M. DEHIMAT Abdelouahab

Developed a robust QSAR model to predict IC50 values for MCF-7 breast cancer cell
line using curated datasets (ChEMBL, PubChem). Leveraged RDKit for molecular
descriptors, applied RFE for feature selection, and trained models with Random Forest
and Gradient Boosting. Achieved high accuracy (low RMSE, strong Pearson correlation)
and identified key structural features via SHAP analysis. Demonstrated the model's utility
for virtual screening and precision oncology.

In this study, we mainly focused on the phytochemical study of the Algerian endemic species, Abies numidica (Asparagaceae), in order to test the antibacterial activity of selected components, 2,6-Octadiene-2,6-dimethyl and Linalyl acetate, from GC-MS profile present in the Essential Oil of this plant. To confirm in silico the antibacterial activity of the Abies numidica essential oil, against S. aureus and M. luteus, the selected components were testing by Molecular docking against 5Z9N, 3AQC proteins of the two bacteria, respectively. The results of the in silico antibacterial activity were showed that the 2,6-Octadiene-2,6-dimethyl showed an energy (-5.3 Kcal/mol) and the three amino acids of active site (Ile 78, Val 71, Ala 47) and for Linalyl acetate with energy (-5.2 Kcal/mol) and the amino acid (Ile 78), respectively for protein 5Z9N. In addition, the 2,6-Octadiene-2,6 dimethyl had an energy (-4.7 Kcal/mol) and amino acids (Val76, Val142, Ala79, Leu116, His83) and Linalyl acetate had an energy (-4.5 Kcal/mol) and amino acids (Leu116, Ala79, Val76, His83), respectively for protein 3AQC. Furthermore, the ADEM(T) profile of the components tested presented in the study. Based on these results, we refer the antibacterial activity of the EO of Abies numidica, against the M. luteus and S. aureus, is due to the presence of the two compounds (2,6-Octadiene-2,6 dimethyl and Linalyl acetate).

Description du sujet : Infections brought on by enterobacteria that produce extended-spectrum beta-lactamases (ESBL) are becoming more common, which is a major global health concern. According to numerous reports, these germs can transmit from animals to humans through the environment.
Objectifs: The main objective of this study was to examine the prevalence of ESBL production in poultry meat and their resistance profile in the region of Msila, Algeria. Samples were taken from three different places in the wilaya of Msila (Msila center, Bousaada, Sidi-Aissa).
Méthode: After screening on selective ESBL agar, confirmation of ESBL production of the isolated strains was determined by the phenotypic method. Then, DNA extraction by boiling was performed.
Résultats et discussion: As a result, all the three regions harbored ESBL-producing E. coli. Hance, 13 (20.97%) of samples contained ESBL producing E. coli. All the isolates were resistant to third generation cephalosporin (cefotaxime) tested and sensitive to Aminosides and some Beta lactamines (imipenem, cefoxitin) and amoxicillin clavulanic acid and Fosfomycin.
Conclusion : In conclusion, the results show a significant prevalence of ESBL-producing Enterobacteriaceae in the region of Msila. Additional research is required to characterize genes of this bacteria responsible for this resistance and their mechanisms.

In this study we examined data on COVID-19 patients recorded in 2020 at the Hakim Sadanne Hospital in the Biskra, Algeria and applied various tests: statistical, epidemiological, etc. In addition, we conducted interviews with herbalists and an ethnobotanical on-line survey of medicinal plants used during the COVID-19 pandemic. A total of 2108 cases of this disease, including 931 deaths, were analysed to link various factors, such as air temperature, age, and sex, with mortality rate. Our results demonstrate that the appearance of symptoms of infection was often associated with pre-existing chronic diseases, particularly diabetes, cardiovascular heart disease, and high blood pressure. As for medicinal plants, Melissa officinalis and Zingiber officinale were the most used during the pandemic, whereas Eucalyptus globulus and Syzygium aromaticum had 100% fidelity levels. In conclusion, primarily the above-mentioned chronic diseases were associated with reduced immunity to COVID-19. Furthermore, traditional medicine has been an accommodating alternative to conventional medical treatment of COVID-19 within Biskra region.

Developed skin healing products from natural extracts
The aim of this study is to analyze the biological activities, chemical compounds, and
toxicology of the plant "Carpobrotus edulis," also known as "griffe de sorcière." The study
encompasses a diverse range of activities, including antibacterial, antifungal, antioxidant, antiinflammatory, anti-proliferative, neuroprotective, and antidiabetic activities. The research
objectives also involve identifying the active chemical and phytochemical compounds present in
the plant, evaluating their effects on the mentioned activities, and studying plant-associated
toxicology.
Through the investigation of these biological activities, this study has demonstrated
multiple traditional medicinal uses of "Carpobrotus edulis," particularly in the treatment of skinrelated diseases. The existence of these traditional uses enhances the value of the study and
highlights the potential applications of this plant in various fields. Additionally, The study aims to
deepen our understanding of the potential health benefits of "Carpobrotus edulis" and its possible
uses in cosmetics and body care, while also examining its toxicity. Furthermore, the study sheds
light on the nutritional aspect of the plant and the benefits it can offer in supporting health and
well-being.

The study will strengthen our project by showcasing our innovative approach to
integrating multi-omics data for cancer research. This conference offers a
platform to present our findings, receive feedback, and engage with experts in
digital pathology and cancer research. The opportunity to discuss our research
with a diverse audience will enhance our project’s visibility, foster collaborations,
and refine our approach based on new insights and feedback.

The presented work case interests pairwise sequence alignment algorithms for two analysis cases. The first analysis is SARS-COV2 Omicron in Algerian compared to the neighbor countries using Global and local alignment algorithms, and the second compared coronaviruses from animal sources like Pangolin, Bat, Civet, and Camal, to predict its origin. Of course, with real applications and samples from GISAID, NCBI, and GenBank, and deal with them using the Biopython command line in the Colab platform. The presented work confirms that the local alignment algorithm for the spike region provides better family classification and grouping by matching score sorting. The identity percentage of SARS-COV-2 compared to animal coronaviruses published previously using Simplot software from other authors have wrong values and reviewed in the presented work with the right values using the Biopython library. A high matching score found in bats and pangolins confirms that the origin of SARS-COV-2 is from animal wildlife

This event will offer insights into refining computational methods and aligning
them with current CRC research trends. It will also explore how to integrate
approaches with existing resources and identify potential collaborations. These
elements will strengthen the credibility, relevance, and feasibility of my study
titled **"A Novel Multi-Omics Analysis Reveals Dose-Dependent and TemporalChanges in Regulatory Pathways Induced by Natural Compounds in Breast
Cancer Cells.

This conference is pivotal for expanding our knowledge of bioinformatics applications
in multiomics sciences and personalized therapy. The exchange of ideas with
leading researchers and industry professionals will provide valuable insights into
advanced bioinformatics techniques and their application in cancer research. This
participation will enhance our project by integrating cutting-edge bioinformatics
approaches and AI-driven insights into our computational models, strengthening
our ability to predict and personalize cancer therapies.

At the 2nd Organoids Conference, I acquired valuable insights into advancing
organoid model systems, including engineering organ-specific cell types and
modeling tissue morphogenesis in 3D. The conference’s focus on innovative
analytic methods and visualization techniques is highly relevant to my
computational cancer research, especially in the clinical validation of GenomeScale Metabolic Models (GSMMs). The knowledge and connections gained will
significantly enhance my project by offering new perspectives and fostering
potential collaborations for better integration of organoid systems with
computational models.

Cancer research has witnessed a dynamic shift in recent years with the incorporation of computational models within the realm of molecular biology. This abstract provides an overview of the pivotal role that computational models play in advancing our understanding of cancer and its therapeutic strategies.
The complex nature of cancer, involving intricate genetic and molecular alterations, necessitates a multifaceted approach to unravel its mechanisms. Computational models, ranging from mathematical simulations of intracellular processes to machine learning-driven analyses of large-scale omics data, offer a means to dissect the underlying intricacies of cancer biology.
Molecular modeling and structural biology simulations are instrumental in elucidating the three-dimensional structures of cancer-related proteins and predicting their interactions with potential therapeutic agents. These techniques aid in the rational design of targeted therapies, contributing to the development of precision medicine approaches.
Systems biology models have enabled a systems-level view of cancer by integrating diverse biological data types, including genomics, transcriptomics, and proteomics. These models provide insights into the dynamic behavior of cancer networks, paving the way for the identification of key regulatory elements and potential drug targets.
Furthermore, machine learning and artificial intelligence approaches have been leveraged to mine vast datasets, enabling the discovery of novel biomarkers, patient stratification, and drug repurposing opportunities. These computational tools enhance the efficiency of biomarker discovery and the personalization of cancer treatment strategies.
This abstract emphasizes the pivotal role of computational models in shaping the landscape of cancer research within the domain of molecular biology. The integration of these models has not only deepened our understanding of cancer but has also expedited the translation of research findings into clinical applications. As the synergy between computational sciences and molecular biology in cancer research continues to evolve, it holds the promise of accelerating the development of more effective cancer therapies and improving patient outcomes.
Key words: Computational Models, Cancer Research, Molecular Biology, Therapeutic Strategies.

The aim of this study is to analyze the biological activities, chemical compounds, and
toxicology of the plant "Carpobrotus edulis," also known as "griffe de sorcière." The study
encompasses a diverse range of activities, including antibacterial, antifungal, antioxidant, antiinflammatory, anti-proliferative, neuroprotective, and antidiabetic activities. The research
objectives also involve identifying the active chemical and phytochemical compounds present in
the plant, evaluating their effects on the mentioned activities, and studying plant-associated
toxicology.
Through the investigation of these biological activities, this study has demonstrated
multiple traditional medicinal uses of "Carpobrotus edulis," particularly in the treatment of skinrelated diseases. The existence of these traditional uses enhances the value of the study and
highlights the potential applications of this plant in various fields. Additionally, The study aims to
deepen our understanding of the potential health benefits of "Carpobrotus edulis" and its possible
uses in cosmetics and body care, while also examining its toxicity. Furthermore, the study sheds
light on the nutritional aspect of the plant and the benefits it can offer in supporting health and
well-being.
Keywords: Carpobrotus edulis, Griffe de sorcière, biological activities, traditional medicine,
nutritional aspect

This study was carried out to evaluate the healing activity of autologous PRP by its topical application to the skin
experimentally in rabbits. The study was carried out on 06 clinically healthy male New Zealand rabbits divided
into two groups of three rabbits for each.. Four full-thickness incision wounds (Group Ι), and complete
excisional wounds of skin (Group Π) were created on the dorsal surface of each animal. Then treated with the
PRP which was obtained by a protocol of Plateltex® in the first wound, the second wound was treated with a
mixture of mastic oil and sesame oil; The third wound was treated with Madécassole; the last treated with
saline solution. The healing process was evaluated by digital planimetry and macroscopic examination of wound
skin at 3, 6, 12, 18 and 24 days of healing. The results obtained showed there was an acceleration of the
contraction of wounds treated with PRP and a reduction of the epithelialization period compared to other
applications, No Synergistic effects were found between mastic oil and sesame oil. PRP application could be a
good therapeutic option for skin wound

Préface

Ce cours retracera le développement de la biotechnologie de ses premiers débuts à l’heure actuelle, expliquer la science derrière la biotechnologie et aider l’étudiant à faire une connaissance de jugement au sujet du rôle de la biotechnologie dans l’avenir.
Prérequis

L’étudiant doit avoir aussi une base solide en Biologie générale, Biochimie, Génétique et Biologie moléculaire.
Objectif

Cette unité vise à introduire les concepts et l'évolution de la biotechnologie en développant une compréhension bien fondée de l'histoire et des définitions de la biotechnologie, notamment des principes généraux, l'intégration de différents domaines, des connaissances spécialisées et des développements sur des sujets spécifique.
Cette matière s’intéresse à donner un aperçu global sur les domaines d’application de la biotechnologie (environnement, agronomie, industrie et médicale) (D’après le Programme Pédagogique/Socle commun
/2èmeannée/Domaine/Sciences de la Nature et de la Vie/ Filière Biotechnologies).

Paclitaxel is one of the two Texans marketed. In 1971, it was isolated from the bark of the
Pacific yew, Taxus brevifolia, this compound of alkaloid origin demonstrated in vivo activity
against Alzheimer's disease which is the most common neurodegenerative pathology affecting
people elderly. This study aimed to provide the activity of Paclitaxel anonymously on
Cholinesterase for treated Alzheimer's using in silico molecular docking software Molegro
Virtual Docker (MVD 2012.5.5.0, Molegro APS). This neuropathology is characterized by a
complex etiology, mainly including the Acetylcholine deficiency due to the two enzymes
Cholinesterase : Acetylcholinesterase (4B82), and Butyrylcholinesterase (7AMZ). Docking
results show that Paclitaxel (CID 133640187 in PubChem) is the best inhibitor because it
forms the more stable complex with acetylcholinesterase than butyrylcholinesterase. Which
we brought to the following score energy values -110.103 kcal / mol and -50.8098 kcal / mol.
This complex formed is stabilized within AChE by hydrogen interactions between the
inhibitor and the following amino acids: Ser 125, Glu 202, Tyr 337, Tyr 124, Phe 295, as
regards Paclitaxel-BuChE we find the following amino acids: Glu 238, Arg 242, Thr 284, Trp
231 at hydrogen bonds, this additional experimental study in vitro and/or in vivo should make
it possible to verify the theoretical results obtained in silico.
Keywords: Paclitaxel (CID 133640187), Alzheimer Diseases, in silico, Acetylcholinesterase
(4B82), and Butyrylcholinesterase (7AMZ)

Background: Machine learning techniques have a significant role in predicting of COVID-19.
Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily
life, public health, and the economy. It is essential to identify the infected people to prevent the
further spread of the pandemic and to treat infected patients quickly.
Objective: In this study, we performed binary classification and detection of new variants
(COVID-19 vs. other types of coronavirus) by extracting features from genome sequences.
Methods: Different models from machine learning support vector machines, naive Bayes, Knearest neighbor, and random forest methods were used for classification. We used viral gene
sequences from the 2019 Novel Coronavirus Resource Database.
Results: Experimental results presented show that a decision tree method achieved a high
percentage of accuracy.
Conclusion et Perspectives : This study allowed to use of different models of Machine learning
with high accuracy to predict and detect new variants of Covid-19 and allow to apply of those
models for detecting new future pandemics caused by other types of viruses.
Keywords: Machine Larning; coronavirus; COVID-19

Background: The COVID-19 virus has reached a 20% death rate amongst chronically ill people; it has beenproved that they are predisposed to hospitalization. But the works published lack regarding the risk and impactof the SARS-CoV-2.Objective: This study aims to assess the statistical data of the outcomes of related chronic diseases to SRAS-COVID 19 infection.Methods: Through recently published papers, the association of COVID-19 and chronic diseases(cardiovascular disease (CVD), chronic liver disease (CLD), diabetes type 2, and hypertension fatal outcomes)in patients with COVID-19 was evaluated to understand and interpret the relationship between COVID-19 andthose diseases.Results: Among 187 patients with confirmed COVID-19, 43 patients (23%) died. With mortality levels of69.44% for those with underlying CVD and elevated troponin T levels [1]. The acute cardiac injury occurs in8-12% of patients and is the most commonly reported cardiac abnormality in COVID-19 [2]. Among patientswith COVID-19, >7% of patients experience myocardial injury from the infection [3]. Chronic Liver disease:the strongest effect was observed in patients with chronic non-alcoholic liver disease, followed by chronichepatitis C [4]. For Diabetes, negative outcomes during COVID-19 have been reported in two clinical cases ofdiabetic ketoacidosis and hyperglycaemic hyperosmolar syndrome. In one analysis, ketosis occurred in 6,4% ofpatients with COVID-19 and its prevalence rose to 11,6% in patients with COVID-19 with diabetes, resultingin a high mortality rate (33,3%) [5]. Hence, these different studies found that the association between the renin-angiotensin-aldosterone system’s (RAAS) master regulator ACE2 and susceptibility to COVID19 hasdisadvantages such as uncontrolled hypertension and cardiac function deterioration [6]. Pre-existingcardiovascular disease increases the risk of death in patients with COVID-19, whereas COVID-19 itself canalso induce myocardial injury, arrhythmia and acute coronary syndrome [7]. Binding with ACE2 receptors thatare also expressed in pancreatic tissue’s β-cells induces an acute loss of insulin secretory capacity which couldlead to rapid metabolic deterioration with the development of diabetic ketoacidosis [5].Conclusion: The presence of preexisting chronic diseases makes the patient not only more susceptible to dieafter infection but also at higher risk of catching the virus than other healthy individuals.Keywords: COVID-19, Chronic diseases, Chronic liver disease, Cardiovascular disease, Diabetes,Hypertension.References:[1]: T. Guo, Y. Fan, M. Chen, X. Wu, L. Zhang, T. He, H. Wang, J. Wan, X. Wang, Z. Lu, Cardiovascular Implications ofFatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19). JAMA cardiology 5(7) (2020), 811-818.[2]: M. Bansal, Cardiovascular disease and COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14(2020), 247-250.[3]: K. Clerkin, J. Fried, J. Raikhelkar, G. Sayer, J. Griffin, A. Masoumi, S. Jain, D. Burkhoff, D. Kumaraiah, L. Rabbani,L. Schwartz, N. Uriel, COVID-19 and Cardiovascular Disease. Circulation 141 (2020), 1648–1655.[4]: Q. Wang, P. Davis, R. Xu, COVID-19 risk, disparities and outcomes in patients with chronic liver disease in the UnitedStates. EClinicalMedicine 31, (2021), 100688.[5]: M. Apicella, M. Campopiano, M. Mantuano, L. Mazoni, A. Coppelli, S. Del Prato, COVID-19 in people withdiabetes: understanding the reasons for worse outcomes. Lancet Diabetes Endocrinol 8 (2020), 782–92.[6]: M. Tadic, C. Cuspidi, G. Mancia, , R. Dell’Oro, G. Grassi, COVID-19, hypertension and cardiovascular diseases:Should we change the therapy. Pharmacological Research 158 (2020), 1043-6618.[7]: M. Nishiga, D. Wang, Y. Han, D. Lewis, J. Wu, COVID-19 and cardiovascular disease: from basic mechanisms toclinical perspectives. Nature Reviews Cardiology 17(9) (2020), 543-558

(PDF) COVID-19 in people with chronic diseases: understanding the reasons for worse outcomes. Available from: https://www.researchgate.net/publication/351870302_COVID-19_in_people_with_chronic_diseases_understanding_the_reasons_for_worse_outcomes [accessed Mar 26 2023].

Diabetic disease is a chronic metabolic disease characterized by postprandial and fasting hyperglycemia. It is believed that
inhibition of α-amylase and α-glucosidase enzymes can be an important strategy in the administration of postprandial
hyperglycemia linked to type 2 diabetes. The aim of this study was to evaluate the α-amylase and α-glucosidase inhibitory
and antioxidant of leaf extracts from Varthemia sericea (Batt. et Trab.) Diels. Methanol extract and its fractions, cold water
and ethanol extracts of Varthemia sericea leaves were initially screened for phytochemical class, antioxidant properties and
α-amylase and α-glucosidase inhibitory activities. Furthermore, all extracts of Varthemia sericea were found to be potent
inhibitors of α-amylase with IC50 values range of 37.98±2.26 and 119.89±0.89 µg/ml. The methanol extracts of Varthemia
sericea had both the strongest inhibitory effect against the α-amylase and the α-glucosidase with IC50 values of 55.63±1.51
and 113.33±1.59 µg/ml, respectively. Besides, all leaf extracts from Varthemia sericea showed a good antioxidant activity.
Whereas, the observed effects were associated with the determination of phytochemical class such as alkaloids, coumarin,
and tannins, etc. Overall, our findings suggest that extract from Varthemia sericea leaves may be useful for diabetic care.
Keywords: Varthemia sericea, α-Amylase, α-Glucosidase, Antioxidant, Phytochemical screening

Abstract
Introduction
Varthemia sericea, is a plant which belongs to the Asteraceae family, and grows wild in the Central Algerian Sahara. According to the traditional medicine pharmacopeia it is used to treat several diseases. This study aimed to assess the phytochemical profile of leaf extracts of Varthemia sericea and investigate in vitro cytotoxicity and antioxidant activities.

Methods
Total phenolic and flavonoids were analyzed through using colorimetric methods. The antioxidant activity was evaluated by using different complementary tests: Free-radical scavenging, β-carotene bleaching inhibition, reducing power and copper chelation ability. The cytotoxicity was determined by the MTT colorimetric assay.

Results
The highest total phenolic content recorded with methanol extract with a value of 283.61 ± 0.58 µg GAE/mg. The ethyl acetate and aqueous residue extracts contained the highest flavonoids contents with values of 54.48 ± 1.29 µg QE/mg and 44.56 ± 0.14 µg QE/mg, respectively. Interesting antioxidant activity was exhibited by the different extracts especially the ethyl acetate and aqueous residue, ethanol and methanol extracts. Furthermore, the petroleum ether, ethyl acetate and aqueous residue extracts showed a significant cytotoxicity activity against MCF-7, HeLa and HL60 cells with IC50 values ranging from 16.15±0.79 to 211.96±3.38 µg/ml, after 72 h of treatment. There is a significant correlation to high level amounts of flavonoids in these extracts which can be attributed to their cytotoxic effects.

Conclusions
The extracts from leaves of V. sericea showed promising antioxidant and cytotoxic effects. It suggests that it could be further explored as a potential natural source for the development of a new cancer drug.

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Keywords
Varthemia sericeaPhenolic compoundsFlavonoidsCancerCytotoxicity activityAntioxidant activity

la Varthemia sericea (Batt &Trab), Diels. est une plante endémique de la pharmacopée traditionnelle du Sahara Algérien. Elle est utilisée comme un remède pour la guérison de certaines maladies comme le diabète, le cancer… Cette plante est peu documentée. L'étude du potentiel bioactif de cette plante constitue l'objectif principal de cette recherche s'inscrivant dans le cadre d’une thèse doctorat. La plante a été achetée à partir d’un spécimen, a été échantillonnée de façon aléatoire à Tamanrasset en Novembre 2015. Les feuilles ont été séchées, broyées en fine poudre et les produits naturels ont été extraits selon une approche d'extraction séquentielle en utilisant des solvants de polarité croissante. L'activité anticancéreuse des extraits de feuilles a été évaluée sur un panel de cellules cancéreuses. L'activité antioxydant a été évaluée par des méthodes spectrophotométries. De plus, la comparaison des facteurs de rétention de ces composés avec des standards et l'étude des comportements chimiques sous l'effet de divers réactifs ont appuyé les identifications spectrométriques. Ainsi, les composés majoritaires des extraits actifs ont été caractérisés par des techniques d’identification. Les analyses préliminaires de la plante ont aussi permis de noter l'abondance de composés phénoliques, tous aussi susceptibles de cacher des vertus thérapeutiques. Cette étude du potentiel bioactif de la Varthemia sericea (Batt &Trab), Diels. démontre ainsi une voie de valorisation de cette plante, souvent considérée comme une source des nouveaux médicaments à base des plantes médicinales du point de vue ethnobotanique.