This study focuses on utilizing deep learning techniques for segmenting medical images, such as MRI and CT scans. The paper explores the limitations of traditional segmentation methods and highlights the potential of deep learning in overcoming these challenges. It provides an overview of Convolutional Neural Networks (CNNs) and their adaptation for medical image segmentation. Various architectures like U-Net, FCNs, and DeepLab are discussed, along with the importance of data augmentation and handling class imbalance. The paper also covers training processes, post-processing techniques, and evaluation metrics. It concludes by discussing current trends, challenges, and future directions in the field.

Traditional herbal medicine has been deeply rooted in the El Haouz region of the High Atlas Mountains in Morocco, where phytotherapy, using medicinal plants for disease prevention and treatment, has been widely practiced for many years. The local community has heavily relied on herbal remedies to alleviate various health conditions, including digestive issues, respiratory infections, joint discomfort, and skin disorders. The primary objective of our study is to meticulously document the specific medicinal plants employed by the region's inhabitants to address prevalent ailments.
To achieve this, an extensive search was conducted across various reputable databases, such as Google Scholar, Semantic Scholar, ResearchGate, Academia.edu, and PubMed. Relevant keywords, such as "High Atlas," "Phytotherapy," and "medicinal and aromatic plants," were employed to ensure comprehensive coverage. Our bibliographic investigation reveals abundant aromatic and medicinal plants in the El Haouz region.
The study findings illustrate that the local population in three areas of El Haouz (Imegdal, Amezmiz, and Asni) utilize 36 well-known remedies, categorized into 14 groups, to address a wide range of ailments. Notably, most of these plants exhibit multiple applications and are not limited to treating a single disease. Among the plant parts employed, leaves are the most commonly used (55%), followed by underground parts (40%, such as roots, tubers, bulbs, and rhizomes), flowers (18%), seeds (16%), and fruits (15%).
Additionally, we present detailed information on five specific aromatic and medicinal plants renowned for their effectiveness in treating various infections. These plants include Rubia tinctorum L., Ziziphus lotus (L.) Lam., Ridolfia segetum (L.) Moris (used for anemia), Thymus saturejoides Coss., and Rosmarinus officinalis L. (used for diabetes). The study delves into the specific utilisation methods for each of these plants.
The results of our inquiry provide substantial evidence of the local knowledge about plant species in the Al Haouz region, which have been traditionally employed for diverse ailments. Further exploration is warranted to investigate these documented plants' phytochemical, pharmacological, and toxicological aspects, with the potential to discover novel medications derived from them.

Background: Malaria is a disease caused by the Plasmodium parasite, which is transmitted through the bites of infected mosquitos. Only the Anopheles genus of mosquito can transmit malaria. The symptoms of this disease can include fever, vomiting, and headache. As millions of people are exposed to the threat of the Plasmodium parasite, it leads to millions of deaths annually. Therefore, there is a need to develop models for predicting compounds that can counteract this disease.
Objective: The primary objective of this research was to employ different techniques of machine learning on molecular descriptors obtained from Atomic Weight Vectors (AWV) and MD-LOVIs tool to predict the activity of potential antimalarial compounds.
Methods: Several machine learning techniques such as Ranger-ES-AWV (accuracy = 0.7714), Random Forest-ES-AWV (accuracy = 0.7718), SVMPoly-IB-AWV (accuracy = 0.787), C5.0-IB-AWV (accuracy = 0.7746), Ranger-IB-AWV (accuracy = 0.7854), GBM-IB-AWV (accuracy = 0.7882), and Treebag-IB-AWV (accuracy = 0.7798) were applied to predict the activity of antimalarial compounds.
Results: The results showed that the models obtained using machine learning techniques can be a powerful tool for predicting the activity of antimalarial compounds.
Conclusion: This study demonstrates the potential of machine learning techniques for predicting the activity of antimalarial compounds. These models can be used to identify new compounds with antimalarial properties and contribute to reducing the number of malaria-related deaths worldwide.

Computational methods have revolutionized the field of drug discovery, playing a vital role in the identification and development of potential therapeutic compounds. Among these methods, virtual screening has emerged as one of the most widely used approaches in the early stages of the drug discovery process. This approach utilizes computational techniques to sift through vast libraries of chemical compounds and predict their potential activity against a target of interest. One of the key tools employed in virtual screening is molecular docking, which allows researchers to simulate the binding interactions between small molecules (ligands) and target proteins (receptors). Scoring functions form a critical component of molecular docking, as they are responsible for evaluating and predicting the binding affinity between ligands and receptors. These scoring functions encompass a range of mathematical algorithms and empirical energy-based models that estimate the strength of the molecular interactions within a complex. By calculating scores based on predicted binding energies, scoring functions enable the ranking of compounds according to their potential to bind and interact with the target protein. This ranking process is crucial in identifying hit compounds that have the potential to be further developed into effective drugs. However, the accuracy of scoring functions is influenced by the inherent complexity of molecular recognition processes. Due to the computational limitations in accurately modeling all aspects of these processes, scoring functions rely on approximations to make predictions within a reasonable timeframe. These approximations introduce unavoidable inaccuracies, leading to a compromise between computational efficiency and predictive accuracy. Consequently, the performance of scoring functions is adversely affected, hindering their ability to effectively prioritize compounds and predict their actual binding affinities. To shed light on the foundations and limitations of current scoring functions, extensive studies and comparative analyses have been conducted. These investigations aim to evaluate the performance of different scoring functions in various scenarios, identify their strengths and weaknesses, and highlight strategies for overcoming the associated limitations. By comparing the results of these studies, researchers can gain insights into the relative performance of different scoring functions and make informed decisions about their implementation.

Furthermore, addressing the inaccuracies and limitations of scoring functions requires the development of innovative strategies and approaches. Researchers have proposed various strategies to improve the performance of scoring functions, such as incorporating more detailed and accurate representation of molecular interactions, refining the energy models used in scoring functions, and integrating machine learning and artificial intelligence techniques into the scoring process. These advancements have the potential to enhance the accuracy and reliability of scoring functions, empowering researchers to make better-informed decisions when selecting potential drug candidates.

When it comes to selecting a scoring scheme for structure-based virtual screening, several factors need to be considered. These include the nature of the target.

Due to their size, flexibility, biocompatibility, large surface area, and variable functionality nanoparticles have enormous industrial, agricultural, pharmaceutical and biotechnological applications. This has led to their widespread use in various fields. The advancement of knowledge in this field of research has altered our way of life from medicine to agriculture. One of the rungs of this revolution, which has somewhat reduced the harmful consequences, is nanotechnology. A helpful ingredient for plants, silicon (Si), is well-known for its preventive properties under adverse environmental conditions. Several studies have shown how biogenic silica helps plants recover from biotic and abiotic stressors. The majority of research have demonstrated the benefits of silicon-based nanoparticles (Si-NPs) for plant growth and development, particularly under stressful environments. In order to minimize the release of brine, heavy metals, and radioactive chemicals into water, remove metals, non-metals, and radioactive components, and purify water, silica has also been used in environmental remediation. Potentially toxic elements (PTEs) have become a huge threat to food security through their negative impact on agroecosystem. Si-NPs have the potentials to remove PTEs from agroecosystem and promote food security via the promotion of plant growth and development. In this review, we have outlined the various sources and ecotoxicological consequences of PTEs in agroecosystems. The potentials of Si-NPs in mitigating PTEs were extensively discussed and other applications of Si-NPs in agriculture to foster food security were also highlighted.

Cystitis is an inflammatory condition that primarily affects the bladder. It is often caused by a bacterial infection, with bacterial cystitis being the most common type among various urinary tract infections. This research aimed to extensively document ethnobotanical knowledge regarding the use of medicinal plants for treating cystitis due to their proven therapeutic properties. The study was conducted in the Rif region from March 1st, 2020, to April 15th, 2020. Semi-structured direct interviews were conducted with 657 participants to gather indigenous therapeutic wisdom. These surveys included information about the interviewees' demographics and ethnomedicinal practices. UR and MUV techniques were employed in data analysis. A total of 60 plant species, distributed among 51 genera and 31 families, were commonly used by our interviewees for cystitis therapy. Apiaceae had the highest representation of seven species, and Capparis spinosa L. was the most frequently recommended medicinal plant by the local population. Leaves were the most commonly utilized plant part (41.5%), and most herbal remedies were prepared through decoction (55%). This study constitutes the initial contribution to the ethnobotanical exploration of this region. It is recommended that the natural plant species identified in this research be further investigated to uncover their therapeutic effects and mechanisms of action. Primary attention should be given to conserving medicinal species, thoroughly documenting widespread medicinal knowledge, and biologically validating the listed species.

The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide. In this study, we use a freely available dataset downloaded from the ChEMBL site composed of 1975 compounds of great structural diversity and with reported IC₅₀ enzyme inhibition against AChE. Using the MATLAB numerical computation system and the molecular descriptors implemented in the Dragon software, a QSAR-SVM classification model was developed; the obtained parameters are adequate for its adjustment (Q_TS = 88.63%), and the validation exercises verify that it is stable (Q_CV = 81.13%), with good predictive power (Q_PS = 81.15%) and is not the product of a casual correlation. In addition, its application domain was determined to guarantee the reliability of the predictions. Finally, the model was used to predict ACh inhibition by a group of quinazolinones and benzothiadiazine 1,1-dioxides obtained by chemical synthesis, resulting in 14 drug candidates with in silico activity comparable to acetylcholine.

Aminonitriles are substances frequently used to obtain other compounds. This circumstance raised the hypothesis that there was intrinsic to such structures relevant therapeutic potential to be scientifically explored. From this perspective, certain studies have identified that the performance of this group of molecules go beyond their participation only as an intermediary of new pharmacological formulations and demonstrate their own antibiotic evidence. In the face of studies carried out to support this research, it was observed that the potential of aminonitrils covers the fight against fungi, parasites, bacteria and even tumors. In this context, the present work aimed to carry out a screening of the antibacterial activity of substituted aminonitriles. Therefore, the disc-diffusion method was used to assess the antibacterial activity of seven new molecules of substituted aminonitrils (HAN-1 to HAN-7), at concentrations of 16, 32, 64, 128, 256, 512 and 1,024 µg /mL, against gram-positive bacteria Staphylococcus epidermidis ATCC 12228; Staphylococcus aureus ATCC 25923 and Enterococcus faecalis ATCC 29212, and gram-negative bacteria Pseudomonas aeruginosa ATCC 27853; Proteus mirabilis ATCC 25933 and Escherichia coli ATCC 25922. After incubation, it was observed that none of the tested molecules were able to form bacterial growth inhibition halo. Thus, it is necessary to continue investigating the antimicrobial potential of this group of compounds using more sensitive methodologies to confirm or rule out their bioactivity in these microorganisms.

There is today a fascination for blockchain technology and how it may improve healthcare systems but its concrete applications are still limited as many questions remain to be solved. Indeed, by definition, all users on a block can see the data shared which will necessarily hurt user’s – patients – privacy. As pointed out by OECD, ‘storing personal health data ‘on chain’ and thus, by definition, visible to other network participants, is a data privacy infringement. Rights under the EU General Data Protection Regulation, particularly the right to erasure, are incompatible with the immutability of blocks in a chain.’[1] Also, public authorities are in a ‘wait and see’ position: few regulations cover specifically the use of blockchain technology. Many states are still trying to understand blockchain technology and its benefits. As a consequence, actors involved in blockchain technology are facing legal and regulatory uncertainty. It is a necessity to adopt specific laws related to the implementation of blockchain technology in a broad manner and in healthcare especially. However, the European Union General Data Protection Regulation (GDPR) 2016 applies here as it deals with data protection and imposes a series of stringent obligations on Internet service providers (ISPs). It is interesting to note that OECD made recommendations regarding the use of blockchain technology in healthcare in order to meet key international standards. ‘Potential blockchain applications should be assessed within the framework provided by the Recommendation of the OECD Council on Health Data Governance and focus on four key aspects: fitness of the technology for the use to which it will be applied; alignment with laws and regulations; incremental adoption to allow time for evaluation; and a training and communications plan.’[2]

[1] See OECD, Blockchain Policy Series, Opportunities and Challenges of Blockchain Technologies in Health Care, p. 2, December 2020. Available online: https://www.oecd.org/finance/Opportunities-and-Challenges-of-Blockchain-Technologies-in-Health-Care.pdf.

[2] Id.

Virtual screening has been the basis for the design of new molecules with a wide variety of pharmacological activities. The great advantage of QSAR (Quantitative Structure-Activity Relationship) methods is that they are a low cost solution which allows the identification of molecules that are likely to present a specific activity.

Currently, the development of antibiotic resistance by microorganisms is one of the most important problems that have appeared in recent years in the treatment of infectious diseases. This increased resistance is associated with increased morbidity and mortality from infections, as well as an increase in healthcare costs.

QSAR methods appear as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indices was used to create a QSAR model to predict antibacterial activity.

The model consists on a hierarchical decision tree in which, in a first step, a combination of discriminant functions capable of predicting antibacterial activity (FD1 and FD2) is applied to a database with 6375 commercial compounds, where 266 compounds were selected as candidates, from which 40.6% have this activity according to bibliography. The second step consists in the application of a discrete index, which is used to divide compounds into groups according to their values for said index in order to construct probability space.

The topological discrete indices R, PR1, PR2 and V4 have proven to have the ability to group active compounds effectively, considerably increasing the bibliographic success activity rate (up to 81.8%, 90.3%, 83.3% and 72.3%, respectively) which suggests a close relationship between them and the antibacterial activity of commercial compounds.

This methodology has proven to be a viable alternative to the traditional methods and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments.