Background: Artificial intelligence (AI) is the way to model the human intelligence to accomplish certain task without much intervention of human being. The term AI was first used in 1956 with The Logic Theorist program, which was designed to simulate problem solving ability of human beings. There has been a significant amount of research using AI in order to determine advantages and disadvantages of the applicability and, the future perspectives that impact on different areas of society. Even the remarkable impact of AI can be transferred to the field of healthcare with its use in pharmaceutical and biomedical studies crucial for the socioeconomic development of the population in general within different studies we can highlight those that have been conducted with the objective of facing diseases, such as cancer, neurodegenerative diseases, among others. In parallel, the long process of drug development also requires the application of AI to accelerate research in medical care. Methods: This review is based on research material obtained from PubMed up to Jan 2020. The search terms include ―artificial intelligence‖, ―machine learning‖ in context of the research in pharmaceutical and biomedical applications. Results: This study aimed to highlight the importance of AI in biomedical research also recent studies support the use of AI to generate tools using patient data to improve outcomes. Other studies have demonstrated the use of AI to create prediction models to determine response to cancer treatment. Conclusion: The application of AI in the field of pharmaceutical and biomedical studies have been extensively utilized, including cancer research, for diagnosis as well as prognosis of disease state. It has become a tool for researchers in the management of complex data obtaining complementary results to conventional statistical analyses. AI increase the precision in estimation of treatment effect in cancer patients and determine prediction outcomes.
Background: The diagnosis and prognosis of pathological conditions, such as age-related macular degeneration (AMD) and cancer still need improvement. AMD is primarily caused due to the dysfunction of retinal pigment epithelium (RPE), whereas endothelial cells (ECs) play one of the major roles in angiogenesis; an important process which occurs in malignant progression of cancer. Several reports suggested about the augmented release of nano-vesicles under pathological conditions, including from RPE as well as cancer-associated ECs, which take part in various biological process including intercellular communication in disease progression. Importantly, these nano-vesicles are around 30-1000 nm and carry fingerprint of their initiating parent cells (IPCs). Therefore, these nano-vesicles could be utilized as the diagnostic tool for AMD and cancer, respectively. However, the analysis of nano-vesicles for biomarker study is confounded by their extensive heterogeneous nature. Methods: To confront this challenge, we utilized artificial intelligence (AI) based machine learning (ML) algorithms such as support vector machine (SVM) and decision tree model on the dataset of nano-vesicles from RPE and ECs cell lines with low dimensionality. Results: Overall, Gaussian SVM demonstrated highest prediction accuracy of the IPCs of nanovesicles, among all the chosen SVM classifiers. Additionally, the bagged tree showed highest prediction among the chosen decision tree-based classifiers. Conclusion: Therefore, overall bagged tree showed the best performance for the prediction of IPCs of nano-vesicles, suggesting the applicability of AI based prediction approach in diagnosis and prognosis of pathological conditions, including non-invasive liquid biopsy via various biofluids-derived nano-vesicles.
Background To localize sound sources accurately in a reverberant environment, human binaural hearing strongly favors analyzing the initial wave front of sounds. Behavioral studies of this “precedence effect” have so far largely been confined to human subjects, limiting the scope of complementary physiological approaches. Similarly, physiological studies have mostly looked at neural responses in the inferior colliculus, the main relay point between the inner ear and the auditory cortex, or used modeling of cochlear auditory transduction in an attempt to identify likely underlying mechanisms. Studies capable of providing a direct comparison of neural coding and behavioral measures of sound localization under the precedence effect are lacking. Results We adapted a “temporal weighting function” paradigm previously developed to quantify the precedence effect in human for use in laboratory rats. The animals learned to lateralize click trains in which each click in the train had a different interaural time difference. Computing the “perceptual weight” of each click in the train revealed a strong onset bias, very similar to that reported for humans. Follow-on electrocorticographic recording experiments revealed that onset weighting of interaural time differences is a robust feature of the cortical population response, but interestingly, it often fails to manifest at individual cortical recording sites. Conclusion While previous studies suggested that the precedence effect may be caused by early processing mechanisms in the cochlea or inhibitory circuitry in the brainstem and midbrain, our results indicate that the precedence effect is not fully developed at the level of individual recording sites in the auditory cortex, but robust and consistent precedence effects are observable only in the auditory cortex at the level of cortical population responses. This indicates that the precedence effect emerges at later cortical processing stages and is a significantly “higher order” feature than has hitherto been assumed.
From the evolution of the mankind, Turmeric has been used in conventional medication. India is in lead for producing, marketing and exporting the Turmeric and its value added products. Curcuma longa (Turmeric) is an Indian rhizomatous medicinal herb from the Zingiberaceae family that is common and widely available across the globe. The components of Turmeric are curcumin, demethoxycurcumin and bisdemethoxycurcumin and these are collectively known as curcuminoids. Curcumin, the active ingredient of Turmeric is generally investigated by the scientific community for its wide range of antioxidant activity, anti-Inflammatory properties and anti-cancer activity, anti-metabolic syndrome activities, neuroprotective activity, antimicrobial effects, anti-arthritis effects, anti-viral effects, anti-asthma and anti-diabetic effects, anti-obesity, cardio and liver toxicity protection activity, anti-depression How to cite this paper:
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