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.