on forearm; toes webbed, webbing formula I1-1 II0.5-2III1-2IV2-0.5V; an inner metatarsal tubercle present; tibio-tarsal articulation reaches between eye and nostril; and skin on forehead co-ossified to cranium. Additionally, males possess paired vocal sacs. The new species is compared with known species of the genus Polypedates.