Since 2007, tuberculosis has been the leading cause of death from an infectious agent, ranking above HIV/AIDS. Thus, despite some progress in the pipeline of new drugs, the identification of new drugs for the treatment of TB is still urgent. The active and non-toxic molecules and the control molecule (rifampicin) were subjected to molecular docking using the Molegro Virtual Docker 6.0 (MVD) software with the proteins chorismate mutase and dihydrofolate reductase . Thus, this study evidenced interactions that favor the action of the compounds studied.

Tuberculosis is an infectious disease chronic Mycobacterium tuberculosis by the microorganism. The global risks of infectious diseases, much is an entity of governmental and non-governmental institutions responsible for public health policies. As tuberculosis, as an infectious disease, remains one of the leading causes of death in the world, it requires effective monitoring, efficient and reliable diagnosis, screening and effective treatment.

According to a 2019 World Health Organization (WHO) estimate report, there was an agreement with the Report of 1.2 million deaths among HIV-negative people in 2018, and a 251,000 deaths among HIV-positive people. Since 2007, tuberculosis has been the leading cause of death from an infectious agent, ranking above HIV/AIDS. Brazil ranked 18th in number of TB cases, representing 0.9% of cases worldwide and 33% of estimated cases in the Americas. In 2016, the disease incidence coefficient was 32.4 cases per 100,000 inhabitants.

The Mycobacterium genome may contain and other structural modifications that may modify the action commonly used to inhibit it. The emergence of resistance makes disease control measures more complicated. Thus, despite some progress in the pipeline of new drugs, the identification of new drugs for the treatment of TB is still urgent.

The Human Immunodeficiency Virus has been
affecting people for years. Leading patients to
acquire several other diseases due to weakening
of the immune system. The use of metabolites
from marine algae have antiviral activities.
Therefore, this work analyzed among 40
molecules, originating from algae, through in
silico techniques, with the objective of proposing
a promising molecule with possible HIV
inhibitory activity.

The disease caused by the Human Immunodeficiency Virus (HIV) has infected people for over 50 years. Currently, 37.7 million people live with the virus, among these 150,000 people were infected with
the HIV virus only in the year 2020, in addition 73% of people use drug therapy with retrovirals.
Therefore, it is important to investigate new possible drugs, one of these ways is through computational
techniques.
The oceans have an enormous biodiversity, with biological sources rich in bioactive molecules,
since the pharmaceutical and cosmetic industries seek technological innovations, numerous marine
sources have been the object of study.
Among these are marine algae that are divided into macroscopic and microscopic. Both are rich
in secondary metabolites that have great pharmacological potential. Seaweeds already have several
studies on their broad biological activities such as: antimicrobial, anticancer, antiallergic, antioxidant,
anticoagulant, antidiabetic, antiparasitic, anti-inflammatory and antiviral.
Thus, this work aimed to analyze the possible inhibitory activity of molecules present in
seaweeds in proteins present in HIV.

Terrapins inhabit brackish water and coastal salt marshes. Terrapins are adapted to intermediate salinities but frequently face saltwater-inundated marsh habitats. To date, 12 species of terrapin have been reported worldwide. The present study aims to determine the global utility of terrapin DNA barcoding using novel COI sequences and compare them to other COI sequences previously published in BOLD systems and GenBank. A total of 26 COI sequences of worldwide terrapins were assessed in this study, including 13 COI sequences generated from field sampling on the east and west coasts of Peninsular Malaysia. Nevertheless, the conservation status of the terrapins is also taken into account for sustainability priority. The assessment of the COI sequences with the UPGMA tree identified three families, with 33% of terrapins being classified as least concern (LC) and 25% of them being classified as critically endangered (CR). In this study, we looked at the genome and bioinformatics of terrapins, which could be used as a starting point for future research on terrapin species all over the world.

The models of healthcare information systems are used for personalized medicine and preventing disease development, which is based on using electronic health records (EHRs) and a huge amount of complex biomedical data and high-quality -omics data. -Omics data, that is, genomics and postgenomics technologies, produce a huge amount of complex biochemical data related to processes in the living organism. According to the objective of the study exist different -omics.

For this reason, big data can be applied in healthcare and medicine, taking into account the large and complex data that exist, which are difficult to analyze and manage with traditional applications. In general, the term big data is described by the following 6 characteristics: value, volume, velocity, variety, and variability, although some authors have used more than these 6 properties. Is important to know that the security and privacy of all patients are guaranteed. To claim this security and privacy, the big data analytics software should use advanced encryption algorithms and pseudo-anonymization of personal data.

In conclusion, applications of big data analytics in medicine and healthcare is a very promising process that can improve patient-based service, detect symptoms and diseases earlier, as well as, supply better treatment methods. As all technology is improving, nowadays, smartphones can be used to deliver personal messages to patients related to their health and the treatment needed.

The process of repositioning drugs is related to the discovery of new clinical benefits of drugs known and already on the market in the treatment of other diseases, presenting an intersectoral and broad panel of advantages. However, it is necessary to verify the regulatory and economic obstacles that may permeate this process. This study aims to present an overview of pharmacological repositioning, focusing on its advantages and disadvantages. A qualitative study was carried out, using the process of narrative and reflective review, using the PICo strategy to support the investigation. The search took place from December 2021 to February 2022, aiming at a broad bibliographic analysis with contemplation of the benefits and challenges of pharmacological reuse, not requiring the opinion of a research ethics committee for its development. The repositioning of drugs essentially presents a simplification of procedures in the face of the introduction of a drug previously approved on the market. This technique allows the final consumer, carrier of the disease, to have access to a therapy more quickly, up to 80% cheaper, with greater chances of remaining in the market. However, in practice, a smaller fraction reaches the final stages effectively. That's because a repositioned drug still needs to go through phase II and III clinical trials for its new purpose. Another theme added to the scenario of limitations is the business model of large pharmaceutical companies, barred from bureaucratic issues related to patents. Therefore, this work highlights the need to seek to mitigate corporate impasses that slow possible innovative results, and it is up to researchers to emphasize the importance of this method in health emergencies.

Leishmaniasis is a neglected disease that accounts for approximately 30,000 deaths annually. Studies demonstrate drug resistance in the treatment of this pathology, thus making it necessary to search for new bioactive molecules for the treatment of this disease. Due to this, the objective of the following study is to verify through a molecular doking the most promising structures from algae selected through a bibliographic search. Presenting two promising molecules through computational data.
Leishmaniasis is a neglected disease that occurs mainly in tropical regions of the world, being found endemically in about 98 countries, where it totals approximately 30,000 deaths annually. Often appearing in its most lethal form as visceral leishmaniasis (VL), caused by the dimorphic parasite Leishmaniasis donovani. Currently, it is known that drugs are losing their effectiveness against this pathology, due to the development of resistance. Therefore, it is essential to search for molecules that may be promising for the manufacture of future drugs that help in the treatment of this pathology.
Natural products of aquatic origin, such as those derived from algae, have been gaining prominence in the pharmaceutical and biometric industry, due to their rich diversity of bioactive molecules with antioxidant, anti-inflammatory, antifungal, antibacterial and neuroprotective activities, but more studies are needed aimed at the interaction of these molecules with the most diverse diseases. Among them those caused by Leishmaniasis, due to the amount of structures from living organisms, studies that have a rapid molecular verification capacity are needed.
The development of chemoinformatics has facilitated the choice of future drug candidates, because of its great ability to verify a huge number of molecules with biological activity, comparing the structures with the targets through molecular doking, and with that to generate a low molecular screening. cost by selecting the most promising drug candidates. Due to this, the objective of the following study is to verify through a molecular doking the most promising molecules from algae, which may be future candidates for studies that help in the treatment of Leishmaniasis caused by the parasite Leishmaniasis donovani.

Leishmaniasis belongs to the neglected disease group, has a high prevalence and is responsible for approximately 70.000 deaths per year worldwide. Recent studies have shown resistance of the parasite to drugs used in the treatment of the disease. From this perspective, the objective of this work is to conduct an in silico study aiming to identify flavonoid derivatives with potential activity against Leishmania braziliensis.

Leishmaniasis belongs to the group of neglected tropical parasitic diseases, it is estimated that the worldwide prevalence is around 12 million cases, and that causes more than 70,000 deaths per year, moreover, studies show an annual incidence of 1.3 million cases and that this number tends to increase. This disease has as etiological agent different species of protozoa of the genus Leishmania, among the main ones is Leishmania braziliensis. Transmission occurs by the bite of infected female sandbrats.

In relation to clinical manifestations, leishmaniasis is characterized in four main forms: cutaneous, mucosal, cutaneomucosa and visceral. Leishmania braziliensis causes a typical tegumentary leishmaniasis, which can evolve to a mucous form.

Leishmania braziliensis is a clinically and epidemiologically important species, due to the wide distribution of the parasite in Latin America and also because of the high resistance to drugs used in the first-line treatment.

Thus, the present work aims to conduct an in silico study of ten flavonoid derivatives, aiming to identify compounds with possible activity against Leishmania braziliensis.

Neglected diseases are caused due to a set of ecological, evolutionary and biological factors, there is a higher incidence in the number of cases in countries with tropical climate. Chagas disease is one of the most important neglected diseases that affects several countries. The etiologic agent that causes this disease is the flagellate protozoan Trypanosoma cruzi. The disease presents in two clinical phases: acute and chronic. This disease pose a significant burden to people's health in general. The drugs available to treat it are not able to meet your clinical needs. Aiming at the analysis of a chemical constituent of pau brasil (Paubrasilia echinata Lam.), the present study aimed to evaluate the action of antichagasic constituents through an in silico study.

Plasmodium falciparum is a protozoan that causes malaria in its most severe form in humans. Its transmission occurs through female mosquitoes of the genus Anopheles infected by Plasmodium, the most common symptoms are: high fever, chills, headaches, tachycardia and muscle pain. Cerebral malaria may even occur, responsible for most fatal cases of the disease. The objective of this study is an in silico analysis of rosemary essential oil, aiming to identify possible molecules with antimalarial action against Plasmodium falciparum.

Malaria is a disease caused by the protozoan of the genus Plasmodium, transmitted by the bite of females of some species of the mosquito of the genus Anopheles. Out of 100 species, 4 of these protozoa infect humans, with Plasmodium falciparum being the one that leads to the most severe form of the disease, which can lead to death. The treatment of malaria is quite complicated, extensive and most of the time it is not effective due to reinfection.

Chloroquine is part of the treatment plan for malaria, but Plasmodium falciparum over time has become resistant to this drug. Thus, the importance of the synthesis of new antimalarial drugs.

Rosmarinus officinalis is a plant known as rosemary and belongs to the Lamiaceae family, it is widely used in traditional medicine against diseases. Rosemary essential oil and extract are biologically active compounds with strong antioxidant activity, so these antioxidant molecules in rosemary essential oil demonstrate strong potential for the synthesis of new natural antimalarial drugs.

Drug discovery is a process that takes several years , as it includes several different plashes, apart from being complicated, expensive, and time-consuming. Because of that, nowadays scientists and informatics are increasingly working together in the processes of drug discovery using technology based on Artificial Intelligence (AI).

Computer tools were developed for being able to identify potential biological active molecules from great numbers of candidate compounds quickly and cheaply. But, when drug discovery moved into the area of AI and big data, using Machine Learning (ML) and Deep Learning (DL) was started to be possible to analyze clinically relevant massive amounts of data that guide the discovery of new potential targets, and consequently drug discovery.

As of today, several drugs were discovered using this technology. For example, the first drug created using AI was DSP-1181, which is a potent serotonin 5-HT1A receptor agonist. The time that took the discovery of it was less than 12 months from initial screening to the end of preclinical testing.