Identification of a potential DNA methyltransferase (DNMT) inhibitor

DNA methyltransferases (DNMTs) play an important role in the epigenetic regulation of gene expression through the methylation of DNA. Since hypermethylation and consequent suppression of tumor suppressor genes are associated with cancer development and progression, DNA hypomethylating agents (HMAs) such as DNMT inhibitors have been proposed for cancer therapy. Two nucleoside analogues approved for the treatment of hematological cancers, decitabine and azacytidine, have poor pharmacokinetic properties, and hence there is a critical need for identifying novel HMAs. Virtual screening of a library of ∼40,000 compounds from the ZINC database, followed by molecular docking of 4,000 compounds having potential druggable properties with DNMT1, DNMT3A and DNMT3B were performed. One unique inhibitor (ZINC167686681) was identified that successfully passed through the Lipinski Rule of 5, geometry constraints as well as ADME/Tox filters and having strong binding energy for DNMTs. Further, molecular dynamics simulations of the docked complexes showed detailed structural features critical for its binding with the DNMTs and the stability of their interaction. Our study identified a compound with potential druggable properties and predicted to bind and inhibit DNMTs. Further investigations involving cellular and animal models of ZINC167686681 will help in potentially taking it into clinical trials for the treatment of cancers.Communicated by Ramaswamy H. Sarma.

Identification of two novel autism genes, TRPC4 and SCFD2, in Qatar simplex families through exome sequencing

This study investigated the genetic underpinnings of autism spectrum disorder (ASD) in a Middle Eastern cohort in Qatar using exome sequencing. The study identified six candidate autism genes in independent simplex families, including both four known and two novel autosomal dominant and autosomal recessive genes associated with ASD. The variants consisted primarily of de novo and homozygous missense and splice variants. Multiple individuals displayed more than one candidate variant, suggesting the potential involvement of digenic or oligogenic models. These variants were absent in the Genome Aggregation Database (gnomAD) and exhibited extremely low frequencies in the local control population dataset. Two novel autism genes, TRPC4 and SCFD2, were discovered in two Qatari autism individuals. Furthermore, the D651A substitution in CLCN3 and the splice acceptor variant in DHX30 were identified as likely deleterious mutations. Protein modeling was utilized to evaluate the potential impact of three missense variants in DEAF1, CLCN3, and SCFD2 on their respective structures and functions, which strongly supported the pathogenic natures of these variants. The presence of multiple de novo mutations across trios underscored the significant contribution of de novo mutations to the genetic etiology of ASD. Functional assays and further investigations are necessary to confirm the pathogenicity of the identified genes and determine their significance in ASD. Overall, this study sheds light on the genetic factors underlying ASD in Qatar and highlights the importance of considering diverse populations in ASD research.

Primary Ovarian Burkitts Lymphoma

Primary ovarian Burkitt lymphoma (BL) is a very rare and aggressive malignancy. We report an 18-year-old female patient who presented with a large, tender abdomen, and highly de-ranged renal and liver functions. Ultrasonography showed hepatosplenomegaly, mild ascites, dilated biliary channels and a heterogeneous pelvic mass of size ~15106.4 cm. Immunohistochemical (IHC) staining of the biopsy sample excised from the left ovary demonstrated reactivity for CD20 and CD10, and negativity for CD3, Bcl-2 and TdT. The C-myc translocation was positive in 60% of tumour cells. Moreover, the proliferation index was ~90%. These features were consistent with BL. After haemodialysis, the patient was planned for multiagent chemotherapy, including cyclophosphamide, doxorubicin, vincristine and prednisone. This case supports the hypothesis that primary ovarian BL is an aggressive malignancy that appears to respond promisingly to multi-agent chemotherapy.

Can growing patients with end-stage TMJ pathology be successfully treated with alloplastic temporomandibular joint reconstruction? - A systematic review

INTRODUCTION

The use of alloplastic total temporomandibular joint reconstruction (TMJR) in growing patients is controversial, mainly due to immature elements of the craniomaxillofacial skeleton. The aim of this systematic review was to evaluate the use of alloplastic TMJR in growing patients, focusing on the patient's clinical presentation, surgical and medical history and efficacy of alloplastic TMJR implantation.

MATERIALS AND METHODS

The literature search strategy was based on the Population, Intervention, Comparator, Outcomes and Study type (PICOS) framework. We searched Pubmed, Google Scholar, Dimension, Web of Science, X-mol, Semantic Scholar and Embase to January 2023, without any restriction on the type of publication reporting alloplastic TMJR in growing patients (age ≤ 18 years for boys and age ≤ 15 years for girls).

RESULTS

A total of 15 studies (case reports: 09, case series: 02, cohort studies: 04) met the inclusion criteria, documenting 73 patients of growing age from 07 countries. Thirty-eight (~ 52%) cases were female. The mean ± SD (range) age and follow-up of patients in all studies was 13.1 ± 3.2 (0-17) years and 34.3 ± 21.5 (7-96) months, respectively. A total of 22 (30%) patients were implanted with bilateral alloplastic TMJR. Over half of the studies (n = 10) were published in the last 3 years. All patients underwent multiple surgeries prior to implantation of alloplastic TMJR. In extreme cases, patients underwent a total of 17 surgeries. Different types of studies reporting inconsistent variables restricted our ability to perform quality assessment measures for evidence building.

CONCLUSIONS

Clinical experience with alloplastic TMJR in growing patients is limited to cases showing poor prognosis with other types of reconstruction. Nevertheless, studies show promising results for the use of alloplastic TMJR in growing patients, highlighting the need for well-controlled prospective studies with long-term follow-up.

Protegrin-2, a potential inhibitor for targeting SARS-CoV-2 main protease Mpro

Background

SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro.

Methods

We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer.

Results

Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro.

Conclusions

Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of &lt;1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

A comprehensive genomic study, mutation screening, phylogenetic and statistical analysis of SARS-CoV-2 and its variant omicron among different countries

BACKGROUND

With the rapid development of the genomic sequence data for the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants Delta (B.1.617.2) and Omicron (B.1.1.529), it is vital to successfully identify mutations within the genome.

OBJECTIVE

The main objective of the study is to investigate the full-length genome mutation analysis of 157 SARS-CoV-2 and its variant Delta and Omicron isolates. This study also provides possible effects at the structural level to understand the role of mutations and new insights into the evolution of COVID-19 and evaluates the differential level analysis in viral genome sequence among different nations. We have also tried to offer a mutation snapshot for these differences that could help in vaccine formulation. This study utilizes a unique and efficient method of targeting the stable genes for the drug discovery approach.

METHODS

Complete genome sequence information of SARS-CoV-2, Delta, and Omicron from online resources were used to predict structure domain identification, data mining, and screening; employing different bioinformatics tools. BioEdit software was used to perform their genomic alignments across countries and a phylogenetic tree as per the confidence of 500 bootstrapping values was constructed. Heterozygosity ratios were determined in-silico. A minimum spanning network (MSN) of selected populations was determined by Bruvo's distance role-based framework.

RESULTS

Out of all 157 different strains of SARS-CoV-2 and its variants, and their complete genome sequences from different countries, Corona nucleoca and DUF5515 were observed to be the most conserved domains. All genomes obtained changes in comparison to the Wuhan-Hu-1 strain, mainly in the TRS region (CUAAAC or ACGAAC). We discovered 596 mutations in all genes, with the highest number (321) found in ORF1ab (QHD43415.1), or TRS site mutations found only in ORF7a (1) and ORF10 (2). The Omicron variant has 30 mutations in the Spike protein and has a higher alpha-helix shape (23.46%) than the Delta version (22.03%). T478 was also discovered to be a prevalent polymorphism in Delta and Omicron variations, as well as genomic gaps ranging from 45 to 65aa. All 157 sequences contained variations and conformed to Nei's Genetic distance. We discovered heterozygosity (Hs) 0.01, mean anticipated Hs 0.32, the genetic diversity index (GDI) 0.01943989, and GD within population 0.01266951. The Hedrick value was 0.52324978, the GD coefficient was 0.52324978, the average Hs was 0.01371452, and the GD coefficient was 0.52324978. Among other countries, Brazil has the highest standard error (SE) rate (1.398), whereas Japan has the highest ratio of Nei's gene diversity (0.01).

CONCLUSIONS

The study's findings will assist in comprehending the shape and kind of complete genome, their streaming genomic sequences, and mutations in various additions of SARS-CoV-2, as well as its different variant strains like Omicron. These results will provide a scientific basis to design the vaccines and understand the genomic study of these viruses.

In silico-prediction of chloroquine as a multi-targeted drug against CDKN2A signaling network associated with cutaneous malignant melanoma

Melanoma is one of the most common skin infections, has triggered significant morbidity and mortality across the globe. Previous studies have reported that mutations in CDKN2A signalling network is associated with cutaneous malignant melanoma. In the present study, initially, the BioGrid database was utilized, and then hierarchical clustering was performed to identify the CDKN2A signature pathways. In addition, a GO Enrichment analysis was investigated using DAVID (n=187 genes) toolkit. Subsequently, the cBioPortal cancer genomic platform was exploited using alteration ranked frequency to determine the role of the CDKN2A signaling network in 363 samples of cutaneous malignant melanoma patients and we find that CDKN2A and its close interactors PTEN and HUWE1 show highest mutations. Further, we systematically employed molecular docking approach via MOE to target PTEN, CDKN2A and HUWE1 with chloroquine which is naturally occurring in medicinal plant Nigella sativa (NS) and observed virtuous interactions between all receptors and ligand molecules with a binding energy of -11.379, -10.324 and -9.06 Kcal/mol, respectively. The outcomes obtained stipulate a vigorous research resource for using chloroquine as a multitargeted anticancer drug. This novel evidence should help the development of effective therapeutic compounds for the treatment of cancer. Our results reveal that chloroquine is a relevant and novel potential therapeutic drug for the treatment of melanoma.

A population study of clinically actionable genetic variation affecting drug response from the Middle East

Clinical implementation of pharmacogenomics will help in personalizing drug prescriptions and alleviate the personal and financial burden due to inefficacy and adverse reactions to drugs. However, such implementation is lagging in many parts of the world, including the Middle East, mainly due to the lack of data on the distribution of actionable pharmacogenomic variation in these ethnicities. We analyzed 6,045 whole genomes from the Qatari population for the distribution of allele frequencies of 2,629 variants in 1,026 genes known to affect 559 drugs or classes of drugs. We also performed a focused analysis of genotypes or diplotypes of 15 genes affecting 46 drugs, which have guidelines for clinical implementation and predicted their phenotypic impact. The allele frequencies of 1,320 variants in 703 genes affecting 299 drugs or class of drugs were significantly different between the Qatari population and other world populations. On average, Qataris carry 3.6 actionable genotypes/diplotypes, affecting 13 drugs with guidelines for clinical implementation, and 99.5% of the individuals had at least one clinically actionable genotype/diplotype. Increased risk of simvastatin-induced myopathy could be predicted in ~32% of Qataris from the diplotypes of SLCO1B1, which is higher compared to many other populations, while fewer Qataris may need tacrolimus dosage adjustments for achieving immunosuppression based on the CYP3A5 diplotypes compared to other world populations. Distinct distribution of actionable pharmacogenomic variation was also observed among the Qatari subpopulations. Our comprehensive study of the distribution of actionable genetic variation affecting drugs in a Middle Eastern population has potential implications for preemptive pharmacogenomic implementation in the region and beyond.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Background

Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

Objective

This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

Methods

The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

Results

We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

Conclusions

This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

Genome wide association study and phylogenetic analysis of novel SARS-COV-2 virus among different countries

Corona Virus (COVID-19) outbreak has threatened the world, since it has become pandemic and spread all over the world. The causative agent SARS-COV2 has proved lethal caused serious public health concern worldwide. Our aims were to describe the SARS-COV-2 genetic connections and check for recombination of all genome. The recombination was investigated by RDP5 and conflicting phylogenetic clustering in individual genomic fragments was established by phylogenetic study by maximum likelihood and Bayesian methods. Our analysis suggests that the available sequences from currently genomes of various strain were retrieved from different countries including Japan, French Republic, Spain, Peru, China, Vietnam, Taiwan, Brazil, U.S.A., South Korea, Sweden, Australia, Nepal, India, Iran, and Italy. The phylogeny of SARS-COV-2 observed the largest number of genome is Vietnam 29891-bp, while France is the smallest member identified with 29679-bp. Using Recombination Detection program5 (RDP5) the china strains was taken as parental strain but there were no recombination in the all strains. In our study we identified the mutation in Pakistani strains in high conserved region of Corona nucleoca super family domain at the nucleotide position (394: C replace with T, Position: 858: C replace with T and Position: 997 G replace A).

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review (Preprint).. [DOI: 10.2196/preprints.29749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

OBJECTIVE

This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

METHODS

The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

RESULTS

We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

CONCLUSIONS

This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

Immunoinformatics approaches to explore B and T cell epitope-based vaccine designing for SARS-CoV-2 Virus

SARS-CoV-2, a new world coronavirus belonging to class Nidovirales of Coronaviridae family causes COVID-19 infection which is the leading cause of death worldwide. Currently there are no approved drugs and vaccines available for the prevention of COVID-19 infection, although couples of immunizations are being tested in clinical trials. However, the present efforts are focused on computational vaccination technique for evaluating candidates to design multi-epitope-based vaccine against pathogenic mechanism of novel SARS-COV-2. Based on recent published evidence, we recognized spike glycoprotein and envelope small membrane protein are the potential targets to combat the pathogenic mechanism of SARS-CoV-2. Similarly, in the present study we identified epitope of both B and T cell associated with these proteins. Extremely antigenic, conserve, immunogenic and nontoxic epitope of B and T cell of Spike protein are WPWYVWLGFI, SRVKNLNSSEGVPDLLV whereas the CWCARPTCIK and YCCNIVNVSL are associated with envelope small membrane protein were selected as potential candidate for vaccine designing. These epitopes show virtuous interaction with HLAA0201 during molecular docking analysis. Under simulation protocol the predicted vaccine candidates show stability. Collectively, this work provides novel potential candidates for epitope-based vaccine designing against COVID-19 infection.