First public dataset to study 2023 Turkish general election.

In the context of Turkiye's most recent parliamentary and presidential elections ("seçim" in Turkish), social media has played an important role in shaping public debate. It is of utmost importance to capture social media trends during the 2023 Turkish elections, since it uncovers a great deal of information of election propaganda, political debates, smear campaigns, and election manipulation by domestic and international actors. We provide a comprehensive dataset for social media researchers to study Turkish elections, develop tools to prevent online manipulation, and gather novel information to inform the public. We are committed to continually improving the data collection and updating it regularly leading up to the election. Using the #Secim2023 dataset, researchers can examine the social and communication networks between political actors, track current trends, and investigate emerging threats to election integrity. Our dataset and analysis code available through Harvard Dataverse and Github, respectively.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer.

BACKGROUND: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. METHODS: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). RESULTS: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). CONCLUSIONS: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Evaluation of the Effect of Cichorium intybus L. on the Liver Enzymes in Burn Patients: A Randomized Double-Blind Clinical Trial.

Burn injuries are considered an important public health problem in the world. Burns are considered the fourth most common kind of trauma in the world, after traffic accidents, falls, and interpersonal violence. Various biochemical agents are involved in the burn healing process such as cytokines (such as IL-6 and TNF-α), antioxidants, and liver and kidney damage biomarkers. Cichorium intybus L. and milk thistle extracts showed a wide range of pharmacological activities such as significant antimicrobial effect and antioxidant activity, as well as anti-inflammatory, antidiabetic, antiproliferative, antiprotozoal, and hepatoprotective effect. Also, these two herbs possess blood-cleansing, detoxifying, laxative, and invigorating activities. Some research confirmed that the preparations of the extract are very suitable for the treatment of nonalcoholic fatty liver disease. This is a double-blind randomized controlled clinical trial. Patients with 2nd and 3rd degree burns have been selected to participate in the study according to the inclusion criteria. A total of 60 patients were selected and divided into intervention and control groups (30 patients in each group). Patients in the intervention group received chicory seed syrup 10 cc three times a day and 1 placebo capsule, and those in the control group received placebo syrup (10 cc three times a day) and one Livergol (140 mg of silymarin in each capsule) capsule. Lab data such as liver function tests, albumin, creatinine, BUN, and hemoglobin were checked every 3 days and 1 week after discharge. The treatment lasted for 4 weeks. According to the results of the study, although the average of liver enzymes at the end of the study does not show a significant difference between the two groups, the level of liver enzymes in each group decreased on the 15th day of the study compared to the first day. This trial is registered with IRCT20180609040016N1.

Immunogenicity of different nanoparticle adjuvants containing recombinant RBD coronavirus antigen in animal model.

Ongoing mutations of SARS-CoV-2 present challenges for vaccine development, promising renewed global efforts to create more effective vaccines against coronavirus disease (COVID-19). One approach is to target highly immunogenic viral proteins, such as the spike receptor binding domain (RBD), which can stimulate the production of potent neutralizing antibodies. This study aimed to design and test a subunit vaccine candidate based on the RBD. Bioinformatics analysis identified antigenic regions of the RBD for recombinant protein design. In silico analysis identified the RBD region as a feasible target for designing a recombinant vaccine. Bioinformatics tools predicted the stability and antigenicity of epitopes, and a 3D model of the RBD-angiotensin-converting enzyme 2 complex was constructed using molecular docking and codon optimization. The resulting construct was cloned into the pET-28a (+) vector and successfully expressed in Escherichia coli BL21DE3. As evidenced by sodium dodecyl-polyacrylamide gel electrophoresis and Western blotting analyses, the affinity purification of RBD antigens produced high-quality products. Mice were immunized with the RBD antigen alone or combined with aluminum hydroxide (AlOH), calcium phosphate (CaP), or zinc oxide (ZnO) nanoparticles (NPs) as adjuvants. Enzyme-linked immunosorbent assay assays were used to evaluate immune responses in mice. In-silico analysis confirmed the stability and antigenicity of the designed protein structure. RBD with CaP NPs generated the highest immunoglobulin G titer compared to AlOH and ZnO after three doses, indicating its effectiveness as a vaccine platform. In conclusion, the recombinant RBD antigen administered with CaP adjuvant NPs induces potent humoral immunity in mice, supporting further vaccine development. These results contribute to ongoing efforts to develop more effective COVID-19 vaccines.

Instabilities in a growing system of active particles: scalar and vectorial systems.

The physics of micron-scale biological colonies usually benefits from different out-of-equilibrium sources. In bacterial colonies and cellular tissues, the growth process is among the important active sources that determine the dynamics. In this article, we study the generic dynamical instabilities associated with the growth phenomena that may arise in both scalar and vectorial systems. In vectorial systems, where the rotational degrees of particles play a role, a phenomenological growth-mediated torque can affect the rotational dynamics of individual particles. We show that such a growth-mediated torque can result in active traveling waves in the bulk of a growing system. In addition to the bulk properties, we analyze the instabilities in the shape of growing interfaces in both scalar and vectorial systems.

CRISPR Typing of Clinical Strains of Salmonella spp. Isolated in Tehran, Iran.

Background: Salmonella is one of the most leading causes of food-born infection and death among infants and people with the poor immunity system. Because Salmonella spp. have diversity in the genome composition and pathogenicity, access to rapid identification and genotyping is necessary to control of salmonellosis. The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) typing is a genotyping method that checks these variable sequences in the bacterial genome in a specific species. This study aimed to differentiate Salmonella strains using CRISPR region. Methods: Salmonella isolates, previously identified via standard microbiological and molecular tests, were subjected to the study. Bacterial DNA was extracted and PCR was done using specific primers. The different PCR products were sequenced and the repeats patterns were used to identify additional or degenerate repeat clusters in the CRISPR region. All different sequences were analyzed using CRISPRtionary tool for dendrogram generation using the binary file. Results: Overall, 119 strains of various Salmonella serovars were used. The result showed unique CRISPR and diversity in spacer both in sequence and the number. Analysis of the extracted sequence and band patterns illustrated that, except for S. infantis, both S. enteritidis and S. typhimurium isolates were classified as a separate cluster. Conclusion: CRISPR genotyping could provide serotype/spacers dictionary and it is performed at low cost and high speed in comparison to the other typing methods. Therefore, the assessment of CRISPR and spacer content can be considered as a powerful and practical discriminatory method for subtyping of Salmonella isolates.

Identification of novel metallo-ß-lactamases inhibitors using ligand-based pharmacophore modelling and structure-based virtual screening.

Metallo-ß-lactamases (MBLs) are a group of enzymes that hydrolyze the most commonly used ß-lactam-based antibiotics, leading to the development of multi-drug resistance. The three main clinically relevant groups of these enzymes are IMP, VIM, and NDM. This study aims to introduce potent novel overlapped candidates from a ZINC database retrieved from the 200,583-member natural library against the active sites of IMP-1, VIM-2, and NDM-1 through a straightforward computational workflow using virtual screening approaches. The screening pipeline started by assessing Lipinski's rule of five (RO5), drug-likeness, and pan-assay interference compounds (PAINS) which were used to generate a pharmacophore model using D-captopril as a standard inhibitor. The process was followed by the consensus docking protocol and molecular dynamic (MD) simulation combined with the molecular mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method to compute the total binding free energy and evaluate the binding characteristics. The absorption, distribution, metabolism, elimination, and toxicity (ADMET) profiles of the compounds were also analyzed, and the search space decreased to the final two inhibitory candidates for B1 subclass MBLs, which fulfilled all criteria for further experimental evaluation.Communicated by Ramaswamy H. Sarma.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Exploring Co-occurrence patterns and microbial diversity in the lung microbiome of patients with non-small cell lung cancer.

BACKGROUND: It has been demonstrated in the literature that a dysbiotic microbiome could have a negative impact on the host immune system and promote disease onset or exacerbation. Co-occurrence networks have been widely adopted to identify biomarkers and keystone taxa in the pathogenesis of microbiome-related diseases. Despite the promising results that network-driven approaches have led to in various human diseases, there is a dearth of research pertaining to key taxa that contribute to the pathogenesis of lung cancer. Therefore, our primary goal in this study is to explore co-existing relationships among members of the lung microbial community and any potential gained or lost interactions in lung cancer. RESULTS: Using integrative and network-based approaches, we integrated four studies assessing the microbiome of lung biopsies of cancer patients. Differential abundance analyses showed that several bacterial taxa are different between tumor and tumor-adjacent normal tissues (FDR adjusted p-value < 0.05). Four, fifteen, and twelve significantly different associations were found at phylum, family, and genus levels. Diversity analyses suggested reduced alpha diversity in the tumor microbiome. However, beta diversity analysis did not show any discernible pattern between groups. In addition, four distinct modules of bacterial families were detected by the DBSCAN clustering method. Finally, in the co-occurrence network context, Actinobacteria, Firmicutes, Bacteroidetes, and Chloroflexi at the phylum level and Bifidobacterium, Massilia, Sphingobacterium, and Ochrobactrum at the genus level showed the highest degree of rewiring. CONCLUSIONS: Despite the absence of statistically significant differences in the relative abundance of certain taxa between groups, it is imperative not to overlook them for further exploration. This is because they may hold pivotal central roles in the broader network of bacterial taxa (e.g., Bifidobacterium and Massilia). These findings emphasize the importance of a network analysis approach for studying the lung microbiome since it could facilitate identifying key microbial taxa in lung cancer pathogenesis. Relying exclusively on differentially abundant taxa may not be enough to fully grasp the complex interplay between lung cancer and the microbiome. Therefore, a network-based approach can offer deeper insights and a more comprehensive understanding of the underlying mechanisms.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data.

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

Spinal Muscular Atrophy with Progressive Myoclonic Epilepsy (SMA-PME): three new cases and review of the mutational spectrum.

BACKGROUND: Spinal muscular atrophy (SMA) could be classified as 5q and non-5q, based on the chromosomal location of causative genes. A rare form of non-5q SMA is an autosomal-recessive condition called spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME), phenotypically characterized by myoclonic and generalized seizures with progressive neurological deterioration. SMA-PME is a clinically heterogeneous disorder that arises from biallelic pathogenic variants in ASAH1 gene. METHODS: Following clinical and primary laboratory assessments, whole-exome sequencing was performed to detect the disease-causing variants in three cases of SMA-PME from different families. Also, Multiplex ligation-dependent probe amplification (MLPA) was employed for determining the copy numbers of SMN1 and SMN2 genes to rule out 5q SMA. RESULTS: Exome sequencing revealed two different homozygous missense mutations (c.109 C > A [p.Pro37Thr] or c.125 C > T [p.Thr42Met]) in exon 2 of the ASAH1 gene in the affected members of the families. Sanger sequencing of the other family members showed the expected heterozygous carriers. In addition, no clinically relevant variant was identified in patients by MLPA. CONCLUSION: This study describes two different ASAH1 mutations and the clinical picture of 3 SMA-PME patients. In addition, previously reported mutations have been reviewed. This study could help to fortify the database of this rare disease with more clinical and genomic data.

Molecular Dynamics Simulation of Antimicrobial Peptide CM15 in Staphylococcus Aureus and Escherichia coli Model Bilayer Lipid.

Background: In animals and plants, antimicrobial peptides (AMPs) are crucial components of defense mechanisms, as they play a crucial role in innate immunity, which protects hosts from pathogenic bacteria. The CM15 has attracted considerable interest as a novel antibiotic against gram-negative and positive pathogens. Objective: The aim of this study was to investigate the permeation potential of the CM15 with membrane bilayers of Staphylococcus aureus and Escherichia coli. Material and Methods: The bilayer membranes of Escherichia coli and Staphylococcus aureus were modelled with the resemblance in lipid composition to its biological sample. This study followed Protein-Membrane Interaction (PMI) through successive applications of molecular dynamics simulation by GROMACS and CHARMM36 force field for two sets of 120-ns simulations. Results: Significant results were obtained from analyzing the trajectory of the unsuccessful insertion of CM15 during simulation. Our data suggested that Lysine residues in CM15 and Cardiolipins in membrane leaflets play a crucial role in stability and interaction terms. Conclusion: The obtained results strengthen the insertion possibility through the toroidal model, which should consider for further studies on AMPs interaction.

An investigation into the relationship between nutritional status, dietary intake, symptoms and health-related quality of life in children and young people with juvenile idiopathic arthritis: a systematic review and meta-analysis.

BACKGROUND: The association between diet, symptoms and health related quality of life in children and young people with Juvenile idiopathic arthritis (JIA) is not clearly understood. The objectives of this systematic review and meta-analysis were to explore the evidence for a relationship between nutritional status, dietary intake, arthritis symptoms, disease activity and health-related quality of life in children and young people with JIA considering both observational and interventional studies separately. METHOD: The databases PubMed, CINAHL, PsycINFO, Web of Science and Cochrane were searched in October 2019, updated in September 2020 and October 2021. Searches were restricted to English language, human and age (2-18 years old). Studies were included if they measured the effect of dietary supplements, vitamins or minerals, or diet in general, on quality of life and/ or arthritis symptom management. Two researchers independently screened titles and abstracts. Full texts were sourced for relevant articles. PRISMA guidelines were used for extracting data. For variables (vitamin D and disease activity), a random-effects meta-analysis model was performed. Two authors using a standardized data extraction form, extracted data independently. RESULTS: 11,793 papers were identified through database searching, 26 studies met our inclusion criteria with 1621 participants. Overall studies quality were fair to good. Results from controlled trial and case control studies with total 146 JIA patients, found that Ɯ-3 PUFA improved the mean active joint count (p < 0.001), Juvenile Arthritis Disease Activity Score (JADAS-27) (p < 0.001) and immune system (≤ 0.05). Furthermore, n-3 and n-6 PUFAs have a negative correlation with CRP (C-reactive protein) and ESR (erythrocyte sedimentation rate) (p < 0.05). Improvement in JIA symptoms were observed in one case, one pilot and one exploratory study with overall 9 JIA patients after receiving Exclusive Enteral Nutrition (EEN) which contains protein and what is required for a complete nutrition, A clinical trial study found Kre-Celazine nutrition (composed of a proprietary alkali buffered, creatine monohydrate and fatty acids mixture) in 16 JIA patients improved symptoms of JIA. No association was found between vitamin D and disease activity from three studies. Height and weight values in relation to healthy controls varied across studies (p = 0.029). CONCLUSIONS: We were only able to include small studies, of lower design hierarchy, mainly pilot studies. We found some evidence of lower height and weight across studies in JIA, but were unable to confirm an association between diet, symptoms and health-related quality of life in children and young people with JIA. Well-designed, carefully measured and controlled interventional studies of dietary patterns in combination with important contributing factors such as medication and lifestyle behaviours, including physical activity, are required to determine the impact of diet in improving symptoms and growth patterns in children and young people with JIA, with an aim to improve the quality of their life. TRIAL REGISTRATION: PROSPERO [CRD42019145587].

Validity and feasibility of remote measurement systems for functional movement and posture assessments in people with axial spondylarthritis.

INTRODUCTION: This study aimed to estimate the criterion validity of functional movement and posture measurement using remote technology systems in people with and without Axial spondylarthritis (axSpA). METHODS: Validity and agreement of the remote-technology measurement of functional movement and posture were tested cross-sectionally and compared to a standard clinical measurement by a physiotherapist. The feasibility of remote implementation was tested in a home environment. There were two cohorts of participants: people with axSpA and people without longstanding back pain. In addition, a cost-consequence analysis was performed. RESULTS: Sixty-two participants (31 with axSPA, 53% female, age = 45(SD14), BMI = 26.6(SD4.6) completed the study. In the axSpA group, cervical rotation, lumbar flexion, lumbar side flexion, shoulder flexion, hip abduction, tragus-to-wall and thoracic kyphosis showed a significant moderate to strong correlation; in the non-back pain group, the same measures showed significant correlation ranging from weak to strong. CONCLUSIONS: Although not valid for clinical use in its current form, the remote technologies demonstrated moderate to strong correlation and agreement in most functional and postural tests measured in people with AxSA. Testing the CV-aided system in a home environment suggests it is a safe and feasible method. Yet, validity testing in this environment still needs to be performed.

Comparative proteomic analysis of mustard lung as a complicated disease using systems biology approach.

During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log2 ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log2 ≤ FC - 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.

An off-the-shelf otoacoustic-emission probe for hearing screening via a smartphone.

Otoacoustic emissions (OAEs) provide information about the function of the outer hair cells of the cochlea. In high-income countries, infants undergo OAE tests as part of the screening protocols for hearing. However, the cost of the necessary equipment hinders early screening for hearing in low- and middle-income countries, which disproportionately bear the brunt of disabling hearing loss. Here we report the design and clinical testing of a low-cost probe for OAEs. The device, which has a material cost of approximately US$10, uses an off-the-shelf microphone and off-the-shelf earphones connected to a smartphone through a headphone jack. It sends two pure tones through each of the headphone's earbuds and algorithmically detects the distortion-product OAEs generated by the cochlea and recorded via the microphone. In a clinical study involving 201 paediatric ears across three healthcare sites, the device detected hearing loss with 100% sensitivity and 88.9% specificity, comparable to the performance of a commercial device. Low-cost devices for OAE testing may aid the early detection of hearing loss in resource-constrained settings.

Engineered cells along with smart scaffolds: critical factors for improving tissue engineering approaches.

In this review, gene delivery and its applications are discussed in tissue engineering (TE); also, new techniques such as the CRISPR-Cas9 system, synthetics biology and molecular dynamics simulation to improve the efficiency of the scaffolds have been studied. CRISPR-Cas9 is expected to make significant advances in TE in the future. The fundamentals of synthetic biology have developed powerful and flexible methods for programming cells via artificial genetic circuits. The combination of regenerative medicine and artificial biology allows the engineering of cells and organisms for use in TE, biomaterials, bioprocessing and scaffold development. The dynamics of protein adsorption at the scaffold surface at the atomic level can provide valuable guidelines for the future design of TE scaffolds /implants.

Performing tympanometry using smartphones.

Background: Tympanometry is used as part of a battery of tests for screening of middle ear function and may help diagnose middle ear disorders, but remains available only on expensive test equipment. Methods: We report a low-cost smartphone-based tympanometer system that consists of a lightweight and portable attachment to vary air pressure in the ear and measure middle ear function. The smartphone displays a tympanogram and reports peak acoustic admittance in realtime. Our programmable and open-source system operates at 226 Hz and was tested on 50 pediatric patient ears in an audiology clinic in parallel with a commercial tympanometer. Results: Our study shows an average agreement of 86 ± 2% between the 100 tympanograms produced by the smartphone and commercial device when five pediatric audiologists classified them into five classes based on the Liden and Jerger classification. Conclusion: Given the accessibility and prevalence of budget smartphones in developing countries, our open-source tool may help provide timely and affordable screening of middle ear disorders.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis.

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.