The citral is a monoterpene composed of two isomers: neral and geranial. These molecules have several pharmacological activities, such as antioxidant, anti-inflammatory, and antifungal. Therefore, this work analyzed the physicochemical differences of the citral isomers and a case study of their antifungal activity.

*Content Disclaimer Note (Added by Committee): This communication is a preprint uploaded under author responsibility. The Congress committee, only do a preliminary inspection of topic suitability. The content of this preprint communication is responsibility of authors and do not express the opinion of the members of committee. The committee is not responsible from content veracity or originality. Using automatic text generation tools, like ChatGPT, is allowed only for AI software/script coding purposes or as a way to improve quality of redaction of the original text. We recommend using text similarity analysis tools but this an author's decision.

We propose an algorithm to calculate the number of weak compositions, wherein each part is restricted to a different range of integers. This algorithm performs different orders of approximation up to the exact solution by using the Inclusion-Exclusion Principle. The great advantage of it with respect to the classical generating function technique is that the calculation is exponentially faster as the size of the numbers involved increases.

*Content Disclaimer Note (Added by Committee): This communication is a preprint uploaded under author responsibility. The Congress committee, only do a preliminary inspection of topic suitability. The content of this preprint communication is responsibility of authors and do not express the opinion of the members of committee. The committee is not responsible from content veracity or originality. Using automatic text generation tools, like ChatGPT, is allowed only for AI software/script coding purposes or as a way to improve quality of redaction of the original text. We recommend using text similarity analysis tools but this an author's decision.

Heterogeneous architecture is an underlining feature of 5G, however deployment and management of HetNets in 5G scenarios is yet to be explored. Given the need to satisfy overwhelming capacity demands in 5G, mm-wave spectrum (3-300 GHz) is expected to offer a very compelling long term solution by providing additional spectrum to 5G networks. Hence, the challenge is the integration of mm-wave in heterogeneous and dense networks as well as the backward compatibility and integration with legacy 4G/3G networks. Furthermore, Cloud radio access networks (C-RAN) contribution to 5G is considered as a cost effective and energy efficient solution for dense 5G deployment. From an energy point of view, cost and energy consumption are major considerations for 5G. C-RAN and energy efficiency techniques could help in performance improvements.

Although HetNets were introduced in 4G networks, their complexity has increased in 5G networks. In this paper, we will try to build a clear image of HetNets in 5G cellular networks. We consider different technologies with a special focus on mm-wave networks given its important role in 5G networks. We then address the available standards in HetNets that allow interworking and multihoming between different radio access technologies. Afterwards, we consider the virtualization of 5G HetNets and its benefits. Different resource allocation strategies in the literature are also presented for single-resource as well as for multi-resources. Finally, we give an overview of existing works addressing energy efficiency strategies in 5G networks.

*Content Disclaimer Note (Added by Committee): This communication is a preprint uploaded under author responsibility. The Congress committee, only do a preliminary inspection of topic suitability. The content of this preprint communication is responsibility of authors and do not express the opinion of the members of committee. The committee is not responsible from content veracity or originality. Using automatic text generation tools, like ChatGPT, is allowed only for AI software/script coding purposes or as a way to improve quality of redaction of the original text. We recommend using text similarity analysis tools but this an author's decision.

Background: Moroccans have used medicinal plants for centuries to treat various human and cattle health issues. However, there is a need for more research to document and share indigenous ethnopharmacological knowledge. In this study, we aimed to identify medicinal plants indigenous people in the Rif region used to treat cardiovascular problems and assess their ethnomedicinal abilities.

Methods: From 2016 to 2018, we conducted an ethnobotanical study in the Moroccan Rif area, surveying 520 traditional herbalists and consumers. We used quantitative ethnobotanical indicators such as family importance value (FIV), the relative frequency of citation (RFC), plant part value (PPV), fidelity level (FL), and informant consensus factor (ICF) to analyze the data.

Results: Our analysis revealed 33 plant species from 20 families, with Poaceae being the most dominant (7 species). Among the cardiovascular disorders treated, cardiac arrhythmias had the highest ICF (0.98). Leaves were the most frequently used plant part (PPV = 0.353), and decoction was the most common preparation method (31%).

Conclusions: Our study found evidence of indigenous ethnomedicinal knowledge of medicinal plants used to treat cardiovascular illnesses in the Moroccan Rif. We recommend further phytochemistry, pharmacology, and toxicology research to discover new drugs from these documented medicinal plants.

*Content Disclaimer Note (Added by Committee): This communication is a preprint uploaded under author responsibility. Consequently, the content of this preprint communication is responsibility of authors and do not express the opinion of the members of committee. The congress committee only do a preliminary inspection of topic suitability. The committee is not responsible from content veracity or originality. Using automatic text generation tools, like ChatGPT, is allowed only for AI software/script coding purposes or as a way to improve quality of redaction of the original text. We recommend using text similarity analysis tools but this an author's decision.

The progressive spread of antibiotic-resistant microorganisms is a public health problem, so
the search for new substances with antimicrobial properties has been growing over the years, especially
natural products. Triterpenes are widely distributed in the plant kingdom and have several
pharmacological activities described. Studies involving the stereochemistry of drugs greatly contribute
to the choice of the isomer that is more active and that promotes fewer adverse effects for humans. The
α,β-amyrin isomers in their isolated form may have different or even similar pharmacological properties,
but with different intensities of activity due to the change in the position of the methyl groups that
influence bioactivity. From the studies previously carried out, it was seen that both have antibacterial
activity against S. aureus when they are separated or mixed, leading to the conclusion that there is no
need to separate these two, because the migration of the C-29 methyl does not influence in this type of
activity.

Tuberculosis is a disease that causes the most deaths in the world, it is estimated that in the last 200 years about 1 billion people died due to this infection, caused by pathogenic organisms such as Mycobacterium tuberculosis Natural products can act as a viable alternative for development of new drugs, due to their rich molecular diversity. Because of that, the objective of the following work is to bring a critical discussion about the isomers of cis-communic, trans-communic acid and their mixture in the development of an antimicrobial potential against Mycobacterium tuberculosis.

Alzheimer's is a neurodegenerative and
irreversible disease. In Alzheimer's disease, it is
possible to identify the presence of insoluble
amyloid β deposits in plaques in the brain.
Retinoic acid isomers are being studied as a new
alternative for Alzheimer's disease. In this work,
the isomers are analyzed: all-trans retinoic acid,
9-cis retinoic acid and 13-cis retinoic acid. They
may play an important role in AD by protecting
neurons from β-amyloid-induced cell death.

The covid-19 is an infectious disease of the repertory tract caused by beta coronavirus, a disease that was considered a pandemic by the World Health Organization (WHO) in March 2020. Currently several vaccines are available for the prevention of covid-19, however few drugs have been shown to be effective in combating the current coronavirus, with remdesivir being the only approved drug Food and Drug Administratio (FDA). The remdesivir has two diastereoisomers due to the presence of a chiral phosphorous atom in its molecule, and the diastereomer-(Sp) is used in the clinic.

HIV-human immunodeficiency virus is an infection caused by a virus that causes the progressive destruction of some white blood cells present in the blood, causing AIDS-acquired immunodeficiency syndrome. Calanolide A is a drug indicated for people with the AIDS-AIDS acquired immunodeficiency syndrome, in which it enables the inhibition of the HIV-protase of the virus in order to prevent the initial development of the virus until its maturation. Calanolide B is a compound present in C. cerasiferum, used as other agents in the therapy of the human immunodeficiency virus (HIV). In this study, the main objective was the study of spatial dispositions and their effects and activities based on the position in which Calanolides A and B are presented in the context of stereochemistry.

In the etiology of pain of discogenic origin, attention is paid to the role of neurotrophic factors, such as brain-derived neurotrophic factor (BDNF). Considering the potential role of BDNF in the etiology of pain during IVDD, this study aimed to assess changes in the number of BDNF-positive nerve fibers and levels of BDNF in the IVDD of the lumbosacral spine in comparison to IVDs of the control group (post mortem samples).

The study group comprised 113 patients with IVDD of the lumbosacral spine. The control group consisted of 81 people (post mortem samples). We performed hematoxylin-eosin staining to assess IVD structures (degeneration), and immunohistochemistry to determine the number of BDNF-positive nerve fibers.

In immunofluorescent staining, we used a primary rabbit anti-BDNF antibody (Novus Biologicals, Centennial, CO, USA; catalog number NB100-98682; dilution 1:200). H&E staining of IVDs obtained from the control group was carried out to assess whether features of IVD degeneration were present in the present samples, which allowed them to be classified as controls. On the other hand, degenerated IVDs show changes in the AP and NF structures and features of reduced IVD height. Based on the analysis, no statistically significant differences were found between the number of BDNF-positive fibers in the study and control groups. We did not find that the number of BDNF-positive nerves differed significantly according to the degree of perceived pain (p = 0.359; one-way ANOVA test). The lowest number of nerve fibers was found in the group of patients reporting a perceived pain level of 6, and the highest at a level of 10. The results indicate an increasing trend in both the number of nerve fibers and the concentration of BDNF with the progress of the degeneration process in IVDD, but only to a certain stage, at which it seems that the intercellular matrix still allows biochemical processes to take place.

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).

Reactive oxygen species (ROS) are mainly produced by the mitochondria under both physiological and pathological conditions. Their production is based on both enzymatic and non-enzymatic reactions. Oxidative stress is therefore caused by an imbalance between the production and accumulation ROS in cells and the ability of the biological system to detoxify them. If left unchecked, it can accelerate aging and induce neurodegenerative and cardiovascular diseases, and even cancer. ROS may therefore contribute to tumor induction and survival, as well as to treatment resistance [15], but their consistently high levels have a cytotoxic effect, which may be helpful in anticancer therapy. The aim of the study was to assess the activity of genes associated with oxidative stress in endometrial cancer. The study included 45 patients with endometrioid endometrial cancer and 45 without neoplastic changes. The expression profile of genes associated with oxidative stress was determined with mRNA microarrays, and RT-qPCR. A one-way ANOVA with the following Tukey’s post hoc test revealed that out of 600 mRNAs representing oxidative stress-related genes, the number of mRNAs differentiating each cancer grade from the control was as follows: G1 vs. C, 56 mRNAs; G2 vs. C, 112 mRNAs; G3 vs. C, 118 mRNA (p < 0.05; FC > 2 or FC < −2). Further analysis indicated that 17 mRNAs were characteristic of G1 cancer, 48 mRNAs for G2 cancer and 56 mRNAs for G3 cancer. In addition, the expression of 25 mRNAs significantly changed regardless of endometrial cancer grade.The next step involved the overrepresentation test for these 25 common mRNAs representing 18 genes and the selection of the “cellular response to reactive oxygen species” biological process and its subprocesses. The experiment showed that AQP1, CYBA, MELK, PKD2, PRDX2 were significantly overexpressed in endometrial cancer, while ATP2B4, FOXO1, KCNMA1, KLF2, PRNP, SNCA, SOD3, THBS1, and TXNIP were downregulated.

Semaphorin 3F (SEMA3F) plays a substantial role in carcinogenesis, because of its role in inducing angiogenesis, and creating a microenvironment for the developing tumor. The purpose of this work was to assess the impact of cisplatin, depending on the concentra- tion and exposure time on the expression pattern of SEMA3F in an endometrial cancer cell line. Cultures of the Ishikawa endometrial cancer cells were incubated with cispla- tin with the following concentrations: 2.5μM; 5μM; and 10μM and for the following periods of time: 12; 24; and 48 hours. Cells not incubated with the drug constituted the control in the experiment. To determine the effect of cisplatin on the expression of SEMA3F, the real-time quantitative reverse tran- scription reaction (RtqPCR; mRNA) was used, as well as the ELISA assay (protein). The statistical analysis was done with the admission of p<0.05. The silencing of SEMA3F expression on the transcriptome and proteome levels in a culture unexposed to the effects of cisplatin in comparison to endometrial cancer cells under the influence of cisplatin (p<0.05) were noted. Along with an increase in the concentration of the drug used, the num- ber of copies of the gene transcript, during the shortest incubation period had a gradual increase. Only for the highest concentration of the drug, substantial statistical differences in the expression of the SEMA3F protein between 24 and 48 hour incubation periods (p<0.05) were determined. Using cisplatin in an endometrial cancer cell culture results in an increased expression of SEMA3F, which advantageously affects the normalization of the neoplastic angiogenic process and lowers the proliferation of the cells making up the mass of the tumor.

Oxidation of DNA results in the formation of hydrolyzed DNA bases, which impairs cell growth by altering the gene expression profile and promoting the occurrence of gene mutations. In addition, damage to the DNA structure may occur, which promotes the formation of cancer. Reactive oxygen species (ROS). may therefore contribute to tumor induction and survival, as well as to treatment resistance [15], but their consistently high levels have a cytotoxic effect, which may be helpful in anticancer therapy [16]. The potential relationship of ROS with microRNAs (miRNAs) is also interesting. These non-coding RNA molecules post-transcriptionally modulate gene expression and can act as oncogenes or tumor suppressors, affecting cancer development, metastasis or survival. The aim of the study was to assess the activity of genes associated with oxidative stress in endometrial cancer and to determine their relationship with miRNAs. Of the 1105 miRNAs found on the microarray, the number of miRNAs differentiating each cancer grade from the control was as follows: G1 vs. C, 131 miRNAs; G2 vs. C, 58 miRNAs; G3 vs. C, 84 miRNAs (p < 0.05; FC > 2 or FC < −2). The next step was to assess which of the differentiating miRNAs could participate in the regulation of the activity of PRDX2, PKD2, AQP1, SOD3, and KLF2. he obtained results indicate that overexpression of PKD2 may be related to significantly reduced activity of miR-195-3p, miR-20a and increased the levels of miR-106a, miR-328 in the early stages of endometrial cancer. At a later stage, the involvement of miR-134 is also possible. Interestingly, miR-183 initially shows a decrease in activity, which changes dramatically in G3 cancer. The reduced expression of SOD3 may be due to the increased activity of miR-328 in G1 cancer and miR-363 in G3 cancer. In the case of KLF2, miR-195-3p level was reduced while miR-363 was overexpressed. PRDX2 and AQP1 expression is most likely not regulated by miRNAs selected in microarray analysis with our criteria. A high level of PKD2 may be the result of a decrease in the activity of miR-195-3p, miR-20a, miR-134. A SOD3 level reduction can be caused by miR-328, miR-363. In addition, miR-363 can also regulate KLF2 expression. In the course of endometrial cancer, the phenomenon of oxidative stress is observed, the regulation of which may be influenced by miRNAs.

Abstract. Cystic fibrosis (CF), a monogenic disease, is the most common autosomal recessive, life-shortening disease affecting people of Northern European descent. According to the American Cystic Fibrosis Foundation patient registry, there are currently more than 30,000 CF patients in the United States and more than 70,000 CF patients worldwide. This disease is caused by dysfunctional transport of chloride and/or other ions (such as sodium and bicarbonate) leading to the generation of thick, viscous secretions (e.g., mucus) in the lungs, pancreas, liver, intestine and reproductive tract and increased salt content in sweat gland secretions. Ultimately, progressive lung disease is the main cause of CF complications and patient mortality. This disease manifests in many organs, but mostly in the upper and lower respiratory tract, pancreas, intestines and reproductive system. For most patients, lung disease is the most important problem in terms of symptoms and the treatment required and the fact that it is the most likely cause of death the
optimal diagnostic test for cystic fibrosis is the measurement of electrolyte levels in sweat. Patients with the disease have elevated sodium and chloride concentrations (>60 mmol/l, diagnostic; 40-60 mmol/l, intermediate (but more likely to be diagnostic in infants); <40 mmol/l, normal). However, undoubted cases of cystic fibrosis have been described with normal sweat electrolytes. Newer techniques have reduced the amount of sweat needed, although cystic fibrosis is currently incurable and greatly reduces life expectancy, the average age of survival of CF has increased significantly over the past 50 years and now exceeds 40 years. Therefore, CF is no longer considered solely as a childhood disease, but is now recognized as a disease of children and adults. Currently, more than half of CF patients are adults up to 60 years of age, indicating that active treatment can improve prognosis, increase quality of life and prolong life expectancy.

Artificial intelligence (AI) is playing an increasingly important role in developing cancer occurrence risk models. AI model can analyze vast amounts of data to identify patterns and correlations that may not be immediately apparent to clinicians, which can reduce overdiagnosis, timely identify risk factors, and lower incidence and mortality rates. This mini-review presented three specific articles that demonstrate the development process and application effectiveness of AI-based cancer occurrence risk models, providing inspiration and reference for future developments. These research allows for more accurate predictions of cancer risk based on a variety of factors such as imaging results, blood test result, etc. By identifying individuals at high risk for developing cancer, preventative measures can be taken to reduce their likelihood of developing the disease. Additionally, AI can help reduce overdiagnosis by distinguishing between benign and malignant conditions with greater accuracy. Overall, the use of AI in developing cancer risk models has the potential to greatly improve our ability to prevent and treat cancers.

Marijuana has been used for decades, and in recent years, interest in its medical qualities has grown. Cannabinoid medications such as dronabinol, nabilone, and nabiximols have been developed as a result of research into their therapeutic characteristics. Marijuana use disorder is becoming more common among marijuana users. Marijuana is an illegal narcotic that is becoming increasingly popular among teenagers and young people, and it is harmful to human health. This review examines the most common concerns that individuals have concerning marijuana use and its impact on human health. The review focuses on the consequences and severity of the effects on human living, including physical, mental, emotional, and behavioral changes. According to this study, the most often used illicit substance (marijuana) has an active ingredient called delta-9-tetrahydrocannabinol (THC), which causes mind-altering effects. THC, the main element in marijuana, goes throughout the body, including the brain, to generate its numerous effects when smoked. THC binds to receptors known as cannabinoid receptors. THC binds to cannabinoid receptors on nerve cells in the brain, altering their function. "Are there treatments for marijuana abusers?" and "Can marijuana be used as a type of medication in humans?" are two of the concerns discussed.

Artificial intelligence (AI) has the potential to revolutionize the domain of medicine, particularly in the realm of medical diagnosis. AI-based diagnostic tools have the ability to analyze large amounts of data and undercover complex patterns that may be hard for humans to detect. Also, it helps to assist healthcare providers to make more precise and prompt diagnoses. This review explores the role of AI in improving medical diagnoses, the limitations associated with this technology, and relevant examples.

Research has indicated higher concentrations of histamine and polyamine in endometrioid tissue in comparison with healthy tissue. The aim of this study was to evaluate changes in the expression patterns of messenger RNA (mRNAs) and microRNA (miRNAs) related to the histaminergic system in endometrial samples and whole blood in women with endometrioid endometrial cancer. The study group consisted of 30 women with endometrioid endometrial cancer qualified for hysterectomy (G1 well-differentiated, 15 cases; G2 moderately differentiated, 8 cases; and G3 poorly differentiated, 7 cases). The control group included 30 women with no neoplastic changes during routine gynecological examinations. The molecular analysis consisted of the microarray analysis of mRNAs and miRNAs related to the histaminergic system, reverse-transcription quantitative polymerase chain reaction (RTqPCR), and enzyme-linked immunosorbent assay (ELISA). Out of 65 mRNAs connected with the histaminergic system, 10 differentiate the samples of tissue and blood obtained from patients with endometrioid endometrial cancer in comparison with the control group (p < 0.05). mRNA histamine receptor 1,3 (HRH1, HRH3), and solute carrier family 22 member 3 (SLC23A2) differentiating samples of endometrioid endometrial cancer independent of either G or control. The selected mRNA transcripts seem to be promising for molecularly targeted therapies in the context of endometrioid endometrial cancer.

Drug interactions, common problems in clinical practice, are one of the leading causes of adverse drug events, including toxicity or adverse reactions during use, failure to treatment, and can even lead to death. The combination of drugs is inevitable, especially in the case of multiple diseases, multiple symptoms. In most cases, physicians actively combine drugs to maximize effectiveness and minimize side effects, or in some cases combine drugs after carefully weighing benefits and risks. ten]. However, adverse drug interactions can be prevented by special precautions or by taking interventions to reduce the risk. Therefore, in order to give appropriate warnings to help doctors weigh the benefits/risks of drug combinations, the research team assessed the current situation of drug-drug interactions at the Department of Medical Examination traditional - the Ministry of Public Security from which to make recommendations and attention in prescribing practices at hospitals.

Abstract.

Medicinal plants have been one of the most important sources of medicine since the dawn of human civilization. Indigenous communities have used products from this plant in different conditions throughout documented history. Cannabis sativa L. is one of the most widely employed herbaceous medicinal plants for textiles and fibers, in medicine, as a source of food, animal food, animal bedding, and agriculture for seeds. This paper highlights the traditional applications, botany, phytochemistry, and pharmacological properties of Cannabis sativa L. Extensive database retrieval, such as Google Scholar, Semantic Scholar, ResearchGate, Academia.edu, PubMed, SciFinder, ChemSpider, CNKI, PubFacts, etc., was performed by using the keywords “Hemp,” “Cannabis,” as well as the scientific name of this plant species (Cannabis sativa L). Besides, reviews of relevant textbooks, documents, and patents were also employed to collect sufficient information. This study revealed numerous pharmacological activities of Cannabis sativa L. that could help with several medical diseases. Besides that, more than 565 bioactive constituents have been isolated and identified from diverse parts of Cannabis sativa L. This could help discover potential therapeutic effects and develop new medications to benefit human health.

RNA molecules are crucial in many biological processes, therefore, they have become potential targets for disease diagnosis and treatment. The design of small molecules that can target RNA structures is a promising approach, as they are tunable and easily taken up by cells. However, it can be challenging without knowing the RNA structure. In this short opinion letter three different examples will be discussed of research groups combining AI to predict the interactions between RNA molecules and small molecules to address the challenges in designing RNA-targeted ligands due to the difficulty in obtaining accurate RNA structures and the lack of understanding of binding kinetics.

Cardiovascular disease, the leading cause of mortality worldwide, is primarily caused by atherosclerosis, which is characterized by lipid and inflammatory cell accumulation in blood vessels and carotid intima thickening, among others. Although disease management has improved significantly, new therapeutic strategies focused on accelerating atherosclerosis regression must be developed. Atherosclerosis models mimicking in vivolike conditions provide essential information for research and new advances towards clinical application. Here a therapeutic strategy to improve cholesterol efflux has been developed based on a twostep administration of rHDL consisting on a first dose of antagomiR33a loaded rHDLs to induce ABCA1 transporter overexpression, followed by a second dose of DPPC rHDLs, which remove efficiently cholesterol from foam cells. A triplecell 2D atheroma plaque model reflecting the cellular complexity of atherosclerosis has been used to overcome the translational gap providing a suitable model to improve the efficiency of the nanoparticles in promoting cholesterol efflux. The results show that sequential administration of rHDL potentiates cholesterol efflux indicating that this approach might be used in vivo to target more efficiently atherosclerotic lesions and improve prognosis of the disease. Sequential targeting of foam cells with nanoparticles including a first antagomiR33a delivery by DPPC:CE:LPC rHDL followed by a second infusion of DPPC rHDL induces a potent cholesterol efflux from foam cells, which may overcome current technique barriers to promote clinical applications.

With the increasing awareness of environmental protection, the greening of flame retardants has
become an inevitable choice for flame retardant technology. The use of bio-based materials in nature
as a flame retardant meets the requirements of a green strategy, which not only mitigates the energy
crisis, but also does not cause environmental pollution.

The reports of cyanobacterial blooms and their impact on ecosystems and human health have increased in the last two decades, becoming of emerging concern for the World Health Organization (WHO). Most of these blooms are non-specific, although a few species showed more dominance, frequently, toxin-producing strains of Microcystis. Likewise, in Cuba, some cyanobacterial water blooms have been published, mainly focused on species composition and abundance, but studies do not approach both morphological and molecular analyses. Herein, we performed the characterization of an unprecedented mixed bloom in the Abreus reservoir using morphological features and molecular biomarkers. We detected high concentrations of some species of cyanobacteria, in addition to some groups of phytoplankton. Our results revealed Microcystis sp. as dominant species, followed by Sphaerospermopsis torques-reginae, being confirmed through molecular biomarker analyses, the presence of the 16S rRNA gene and Microcystis-specific 16S rRNA gene. Besides, the screening on cyanotoxin genes, revealed the gene mycE, which is involved in the biosynthesis of microcystins. This study constitutes one the few records of non-specific Harmful Algal Blooms (HABs) in Cuba, based on both morphological and molecular level. Although this study shows unpublished data, we consider this work as an extended version of the article “Valle-Pombrol A., et al., Planktonic cyanobacteria from the Abreus Reservoir, Cienfuegos, Cuba. Pan-American Journal of Aquatic Sciences (2021), 16(1): 20-29; http://panamjas.org/pdf_artigos/PANAMJAS_16(1)_20-29.pdf”. Indeed, our findings reinforce the importance of monitoring program of cyanobacteria in reservoir waters used for agricultural activities, animals, and human consumption.

Computational methods have gone through enormous improvements during the recent years. In this context, different enhancements in chemoinformatic and anticancer drug predictions will be listed as follow.

High-throughput screening (HTS), as one of the key techniques in drug discovery, is frequently
used to identify promising drug candidates in a largely automated and cost-effective way. In this report
different methods and applications of HTS will be commented.

In the last four decades, nanotechnology has gained a lot of importance with no sign of slowing
down. Nanoparticles have made it possible to significantly extend the shelf lives of food product,
improve intracellular delivery of hydrophobic drugs and so on. The improvement that nanotechnology
has reached in the field of anticancer agents and new drug delivery systems will be seen.

Alzheimer's disease (AD), the most common type of dementia in older people, causes
neurological problems associated with memory and thinking. Different computational analysis could
act as useful tools in gaining more information about the development of the disease or potential target
drugs. That is why different computational methods will be reviewed.

This paper presents a preliminary study of the relationship between IoT and cyber security in the context of predictive data analytics. The study examines the key security challenges associated with IoT devices and the measures that can be taken to mitigate these risks. The paper also explores the role of predictive data analytics in managing and analyzing data collected by IoT devices and the potential benefits and challenges of this approach. The paper further explores the potential benefits of using predictive data analytics in managing and analyzing data collected by IoT devices. These benefits include the ability to identify patterns and trends in data, optimize resource allocation, and improve decision-making. However, the study also identifies challenges associated with the use of predictive data analytics, such as the need for high-quality data, the complexity of analytics algorithms, and the potential for bias and discrimination. Overall, the paper highlights the importance of addressing cyber security challenges associated with IoT devices in the context of predictive data analytics. The study emphasizes the need for comprehensive security measures to be implemented to protect IoT devices and the data they collect. The paper also highlights the potential benefits of using predictive data analytics in managing and analyzing IoT data but cautions that careful consideration of the associated risks and challenges is necessary. Finally, the study identifies areas for future research, such as the development of more effective security measures and the exploration of new data analytics algorithms and techniques.

*Content Disclaimer Note (Added by Committee): This communication is a preprint uploaded under author responsibility. The Congress committee, only do a preliminary inspection of topic suitability. The content of this preprint communication is responsibility of authors and do not express the opinion of the members of committee. The committee is not responsible from content veracity or originality. Using automatic text generation tools, like ChatGPT, is allowed only for AI software/script coding purposes or as a way to improve quality of redaction of the original text. We recommend using text similarity analysis tools but this an author's decision.

Terpenes are hydrocarbons from secondary metabolism of plants, which have known biological activities (e.g., antimicrobial, antioxidants, chemotherapy, and immunomodulatory agents). However, it is necessary to better explore its effects on neuropsychological disorders. This study presents a brief overview of the neuropsychotropic action of terpenes, based on a narrative review using a qualitative descriptive approach. Among the articles found, the most studied applications were for ischemia, seizures, Alzheimer's disease, multiple sclerosis, depression, and anxiety. The action of these terpenes against diseases such as Amyotrophic Lateral Sclerosis, Parkinson's and Huntington's Disease is still a little explored field that needs further studies.

Imaging procedures in medicine are beneficial in diagnosing and treating a wide range of medical disorders. Because the images recorded by the many different imaging modalities are often complicated, image processing methods are required to improve their quality and get relevant information from them. Image processing has made it possible to construct computer-aided diagnostic (CAD) systems, which are designed to assist medical practitioners in detecting and identifying disorders. Isolating specific structures inside the body and following their evolution over time is now feasible because of recent advances in imaging techniques such as image segmentation and registration. Additionally, the use of image processing to build virtual and augmented reality environments has led to improvements in medical education and training and in the planning and execution of surgical procedures. This abstract focuses on how image processing has revolutionized medical imaging and made it possible for medical practitioners to identify and treat illnesses more efficiently.

A research initiative that attempts to categorize the many forms of cancer using machine learning algorithms. Machine learning strategies are being researched to enhance the precision and speed with which cancer is diagnosed. The researchers compiled a dataset consisting of cancer patients. They used several machine learning algorithms to analyze the data to identify patterns and characteristics that may be used to differentiate between the various forms of cancer. According to the research findings, the machine learning algorithms were practical in correctly categorizing the multiple forms of cancer, and the accuracy of the models was greater than that of conventional diagnosis techniques. The research results indicate that machine learning algorithms can be a valuable cancer detection tool. These algorithms have the potential to assist in improving patient outcomes by more rapidly and correctly detecting the proper diagnosis.

For thousands of years, plants have been utilized for their medicinal properties, and even today, numerous modern medicines are derived from plant compounds or draw inspiration from them. A research project was conducted in the Rif region of Morocco to uncover medicinal plants used for treating digestive system diseases and gather associated ethnomedicinal information. From July 2016 to July 2018, a comprehensive ethnomedicinal review was conducted, involving semi-structured interviews with 732 traditional healers serving as informants. Various quantitative indices, including family cultural importance, salience index, plant part value, informant agreement ratio, and fidelity level, were used to analyze the collected data statistically. The identified medicinal herb species were collected, documented, and preserved at the Plant, Animal Production, and Agro-industry Laboratory. Eighty-seven plant species were identified, belonging to 73 genera and 42 families. Among them, Apiaceae, with ten species, was the most commonly utilized plant family. Gastric ulcers were reported as the most prevalent illness (IAR = 0.97). Most herbal treatments consisted of decoctions (42.12%); the leaf was the most frequently employed plant part (PPV = 0.344). Thymus saturejoides Coss. was the most commonly recommended medicinal species by local traditional healers (SI = 0.240). This study highlights the reliance of traditional healers in the Moroccan Rif region on medicinal herbs. Further exploration of these documented plants' phytochemical, pharmacological, and toxicological properties is necessary to uncover new drugs and explore potential synergies between traditional and modern medicine.

Nan robotics is an emerging field of nanotechnology having nanoscale dimensions and is predictable to work at an atomic, molecular, and cellular level. The Nan robot skeleton is made up of carbon and its toolkit contains components like a medicine cavity containing medicine, a micro camera, a payload, a capacitor and a swimming tail. As nanorobots have special sensors i.e. physical or chemical which detect the target molecules in the human body can be used for the diagnosis and treatment of various vital diseases i.e. cancer, diabetes, atherosclerosis, haemophilia, kidney stones, etc. Nan robots to date are under the line of investigation, but some primary molecular models of these medically programmable machines have been tested. This review on nanorobots presents the various aspects allied i.e. introduction, history, ideal characteristics, Approaches in Nan robotics, basis for the development, tool kit recognition, and retrieval from the body, application considering diagnosis and treatment.

Leishmaniasis is a significant global health problem caused by parasites transmitted through sandflies. Current treatments have limitations, and the lack of vaccines necessitates the exploration of new therapeutic options. One potential target is a protein called glycoprotein 63 (gp63), found on the surface of Leishmania parasites, which plays a role in the parasite's virulence. Computational methods, including molecular docking, can help predict protein structures and interactions with small molecules. This study presents a three-dimensional model of gp63 from Leishmania panamensis, validated for reliability. Molecular docking was used to analyze the binding of flavonoid compounds with gp63 from L. major and L. panamensis, providing insights for potential inhibitors. Another study discusses the importance of arginase, an enzyme involved in the parasite's survival, as a therapeutic target for Leishmania. Arginase inhibitors have shown promise in controlling infection by inducing oxidative stress in the parasite. Natural compounds, particularly flavonoids, have shown promise as arginase inhibitors and potential therapeutic agents against leishmaniasis.

This document discusses the physicochemical properties of nanoparticles (NPs) and their importance in various applications. NPs are materials formed by particles with dimensions between 1 and 100 nm, which possess unique properties due to their relatively large surface area. However, characterization of these properties is challenging due to the polydispersity and wide distribution of sizes, shapes and defects that occur in their synthesis. Size, shape, surface charge and porosity are key parameters that determine the properties of NPs. The physicochemical properties of NPs include electronic and optical, magnetic, mechanical and thermal properties.

The study of bioactive compounds based on platinum is associated with the discovery of the inhibitory activity of cisplatin on the cell division of Escherichia Coli bacteria, in 1965. Its antimitotic activity was studied in malignant tumors, such as Kaposi 180, and its effectiveness was appreciated in low doses, being approved in 1978 by the Food and Drug Administration (FDA). Since the marketing approval, around 3000 platinum analogues have been synthesized, 185 of which have registered activity, but only five were approved, namely. The mechanism of action of the cisplatin complex and its analogues begins with their entry into the cell through active diffusion, carried out by copper and organic cationic transporters. In the cytoplasm, it undergoes successive hydrolysis reactions, due to the low concentration of chloride anions. In sequence, platinum interacts with purines and triggers their cytotoxic action. But the side effects related to his therapy resistance to inert cancers such as colon and nonsmall cell lung; or acquired, caused by biomolecules such as cysteine, methionine and GSH, it is necessary to study new compounds. In silico approaches, despite having a smaller number of works, are increasing, as computational chemical methods at molecular, quantum or hybrid modeling levels (QM/MM) are providing relevant data to biological systems. In the field of molecular docking, methods are used to predict the preferred orientation of one molecule over a second when linked together to form a stable complex. In addition to providing descriptors for the study of the quantitative structure-activity relationship (QSAR). This is appreciated in the work by Chojnaki and team in which the electrostatic potentials at the DFT level describe cisplatin with greater interaction with serum sulfur atoms compared to transplatin. The abstract describes two in silico studies that presented structure-activity data involving platinum
antitumor agents, which provide promising data for the class. The first article describes the development of new platinum complexes, in which it proposes four medicinal compounds depending on the type of binder, as the first binder has anticancer efficacy, such as tamoxifen or methotrexate, and the second chemical compounds, such as curcumin or xanthine, were used as ligands that are bound to Pt+4 (prodrug). While the second presents new Pt(II) complexes [R′2Pt(CNR)2] (1a–c; R′ = Me and 2a–c; R′ = p-tolyl) were synthesized by the reaction of the precursor complexes cis, cis-[Me2Pt(μ SMe2)2PtMe2], A, and cis-[(p-tolyl)2Pt(SMe2)2], B, with four and two equivalents of different types of isocyanide ligands (CNR; R = a; t-butyl, b; benzyl, and c; cyclohexyl isocyanide), respectively.

During the study, the influence of the isomerism of monohydric alcohols on their time dynamics in an ionic liquid was studied. Based on the data obtained as a result of molecular dynamics calculations, the time intervals were determined during which the nature of the motion of a dissolved substance (iso-alcohol molecules) in an ionic liquid changes, model representations were constructed to describe the mechanisms of diffusion of the components of the systems under study, which made it possible to analyze the effect of isomerism of monatomic alcohols on the dynamic properties of alcohols in an ionic liquid.

Metal Organic Framework (MOF) drug delivery systems are interesting for Gastrointestinal tract (GIT) inflammatory, parasitic, cancer, and other diseases therapy. Artificial Intelligence - Machine Learning (AI/ML) can be used for a more rational design of these systems. However, the low abundance of data difficult these studies. In this communication we presented preliminary results of the project AIMOFGIFT funded by the SPRI Group Elkratek program in this area. A new database of MOF-Drug systems was created from public sources. In addition, different AI/ML preliminary models were developed to predict new MOF-Drug systems.

Cancer is one of the main causes of death in the world, this disease is characterized by the uncontrolled proliferation of mutant cells, by their ability to spread throughout the body through the invasion of blood or lymphatic vessels, and by inducing the process of angiogenesis and metastasis. According to the world health organization, in the year 2020 there were about 19.3 million cases, in 2040, this number could reach 30.2 million new cases. The search for new drugs for the treatment of this disease has been motivating generations of researchers, in organic chemistry heterocyclics appear as an alternative compound to be explored due to their oxygen and nitrogen atoms in their nucleus. In this summary we will report two works on oxadiazole, the same has been studied because its derivatives have different biological activities, some of these biological activities can be predicted using the molecular docking technique, this technology allows simulating the interaction of the molecule against different proteins and predicting the possibility of molecules presenting biological activities, the use of molecular docking is very important to have a brief interpretation of the behavior of the molecule in the studied organism. This present study aims to bring two different articles that present the molecular docking technique in oxadiazole derivatives aiming at antitumor activity.