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Acute inflammation is the body’s first defense in response to pathogens or injury that is partially governed by a novel genus of endogenous lipid mediators that orchestrate the resolution of inflammation, coined specialized pro-resolving mediators (SPMs). SPMs, derived from omega-3-polyunstaturated fatty acids (PUFAs), include the eicosapentaenoic acid-derived and docosahexaenoic acid-derived Resolvins, Protectins, and Maresins. Herein, we review their biosynthesis, structural characteristics, and therapeutic effectiveness in various diseases such as ischemia, viral infections, periodontitis, neuroinflammatory diseases, cystic fibrosis, lung inflammation, herpes virus, and cancer, especially focusing on therapeutic effectiveness in respiratory inflammation and ischemia-related injuries. Resolvins are sub-nanomolar potent agonists that accelerate the resolution of inflammation by reducing excessive neutrophil infiltration, stimulating macrophage functions including phagocytosis, efferocytosis, and tissue repair. In addition to regulating neutrophils and macrophages, Resolvins control dendritic cell migration and T cell responses, and they also reduce the pro-inflammatory cytokines, proliferation, and metastasis of cancer cells. Importantly, several lines of evidence have demonstrated that Resolvins reduce tumor progression in melanoma, oral squamous cell carcinoma, lung cancer, and liver cancer. In addition, Resolvins enhance tumor cell debris clearance by macrophages in the tumor’s microenvironment. Resolvins, with their unique stereochemical structure, receptors, and biosynthetic pathways, provide a novel therapeutical approach to activating resolution mechanisms during cancer progression.

For either healthy or diseased organisms, lipids are key components for cellular membranes; they play important roles in numerous cellular processes including cell growth, proliferation, differentiation, energy storage and signaling. Exercise and disease development are examples of cellular environment alterations which produce changes in these networks. There are indications that alterations in lipid metabolism contribute to the development and progression of a variety of cancers. Measuring such alterations and understanding the pathways involved is critical to fully understand cellular metabolism. The demands for this information have led to the emergence of lipidomics, which enables the large-scale study of lipids using mass spectrometry (MS) techniques. Mass spectrometry has been widely used in lipidomics and allows us to analyze detailed lipid profiles of cancers. In this article, we discuss emerging strategies for lipidomics by mass spectrometry; targeted, as opposed to global, lipid analysis provides an exciting new alternative method. Additionally, we provide an introduction to lipidomics, lipid categories and their major biological functions, along with lipidomics studies by mass spectrometry in cancer samples. Further, we summarize the importance of lipid metabolism in oncology and tumor microenvironment, some of the challenges for lipodomics, and the potential for targeted approaches for screening pharmaceutical candidates to improve the therapeutic efficacy of treatment in cancer patients.

Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficult task since it necessitates high data rates and high energy consumption. Long Range (LoRa) is one such protocol, which is excellent for transferring data over long distances but has generated severe doubts regarding the viability of image transmission due to its low data rate. This paper demonstrates the application results of an integrated LoRa and Deep Learning-based computer vision system that can efficiently identify grape leaf diseases using low-resolution images. In particular, the focus in this paper is to combine the two technologies, LoRa and Deep Learning, to make the transmission of the images and the identification of the diseases possible. To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model fine-tuning. Based on the evaluation, the proposed framework proved that the transmission of images using LoRa is possible within the protocol limitations (such as limited bandwidth and low duty cycle). Our fine-tuned model can efficiently identify grape leaves diseases. The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). In this study, we use the Grad-CAM method to visualize the output layer judgments of the CNN. The disease’s spot region is highly activated, according to the visualization findings. This is how the network distinguishes between different grape leaf diseases.

In this study, the electron coefficients; Mean energy , Mobility and Drift velocity of different gases Ar, He, N2 and O2 in the ionosphere have been calculated using BOLSIG+ program to check the solution results of Boltzmann equation results, and effect of reduced electric field (E/N) on electronic coefficients. The electric field has been specified in the limited range 1-100 Td. The gases were in the ionosphere layer at an altitude frame 50-2000 km. Furthermore, the mean energy and drift velocity steadily increased with increases in the electric field, while mobility was reduced. It turns out that there is a significant and obvious decrease in mobility as a result of inelastic collisions and in addition little energy gained by the reduced electric field. A clear mathematical model was obtained to find out the electronic coefficient values without a simulation program (BOLSIG +). In addition, this model shows a strong correlation between the current work and the electronic transaction values calculated through the BOLSIG+ program.

Acute inflammation is the body's first defense in response to pathogens or injury that is partially governed by a novel genus of endogenous lipid mediators that orchestrate the resolution of inflammation, coined specialized pro-resolving mediators (SPMs). SPMs, derived from omega-3-polyunstaturated fatty acids (PUFAs), include the eicosapentaenoic acid-derived and docosahexaenoic acid-derived Resolvins, Protectins, and Maresins. Herein, we review their biosynthesis, structural characteristics, and therapeutic effectiveness in various diseases such as ischemia, viral infections, periodontitis, neuroinflammatory diseases, cystic fibrosis, lung inflammation, herpes virus, and cancer, especially focusing on therapeutic effectiveness in respiratory inflammation and ischemia-related injuries. Resolvins are sub-nanomolar potent agonists that accelerate the resolution of inflammation by reducing excessive neutrophil infiltration, stimulating macrophage functions including phagocytosis, efferocytosis, and tissue repair. In addition to regulating neutrophils and macrophages, Resolvins control dendritic cell migration and T cell responses, and they also reduce the pro-inflammatory cytokines, proliferation, and metastasis of cancer cells. Importantly, several lines of evidence have demonstrated that Resolvins reduce tumor progression in melanoma, oral squamous cell carcinoma, lung cancer, and liver cancer. In addition, Resolvins enhance tumor cell debris clearance by macrophages in the tumor's microenvironment. Resolvins, with their unique stereochemical structure, receptors, and biosynthetic pathways, provide a novel therapeutical approach to activating resolution mechanisms during cancer progression. [ABSTRACT FROM AUTHOR]

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