Molecular cloning,characterization, and expression of Cuc m 2, a major allergen in Cucumis melo

Background:

Several studies reported the clinical features of IgE-mediated hypersensitivity after ingestion of melon. Melon allergy is a common IgE-mediated fruit allergy in Iran. This prompted us to investigate immunochemical and molecular properties of the major allergen in melon fruit, to compare the IgE-binding capacity of the natural protein with the recombinant allergen, and to determine cross-reactivity of the major allergen with closely-related allergens from other plants displaying clinical cross-reactivity with melon.

Methods:

Identification and molecular characterization of the major melon allergen were performed using IgE immunoblotting, allergen-specific ELISA, affinity-based purifications, cross-inhibition assays, cloning, and expression of the allergen in Escherichia coli.

Results:

Melon profilin was identified and isolated as a major IgE-binding component and designated as Cuc m 2. Sequencing corresponding cDNA revealed an open reading frame of 363 bp coding for 131 amino acid residues and two fragments of 171 bp and 383 bps for the 5’and 3’ UTRs, respectively. Significant cross-reactivity was found between melon profilin and Cynodon dactylon, tomato, peach, and grape profilins in cross-inhibition assays. Although the highest degree of amino acid identity was revealed with watermelon profilin, there was no significant cross-reactivity between melon and watermelon profilins.

Conclusion:

Melon profilin is the major IgE-binding component in melon extract, and the recombinant and natural forms exhibited similar IgE-binding capacities. A part of the fruit-fruit and pollen-fruit cross-reactions could be explained by the presence of this conserved protein; however, sequence homology provides insufficient information to predict IgE cross-reactivity of profilins. Key Words: Melon, Profilin, Cross-reactivity, Fruit allergy, Recombinant allergen     

Otomycosis Due to the Rare Fungi Talaromyces purpurogenus,Naganishia albida and Filobasidium magnum

Mycopathologia - Otomycosis is a common finding in otorhinolaryngology clinics and is usually caused by species of Candida and Aspergillus, particularly black aspergilli. Meanwhile, other fungi can...     

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.     

A symbiotic brain-machine interface through value-based decision making

Background

In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users'' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC).

Methodology

The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc.

Conclusions

Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain.     

A common variant in the adiponectin gene and polycystic ovary syndrome risk

In this study, we explored whether polymorphisms in insulin receptor (INSR), adiponectin (ADIPOQ), parathyroid hormone (PTH), and vitamin D receptor (VDR) genes are associated with polycystic ovary syndrome (PCOS). A total of 362 subjects, including 181 women with PCOS and 181 controls were enrolled in this case-control study. Two SNPs (rs2059806 and rs1799817) in the INSR gene, two SNPs (rs2241766 and rs1501299) in the ADIPOQ gene, one SNP (rs6256) in the PTH gene, and one SNP (rs757343) in the VDR gene were analyzed using PCR-RFLP method. We observed no significant difference in genotype and allele frequencies between the women with PCOS and controls for the rs2059806, rs1799817, rs1501299, rs6256, and rs757343 polymorphisms either before or after adjustment for confounding factors including age and BMI. However, the ADIPOQ rs2241766 “TT” genotype compared with “TG and GG” genotypes was associated with a 1.93-fold increased risk for PCOS (P = 0.006, OR = 1.93, 95% CI = 1.20–3.11), and the differences remained significant after adjustment for age and BMI (P = 0.039, OR = 1.72, 95% CI = 1.03–2.86). Furthermore, the ADIPOQ rs2241766 “T” allele was significantly overrepresented in women with PCOS than controls (P = 0.006; OR = 1.80, 95% CI = 1.18–2.70), and the difference remained significant after Bonferroni correction. Our findings suggest that the ADIPOQ rs2241766 “TT” genotype is a marker of increased PCOS susceptibility. This study also indicates for the first time that there are no significant association between INSR rs2059806, PTH rs6256, and VDR rs757343 gene polymorphisms and PCOS risk. However, these data remain to be confirmed in larger studies and in other populations.     

The role of vascular endothelial growth factor +405 G/C polymorphism and albuminuria in patients with type 2 diabetes mellitus

Observations on the association between the vascular endothelial growth factor (VEGF) gene polymorphism and nephropathy have been inconsistent, which might be due to ethnic and geographical variations. Furthermore, the relationship between +405 G/C polymorphism and albuminuria in the diabetic population has not been sufficiently studied. The aim of this study was to evaluate for the first time the possible association between +405 G/C polymorphism and albuminuria in an population from Tehran of Iran. A total of 255 consecutive patients with type 2 diabetes and microalbuminuria (Group A) and 235 patients with type 2 diabetes and normoalbuminuria (Group B) were included. Polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) were used to detect the VEGF alleles. In univariate analysis, the groups were statistically similar in all variables except for HbA1c (8.53 ± 1.7 in Group A vs. 8.2 ± 1.73 in Group B; P = 0.034), 24-h urinary albumin (201.33 ± 84.8 in Group A vs. 22.88 ± 3.5 in Group B; P < 0.001), and the frequency of GG genotype (31% in Group A vs. 18.7% in Group B; P = 0.006). The GG genotype was the independent predictor of albuminuria [P = 0.014, OR = 1.771, 95% confidence interval (CI) = 1.124–2.790]. Our study showed that the G allele was not associated with albuminuria, but the GG genotype in the VEGF gene is independently associated with development of nephropathy in the our diabetic population.     

Targeting siRNA in colorectal cancer therapy: Nanotechnology comes into view

Colorectal cancer (CRC) is known as one of the most important causes of death and mortality worldwide. Although several efforts have been made for finding new therapies, no achievements have been made in this area. Multidrug resistance (MDR) mechanisms are one of the key factors that could lead to the failure of chemotherapy. Moreover, it has been shown that various chemotherapy drugs are associated with several side effects. Hence, it seems that finding new drugs or new therapeutic platforms is required. Among different therapeutic approaches, utilization of nanoparticles (NPs) for targeting a variety of molecules such as siRNAs are associated with good results for the treatment of CRC. Targeting siRNA-mediated NPs could turn off the effects of oncogenes and MDR-related genes. In the current study, we summarized various siRNAs targeted by NPs which could be used for the treatment of CRC. Moreover, we highlighted other routes such as liposome for targeting siRNAs in CRC therapy.     

PASylation Enhances the Stability,Potency, and Plasma Half‐Life of Interferon α‐2a: A Molecular Dynamics Simulation

In this study, the effectiveness of PASylation in enhancing the potency and plasma half‐life of pharmaceutical proteins has been accredited as an alternative technique to the conventional methods such as PEGylation. Proline, alanine, and serine (PAS) chain has shown some advantages including biodegradability improvement and plasma half‐life enhancement while lacking immunogenicity or toxicity. Although some experimental studies have been performed to find the mechanism behind PASylation, the detailed mechanism of PAS effects on the pharmaceutical proteins has remained obscure, especially at the molecular level. In this study, the interaction of interferon α‐2a (IFN) and PAS chain is investigated using molecular dynamics simulation method. Several important parameters including secondary structure, root‐mean‐square distance, and solvent accessible surface area to investigate the stability, bioavailability, and bioactivity of the PASylated protein are studied. The results demonstrate that IFN conformation is not affected critically through PASylation while it results in improvement of the protein stability and bioactivity. Therefore, PASylation can be considered as a proper biological alternative technique to increase the plasma half‐life of the biopharmaceutical proteins through enlarging apparent volume. The proposed simulation represents a computational approach that would provide a basis for the study of PASylated pharmaceutical proteins for different future applications.