COVID-19: Policy Interventions and Socio-economic Impact in Andhra Pradesh, India

The COVID-19 pandemic has claimed several lives and has already adversely affected the livelihoods of millions of vulnerable households. This policy brief surveys the current status of the disease, its spread and its likely socio-economic effects in the context of Andhra Pradesh, India. Given the global scope of the shock, the policy brief emphasizes the need for innovative and bold policy measures, particularly from the states’ perspective

ZERUMBONE, A NATURAL PLANT DIETARY COMPOUND INDUCES EXPRESSION OF INTERLEUKIN-12P70 CYTOKINE IN HUMAN PERIPHERAL BLOOD MONONUCLEAR CELLS

ABSTRACTObjective: Despite possessing many biological activities as antiproliferative, antioxidant, anti-inflammatory, and anticancerous, and zerumbone lacksany evidence for its immunomodulatory activity. This naturally occurring dietary compound needs to be developed as drug to support therapeuticclaims in various infections and diseases.Methods: Hence, in this study, the immunomodulatory effects of zerumbone were investigated by evaluating the effect of this compound toward thelymphocytes proliferation in human peripheral blood mononuclear cells.Results: Lymphocyte proliferation assay showed that zerumbone was able to activate human lymphocytes at dosage-dependent manner at the highestconcentration 40 μl/mL. The production of human interleukin-12p70 cytokine in culture supernatant from activated lymphocytes was upregulatedby zerumbone at 24 hrs and gradually decreased at 48 hrs. Hence, the study confirms the immunomodulatory activity of zerumbone which play animportant role in boosting up the immune system through cytokine production in dosage dependent manner.Conclusion: The study concludes that zerumbone could be used as a lead molecule in herbal therapeutic world as an immunomodulatory drug in thetreatment of chronic infections and various autoimmune disorders.Keywords: Zerumbone, Peripheral blood mononuclear cells, Immunomodulation, Cytokine, Lymphocyte proliferation

Fungal carriage on healthcare workers’ hands, clothing, stethoscopes and electronic devices during routine patient care: a study from a tertiary care center

Background – Invasive fungal infections are a constant threat to immunocompromised and critically ill patients. Healthcare workers caring for such patients act as conduits of transmission through their contaminated hands and belongings. Although bacterial contamination of healthcare workers is known, our knowledge about fungal carriage is sparse. Aim– To study the prevalence and type of fungal carriage on healthcare workers hands, aprons/hospital scrubs, electronic devices and stethoscopes. Methods– Healthcare workers working in Medicine ward and ICU during November and December 2019 were sampled. Hand washes were collected in Brain Heart Infusion (BHI) broth with gentamycin. Direct impression smears on blood agar were taken from aprons/hospital scrubs. Electronic devices and stethoscopes were sampled using moist cotton swabs. Subculture and plating was done on Sabarouds Dextrose Agar (SDA). Yeasts were identified using Matrix Assisted Laser Desorption Ionisation Time of Flight (MALDI TOF) and moulds were identified using microscopy. Findings – Out of 60 health care workers, 20 (33.3%) had fungal carriage. Aprons/hospital scrubs and hands were contaminated in 17 (28.3%) and 3 (5%) respectively. Aprons/hospital scrubs mainly constituted moulds belonging to species of Aspergillus. Hands were contaminated with Candida tropicalis, Candida parapsilosis and Candida auris. Electronic devices and stethoscopes had no fungal contamination. Sex (p=0.77), designation (p=0.32) and unit of surveillance (p=0.06) were not significantly associated with fungal isolation from health care workers. Conclusion – Active fungal surveillance provides prevalent carriage rates and serve as a feedback to improve our disinfection and hand hygiene practices. It also aids in identification of potential source of hospital outbreaks

A robust pest identification system using morphological analysis in neural networks

Timely detection of pests play a major role in agriculture. There exist many pest identification systems, but almost all of them suffer from the misclassification due to lighting, background clutter, heterogeneous capturing devices as well as the pest being partially visible or in the different orientation. This misclassification may cause tremendous yield loss. To mitigate this situation, we proposed an architecture to provide high classification accuracy under the aforementioned conditions using morphology and skeletonization along with neural networks as classifiers. We have considered the crop rice as a use case as it is the staple food grain of almost the entire population of India. The amount of pesticides used is highest in rice as compared to all other food grains. This paper offers a robust technique to identify the pests in rice crops. The performance of the proposed architecture is tested with an image dataset, and the experimental results reveal that our proposed approach provides better classification accuracy than the existing pest detection approaches in the literature. Furthermore, the experimental results also provide the performance comparison among the popular classifiers

Intermittent PI3Kδ inhibition sustains anti-tumour immunity and curbs irAEs

Phosphoinositide 3-kinase δ (PI3Kδ) has a key role in lymphocytes, and inhibitors that target this PI3K have been approved for treatment of B cell malignancies1-3. Although studies in mouse models of solid tumours have demonstrated that PI3Kδ inhibitors (PI3Kδi) can induce anti-tumour immunity4,5, its effect on solid tumours in humans remains unclear. Here we assessed the effects of the PI3Kδi AMG319 in human patients with head and neck cancer in a neoadjuvant, double-blind, placebo-controlled randomized phase II trial (EudraCT no. 2014-004388-20). PI3Kδ inhibition decreased the number of tumour-infiltrating regulatory T (Treg) cells and enhanced the cytotoxic potential of tumour-infiltrating T cells. At the tested doses of AMG319, immune-related adverse events (irAEs) required treatment to be discontinued in 12 out of 21 of patients treated with AMG319, suggestive of systemic effects on Treg cells. Accordingly, in mouse models, PI3Kδi decreased the number of Treg cells systemically and caused colitis. Single-cell RNA-sequencing analysis revealed a PI3Kδi-driven loss of tissue-resident colonic ST2 Treg cells, accompanied by expansion of pathogenic T helper 17 (TH17) and type 17 CD8+ T (TC17) cells, which probably contributed to toxicity; this points towards a specific mode of action for the emergence of irAEs. A modified treatment regimen with intermittent dosing of PI3Kδi in mouse models led to a significant decrease in tumour growth without inducing pathogenic T cells in colonic tissue, indicating that alternative dosing regimens might limit toxicity

AnD: A Many-Objective Evolutionary Algorithm with Angle-based Selection and Shift-based Density Estimation

Evolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms, such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose an alternative algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals have the most similar search directions. The latter, which takes both diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems