In recent years it is increasingly normal to hear information about rare diseases, where the lack of knowledge about these rare diseases causes a high number of deaths without being able to treat them due to not having an effective medicine. To be called a rare disease, it has to affect at least 5 patients per 10,000 and of these we know that there are at least 6,000 diseases. In a portion of 80% rare diseases affect at least one patient in a million, making it very difficult to diagnose the patient even for the most experienced doctors.
New methods of machine learning are transforming medicine and health care information is very useful for the diagnosis and treatment of these diseases. This study tries to investigate how machine learning through types of algorithms and input data can be useful in diagnosis, prognosis or treatment.
The procedural method of the study was as follows, a complex search was made with generic terms on the official PubMed page, where they have found a result of 381 names of individual diseases in the last 10 years. Terms such as the location of the different types of trials were added to obtain a greater source of information. To proceed with the analysis, the studies have been arranged according to different types of dimensions.

The analyzes obtained showed us that there are a total of 211 studies in a total of 32 countries that studied 74 rare diseases. It has also been seen that diseases with a higher prevalence had a higher frequency in clinical studies, although with the exception of some rare neurological diseases, systemic or rare rheumatological diseases. Learning as a whole (36%), support vector machines (32.2%) and artificial neural networks (31.8) has been the most applied algorithm in studies and, above all, it should be noted that almost 40.8% of machine learning studies for diagnosis and prognosis in 38.40%. With a too low percentage, around 4.7% of machine learning studies have been dedicated to the treatment of these rare diseases.

We can conclude that this new learning model is increasingly being used to help with these rare diseases, where we could obtain greater knowledge of their origin, spread and especially in the treatment of these.

Senna splendida (Vogel) H.S.Irwin & Barneby (Leguminosae) is a ethnomedicinal species native to Brazil, popularly known in the country as “fedegoso-grande and “feijão-brabo”, it is used against kidney diseases, bronchitis, rheumatism, anemia and inflammations. A pharmacobotanical study of the aerial vegetative organs of S. splendida was carried out, looking for morphoanatomical data that can contribute to the taxonomy and quality control of its ethno-drugs. Paradermic sections of both epidermal surfaces and cross sections of the leaf and stem were made freehand. Histochemical tests were also performed to indicate the types of plant metabolites. Senna splendida is a sub-shrubby to shrubby, with cylindrical stems, leaves with 4-leaflets, sub-cylindrical petiole, leaflet blade are oblong or narrowly ovate, and carthaceous. In transverse section, the stem has the vascular system syphonostelic, ectophloic, continuous, consisting of a single circular vascular bundle, and a well-developed medullary parenchyma. The vascular system is collateral in the rachis, petiole, and midrib; the petiole and midrib showed four main bundles and two accessories; and the leaf rachis showed a main bundle and two accessories. The leaflet epidermis is hypostomatic, which has sinuous anticlinical cell walls on the both surfaces with anisocytic and paracytic stomata. The mesophyll is dorsi-ventral with a single-palisade, and 4-5-seriate spongy parenchyma. Inorganic idioblasts druse-type and prismatic crystals were observed near de vascular system of rachis, leaflet midrib, and petiole. Histochemical tests indicated the presence of starch, structural and non-structural phenolic compounds, alkaloids, and proteins in various parts of the leaflets, and also in the stem. The pharmacobotanical study of Senna splendida provided subsidies for its taxonomy and, in addition, it contributed to the quality control of its ethno-drugs and, thus, expanded the anatomical knowledge for the genus Senna. Financial support: CNPq and CAPES.

Introduction: Piperine is a natural alkaloid found in Piper nigrum. This work aims to perform a virtual screening of 20 synthetic piperine derivatives with potential anti-leishmanial activity. Methodology: A classificatory prediction model concerning the Leishmania infantum Promastigote cell form in Knime software was built. The compounds were subjected to Molecular Docking in the Molegro Virtual Docker v.6.0.1 software, using the CYP51 target complexed to the inhibitor Fluconazole obtained from the Protein Data Bank. The molecules under study were submitted to a consensus calculation involving the activity probability obtained in the elaborated model, as well as in the molecular docking simulation performed. Results: The prediction model was more than 78% accurate in the test and in the cross-validation, selecting the 20 synthetic derivatives under study as potentially active for the promastigote form. In Docking, only 9 of the molecules of the 20 molecules selected by the model had better energy than the ligand. In the consensus calculation, nine compounds had a probability above 50%. Among the structures, molecule 20 was considered the one with the best performance in the study developed.Conclusions: The virtual screening performed was able to identify the compounds with the highest probability of activity.

Deaths from sepsis are the most common cause of death and rank among the leading causes of death. In these diseases caused by bacteria, the time for the detection of the bacteria causing the problems and their immediate treatment is important so as not to produce serious effects of sepsis and to avoid septic shock. But conventional methods such as blood culture, biochemical identification, immunological testing or PCR amplification only give us long-term resolution of a small fraction of bacteria.
According to the latest published studies on the metabolomic changes produced by infecting microorganisms in patients, they produce a type of biomarkers or biosignatures that are produced when these pathogens proliferate and grow. Trials have shown that using specific metabolomic biosignatures of plasma pathogens, these pathogens that cause sepsis could be detected in a much faster and more efficient way. The big problem with this novel idea is the small number of samples that are registered against the large number of characteristics that occur in different sepsis infections.
The solution of using the machine learning model has been seen to this problem, which could integrate a large amount of clinical information and through the use of predictive algorithms could give us a greater identification of pathogens.
A retrospective cohort study of some clinical cases of sepsis has been carried out, where the information of the 1152 patients with signs of the disease has been collected and 100 individuals with a resolved clinical case have been chosen and compared with 29 controls. Both Gram-negative and Gram-positive organisms have been analyzed, among them the most prominent Streptococcus pneumoniae, Staphylococcus aureus and Escherichia coli. The variables of the studies have been used the sensitivity, the specificity and the value of AUC of the clinical and metabolomic characteristics in the prediction of the diagnostic results.

We can conclude that the studies analysed by the machine learning model gave us abnormal nitrogen metabolism results, cellular respiratory disorders, and kidney or intestinal failure. These data are important in order to detect sepsis and discriminate against other possible common causes. This advance in septic diseases could help us to identify the causative pathogens in a much greater way and try to take advantage of more time in treating the disease. We can highlight the great advantage of the machine learning model of great information management and a great speed in searching for a common factor to a problem for its greater resolution and detection.

Face masks, face shields and other personal protective equipment (PPE) have been accepted to be an effective tool in order to avoid bacterial and viral transmission, especially against indoor aerosol transmission. However, commercial PPE are made of materials that are not capable of inactivating pathogenic particles such as SARS-CoV-2 or multidrug-resistant bacteria. In this context, we describe here the development of new antimicrobial materials that can be used in PPE manufacturing, which include composite materials with a biofunctional coating of benzalkonium chloride (BAK) or solidified hand soap. These coatings were capable of inactivating SARS-CoV-2 in less than 1 minute of viral contact. Moreover, the BAK coating was also effective against the life-threatening methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis. These novel protective materials will be useful to combat the current COVID-19 pandemic in the current bacterial-resistant era.

Brugmansia suaveolens (Willd.) Sweet (Solanaceae), widely distributed around the world, is a source of several secondary metabolites, mainly tropane alkaloids, such as atropine and scopolamine. In addition, its large, white and showy flowers are used as ornamental, and as medicinal also, and involved in events of intoxication. Although it is a species of ethnobotanical importance, with chemical constituents already isolated, morpho-anatomical and histochemical studies are still lacking for its floral structures. In this work, a morpho-anatomical and histochemical study of flowers of Brugmansia suaveolens was carried out, with the aiming to find additional characters that could support its characterization, taxonomy and the quality control of its ethno-drugs. The anatomical study was conducted following the usual techniques in plant anatomy. Brugmansia suaveolens has big flowers, 20-30 cm long x 10 cm diameter, calyx symsepalous, and corolla sympetalous. Ovary superior, elongate-conical, 2-loculed with many ovules on an enlarged placent; the stigma elongate, 2-lobed and exceeding the anthers. Stamens isodynamous, anthers with longitudinal dehiscence, the filaments inserted near the top of the tube, sometimes geniculate, the anthers linear. The anatomic study revealed the peduncle with eustelic vascular system, with a central cylinder with vascular bundles that is separated by inter-fascicular parenchyma. The petal epidermis has straight to curved anticlinal cell walls on the both faces, unlike the sepal epidermis with sinuous anticlinal cell walls, on both sides, both structures are hypostomatic with anisocytic and anomocytic stomata. In transverse-section, sepals and petals showed epidermis uniseriate, the mesophyll with homogeneous parenchyma. Histochemical tests in the peduncle and sepals revealed more expressive reaction for alkaloids. Anatomical characteristics of the of the petals and sepals epidermis, and the vascular system of the peduncle are described here for the first time, as well as the histo-localization of alkaloids in the flowers of B. suaveolens, which may contribute to the knowledge of the species and the genus Brugmansia, providing subsidies for the taxonomy, and for the quality control of its medicinal potential drugs. Financial support: CAPES and CNPq.

Introduction: Lactoferrin (Lf) is a iron ligating glycoprotein with a molecular mass of approximately 80 kDa, present in mammalian whey, belonging to the transferrin family. Lf can be found in various mucous secretions, such as tears, saliva, gastrointestinal fluids, urine and seminal fluid, as well as in secondary neutrophil granules, being released in places where there is an inflammatory response. Lf is a multifunctional protein possessing functions such as antibacterial, antiviral, antifungal, anti-inflammatory and immunomodulatory activities. This study aimed to purify, characterizing buffalo milk lactoferrin by monitoring the purification by spectroscopic techniques as well as investigating the interaction of protein with antibiotic amoxicillin. Materials and methods: The processing of the buffalo milk began with the separation of fat by centrifugation. The skimmed milk was acidified with HCl 0.1 M up to pH 4.6, obtaining acidified whey and the sour serum was neutralized with NaOH 0.1 M until pH 6.8 and then centrifuged. The supernatant was submitted to saline precipitation profiles of 0-20%, 20-40%, 40-60% and 60-80% saturation of (NH₄)₂SO₄. Fluorimetric analyses of salt fractions were performed under excitation length conditions at 290 nm and emission wavelengths between 300-550 nm. Spectrophotometric studies were carried out with additions of 100 μL of saline fraction 40-60% (0.421 mg/mL), 100 μL of purified lactoferrin (0.421 mg/mL) and 100 μL amoxicillin (2.5x10^-6 mol). L^-1). Uv-vis absorption spectra were recorded from 190 to 450 nm. Results and discussions: The saline profile of the precipitate was resuspended 40-60% showed the spectrum of fluorescence extinction characteristic of lactoferrin (peak at 332 nm). The 40-60% precipitate was resuspended and submitted to liquid chromatography in a Sephacryl S-100 gel column. Fractions 12 to 16 showed the fluorescence extinction spectrum characteristic of lactoferrin. SDS-PAGE 8%, using commercial lactoferrin (SIGMA) as standard, showed the presence of two protein bands in the standard range. The UV-vis spectrum of maximum absorption showed that the interaction between lactoferrin and amoxicillin play an important role, with the decrease and displacement to the red peak on the UV-vis absorption spectrum when compared with that of lactoferrin partially purified with lactoferrin in the presence of the amoxicillin. Conclusion: Buffalo lactoferrin was partially purify by liquid chromatography in a Sephacryl s-100 resin and SDS-PAGE 8%, using commercial lactoferrin (SIGMA) as standard, showed two protein bands in the standard range. Partially purified buffalo lactoferrin exhibited a UV-vis absorption spectrum with two peaks; the first strong peak centered at absorption maximum in the region 220 to 230 nm is characteristic of the peptide structure and the second peak absorption maximum in the region from 270 to 280 nm due to conjugate double bonds in tyrosine and tryptophan residues. On assays of partial purified Lf incubated with amoxicillin was observed observe a redshift shift from 222.50 nm to 225 nm followed by the hypochromic effect on the UV-vis spectrum of maximum absorption of lactoferrin. The UV-visible absorption spectra studies showed that amoxicillin when coupled with lactoferrin induced alterations in the protein structure.

We present a systematic study of the effect of temperature and pressure on the microscopic dynamics of argon near the triple point. We also provide a detailed description of the argon diffusion model and discuss the time-dependent dynamics of argon, as well the relaxation processes in the temperature and pressure range near the argon’s triple point. The main goal of this work is to develop a method for determining the P-T parameters on the coexistence curve, for which there is a transition from a mixture ("solid (glass)- dense liquid" )→ ( "dense liquid" → "liquid") → ("liquid" -gas). We present and compare the results of a dynamic analysis of the system in various states obtained using several approaches.

The use of algorithms to predict optimal results in preclinical malaria assays from a data set that includes the experimental conditions of preclinical assays and characteristics of proteins, genes and chromosomes is a breakthrough in research. The process from data collection to data processing takes 70 percent of the time to develop. The process from the creation of preliminary models to the production of the model takes 30 percent of the total research time. There are several databases such as ChEMBL, Uniprot and NCBI-GDV that allow the collection of information on both preclinical assays and characteristics of any species, in this case study is plasmodium falciparum. This species is a major public health problem in tropical and subtropical countries. P. falciparum usually causes high fever, diarrhea, chills and in a few hours, it can evolve to a severe case causing death. The use of different algorithms such as: Linear Discriminant Analysis (LDA), Classification Tree with Univariate Splits (CTUS), Classification Tree with Linear Combinations (CTLC), and so on. The use of these algorithms and the perturbation theory allows pharmaceutical industries to optimize preclinical testing processes obtaining the most optimal models with a high percentage of specificity and sensitivity.

A new term called Chromosomics appeared, probably for the first time, in the research conducted by Deakin et al. in the paper entitled Chromosomics: Bridging the Gap between Genomes and Chromosomes. Chromosomics emerges from the necessity of going beyond the information about the sequences of DNAs (gene), RNAs, and Proteins (Genomics, Rnomics, Proteomics). Chromosomics focus them on the study of the formation and structure of chromosomes and the effect of this structure over biological process. Consequently, it also focus on the mechanisms, kinetics, and dynamics, of DNA folding, DNA-interaction with Histones, the assembly, disposition, distribution, organization, and packing of all the genome in chromosomes and the ultimate effect of this processes over the outcomes of biological process. It also studies the relative orientation of gene within the chromosome, the interaction between them inside the Chromosome of inter-chromosomes, the interaction between chromosomes in the cell. One of the ultimate goals of Chromosomics is the study of the relationship of all this information related to chromosomes with Genomics, Rnomics, and Proteomics information and their final contribution to the outcome of biological process. Chromosomics should answer questions like in which degree a determined Genomics, Rnomics, and Proteomics information pre-determines and/or is determined by chromosomes structure.

Very importantly, Chromosomics have work to do with the effect of changes of building blocks of chromosomes on their structure/function relationships and the ultimate effect on pathogenesis. Chromosomics experts may play an important role in the same sense but in inverse direction. We refer to determining the effect over biological process of intentional changes of building blocks of chromosomes (gene, histones). On our opinion different kind of changes and different levels of the process may be carry out. For instance, at a first level, we can change the chemical structure of the building blocks by replacing natural nitrogen bases of DNA or natural aminocids of histones by other natural or artificial elements (other nitrogen bases, aminoacids, or other building blocks). At a second level we can change the orientation of genes inside the chromosome to see the effect of these changes. At a third level we can chemically modified the 3D structure of histones, etc. All these kind of actions may led us to enter in the field of Synthetic biology with the aim of designing for instance new microorganisms (bacteria, yeast, etc.) with optimal capabilities for producing/metabolizing different substances. This in turn may be of importance for Biotechnology and Environmental biotechnology. However, important bioethics and regulatory affairs consideration emerge of this field that has to be taken into consideration carefully.

Experimental Chromosomics may use a direct approach for determination of all this information. It starts by the ab initio experimental approaching to the problem. This begins by the direct study of the building blocks of chromosomes (nucleotides, nucleic acids, gene, histone proteins). Next, it can study the determination of mechanism of the assembly of these blocks to determine the structure and position of chromosomes. This implies the study of the interaction of genes with histones to form structural units called nucleosomes and in turn the packing chromatin or chromosomes. In so doing, it may use biophysics techniques like Electronic Microscopy (EM), CryoEM, X-Ray, Nuclear Magnetic Resonance (NMR), etc.

It is also interesting how the use of Big Data analysis tools could lead us to new discoveries in in this area. However, the amount of information to be taking into consideration here is vast and comes from many diverse sources. Consequently, on our opinion, Artificial Intelligence and Machine Learning (AI/ML) algorithms are called to play an important role here. In particular the algorithms of the family Artificial Intelligence and Network Information Fusion with Perturbation Theory Machine Learning (NAIF.PTML = N + AI + IF + PT + ML) developed by us may play an interesting role. They has an N-stage devoted to represent as complex networks and molecular graph and encode numerically all the information related to the structure of DNA, RNA, Proteins, and Chromosomes. Next we can use the IF-stage to curate and fusion all information about the previous biological assays reported for the properties and behaviors of these networks. Next, in the AI/ML phase we can train/validate predictive models useful to predict the desired outcomes of the new systems.