Drosophila hemocytes are majorly associated with immune responses, but they also undertake several non-immune functions that are crucial during various stages of development. The activity and behaviour of hemocytes are least documented during the metamorphic phase of fly development. Here we describe the activity, form and behaviour of the most abundant type of hemocyte in Drosophila melanogaster, the “plasmatocyte,” throughout pupal development. Our study reveals different forms of plasmatocytes laden with varying degrees of histolyzing debris (muscle and fat) which extend beyond the size of the cell itself, highlighting the phagocytic capacity of these plasmatocytes. Interestingly, the engulfment of apoptotic debris by plasmatocytes is an actin-dependent process, and by the end of metamorphosis, clearance is achieved. The uptake of apoptotic debris consisting of muscles and lipids by the plasmatocytes provides us a model that can be employed to dissect out the relevant components of macroendocytosis and lipid-loaded phagocytosis. This understanding, by itself, is crucial for addressing the emerging role of phagocytes in physiology and pathophysiology.

Tumour segmentation from brain MRI is more than a decade old problem in the field of medical imaging. Till date automated brain tumour segmentation happens to be a difficult task due to the variance and complexity of tumour growth. In this paper, we present this segmentation problem for the purpose of determining the exact location of brain tumour using refractive index study on the structural analysis of both tumorous and normal tissues. Initially, as per existing survey 3 kinds of features namely, intensity-based, texture-based, and symmetry-based are extracted from the structural elements. Then reduction of this feature set is performed and similar features are clustered together. Refractive index analysis is performed on each of the clusters from the MR T2 relaxation time. Deviation from a threshold value of RI for majority of pixels in a particular cluster denotes it to be the tumorous region.

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Targeting checkpoint proteins expressed on surfaces of T cells as a consequence of sustained tumour antigen stimulation leads to reinvigoration of immune responses and durable clinical benefits in cancer patients. However, the therapeutic interventionsusing checkpoint inhibitors like anti-CTLA4 and anti-PD1 produce different outcomes in different individuals as a consequence of complex interactions between tumour antigens, immune cells and immune responses. Here a discrete time mathematical model is proposed for establishing the quantitative underpinningsof the tumour-immune interplay occurring during tumour growth. The mathematical motif succintlydescribes the tumour growth history with time, the dynamics of tumour antigens and effector T cells and emphasizes on the effect of primary immune responses alone and combination of primary and secondary immune responses on tumour regression. The model compares the tumour growth dynamics observed in persons lacking secondary immune responses with that observed in persons with both immune responses. The anti-tumour immune responses comprising the primary and secondary responses are downregulated by the checkpoint proteins like PD1 and CTLA4 upon continuous stimulation by tumour antigens. The anti-PD1 and anti-CTLA4 inhibitors inhibit the checkpoint proteins and re-strengthen the immune responses resulting in sustained tumour growth or elimination of the tumour. The model developed further forecasts the effect of variation in the strength of immune responses on tumour growth dynamics, and the various outcomes observed when the monotherapy or combination therapy is discontinued after a certain period of time. Further, the model predictions are used to explore the synergistic interactions between the inhibitors and report the maximum synergy achievable for the drug combination.

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