Comparative Analysis of Keratometric and Pachymetry Values From Corneal Topography Scans: A Comparison Between Pentacam and Galilei.

Objective The objective of this study was to compare K1, K2, Kmax, and pachymetry values from Pentacam and Galilei scans of corneal topography in order to assess their correlation and interchangeability in clinical practice. Methodology A total of 34 patients (68 eyes) were enrolled in the study. Corneal topography was performed using Pentacam and Galilei devices on the same day. K1, K2, Kmax, and pachymetry readings were obtained from the scans and analyzed using paired t-tests and Bland-Altman plots. Results There were minimal differences in clinical settings between Pentacam and Galilei for K1, K2, Kmax (>0.75 D), and pachymetry values (>15 um). However, there was a statistically significant difference found between Kmax and pachymetry, making their interchangeability questionable. Conclusion Pentacam and Galilei demonstrate a good correlation between corneal keratometric values (K1, K2, and Kmax) and pachymetry values in clinical settings, and they should be used interchangeably with caution.

Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets.

Fagonia indica from Zygophyllaceae family is a medicinal specie with significant antidiabetic potential. The present study aimed to investigate the in vitro antidiabetic activity of Fagonia indica crude extract followed by an in silico screening of its phytoconstituents. For this purpose, crude extract of Fagonia indica was prepared and divided in three different parts, i.e., n-hexane, ethyl acetate, and methanolic fraction. Based on in vitro outcomes, the phytochemical substances of Fagonia indica were virtually screened through a literature survey and a screening library of compounds (1-13) was prepared. The clinical potential of these novel drug candidates was assessed by applying an ADME screening profile. Findings of SwissADME indicators (Absorption, Distribution, Metabolism, and Excretion) for the compounds (1-13) presented relatively optimal physicochemical characteristics, drug-likeness, and medicinal chemistry. The antidiabetic action of these leading drug candidates was optimized through molecular docking analysis against 3 different human pancreatic α-amylase macromolecular targets with (PDB ID 1B2Y), (PDB ID 3BAJ), and (PDB ID: 3OLI) by applying Virtual Docker (Molegro MVD). Metformin was taken as a reference standard for the sake of comparison. In vitro antidiabetic evaluation gave good results with promising α-amylase inhibitory action in the form of IC50 values, as for n-hexane extract = 206.3 µM, ethyl acetate = 41.64 µM, and methanolic extract = 9.61 µM. According to in silico outcomes, all 13 phytoconstituents possess the best binding affinity with successful MolDock scores ranging from - 97.2003 to - 65.6877 kcal/mol and show a great number of binding interactions than native drug metformin. Therefore, the current work concluded that the diabetic inhibition prospective of extract and the compounds of Fagonia indica may contribute to being investigated as a new class of antidiabetic drug or drug-like candidate for further studies.

Exploring Type II Diabetes Inhibitors from Genus Daphne Plant-species: An Integrated Computational Study.

BACKGROUND: Plant species of the genus Daphne clasps a historical background with a potential source of bioactive phytochemicals such as flavonoids and daphnodorins. These compounds manifest a significant chemotaxonomic value in drug discovery. Their flair comprehensive pharmacological, phytochemical, biological, catalytic, and clinical utilities make them exclusively unique. This study was conducted to investigate the optimization and structure-based virtual screening of these peculiar analogs. The majority of the active constituents of medicines are obtained from natural products. Previously, before the invention of virtual screening methods or techniques, almost 80% of drugs were obtained from natural resources. Comparing reported data to drug discovery from 1981 to 2007 signifies that half of the FDA-approved drugs are obtained from natural resources. It has been reported that structures of natural products that have particularities of structural diversity, biochemical specification, and molecular properties make them suitable products for drug discovery. These products basically have unique chiral centers which increase their structural complexity than the synthesized drugs. METHOD: This work aimed to probe the use of daphnodorins analogs for the first time as antidiabetic inhibitors based on significant features and to determine the potential of daphnodorin analogs as antidiabetic inhibitors through computational analysis and structure-based virtual screening. A dataset of 38 compounds was selected from different databases, including PubChem and ZINC, for computational analysis, and optimized compounds were docked against various co-crystallized structures of inhibitors, antagonists, and receptors which were downloaded from PDB by using AutoDock Vina (by employing Broyden-Fletcher-Goldfarb-Shanno method), Discovery studio visualizer 2020, PYMOL (Schrodinger). Docking results were further validated by Molecular dynamic simulation and MM-GBSA calculation. Quantitative structure-activity relationship (QSAR) was reported by using Gaussian 09W by intimating Density Functional Theory (DFT). Using this combination of multi-approach computational strategy, 14 compounds were selected as potential exclusive lead compounds, which were analyzed through ADMET studies to pin down their druglike properties and toxicity. RESULT: At significant phases of drug design approaches regular use of molecular docking has helped to promote the separation of important representatives from 38 pharmaceutically active compounds by setting a threshold docking score of -9.0 kcal/mol which was used for their exposition. Subsequently, by employing a threshold it was recognized that 14 compounds proclaimed this threshold for antidiabetic activity. Further, molecular dynamic simulation, MM-GBSA, ADMET, and DFT results screened out daphnegiralin B4 (36) as a potential lead compound for developing antidiabetic agents. CONCLUSION: Our analysis took us to the conclusion that daphnegiralin B4 (36) among all ligands comes out to be a lead compound having drug-like properties among 38 ligands being non-carcinogenic and non-cytotoxic which would benefit the medical community by providing significant drugs against diabetes. Pragmatic laboratory investigations identified a new precursor to open new doors for new drug discovery.

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential.

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (µ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

The selected compounds from the mushroom were evaluated as potential breast cancer MCF-7 cell line inhibitor.Ligand-based 3D-QSAR study to analyze the structurally diverse compounds with experimentally reported IC₅₀.Pharmacophore-based virtual screening of compounds.Molecular docking analysis pointed out the vital interaction of the hit with the protein's amino acids.Absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of the lead compounds were examined.

A discovery of potent kaempferol derivatives as multi-target medicines against diabetes as well as bacterial infections: an in silico approach.

Flavonoids demonstrate beneficial effects on human health because flavonoids contain important biological properties. Kaempferol is a flavonol, type of flavonoid found in eatable plants and in plants usually employed in ancient drugs (Moringa oleifera, Tilia spp., fern genus spp. and gingko etc.). Some medicinal studies have shown that the use of foods full of kaempferol decreases the risk of many (cancer, vascular) diseases. All the data of 50 kaempferol derivatives were collected from PubChem database. Through Schrödinger software, 3D-QSAR study was performed for 50 compounds by using method of field base. Conformer of kaempferol derivatives was docked against anti-diabetic, anti-microbial co-crystal structures and protein. To monitor the best anti-diabetic and antibacterial agent, particular kaempferol derivatives were downloaded from PubChem database. Virtual screening by molecular docking provided four lead compounds with four different proteins. These hit compounds were found to be potent inhibitor for diabetic enzymes alpha-amylase and DPP IV and had the potential to suppress DNA gyrase and dihydrofolate reductase synthesis. Molecular dynamic simulation of docked complexes evaluates the value of root mean square fluctuation by iMOD server. Kaempferol 3-O-alpha-L-(2, 3-di-Z-p-coumaroyl) rhamnoside (42) compound used as anti-diabetic and kaempferol 3-O-gentiobioside (3) as antibacterial with good results can be used for drug discovery.Communicated by Ramaswamy H. Sarma.

Combined Experimental and Theoretical Insights: Spectroscopic and Molecular Investigation of Polyphenols from Fagonia indica via DFT, UV-vis, and FT-IR Approaches.

This review deals with computational study of polyphenolic compounds of medicinal importance and interest for drug development. Herein, four polyphenolic compounds comprising catechol (1), caffeic acid (II), gallic acid (III), and pyrogallol (IV) have been isolated from a medicinal specie, Fagonia indica, by applying silica gel column chromatography. These compounds were identified by using gas chromatography-mass spectrometry (GC-MS) analysis and confirmed by geometric computational analysis. According to computational results, caffeic acid has shown the highest biological activation due to higher chemical softness, electronegativity (χ (eV) = -648.644), and electrostatic potential value (-8.424 × 10-2 to +8.424 × 10-2), while smaller values of chemical potential (-0.269), ELUMO (-0.080), and energy gap (ΔE = 0.149). The Mulliken atomic charges were calculated by using DFT/B3LYP with basis set 6-311G for the determination of active sites. The oxygen atom of catechol showed highest nucleophilic characteristic with a more negative charge (08 = -0.695), and pyrogallol indicated a strong electrophilic center at C14 = 0.415 with a higher positive charge. Moreover, UV-visible absorption spectra and a detailed study of vibrational frequencies for all phenolic compounds by employing the DFT approach with 3-21G, 6-31G, and 6-311G basis sets at the ground-state level showed the great agreement with experimental results. ANOVA has been applied to validate the theoretical data. Results suggest that compounds I-IV are suitable in diverse fields.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis.

BACKGROUND: Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software. CONCLUSIONS: This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning.

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

Exploration of phenolic acid derivatives as inhibitors of SARS-CoV-2 main protease and receptor binding domain: potential candidates for anti-SARS-CoV-2 therapy.

Severe acute respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) is the etiological virus of Coronavirus Disease 2019 (COVID-19) which has been a public health concern due to its high morbidity and high mortality. Hence, the search for drugs that incapacitate the virus via inhibition of vital proteins in its life cycle is ongoing due to the paucity of drugs in clinical use against the virus. Consequently, this study was aimed at evaluating the potentials of natural phenolics against the Main protease (Mpro) and the receptor binding domain (RBD) using molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and density functional theory (DFT) calculations. To this end, thirty-five naturally occurring phenolics were identified and subjected to molecular docking simulation against the proteins. The results showed the compounds including rosmarinic acid, cynarine, and chlorogenic acid among many others possessed high binding affinities for both proteins as evident from their docking scores, with some possessing lower docking scores compared to the standard compound (Remdesivir). Further subjection of the hit compounds to drug-likeness, pharmacokinetics, and toxicity profiling revealed chlorogenic acid, rosmarinic acid, and chicoric acid as the compounds with desirable profiles and toxicity properties, while the study of their electronic properties via density functional theory calculations revealed rosmarinic acid as the most reactive and least stable among the sets of lead compounds that were identified in the study. Molecular dynamics simulation of the complexes formed after docking revealed the stability of the complexes. Ultimately, further experimental procedures are needed to validate the findings of this study.

Developmental landscape of computational techniques to explore the potential phytochemicals from Punica granatum peels for their antioxidant activity in Alzheimer's disease.

Alzheimer's disease (AD) is more commonly found in women than in men as the risk increases with age. Phytochemicals are screened in silico from Punica granatum peels for their antioxidant activity to be utilized for Alzheimer's disease. Alzheimer's disease is inhibited by the hormone estrogen, which protects the brain from the bad effects of amyloid beta and acetylcholine (ACh), and is important for memory processing. For the purpose, a library of about 1,000 compounds from P. granatum were prepared and studied by applying integrated computational calculations like 3D-QSAR, molecular docking, MD simulation, ADMET, and density functional theory (DFT). The 3D-QSAR model screened the active compounds B25, B29, B35, B40, B45, B46, B48, B61, and B66 by the field points and activity atlas model from the prepared library. At the molecular level, docking was performed on active compounds for leading hit compounds such as B25 and B35 that displayed a high MolDock score, efficacy, and compatibility with drug delivery against the antioxidant activity. Optimization of the structure and chemical reactivity parameter of the hit compound was calculated by DFT. Moreover, ADMET prediction was evaluated to check the bioavailability and toxicity of the hit compound. Hesperidin (B25) is found to be a hit compound after the whole study and can be synthesized for potent drug discovery in the future.

One-pot multicomponent synthesis of novel pyridine derivatives for antidiabetic and antiproliferative activities.

Background: Due to the close relationship of diabetes with hypertension reported in various research, a set of pyridine derivatives with US FDA-approved drug cores were designed and integrated by artificial intelligence. Methods: Novel pyridines were designed and synthesized. Compounds MNS-1-MNS-4 were evaluated for their structure and were screened for their in vitro antidiabetic (α-amylase) activity and anticancer (HepG2) activity by methyl thiazolyl tetrazolium assay. Comparative 3D quantitative structure-activity relationship analysis and pharmacophore generation were carried out. Results: The study revealed MNS-1 and MNS-4 as good alternatives to acarbose as antidiabetic agents, and MNS-2 as a more viable, better alternative to doxorubicin in the methyl thiazolyl tetrazolium assay. Conclusion: This combination of studies identifies new and more active analogs of existing FDA-approved drugs for the treatment of diabetes.

Exploring the potential of FDA approved anti-diabetic drugs for repurposing against COVID-19: a core combination of multiple computational strategies and integrated artificial intelligence.

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of concern (VOC) on 26 November 2021. By September 2020, it will have infected over 16 million patients and killed over 600,000 people over the world. This very infectious viral illness still poses a danger to world health; it has also become the greatest problem the world is facing and become the main area of research. The development of vaccines is insufficient to stop their spread and serious effects. Despite several reputable pharmaceutical firms claiming to have developed a cure for COVID-19. For that purpose, the field-based 3D-QSAR model has been used to analyze a series of anti-diabetic drugs to repurpose them against COVID-19. The LOO verified partial least square (PLS) model generates satisfactory q2 (0.4) and r2 (0.5) values. By using this model 10 compounds were screened out of 55 FDA approved anti-diabetic drugs (built-up library). Additionally, these substances were examined using molecular docking screening and ADMET. Finally, the drugs L8, and L23 were discovered to be the lead drugs. Density functional theory at the B3LYP/6-311G* technique was used to examine structural geometries, electronic characteristics, and molecular electrostatic potential (MEP). This work will greatly assist in the detection and development of leads for early drug development to control COVID-19.Communicated by Ramaswamy H. Sarma.

The combination of multi-approach studies to explore the potential therapeutic mechanisms of imidazole derivatives as an MCF-7 inhibitor in therapeutic strategies.

Breast cancer covers a large area of research because of its prevalence and high frequency all over the world. This study is based on drug discovery against breast cancer from a series of imidazole derivatives. A 3D-QSAR and activity atlas model was developed by exploring the dataset computationally, using the machine learning process of Flare. The dataset of compounds was divided into active and inactive compounds according to their biological and structural similarity with the reference drug. The obtained PLS regression model provided an acceptable r 2 = 0.81 and q2 = 0.51. Protein-ligand interactions of active molecules were shown by molecular docking against six potential targets, namely, TTK, HER2, GR, NUDT5, MTHFS, and NQO2. Then, toxicity risk parameters were evaluated for hit compounds. Finally, after all these screening processes, compound C10 was recognized as the best-hit compound. This study identified a new inhibitor C10 against cancer and provided evidence-based knowledge to discover more analogs.

Antagonist Impact of Selenium-Based Nanoparticles Against Mycobacterium tuberculosis.

One of the cardinal causes of global deaths from a single-point infectious agent has been reported to be tuberculosis (or TB). At present times, the incidence of TB cases occurs mostly due to multi-drug resistance, which is expected to boost further in the upcoming times. Accordingly, the development of alternative treatment methodologies has received significant research interest. In this regard, the application of nanoparticles has notable cognizance. The literature suggested that nanoparticles have substantial potential to be used as the delivery medium for drug injection as well as they also serve as a potential bactericidal agent. In this present study, the efficacy of the selenium nanoparticles against the inhibition of growth of Mycobacterium tuberculosis was evaluated. The obtained results indicated that the synthesized selenium nanoparticles have notable cognizance towards the inhibition of growth of Mycobacterium tuberculosis by disrupting the integrity of their cell envelope. This study thus proposes a novel approach and opens new dimensional avenues in the field of nanoparticle-induced cell disruption strategies.

Characterization of stenocephol from Seriphidium stenocephalum as potent HepG2 cell growth and glycogen phosphorylase inhibitor.

Plant-derived compounds represent an important source for developing innovative drugs. One of the widely distributed plants, especially in Afghanistan and Pakistan, Seriphidium stenocephalum, was investigated in this study to identify bioactive compounds. The plant extract was subjected to silica gel column chromatography, four phenolic acid derivatives were isolated, while stenocephol was obtained by ethyl acetate fraction. Stenocephol was subjected to experimental screening for anti-diabetic and anti-cancer activities, measuring its inhibitory potency against glycogen phosphorylase, and its cytotoxicity against HepG2 cells. Further insights into the mechanism of action of stenocephol were obtained from a computational investigation. Stenocephol showed a dose-dependent manner of inhibition against glycogen phosphorylase and HepG2 cells in the low micromolar range. Notably, coupling in vitro and computational investigation, we identified the natural product stenocephol as a possible anti-diabetic and anti-cancer agent, representing a possible starting point for developing novel therapeutics, enriching the armamentarium against the mentioned diseases.

Awareness and Utilization of Unani Medicine among the Adult Population from East Delhi: A cross Sectional Survey.

Objectives: To assess the awareness, practice, and utilization pattern of Unani medicine among the general population of Trilokpuri, East Delhi. Materials and Methods: A cross-sectional, observational descriptive survey was conducted in New Delhi using pretested, predesigned, and structured questionnaire to assess the awareness and utilization pattern of Unani medicine on a sample size of 100 subjects. Result: Out of 100 subjects, 60% were males and 40% were females with mean age 38.96 ± 9.12 years, ranged between 18 and 65 years. The awareness about Unani medicine was found in 67% of adopted population, while 57% population among them preferred Unani system of medicine as a mode of treatment. Unani is preferred because of minimal side effects associated as reported by 47% users. On the other hand, among nonuser (n = 29), 48% population do not prefer Unani medicine because of its slow action. Very few (2.6%) users adopted Unani medicine exclusively, while most of them preferred Unani as an adjuvant with allopathic. Unani medicine is preferred mainly for the management of musculoskeletal disorders (35%), followed by GIT disorders (19%) and their related disorders. Conclusion: The awareness level regarding Unani medicine is good rather utilization of is relatively lower as compared to modern system of medicine. There is a need and scope for promotion of health education as well as improvement in the people's attitude towards Unani system of medicine.

Exploring the anti-SARS-CoV-2 main protease potential of FDA approved marine drugs using integrated machine learning templates as predictive tools.

Since the inception of COVID-19 pandemic in December 2019, socio-economic crisis begins to rise globally and SARS-CoV-2 was responsible for this outbreak. With this outbreak, currently, world is in need of effective and safe eradication of COVID-19. Hence, in this study anti-SAR-Co-2 potential of FDA approved marine drugs (Biological macromolecules) data set is explored computationally using machine learning algorithm of Flare by Cresset Group, Field template, 3D-QSAR and activity Atlas model was generated against FDA approved M-pro SARS-CoV-2 repurposed drugs including Nafamostat, Hydroxyprogesterone caporate, and Camostat mesylate. Data sets were categorized into active and inactive molecules on the basis of their structural and biological resemblance with repurposed COVID-19 drugs. Then these active compounds were docked against the five different M-pro proteins co-crystal structures. Highest LF VS score of Holichondrin B against all main protease co-crystal structures ranked it as lead drug. Finally, this new technique of drug repurposing remained efficient to explore the anti-SARS-CoV-2 potential of FDA approved marine drugs.

Neural Basis of Dental Pulp Stem Cells and its Potential Application in Parkinson's Disease.

Parkinson's Disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease. Though significant insights into the molecular-biochemical-cellular-behavioral basis of PD have been understood, there is no appreciable treatment available till date. Current therapies provide symptomatic relief without any influence on the progression of the disease. Stem cell therapy has been vigorously explored to treat PD. In this comprehensive review, we analyze various stem cell candidates for treating PD and discuss the possible mechanisms. We advocate the advantage of using neural crest originated Dental Pulp Stem Cells (DPSC) due to their predisposition towards neural differentiation and their potential to regenerate neurons far better than commonly used bone marrow derived mesenchymal stem cells (BM-MSCs). Eventually, we highlight the current challenges in the field and the strategies, which may be used for overcoming the impediments.

Incessant threat of COVID-19 variants: Highlighting need for a mix of FDA-approved artificial intelligence tools and community pharmacy services.

Seamless integration of artificial intelligence (AI) technology, especially FDA-approved AI tools, in community pharmacies can build increasingly effective and easy care pathways, thereby minimizing burden on healthcare system, and ensuring compliance to preventive measure through limiting hospital visits. In this regard, the WHO needs to enlist and provide guidance on most promising AI tools that can be implemented in community pharmacy settings.

Generation of Induced Pluripotent Stem Cells from Turner Syndrome (45XO) Fetal Cells for Downstream Modelling of Neurological Deficits Associated with the Syndrome.

Chromosomal aneuploidies cause severe congenital malformations including central nervous system malformations and fetal death. Prenatal genetic screening is purely diagnostic and does not elucidate disease mechanism. Although cells from aneuploid fetuses are valuable biological material bearing the chromosomal aneuploidy, these cells are short lived, limiting their use for downstream research experiments. Generation of induced pluripotent stem cell (iPSC) models is an effective method of cell preparation for perpetual conservation of aneuploid traits. They are self-renewing and differentiate into specialized cells reminiscent of embryonic development. Thus, iPSCs serve as excellent tools to study early developmental events. Turner syndrome (TS) is a rare condition associated with a completely or partially missing X chromosome. The syndrome is characterized by infertility, short stature, endocrine, metabolic, autoimmune and cardiovascular disorders and neurocognitive defects. The following protocol describes isolation and culturing of fibroblasts from TS (45XO) fetal tissue, generation of integration free TSiPSCs through delivery of episomal reprogramming plasmids by nucleofection followed by characterization. The reprogramming TSiPSCs were initially screened by live cell alkaline phosphatase staining followed by extensive probing for pluripotency biomarkers. Selected colonies were mechanically dissected, passaged several times and stable self-renewing cells were used for further experiments. The cells expressed pluripotency transcription factors OCT4, NANOG, SOX2, cell surface markers SSEA 4 and TRA1-81 typical of pluripotent stem cells. The original 45XO karyotype was retained post reprogramming. The TSiPSCs were able to form embryoid bodies and differentiate into cells of endoderm, mesoderm and ectoderm expressing lineage specific biomarkers ((SRY BOX17), (MYOSIN VENTRICULAR HEAVY CHAINα/ß), (ßIII TUBULIN)). The exogenous episomal plasmids were lost spontaneously and not detected after passage 15 in cells. These TSiPSCs are a valuable cellular resource for modelling defective molecular and cellular neurodevelopment causing neurocognitive deficits associated with Turner syndrome.