Multivalent systems are biotechnological tools that utilize multiple, specific interactions to achieve a desired function. These systems often involve the use of multivalent ligands, which are molecules that can bind to multiple target molecules simultaneously, and multivalent receptors. By leveraging the power of multiple, specific interactions, multivalent systems can achieve higher levels of effectiveness and specificity than traditional monovalent approaches. Research on multivalency is currently an interplay of the fields of biochemistry and supramolecular chemistry. In biotechnology, multivalent systems have been used in a variety of applications, including drug delivery, protein engineering, and immune system modulation. The use of multivalent systems has the potential to revolutionize the way that biotechnology approaches complex problems and has already led to numerous breakthroughs in a variety of fields. In this review, we focus on the application of multivalent systems in biotechnology and their potential in nucleic acid therapies.

In the last decades, we have experienced a revolution in data science in terms of the huge amount of data to be analyzed (era of big data) and the availability of high-performance processors. In drug discovery, this scenario is not different: the large volume of data (chemical, biological, etc.) along with the automation of techniques have generated a fertile ground for the use of artificial (or computational) intelligence/Machine Leaning (AI/ML). This powerful tool helped the researchers to achieve several major theoretical and applied breakthroughs. In this mini-review, recent research work of AI/ML in drug discovery and design will be introduced.

Synthesis of organic molecules is one of the most essential tasks in organic chemistry. The standard methodology started by a chemist solving a problem centered on experience, heuristics, and rules of thumb. Generally, experimentalists often work backward, starting with the molecule desired design and then analyzing the retrosynthesis in which readily available reagents and sequences of reactions could be used to produce it. All this his process is time-consuming and source- consuming, it can result in non-optimized solutions or even failure in finding reaction pathways because of human errors. In this sense, AI/ML (Artificial Intelligence/Machine Learning) is gaining more and more attention in organic chemistry because it can speed up this process. In this mini-Review provided a guide map to review the digitalization and computerization of organic chemistry principles.

In this day and age, the deficiency of resources for synthetic chemicals and massive challenges for waste carries the circular economy, including re-cycling waste, into focus. Consequently, it would provide waste a value that is one of the most essential incentives for all researchers to take better care and to avoid non-recyclable waste. In fact, the researchers established how computers equipped with wide synthetic knowledge (forward-synthesis with well-known reactions in chemical and related industries) can help to address the chemical waste challenge. In this context, Artificial Intelligence/Machine learning (AI/ML) can automatically learn from data and can perform tasks such as predictions and decision-making. Interdisciplinary studies combining AI/ML with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this summary, recent research work of AI/ML in sustainable and cycle chemistry will be introduced.

Diabetes mellitus represents one of the most
widespread metabolic illnesses, with ef ects on the
micro and macrovascular system that dramatically
raise morbidity and death. Diabetes mellitus is the
most prevalent endocrine illness in the world and is
predicted to cause the largest epidemic in the history
of mankind. Popular anti-diabetic medications have
lately been created and placed on the market,
although artificial pharmaceutical use for the
semi-permanent treatment of diabetes is restricted.
Healthy vegetables are extremely important in the
management of diabetic.Around the world, a number
of beneficial plants and the associated traditional
diabetes remedies they relate to are employed, and
they provide potential alternatives for the management
of diabetes therapy. Additionally, during the past ten
years, numerous metabolomics research have focused
on how various herbal medications work. The current
study intends to review several plant species of Indian
ancestry and their ingredients, which are used in the
standard medicine delivery system and have
demonstrated clinical action.The purpose is to find out
if plants, plant parts, or plant extracts can be utilised
to treat diabetes mellitus, the current review's goal is
to examine the available evidence. The Indian
aesthetic has extremely deep roots in the creation of
natural treatments. People still rely on herbal
medication systems for primary healthcare in the
majority of agricultural area units today.

Drug design and discovery is a complex, expensive and arduous procedure taking into account the multiple existing diseases and their variants. This long process includes the identification of potential targets and the development of therapeutically safe and effective drugs.1 Computer-aided drug design (CADD) can make it less time- and resource-consuming. In recent research, computational and statistical techniques are used in an effective way to study biomedical compounds for target identification and hit hunting. The arrival of ML in this field of study offers important enhancement in the efficacy of drug design and discovery process. The success drug design, discovery and development are in concordance with the computational methods and tools. They need to be accurate and use a reliable pre-processed data. Henceforward, Artificial Intelligence/Machine Leaning (AI) approaches to data pre-processing, modeling and representative applications in drug design and discovery will be introduced.

Machine learning (ML), the sub-set of AI, is a part of computer science which enables computers to have the ability to learn without being explicitly programmed. This process of leaning involves from the study of pattern recognition and computational learning theory. In addition, these algorithms can learn from and make predictions on data. These models obtained enable researchers, data scientists, engineers, and analysts to get reliable, repeatable decisions. Furthermore, the results analysis and discover hidden intuitions, through learning from historical relationships and trends in the data. ML models have been demonstrated the capacity of decision-making by clinicians in neurosurgical propose. In this mini-review, AI/ML approaches to data pre-processing, modeling and representative applications in neuroscience-related-topic will be introduced.

The fast progress in environmental science and monitoring technologies has headed to a big deal of growth in the quantity and complexity in data generation. The environmental study demands more innovative and powerful computational and data analytical methods. Data analytical focus on having less dependence on previous knowledge. In this context, machine learning (ML) has shown as a promising tool in tackling complex data patterns due to their powerful fitting abilities. Therefore, the past few year has seen a quick development of ML, particularly deep learning (DL). Henceforth, in this communication some research work environmental science related topic by using Artificial Intelligence/Machine Leaning (AI/ML) approaches will be introduced. Furthermore, diverse startup, spin-off, Small and Medium Enterprises (SMEs), and also some Tech companies, etc. are increasing the use of AI-based environmental science.

Abstract.

Every year, mankind and the environment are exposed to chemicals. Numerous chemicals may present a risk to health or the environment during production, processing, distribution in commerce, use or end of use.

Through data engineering it is possible to trace chemicals, estimate emissions and identify possible exposure scenarios for the different chemical compounds at the end of life of the industrial processes involved. This mini review identified case studies based on food, pharmaceuticals and N-hexane, concluding that data engineering can help to track chemicals in waste streams generated in industrial activities handled, identifying possible exposure scenarios to a chemical in question.

Enterococcus spp are microorganisms described in the literature as the main cause of endocarditis and bacteremia, which are both severe conditions that can end the life of the patient, has a morbidity between 5 and 12% of cases and a mortality rate 23-46%; Its rapid dissemination, both intrahospital and interhospital, with a clonal expansion of pathogenic and antimicrobial-resistant strains, makes this situation worrying in the province of Villa Clara, where it also represents a serious problem and the number of patients who have presented Enterococcus in blood cultures, as there is no research on this subject. Therefore, there is a knowledge gap in the matter and it is necessary to deepen the epidemiology and microbiological characteristics of the Enterococcus isolates circulating in the different hospitals of the province of Villa Clara. In this project we are going to focus / pursue the following objectives. Scientific Problem: Determining what microbiological characteristics will the Enterococcus isolated in blood cultures of different provincial hospitals of Villa Clara have. General Objective: Characterize, according to microbiological aspects, Enterococcus spp. Isolates in blood cultures of patients admitted to the different provincial hospitals of Villa Clara, in the period January - December 2022. Specific Objectives: Describe cultural characteristics of the Enterococcus isolates in the period of study. Describe microscopic characteristics of the colonies. Determine the species of Enterococcus to obtain according to hospitals and services studied. Identify susceptibility of Enterococcus against tested antimicrobials according to Clinical and Laboratory Standards Institute (CLSI).