Development of a mono‐specific anti‐VEGF bivalent nanobody with extended plasma half‐life for treatment of pathologic neovascularization

Vascular endothelial growth factor (VEGF) plays a crucial role in angiogenesis within solid cancers. Thus, targeting VEGF might be part of a feasible therapy for treating pathological neovascularization, and nanobodies ? derived from heavy chain‐only antibodies occurring within Camelidae ? are a novel class of nanometer‐sized antibodies possessing unique properties that could be developed into a promising therapeutic. However, nanobodies have a very short half‐life in vivo due to their small size. Development of a bivalent nanobody is one way to remediate the half‐life problem of nanobodies. Two identical anti‐VEGF nanobodies were connected using the hinge region of llama IgG2c. The recombinant plasmid (pHEN6c‐bivalent nanobody) was transformed into E.coli WK6 cells and expression of the bivalent nanobody construct was induced with 1mM Isopropyl β‐D‐1‐thiogalactopyranoside (IPTG). Recombinant bivalent nanobody was purified using nickel affinity chromatography and its activity on human endothelial cells was assessed using 3‐(4,5‐Dimethylthiazol‐2‐yr)‐2,5‐diphenyltetrazolium bromide (MTT), tube formation, and cell migration assays. The pharmacokinetic study was performed after intravenous (i.v.) injection of recombinant bivalent nanobody into six‐week‐old C57BL/6 mice. Recombinant bivalent nanobody performed significantly better than monovalent nanobody in inhibiting proliferation, tube formation, and migration of human endothelial cells. Pharmacokinetic results showed a 1.8‐fold longer half‐life of bivalent nanobody in comparison with the monovalent nanobody. These results underscore the potential of recombinant anti‐VEGF bivalent nanobody as a promising tool for development of a novel therapeutic with an extended plasma half‐life for VEGF‐related diseases.     

Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.     

Potential role of Nigella sativa supplementation with physical activity in prophylaxis and treatment of COVID-19: a contemporary review

Sport Sciences for Health - The widespread prevalence and mortality of coronavirus diseases-2019 (COVID-19) lead many researchers to study the SARS-CoV-s2 infection to find a treatment for this...     

The Production and Characterization of Novel Heavy-Chain Antibodies Against the Tandem Repeat Region of MUC1 Mucin

Camelidae are known to produce immunoglobulins (Igs) devoid of light chains and constant heavy-chain domains (CH₁). Antigen-specific fragments of these heavy-chain IgGs (VHH) are of great interest in biotechnology applications. This paper describes the first example of successfully raised heavy-chain antibodies in Camelus dromedarius (single-humped camel) and Camelus bactrianus (two-humped camel) against a MUC1 related peptide that is found to be an important epitope expressed in cancerous tissue. Camels were immunized against a synthetic peptide corresponding to the tandem repeat region of MUC1 mucin and cancerous tissue preparation obtained from patients suffering from breast carcinoma. Three IgG subclasses with different binding properties to protein A and G were purified by affinity chromatography. Both conventional and heavy-chain IgG antibodies were produced in response to MUC1-related peptide. The elicited antibodies could react specifically with the tandem repeat region of MUC1 mucin in an enzyme linked immunosorbant assay (ELISA). Anti-peptide antibodies were purified after passing antiserum over two affinity chromatography columns. Using ELISA, immunocytochemistry and Western blotting, the interaction of purified antibodies with different antigens was evaluated. The antibodies were observed to be selectively bound to antigens namely: MUC1 peptide (tandem repeat region), human milk fat globule membrane (HMFG), deglycosylated human milk fat globule membrane (D-HMFG), homogenized cancerous breast tissue and a native MUC1 purified from ascitic fluid. K_a values of specific polyclonal anti-peptide antibodies were estimated in C. dromedarius and C. bactrianus, as 7 × 10¹⁰ M^{? 1} and 1.4 × 10¹⁰ M^{? 1} respectively.     

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

Methods

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

Results

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

Conclusion

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

     

Distress tolerance and special alcohol metacognitions behave differently in the association of negative affect with alcohol-related patterns in men with problematic alcohol use in the abstinence phase

Negative affect may be related to alcohol-related patterns (e.g., craving and problematic alcohol use). Distress intolerance and positive and negative alcohol-related metacognitions may be underlying mechanisms in this link. This study aimed to evaluate the effect of negative affect including depressive, anxious, and stress symptoms on alcohol craving and problematic alcohol use via the paths of distress tolerance and both positive and negative alcohol-related metacognitions. Three hundred men with problematic alcohol use during the abstinence phase completed psychological and clinical measures. Results showed that craving and negative alcohol metacognitions mediated the relationship between negative affect and problematic alcohol use. Negative affect had a direct and positive effect on craving and indirect effect via distress intolerance and positive alcohol metacognitions. In turn, distress intolerance and positive alcohol metacognitions indirectly and positively affected problematic alcohol use via craving. The study indicates that distress tolerance and distinct alcohol metacognitions may be differently related to various patterns of alcohol-related problems, such that alcohol drinkers with high levels of negative affect, distress intolerance, and positive alcohol metacognitions show higher levels of craving, while high negative affect in relation to high negative alcohol metacognitions and alcohol craving is related to the perpetuation of alcohol use or problematic alcohol use.     

Cone Beam Computed Tomography Analysis of Sphenoid Sinus Pneumatization and Relationship with Neurovascular Structures

BackgroundThe sphenoid sinus is considered as the most variable pneumatized structure of the skull.PurposeThe aim of the present study was to determine the prevalence of the Onodi cell as well as to evaluate the relationship between the sphenoid sinus type of pneumatization and the presence of surrounding neurovascular protrusion using cone beam computed tomography (CBCT).MethodsThe CBCT images of 500 patients/996 sides [203 males (40.6%) and 297 females (59.4%)] were analyzed in this study. The type of sphenoid sinus pneumatization, prevalence of internal carotid artery (ICA) and optic nerve (ON) protrusion and dehiscence, and also the frequency of Onodi cell were assessed.ResultsThe percentages of the conchal, presellar, sellar, postsellar (a), and postsellar (b) types of pneumatization were 1%, 11.5%, 35.5%, 38.9%, and 13.1%, respectively. The more the sphenoid sinuses pneumatized, the greater the frequency of ON and ICA protrusion and dehiscence of their wall to the sinus. The prevalence of Onodi cell was 38.8%. A significant correlation was found between ON dehiscence and the presence of Onodi cells.ConclusionThe present study demonstrated a significant relationship between the sinus type and frequency of neurovascular protrusions. Therefore, the sphenoid sinus extent of pneumatization might be useful in predicting the risk of iatrogenic damage to the surrounding structures.     

Dynamics of host immune responses to SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most recent global health threat, is spreading throughout the world with worrisome speed, and the current wave of coronavirus disease 2019 (COVID-19) seems to have no mercy. While this mysterious virus challenges our ability to control viral infections, our opportunities to control the COVID-19 pandemic are gradually fading. Currently, pandemic management relies on preventive interventions. Although prevention is a good strategy to mitigate SARS-CoV-2 transmission, it still cannot be considered an absolute solution to eliminate this pandemic. Currently, developing a potent immunity against this viral infection seems to be the most promising strategy to drive down this ongoing global tragedy. However, with the emergence of new challenges in the context of immune responses to COVID-19, the road to control this devastating pandemic seems bumpier; thus, it is pivotal to characterize the dynamics of host immune responses to COVID-19, in order to develop efficient prophylactic and therapeutic tools. This begs the question of whether the effector mechanisms of the immune system are indeed potent or a possible contributing factor to developing more severe and lethal forms of COVID-19. In this review, the possible role of the immunopathologic phenomena including antibody-dependent enhancement, cytokine storm, and original antigenic sin in severity and mortality of COVID-19 will be discussed.