Tumor-associated macrophages (TAMs) are among the most abundant immune cells in the solid tumor microenvironment, making them an attractive target for cancer immunotherapy. However, there are two important challenges. First, tumors repolarize the TAMs predominantly to M2 tumor-aiding phenotype by secreting various immunosuppressive cytokines. Second, CD47 on cancer cells interacts with signal-regulating protein a (SIRPa) expressed on macrophages. This crosstalk provides a downregulatory signal in the form of activation of SHP1/2 that inhibits cancer cell phagocytosis. These challenges can be overcome by engineering a nanoparticle that can deliver a rationale combination of immunomodulatory agents to the TAMs that can repolarize the M2 macrophages to M1 phenotype efficiently and concurrently block CD47-SIRPa interactions by inhibiting SHP2 signaling. A lipid nanoparticle (LNP) system loaded with amphiphilic R848-cholesterol (TLR7/8 agonist) and SHP099 (SHP2 inhibitor) in a predefined ratio has been designed. In vitro studies show that the LNPs system repolarized to M2 macrophages to M1 phenotype and expressed co-stimulatory molecules while enhancing phagocytic potential. In vivo efficacy studies in 4T1 tumor-bearing mice show that LNPs exhibit superior anti-tumor efficacy than other treatments. Thus, the lipid nanoparticle-mediated co-delivery of a rational combination of TLR7/8 agonist and SHP2 inhibitor in the TAMs can enhance macrophage immunotherapy.

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Tumor-associated macrophages (TAMs) exist in multiple phenotypes across the spectrum, defined by an M1 antitumorigenic phenotype and an M2 pro-tumorigenic phenotype on two ends of the spectrum. A largely immunosuppressive tumor-microenvironment aids the polarization of the infiltrating macrophages to a pro-tumorigenic M2 phenotype that promotes tumor progression and metastasis. Recent developments in macrophage immunotherapy have focused on strategies to re-educate TAMs from an M2 to M1 phenotype.Recent findings in the realm of immuno-metabolism have indicated that distinct metabolic signatures accompany macrophages based on their polarization states (M1-Glycolysis and M2-TCA cycle). These metabolites are important drivers of cellular signaling responsible for acquiring these polarization states, with evidence showing that metabolism is essential to facilitate the energy requirements of immune cells and regulate immune cell response. We hypothesized that TAMs could be reprogrammed metabolically by co-delivery of drugs using a supramolecular nanoparticle system that could effectively rewire macrophage metabolism by simultaneous inhibition of the TCA cycle and upregulation of the glycolytic metabolic pathway. TLR7/8 agonist and Fatty Acid Oxidation (FAO) inhibitor loaded metabolic supramolecular nanoparticles (MSNPs) were synthesized. In vitro assays showed macrophages treated with MSNPs were reprogrammed from an M2 phenotype to an M1 phenotype while significantly upregulating phagocytosis. When injected in 4T1 tumorbearing mice, MSNPs treatment reduced tumor growth progression more than other treatments. Hence, the delivery of TLR7/8 agonist combined with an FAO inhibitor can enhance antitumor efficacy through metabolic reprogramming of tumor-associated macrophages.

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The primary goal of trending technology artificial intelligence (AI) is to realize natural human-machine dialogue. Various IT-based companies also utilized dialogue networks technology to create various types of Virtual Personal Assistants focused on their products and areas for expanding human-machine contact, such as Alexa, Cortana, Google's Assistant, Siri and so more. Just like the Microsoft voice assistant named 'Cortana', we designed our virtual assistant which performs basic tasks based on the instruction provided to it on the Windows platform using Python. Here, Python is used as a scripting language as it has a large library that is used to perform instructions. Using Python packages, a personalized virtual assistant recognizes and processes the user's voice. Voice assistants are a fantastic advancement in the sector of Artificial Intelligence that can transform people's lives in a variety of ways. The voice-based assistant was initially given on cellphones and quickly gained popularity. It was widely acknowledged by all. Previously, voice assistants were largely found in smartphones and laptops, but they are now increasingly available in various home automation setups and smart speakers. Many technologies seem to become wiser in their very own way, allowing them to converse with humans in a simple language. Desktop voice assistants are programme that can identify people's speech and answer through an integrated speech system. This paper will outline how different voice assistants work, as well as their primary challenges and limitations. The way of developing a voice-based assistant without requiring cloud services is discussed in this paper, which would promote the future growth of such devices. Keywords: Voice Assistant, Speech Recognition, Python, Smtplib, Automation.