Nishanth Gopinath

Bio: Nishanth Gopinath is an academic researcher from University at Buffalo. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Artificial Intelligence in Pharmaceutical and Healthcare Research

Abstract: Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

A study on the processing technology for Rhizoma Coptidis

Abstract: The present study intends to optimize the processing technology for the wine-processing of Rhizoma Coptidis, using alkaloids as indicators.In the present study, the Box-Behnken design method was adopted to optimize the processing technology for Rhizoma Coptidis, using the alkaloid component quantities as the index. 100 g of Rhizoma Coptidis slices and 12.5 g of Rhizoma Coptidis wine were used. After full mixing, box-Behnken design method was used to optimize the processing time, processing temperature and processing time of coptis chinensis by taking alkaloid content as index. After mixing well, these components were fried in a container at 125 °C for 6 min and exhibited good parallelism.The content of alkaloids in coptis chinensis was the highest after roasting at 125 °C for 6 min. The characteristic components were berberine hydrochloride, and the relative content was about 15.96%. And showed good parallelism. The effective components of Rhizoma Coptidis were primarily alkaloids.The optimized processing technology for Rhizoma Coptidis is good.

The Impact of the Integrated Development of AI and Energy Industry on Regional Energy Industry: A Case of China.

Abstract: With the advent of the Energy 4.0 era, the adoption of “Internet + artificial intelligence” systems will enable the transformation and upgrading of the traditional energy industry. This will alleviate the energy and environmental problems that China is currently facing. The integrated development of artificial intelligence and the energy industry has become inevitable in the development of future energy systems. This study applied a comprehensive evaluation index to the energy industry to calculate the comprehensive development index of the energy industry in 30 provinces of China from 2000 to 2017. Then, taking Guangdong and Jiangsu as examples, the synthetic control method was used to explore the direction and intensity of the integrated development of artificial intelligence and the energy industry on the comprehensive development level of the local energy industry. The results showed that when artificial intelligence (AI) and the energy industry achieved a stable coupled development without the need to move to the coordination stage, the coupling effect promoted the development of the regional energy industry, and the annual growth rate of the comprehensive development index was above 20%. This coupling effect passed the placebo test and ranking test and was significant at the 10% level, indicating the robustness and validity of the experimental results, which strongly confirmed the great potential of AI in re-empowering traditional industries from the data perspective. Based on the findings, corresponding policy recommendations were proposed on how to promote the development of inter-regional AI, how the government, enterprises, and universities could cooperate to promote the coordinated development of AI and energy, and how to guide the integration process of regional AI and energy industries according to local conditions, in order to maximize the technological dividend of AI and help the construction of smart energy in China.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

Abstract: The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Artificial Intelligence and Neuroscience: An Update on Fascinating Relationships

Abstract: Innovative technologies such as Artificial Intelligence (AI), deep learning, Machine learning and optogenetics have been considered key components in the contribution to the acceleration of numerous discoveries in life sciences, particularly in the field of neuroscience. With the inherent progress of AI in particular, it is no surprise that ‘neuroscience’, a complex study of the nervous system could benefit from the endless capabilities that AI has to offer with its magnification of the human mind. Although our minds are capable of extraordinary endeavours, there is a limit as to how much information we may mentally be able to process. Alongside the advancements of AI systems, we may be able to drive neuroscience forward and unlock the secrets of the human brain with one of its applications being the ability to identify neurological problems and detect neurotransmitters. This review therefore discusses the fruitful relationship between AI and neuroscience and its applications to furthering our knowledge in this field.