An ensemble-based strategy for robust predictive volcanic rock typing efficiency on a global-scale: A novel workflow driven by big data analytics.

Laboratory measurements, paleontological data, and well-logs are often used to conduct mineralogical and chemical analyses to classify rock samples. Employing digital intelligence techniques may enhance the accuracy of classification predictions while simultaneously speeding up the whole classification process. We aim to develop a comprehensive approach for categorizing igneous rock types based on their global geochemical characteristics. Our strategy integrates advanced clustering, classification, data mining, and statistical methods employing worldwide geochemical data set of ~25,000 points from 15 igneous rock types. In this pioneering study, we employed hierarchical clustering, linear projection analysis, and multidimensional scaling to determine the frequency distribution and oxide content of igneous rock types globally. The study included eight classifiers: Logistic Regression (LR), Gradient Boosting (GB), Random Forest (RF), K-nearest Neighbors (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and two ensemble-based classifier models, EN-1 and EN-2. EN-1 consisted of LR, GB, and RF aggregates, whereas EN-2 comprised the predictions of all ML models used in our study. The accuracy of EN-2 was 99.2 %, EN-1 achieved 98 %, while ANN yielded 98.2 %. EN-2 provided the best performance with highest initial curve for longest time on the receiver operating characteristic (ROC) curve. Based on the ranking features, SiO2 was deemed most important followed by K2O and Na2O. Our findings indicate that the use of ensemble models enhances the accuracy and reliability of predictions by effectively capturing diverse patterns and correlations within the data. Consequently, this leads to more precise results in rock typing globally.

Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods.

Geoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile-quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) and qualitative log curve assessment through three wells (X4, X5, X6) in complex geological formation to distinguish coal from tight sand and shale. Also, we identify the reservoir rock typing (RRT), gas-bearing and non-gas bearing potential zones. Results showed gamma-ray and resistivity logs are not reliable tools for coal identification. Further, coal layers highlighted high acoustic (AC) and neutron porosity (CNL), low density (DEN), low photoelectric, and low porosity values as compared to tight sand and shale. While, tight sand highlighted 5-10% porosity values. The SOM and clustering assessment provided the evidence of good-quality RRT for tight sand facies, whereas other clusters related to shale and coal showed poor-quality RRT. A t-SNE algorithm accurately distinguished coal and was used to make CNL and DEN plot that showed the presence of low-rank bituminous coal rank in study area. The presented strategy through conventional logs shall provide help to comprehend coal-tight sand lithofacies units for future mining.

Computer-aided anti-cancer drug discovery of EGFR protein based on virtual screening of drug bank, ADMET, docking, DFT and molecular dynamic simulation studies.

Numerous malignancies, including breast cancer, non-small cell lung cancer, and chronic myeloid leukemia, are brought on by aberrant tyrosine kinase signaling. Since the current chemotherapeutic medicines are toxic, there is a great need and demand from cancer patients to find novel chemicals that are toxic-free or have low toxicity and that can kill tumor cells and stop their growth. This work describes the in-silico examination of substances from the drug bank as EGFR inhibitors. Firstly, drug-bank was screened using the pharmacophore technique to select the ligands and Erlotinib (DB00530) was used as matrix compound. The selected ligands were screened using ADMET and the hit compounds were subjected to docking. The lead compound from the docking was subjected to DFT and MD simulation study. Using the pharmacophore technique, 23 compounds were found through virtual drug bank screening. One hit molecule from the ADMET prediction was the subject of docking study. According to the findings, DB03365 molecule fits to the EGFR active site by several hydrogen bonding interactions with amino acids. Furthermore, DFT analysis revealed high reactivity for DB03365 compound in the binding pocket of the target protein, based on ELUMO, EHOMO and band energy gap. Furthermore, MD simulations for 100 ns revealed that the ligand interactions with the residues of EGFR protein were part of the essential residues for structural stability and functionality. However, DB03365 was a promising lead molecule that outperformed the reference compound in terms of performance and in-vitro and in-vivo experiments needs to validate the study.Communicated by Ramaswamy H. Sarma.

Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F.

Interleukin 17 F is a member of IL-17 cytokine family with a 50% structural homology to IL-17A and plays a significant role either alone or in combination with IL-17A towards inflammation in Rheumatoid arthritis (RA). A growing number of drugs targeting IL-17 pathway are being tested against population specific disease markers. The major objective of this research was to investigate the anti-inflammatory effect of Anakinra (an IL-1 R1 inhibitor) and Ustekinumab (an IL-12 and IL-23 inhibitor) by targeting IL17F. The three dimensional structures of IL17F was taken from PDB while structures of drugs were taken from PubChem database. Docking was performed using MOE and Schrodinger ligand docking software and binding energies, including s-score using London-dG fitness function and glide score using glide internal energy function, between drug and targets were compared. Furthermore, Protein-Drug complex were subjected to 150 ns Molecular Dynamics (MD) Simulations using Schrodinger's Desmond Module. Docking and MD simulation results suggest anakinra as a more potent IL17F inhibitor and forming a more structurally stable complex.Communicated by Ramaswamy H. Sarma.

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling.

The Meyal oil field (MOF) is among the most important contributors to Pakistan's oil and gas industry. Northern Pakistan's Potwar Basin is located in the foreland and thrust bands of the Himalayan mountains. The current research aims to delineate the hydrocarbon potential, reservoir zone evaluation, and lithofacies identification through the utilization of seven conventional well logs (M-01, M-08, M-10, M-12, M-13P, and M-17). We employed the advanced unsupervised machine-learning method of self-organizing maps for lithofacies identification and the novel Quanti Elan model technique for comprehensive multimineral evaluation. The shale volume, porosity, permeability, and water saturation (petrophysical parameters) of six wells were evaluated to identify the reservoir potential and prospective reservoir zones. Well-logging data and self-organizing maps were used in this study to provide a less costly method for the objective and systematic identification of lithofacies. According to the SOM and Pickett plot analyses, the zone of interest is mostly made up of pure limestone oil zone, whereas the sandy and dolomitic behavior with a mixture of shale content shows non-reservoir oil-water and water zones. The reservoir has good porosity values that range from 0 to 18%, but there is a high water saturation of up to 45% in reservoir production zones. The presence of shale in the entire reservoir interval has a negative effect on the permeability value, but the petrophysical properties of the Meyal oil reservoir are good enough to permit hydrocarbon production. According to the petrophysical estimates, the Meyal oil field's Sakesar and Chorgali Formations are promising reservoirs, and new prospects for drilling wells in the southern and central portions of the eastern portion of the research area are recommended.

Effectiveness of Mavrilimumab in Viral Infections Including SARS-CoV-2Infection - A Brief Review / 감염과화학요법

Hyperinflammation and cytokine storm has been noted as a poor prognostic factor in patients with severe pneumonia related to coronavirus disease 2019 (COVID-19). In COVID-19, pathogenic myeloid cell overactivation is found to be a vital mediator of damage to tissues, hypercoagulability, and the cytokine storm. These cytokines unselectively infiltrate various tissues, such as the lungs and heart, and nervous system. This cytokine storm can hence cause multi-organ dysfunction and life-threatening complications. Mavrilimumab is a monoclonal antibody (mAb) that may be helpful in some cases with COVID-19. During an inflammation, Granulocyte-macrophage colony-stimulating factor (GM-CSF) release is crucial to driving both innate and adaptive immune responses. The GM-CSF immune response is triggered when an antigen attaches to the host cell and induces the signaling pathway. Mavrilimumab antagonizes the action of GM-CSF and decreases the hyperinflammation associated with pneumonia in COVID-19, therefore strengthening the rationale that mavrilimumab when added to the standard protocol of treatment could improve the clinical outcomes in COVID-19 patients, specifically those patients with pneumonia. With this review paper, we aim to demonstrate the inhibitory effect of mavrilimumab on cytokine storms in patients with COVID-19 by reviewing published clinical trials and emphasize the importance of extensive future trials.

Effectiveness of Mavrilimumab in Viral Infections Including SARS-CoV-2Infection - A Brief Review / 감염과화학요법

Hyperinflammation and cytokine storm has been noted as a poor prognostic factor in patients with severe pneumonia related to coronavirus disease 2019 (COVID-19). In COVID-19, pathogenic myeloid cell overactivation is found to be a vital mediator of damage to tissues, hypercoagulability, and the cytokine storm. These cytokines unselectively infiltrate various tissues, such as the lungs and heart, and nervous system. This cytokine storm can hence cause multi-organ dysfunction and life-threatening complications. Mavrilimumab is a monoclonal antibody (mAb) that may be helpful in some cases with COVID-19. During an inflammation, Granulocyte-macrophage colony-stimulating factor (GM-CSF) release is crucial to driving both innate and adaptive immune responses. The GM-CSF immune response is triggered when an antigen attaches to the host cell and induces the signaling pathway. Mavrilimumab antagonizes the action of GM-CSF and decreases the hyperinflammation associated with pneumonia in COVID-19, therefore strengthening the rationale that mavrilimumab when added to the standard protocol of treatment could improve the clinical outcomes in COVID-19 patients, specifically those patients with pneumonia. With this review paper, we aim to demonstrate the inhibitory effect of mavrilimumab on cytokine storms in patients with COVID-19 by reviewing published clinical trials and emphasize the importance of extensive future trials.

Clinical Profile and HLA Typing of Autoimmune Hepatitis From Pakistan.

BACKGROUND: Human leukocyte antigen (HLA) typing in autoimmune hepatitis (AIH) has been investigated in different populations and ethnic groups, but no such data is available from Pakistan. OBJECTIVES: The aim of this study was to evaluate the clinical profile of autoimmune hepatitis (AIH), and determine the associated antigens and alleles by performing HLA typing. PATIENTS AND METHODS: A total of 58 patients, diagnosed and treated as AIH in the last 10 years were reviewed. Diagnosis was based on International AIH Group criteria. Forty one patients underwent liver biopsy. HLA typing was performed in 44 patients and 912 controls by serological method for HLA A and B, and by PCR technique using sequence specific primers for DR alleles. RESULTS: Of 58 cases, 35 were females (60.3%). The median age was 14.5 (range 4-70 years), and AIH score was 14 (10-22). Thirty-six (62.0%) patients had type 1 AIH, 10 (17.2%) type 2, and the remaining 12 were seronegative with biopsy proven AIH. Forty-nine patients (84.4%) had cirrhosis. Twenty-four (41.4%) patients had ascites at the time of presentation. Among 41 patients who underwent liver biopsy, thirty-two had advance stages III and IV disease, and twenty had severe grade of inflammation. Fifteen patients had other associated autoimmune diseases and one developed hepatocellular carcinoma. HLA A2 (P = 0.036), HLA A9 (23) (P = 0.018), HLA A10 (25) (P = 0.000), HLA A19 (33) (P = 0.000), HLA B15 (63) (P = 0.007), HLA B40 (61) ( P = 0.002), HLA DR6 (P = 0.001) with its subtypes HLA-DRB1*13 (P = 0.032) and HLA-DRB1*14 (p = 0.017) were more prevalent in AIH with statistical significance than controls. CONCLUSIONS: AIH in our region presents with advanced disease affecting predominantly children and adolescents. There is a genetic association of HLA DR6 along with other alleles and antigens in our patients with AIH.