HPV16-E7 Protein T Cell Epitope Prediction and Global Therapeutic Peptide Vaccine Design Based on Human Leukocyte Antigen Frequency: An In-Silico Study

International Journal of Peptide Research and Therapeutics - Cervical cancer is the second most common leading cause of women's death due to cancer worldwide, about 528,000 patients’...     

Extra EF Hand Unit (DX) Mediated Stabilization and Calcium Independency of α-Amylase

It is the common feature of α-amylases that calcium ion is required for their structural integrity and thermal stability. All amylases have at least one Ca²⁺ per molecule; therefore amino acids involved in calcium binding are specific and conserved. In this study, sequence analysis revealed the presence of EF-hand-like motif in calcium-binding loop of Bacillus megaterium WHO (BMW)-amylase that was previously isolated from BMW. The EF-hand motif and its variants (EF-hand-like motif) are the most common calcium-binding motifs found in a large number of protein families. To investigate the effect of calcium ion on the thermal stability and activity of BMW-amylase, we used site-directed mutagenesis to replace histidine 58 with Asp (D), Ile (I), Tyr (Y), Phe (F), and Arg (R) at the seventh position of EF-hand-like motif. Upon the addition of an extra DX unit to the calcium-binding loop in H58D variant, thermal stability, catalytic activity, and chelating power of the enzyme improved due to higher affinity toward calcium. H58D variant demonstrated calcium independency compared to the wild type and other created mutants. Conformational changes in the presence and absence of Ca²⁺ were monitored using fluorescence technique.     

Bovine serum albumin/gold nanoparticles as a drug delivery system for Curcumin: experimental and computational studies

Abstract

Communicated by Ramaswamy H. Sarma     

Fatal Invasive Pulmonary Aspergillosis in COVID-19 Patient with Acute Myeloid Leukemia in Iran

Mycopathologia - Although patients with severe immunodeficiency and hematological malignancies has been considered at highest risk for invasive fungal infection, patients with severe pneumonia due...     

Blood and tissue levels of lncRNAs in periodontitis

Periodontitis is a complex disorder that affects a large number of human beings from different ethnic groups. This condition has been associated with dysregulation of a number of genes, among them are long noncoding RNAs (lncRNAs). In the current study, we assessed the expression of four lncRNAs (BDNF-AS, MIAT, MIR137HG, and PNKY) as well as BDNF in the peripheral blood and gingival tissues obtained from patients with periodontitis and healthy subjects. The expression of BDNF was significantly lower in blood samples of male patients with periodontitis compared with male controls (posterior β of RE = −4.754, p = .048). However, there was no significant difference in the expression of BDNF in tissue samples from the cases and controls. The expression of BDNF-AS was significantly lower in the tissue samples of patients compared with control tissue samples (posterior β of RE = −2.151, p = .019). Such an expression difference was detected between male subgroups as well (posterior β of RE = −3.679, p = .009). However, expression of this lncRNA was not different in blood samples obtained from patients compared with healthy subjects. The expression of PNKY was significantly higher in tissue samples obtained from female patients compared with sex-matched controls (posterior β of RE = 6.23, p = .037). Blood levels of this lncRNA were not different between cases and controls. There was no significant difference either in the tissue expression or in blood expression of MIR137HG or MIAT between cases and controls. The current study indicates the putative role of BDNF, BDNF-AS, and PNKY in the pathophysiology of periodontitis and potentiates these genes as candidates for functional studies.     

Effects of Probiotic Yogurt on Serum Omentin-1, Adropin,and Nesfatin-1 Concentrations in Overweight and Obese Participants Under Low-Calorie Diet

Probiotics and Antimicrobial Proteins - Data on the effects of probiotics on adipokines such as omentin-1, nesfatin-1, and adropin are limited. The aim of this study was to evaluate the effects of...     

A novel high sensitivity HPLC assay for topiramate, using 4-chloro-7-nitrobenzofurazan as pre-column fluorescence derivatizing agent

A new, sensitive and simple high-performance liquid chromatographic method for analysis of topiramate, an antiepileptic agent, using 4-chloro-7-nitrobenzofurazan as pre-column derivatization agent is described. Following liquid-liquid extraction of topiramate and an internal standard (amlodipine) from human serum, derivatization of the drugs was performed by the labeling agent in the presence of dichloromethane, methanol, acetonitrile and borate buffer (0.05 M; pH 10.6). A mixture of sodium phosphate buffer (0.05 M; pH 2.4): methanol (35:65 v/v) was eluted as mobile phase and chromatographic separation was achieved using a Shimpack CLC-C18 (150 x 4.6 mm) column. In this method the limit of quantification of 0.01 microg/mL was obtained and the procedure was validated over the concentration range of 0.01 to 12.8 microg/mL. No interferences were found from commonly co-administrated antiepileptic drugs including phenytoin, phenobarbital carbamazepine, lamotrigine, zonisamide, primidone, gabapentin, vigabatrin, and ethosuximide. The analysis performance was carried-out in terms of specificity, sensitivity, linearity, precision, accuracy and stability and the method was shown to be accurate, with intra-day and inter-day accuracy from -3.4 to 10% and precise, with intra-day and inter-day precision from 1.1 to 18%.     

In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes

The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development.Trial Registration: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00881920","term_id":"NCT00881920"}}NCT00881920.