Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models.

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs' solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

Prevalence of Dental Abnormalities, Soft Tissue Pathologies and Occlusion Disorders in Patients with High BMI: A Cross-sectional Study.

PURPOSE: The relationship between body mass index (BMI) and oral disorders remains unclear. This study examined the prevalence and types of dental abnormalities and oral mucosal lesions among female students with obesity attending a Taif University sports centre. MATERIALS AND METHODS: This non-interventional cross-sectional study enrolled female students with high BMI from a university sports facility using a convivence sampling method. The participants were divided into three BMI groups. Data were collected using an interview and by clinical oral examination. Prevalence and oral disorder types and possible mechanisms linking BMI and dental development were evaluated. RESULTS: Ultimately, 86 female students with obesity were analysed. The mean BMI was 42.8 kg/m2, indicating high obesity levels. A weak although statistically significant correlation was observed between age and BMI (r=0.27), indicating that older students had higher BMI. A statistically significant association was observed between BMI and dental abnormalities (p0.05). The dental abnormality prevalence increased with BMI, ranging from 37.5% to 40.7% in the ≤40 and >45 kg/m2 groups, respectively. Most participants (66.3%) had oral mucosal lesions, with the highest prevalence among participants in the 40-45 kg/m2 group (71.4%). CONCLUSION: A statistically significant relationship was observed between BMI and dental abnormalities; obesity may negatively affect oral health.

Mesenchymal stem cells and their potential therapeutic benefits and challenges in the treatment and pathogenesis of gastric cancer.

Mesenchymal stem/stromal cells (MSCs) are acknowledged for their remarkable ability to undergo differentiation into various cell types. In addition, they exhibit anti-tumor characteristics, prompting endeavors to modify MSCs for employment in cancer therapies. On the contrary, it is imperative to recognize that MSCs have been extensively linked to pathways that facilitate the advancement of tumors. Numerous research studies have sought to modify MSCs for clinical application; however, the outcomes have been ambiguous, potentially due to the heterogeneity of MSC populations. Furthermore, the conflicting roles of MSCs in suppressing and promoting tumor growth present a challenge to the appropriateness of their use in anti-cancer therapies. Currently, there exists a lack of comprehensive comprehension concerning the anti-tumor and pro-tumor characteristics of MSCs for gastric cancer (GC). This article discusses the influence of MSCs on GC, the underlying mechanisms, the origins of MSCs, and their effects. This review article also elucidates how MSCs exhibit dual characteristics of promoting and inhibiting tumor growth. Hence, it is of utmost importance that clinical inquiries aimed at utilizing MSCs as a therapeutic intervention for cancer consider the potentiality of MSCs to accelerate the progression of GC. It is crucial to exercise caution throughout the process of developing MSC-based cellular therapies to enhance their anti-cancer attributes while simultaneously eliminating their tumor-promoting impacts.

The role of miR-765 in human cancers.

MicroRNAs, a collection of short noncoding RNAs, are promising biomarkers for identifying cancer in its early stages and tracking the effectiveness of treatment. This is due to their critical role in regulating gene expression and other vital biological functions via cell-level epigenetic mechanisms. This review brings together data on the molecular and clinical effects of miR-765 on different types of cancer. Significant variation in miR-765 levels has been observed in a variety of cancer types, suggesting that it could have an oncogene or tumor suppressor role. A number of pathways, including PLP2/Notch, VEGFA/Akt1, PDX1, KLK4, RUNX2, DPF3, EMP3, APE1, ERK/EMT axis, and others, are impacted by the inclusion of miR-765 in their analysis. MiR-765 is an essential biomarker that shows promise as a diagnostic tool for various types of cancer. The latest research has identified them as reliable predictive markers for detecting tumor development at an early stage. Based on our study, miR-765 shows promising potential as a biomarker for prognosis in multiple types of cancer. Specifically, we suggest that miR-765 could be an early detection marker for tumor development, progression, and metastasis.

Exploring molecular mechanisms, therapeutic strategies, and clinical manifestations of Huntington's disease.

Huntington's disease (HD) is a paradigm of a genetic neurodegenerative disorder characterized by the expansion of CAG repeats in the HTT gene. This extensive review investigates the molecular complexities of HD by highlighting the pathogenic mechanisms initiated by the mutant huntingtin protein. Adverse outcomes of HD include mitochondrial dysfunction, compromised protein clearance, and disruption of intracellular signaling, consequently contributing to the gradual deterioration of neurons. Numerous therapeutic strategies, particularly precision medicine, are currently used for HD management. Antisense oligonucleotides, such as Tominersen, play a leading role in targeting and modulating the expression of mutant huntingtin. Despite the promise of these therapies, challenges persist, particularly in improving delivery systems and the necessity for long-term safety assessments. Considering the future landscape, the review delineates promising directions for HD research and treatment. Innovations such as Clustered regularly interspaced short palindromic repeats associated system therapies (CRISPR)-based genome editing and emerging neuroprotective approaches present unprecedented opportunities for intervention. Collaborative interdisciplinary endeavors and a more insightful understanding of HD pathogenesis are on the verge of reshaping the therapeutic landscape. As we navigate the intricate landscape of HD, this review serves as a guide for unraveling the intricacies of this disease and progressing toward transformative treatments.

Elucidating the Impact of Deleterious Mutations on IGHG1 and Their Association with Huntington's Disease.

Huntington's disease (HD) is a chronic, inherited neurodegenerative condition marked by chorea, dementia, and changes in personality. The primary cause of HD is a mutation characterized by the expansion of a triplet repeat (CAG) within the huntingtin gene located on chromosome 4. Despite substantial progress in elucidating the molecular and cellular mechanisms of HD, an effective treatment for this disorder is not available so far. In recent years, researchers have been interested in studying cerebrospinal fluid (CSF) as a source of biomarkers that could aid in the diagnosis and therapeutic development of this disorder. Immunoglobulin heavy constant gamma 1 (IGHG1) is one of the CSF proteins found to increase significantly in HD. Considering this, it is reasonable to study the potential involvement of deleterious mutations in IGHG1 in the pathogenesis of this disorder. In this study, we explored the potential impact of deleterious mutations on IGHG1 and their subsequent association with HD. We evaluated 126 single-point amino acid substitutions for their impact on the structure and functionality of the IGHG1 protein while exploiting multiple computational resources such as SIFT, PolyPhen-2, FATHMM, SNPs&Go mCSM, DynaMut2, MAESTROweb, PremPS, MutPred2, and PhD-SNP. The sequence- and structure-based tools highlighted 10 amino acid substitutions that were deleterious and destabilizing. Subsequently, out of these 10 mutations, eight variants (Y32C, Y32D, P34S, V39E, C83R, C83Y, V85M, and H87Q) were identified as pathogenic by disease phenotype predictors. Finally, two pathogenic variants (Y32C and P34S) were found to reduce the solubility of the protein, suggesting their propensity to form protein aggregates. These variants also exhibited higher residual frustration within the protein structure. Considering these findings, the study hypothesized that the identified variants of IGHG1 may compromise its function and potentially contribute to HD pathogenesis.

Medicinal and chemosensing applications of chitosan based material: A review.

Chitosan (CTS), has emerged as a highly intriguing biopolymer with widespread applications, drawing significant attention in various fields ranging from medicinal to chemosensing. Key characteristics of chitosan include solubility, biocompatibility, biodegradability and reactivity, making it versatile in numerous sectors. Several derivatives have been documented for their diverse therapeutic properties, such as antibacterial, antifungal, anti-diabetic, anti-inflammatory, anticancer and antioxidant activities. Furthermore, these compounds serve as highly sensitive and selective chemosensor for the detection of various analytes such as heavy metal ions, anions and various other species in agricultural, environmental and biological matrixes. CTS derivatives interacting with these species and give analytical signals. In this review, we embark on an exploration of the latest advancements in CTS-based materials, emphasizing their noteworthy contributions to medicinal chemistry spanning the years from 2021 to 2023. The intrinsic biological and physiological properties of CTS make it an ideal platform for designing materials that interact seamlessly with biological systems. The review also explores the utilization of chitosan-based materials for the development of colorimetric and fluorimetric chemosensors capable of detecting metal ions, anions and various other species, contributing to advancements in environmental monitoring, healthcare diagnostics, and industrial processes.

Identification of potential inhibitors of tropomyosin receptor kinase B targeting CNS-related disorders and cancers.

Tropomyosin receptor kinase B (TrkB), also known as neurotrophic tyrosine kinase receptor type 2 (NTRK2), is a protein that belongs to the family of receptor tyrosine kinases (RTKs). NTRK2 plays a crucial role in regulating the development and maturation of the central nervous system (CNS) and peripheral nervous system (PNS). Elevated TrkB expression levels observed in different pathological conditions make it a potential target for therapeutic interventions against neurological disorders, including depression, anxiety, Alzheimer's disease, Parkinson's disease, and certain types of cancer. Targeting TrkB using small molecule inhibitors is a promising strategy for the treatment of a variety of neurological disorders. In this research, a systematic virtual screening was carried out on phytoconstituents found in the IMPPAT library to identify compounds potentially inhibiting TrkB. The retrieved compounds from the IMPPAT library were first filtered using Lipinski's rule of five. The compounds were then sorted based on their docking score and ligand efficiency. In addition, PAINS, ADMET, and PASS evaluations were carried out for selecting drug-like compounds. Finally, in interaction analysis, we found two phytoconstituents, Wedelolactone and 3,8-dihydroxy-1-methylanthraquinone-2-carboxylic acid (DMCA), which possessed considerable docking scores and specificity on the TrkB ATP-binding pocket. The selected compounds were further assessed employing molecular dynamics (MD) simulations and essential dynamics. The results revealed that the elucidated compounds bind well with the TrkB binding pocket and lead to fewer conformations fluctuations. This study highlighted using phytoconstituents, Wedelolactone and DMCA as starting leads in developing novel TrkB inhibitors.Communicated by Ramaswamy H. Sarma.

Identification of natural product-based effective inhibitors of spleen tyrosine kinase (SYK) through virtual screening and molecular dynamics simulation approaches.

Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase that plays an essential role in signal transduction across different cell types. In the context of allergy and autoimmune disorders, it is a crucial regulator of immune receptor signaling in inflammatory cells such as B cells, mast cells, macrophages, and neutrophils. Developing SYK kinase inhibitors has gained significant interest for potential therapeutic applications in neurological and cancer-related conditions. The clinical use of the most advanced SYK inhibitor, Fostamatinib, has been limited due to its unwanted side effects. Thus, a more targeted approach to SYK inhibition would provide a more comprehensive treatment window. In this study, we used a virtual screening approach to identify potential SYK inhibitors from natural compounds from the IMPPAT database. We identified two compounds, Isolysergic acid and Michelanugine, which showed strong affinity and specificity for the SYK binding pocket. All-atom molecular dynamics (MD) simulations were also performed to explore the stability, conformational changes, and interaction mechanism of SYK in complexes with the identified compounds. The identified compounds might have the potential to be developed into promising SYK inhibitors for the treatment of various diseases, including autoimmune disorders, cancer, and inflammatory diseases. This work aims to identify potential phytochemicals to develop a new protein kinase inhibitor for treating advanced malignancies by providing an updated understanding of the role of SYK.Communicated by Ramaswamy H. Sarma.

Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies.

Cathepsin L (CTSL) expression is dysregulated in a variety of cancers. Extensive empirical evidence indicates their direct participation in cancer growth, angiogenic processes, metastatic dissemination, and the development of treatment resistance. Currently, no natural CTSL inhibitors are approved for clinical use. Consequently, the development of novel CTSL inhibition strategies is an urgent necessity. In this study, a combined machine learning (ML) and structure-based virtual screening strategy was employed to identify potential natural CTSL inhibitors. The random forest ML model was trained on IC50 values. The accuracy of the trained model was over 90%. Furthermore, we used this ML model to screen the Biopurify and Targetmol natural compound libraries, yielding 149 hits with prediction scores >0.6. These hits were subsequently selected for virtual screening using a structure-based approach, yielding 13 hits with higher binding affinity compared to the positive control (AZ12878478). Two of these hits, ZINC4097985 and ZINC4098355, have been shown to strongly bind CTSL proteins. In addition to drug-like properties, both compounds demonstrated high affinity, ligand efficiency, and specificity for the CTSL binding pocket. Furthermore, in molecular dynamics simulations spanning 200 ns, these compounds formed stable protein-ligand complexes. ZINC4097985 and ZINC4098355 can be considered promising candidates for CTSL inhibition after experimental validation, with the potential to provide therapeutic benefits in cancer management.

Deciphering the binding mechanism of an anti-cancer phytochemical plumbagin with calf thymus DNA using biophysical and in silico techniques.

Plumbagin (PLM), a plant derivative, is well known for a wide range of therapeutic effects in humans including anti-cancer, anti-inflammatory, anti-oxidant, and anti-microbial. Cytotoxic and genotoxic potential of this phytochemical has been studied which demands further insight. DNA being a major target for several drugs was taken to study against PLM to understand its effects on the cellular system. UV-Vis spectroscopy has indicated the binding of PLM to ctDNA and dye displacement assays have confirmed the formation of PLM-ctDNA complex. The insignificant changes in circular dichroism spectra suggested that PLM is not affecting the structural makeup of the ctDNA, hence the binding could be peripheral and not intercalating. Further, the relative viscosity and minimal change in melting temperature upon the complex formation supported this finding and confirmed the groove binding of PLM. Molecular docking analysis and simulation studies also show PLM as a minor groove binder to DNA and provide details on the interaction dynamics of PLM-DNA complex. Docking followed by a 100 ns simulation reveals the negative Gibbs free energy change (∆G = -6.6 kcal mol-1), and the formation of a stable complex. The PLM- DNA complex with stable dynamics was further supported by different parameters including RMSD, RMSF, SASA, Rg, and the energy profile of interaction. This study provides an insight into the cytotoxic and genotoxic mechanism of PLM which can be a crucial step forward to exploit its therapeutic potential against several diseases including cancer.

Genome-Wide Analysis of Kidney Renal Cell Carcinoma: Exploring Differentially Expressed Genes for Diagnostic and Therapeutic Targets.

Kidney renal cell carcinoma (KIRC) is the most common type of renal cancer. Kidney malignancies have been ranked in the top 10 most frequently occurring cancers. KIRC is a prevalent malignancy with a poor prognosis. The disease has risen for the last 40 years, and robust biomarkers for KIRC are needed for precision/personalized medicine. In this bioinformatics study, we utilized genomic data of KIRC patients from The Cancer Genome Atlas for biomarker discovery. A total of 314 samples were used in this study. We identified many differentially expressed genes (DEGs) categorized as upregulated or downregulated. A protein-protein interaction network for the DEGs was then generated and analyzed using the Search Tool for the Retrieval of Interacting Genes plugin of Cytoscape. A set of 10 hub genes was selected based on the Maximum Clique Centrality score defined by the CytoHubba plugin. The elucidated set of genes, that is, CALCA, CRH, TH, CHAT, SLC18A3, FSHB, MYH6, CAV3, KCNA4, and GBX2, were then categorized as potential candidates to be explored as KIRC biomarkers. The survival analysis plots for each gene suggested that alterations in CHAT, CAV3, CRH, MYH6, SLC18A3, and FSHB resulted in decreased survival of KIRC patients. In all, the results suggest that genomic alterations in selected genes can be explored to inform biomarker discovery and for therapeutic predictions in KIRC.

Identifying promising GSK3ß inhibitors for cancer management: a computational pipeline combining virtual screening and molecular dynamics simulations.

Glycogen synthase kinase-3 (GSK3ß), a serine/threonine protein kinase, has been discovered as a novel target for anticancer drugs. Although GSK3ß is involved in multiple pathways linked to the etiology of various cancers, no specific GSK3ß inhibitor has been authorized for cancer therapy. Most of its inhibitors have toxicity effects therefore, there is a need to develop safe and more potent inhibitors. In this study, a library of 4,222 anti-cancer compounds underwent rigorous computational screening to identify potential candidates for targeting the binding pocket of GSK3ß. The screening process involved various stages, including docking-based virtual screening, physicochemical and ADMET analysis, and molecular dynamics simulations. Ultimately, two hit compounds, BMS-754807 and GSK429286A, were identified as having high binding affinities to GSK3ß. BMS-754807 and GSK429286A exhibited binding affinities of -11.9, and -9.8 kcal/mol, respectively, which were greater than that of the positive control (-7.6 kcal/mol). Further, molecular dynamics simulations for 100 ns were employed to optimize the interaction between the compounds and GSK3ß, and the simulations demonstrated that the interaction was stable and consistent throughout the study. These hits were also anticipated to have good drug-like properties. Finally, this study suggests that BMS-754807 and GSK429286A may undergo experimental validation to evaluate their potential as cancer treatments in clinical settings.

Identification of human immunodeficiency virus -1 E protein-targeting lead compounds by pharmacophore based screening.

OBJECTIVES: To identify potential compounds by seeking the knowledge of molecular interactions between human immunodeficiency virus (HIV) glycoprotein (gp) 120 protein and anti-HIV drug (BMS-488043). METHODS: This study is a computational structure-based drug design study, carried out at University of Taif, Saudi Arabia and African Genome Centre (AGC), Mohammed VI Polytechnic University, Benguerir, Morocco from January 2021 to March 2022. Initially, using the docked structure of gp120 with BMS-488043, a structure-based pharmacophore model was created. The generated model was utilized for virtual screening of the ZINC and ChemBridge database in order to identify hit compounds. To further assess the time-dependent stability of the selected complexes, computer simulation was performed. RESULTS: From pharmacophore-based screening, 356 hits were obtained from both the database. The docking studies of the retrieved hit compounds reveal that all the compounds fit into the binding site of gp120. However, based on the significant interactions with the crucial residues and docking scores four compounds were suggested as potential hits. MD simulation of ChemBridge14695864 and ZINC06893293 in complex with gp120 suggested that both compounds significantly stabilized the receptor. CONCLUSION: These findings could aid in the design of effective drugs against HIV by inhibiting the interaction between gp120 and CD4.

Comparison and Advanced Antimicrobial Strategies of Silver and Copper Nanodrug-Loaded Glass Ionomer Cement against Dental Caries Microbes.

Caries lesions during cement repairs are a severe issue, and developing a unique antimicrobial restorative biomaterial can help to reduce necrotic lesion recurrence. As a result, Thymus vulgaris extract was used to biosynthesize copper nanoparticles (TVE-CuNPs) exhibiting different characteristics (TVE). Along with TVE-CuNPs, commercial silver nanoparticles (AgNPs) and metronidazole were combined with glass ionomer cement (GIC) to test its antibacterial efficacy and compressive strength. FTIR, XRD, UV-Vis spectrophotometry, and TEM were applied to characterize the TVE-CuNPs. Additionally, AgNPs and TVE-CuNPs were also combined with metronidazole and GIC. The modified GIC samples were divided into six groups, where groups 1 and 2 included conventional GIC and GIC with 1.5% metromidazole, respectively; group 3 had GIC with 0.5% TVE-CuNPs, while group 4 had 0.5% TVE-CuNPs with metronidazole in 1.5%; group 5 had GIC with 0.5% AgNPs, and group 6 had 0.5% AgNPs with metronidazole at 1.5%. An antimicrobial test was performed against Staphylococcus aureus (S. aureus) and Streptococcus mutans (S. mutans) by the disc diffusion method and the modified direct contact test (MDCT). GIC groups 4 and 6 demonstrated a greater antimicrobial efficiency against the two tested strains than the other groups. In GIC groups 4 and 6, the combination of GIC with two antimicrobial agents, 1.5% metronidazole and 0.5% TVE-CuNPs or AgNPs, enhanced the antimicrobial efficiency when compared to that of the other groups with or without a single agent. GIC group specimens combined with nanosilver and nanocopper had similar mean compressive strengths when compared to the other GIC groups. Finally, the better antimicrobial efficacy of GIC boosted by metronidazole and the tested nanoparticles against the tested strains may be relevant for the future creation of more efficient and modified restorations to reduce dental caries lesions.

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images.

The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods.

Association between gastric reflux, obesity and erosive tooth wear among psychiatric patients.

ABSTRACT: The prevalence of erosive tooth wear and obesity are high in psychiatric patients and soft drink consumption is a common risk factor associated with both diseases. This study aimed to assess the association between soft drink consumption, gastric reflux, erosive tooth wear, and obesity among resident patients at the Psychiatric Hospital, Taif, Saudi Arabia.This descriptive, cross-sectional study included 223 adult psychiatric inpatients (126 male, 97 female) with a mean age of 42.3 years (± 2.2). Dental erosion detection was performed according to World Health Organization criteria. The medical evaluation included assessment of the body mass index (BMI). With appropriate sample weighting, relationships between erosive tooth wear, gastric reflux, and obesity were assessed using multivariable logistic regression.Ninety eight patients (43.9%) presented with erosive tooth wear. The mean BMI for the entire study population was 27.7 ±â€Š6.3 kg/m2. Regression analysis showed a strong association between erosive tooth wear and chronic vomiting or bulimia (adjusted odds ratio = 2.11; 95% confidence interval [CI] = 1.98-5.07, P < .001), gastric reflux (adjusted odds ratio = 2.13; 95% CI = 1.34-6.23, P < .001), consumption of soft drinks (adjusted odds ratio = 2.14; 95% CI = 1.03-6.08, P < .001), and schizophrenia and delusional disorders (adjusted odds ratio = 2.07; 95% CI = 1.98-5.08, P < .001).This study demonstrates a significant association between erosive tooth wear prevalence and chronic vomiting or bulimia, consumption of soft drinks, and gastric reflux among resident patients at psychiatric hospital.

Association Between Early Childhood Caries and Obesity among Preschool Children.

PURPOSE: Early childhood caries (ECC) and childhood obesity are among the most prevalent health conditions affecting children. ECC is associated with obesity through the common risk factor of sugar consumption. The present study aimed to assess the association between ECC and obesity in preschool children. MATERIALS AND METHODS: A cross-sectional study was conducted among 1250 preschool children (698 girls, 552 boys; mean age: 4.3 [1.1] years). The children's body mass index was determined (BMI: weight/height in kg/m2). The World Health Organization criteria were used for the diagnosis of caries. Multivariable logistic regression was used to analyse the relationship between ECC prevalence and childhood obesity. RESULTS: ECC was detected in 929 (74.3%) children. The mean dmft and dmfs was 5.91 (1.13) and 8.92 (2.07), respectively. The multiple regression model showed a statistically significant association between ECC prevalence and obesity with an adjusted odds ratio (OR) of 2.59 (95% CI: 1.88 - 3.57; P = 0.001). The logistic regression model showed that in children with a monthly family income > $2666, sugar consumption, preterm low birth-weight/full-term low birth-weight (PTLBW/FTLBW), and toothbrushing frequency ≤ 1 time/day were statistically significantly associated with ECC prevalence. CONCLUSION: ECC was positively associated with obesity.

Antimicrobial Efficacy of Glass Ionomer Cement in Incorporation with Biogenic Zingiber officinale Capped Silver-Nanobiotic, Chlorhexidine Diacetate and Lyophilized Miswak.

In the present study, Zingiber officinale is used for the synthesis of Zingiber officinale capped silver nanoparticles (ZOE-AgNPs) and compares the antimicrobial efficacy and compressive strength of conventional glass ionomer cement (GIC) combined with ZOE-AgNPs, lyophilized miswak, and chlorhexidine diacetate (CHX) against oral microbes. Five groups of the disc-shaped GIC specimens were prepared. Group A: lyophilized miswak and GIC combination, Group B: ZOE-AgNPs and GIC combinations, Group C: CHX and GIC combination, Group D: ZOE-AgNPs + CHX + GIC; Group E: Conventional GIC. Results confirmed the successful formation of ZOE-AgNPs that was monitored by UV-Vis sharp absorption spectra at 415 nm. The X-ray diffractometer (XRD) and transmission electron microscope (TEM) results revealed the formation of ZOE-AgNPs with a mean size 10.5-14.12 nm. The peaks of the Fourier transform infrared spectroscopy (FTIR) were appearing the involvement of ZOE components onto the surface of ZOE-AgNPs which played as bioreducing, and stabilizing agents. At a 24-h, one-week and three-week intervals, Group D showed the significantly highest mean inhibitory zones compared to Group A, Group B, and Group C. At microbe-level comparison, Streptococcus mutans and Staphylococcus aureus were inhibited significantly by all the specimens tested except group E when compared to Candida albicans. Group D specimens showed slightly higher (45.8 ± 5.4) mean compressive strength in comparison with other groups. The combination of GIC with ZOE-AgNPs and chlorhexidine together enhanced its antimicrobial efficacy and compressive strength compared to GIC with ZOE-AgNPs or lyophilized miswak or chlorhexidine combination alone. The present study revealed that The combination of GIC with active components of ZOE-AgNPs and chlorhexidine paves the way to lead its effective nano-dental materials applications.

Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans.

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.