Evaluation of human papillomavirus type 16 viral load and genome physical status in Iranian women with cervical disease

BACKGROUND

This study examined the viral load and physical status of the human papillomavirus 16 (HPV-16) genome in non-cancerous, precancerous and cancerous cervical lesions.

METHODS

Quantitative real-time PCR was performed to determine HPV-16 E2 and E6 viral load in 132 cervical specimens. E2/E6 viral load ratio was used to determine the physical status of HPV-16 genome.

RESULTS

E2 gene viral load was a significant (P < 0.001) predicting biomarker in differentiating non-cancerous from precancerous and cancerous samples. E6 gene viral load was significantly different between the groups (P < 0.001). The specificity and sensitivity of E2 and E6 in distinguishing SCC samples were 100% and 95% respectively.

CONCLUSION

HPV-16 viral load measured through E2 and E6 genes is a reliable indicator of lesion type.

Retinal Pigment Epithelium Regeneration by Induced Pluripotent Stem Cells; Therapeutic and Modelling Approaches on Retinal Degenerative Diseases

Retinal degenerative diseases (RDDs) are irreversible ocular damages categorized as retinopathies. RDDs affect about 0.05% of individuals worldwide. The degenerations of RPE cells are involved in inherited and age-related RDDs. After the invention of induced pluripotent stem cells (iPSC) by Yamanaka, a promising avenue has been opened to regenerative medicine and disease modeling. Retinal pigment epithelium (RPE) degeneration related-RDDs are also affected by iPSCs. IPSC-derived RPE cells created a novel method for treating the RPE degeneration related- RDDs and retinal diseases modeling to find a new therapeutic approach or drug development. There are various studies based on iPSC-derived RPE cells reporting the investigation of the role of a specific mutation, protein, signaling pathway, etc., responsible for a type of RDD. Furthermore, iPSC-based RPE therapy is expanded to include some clinical trials. Despite the incredible growth rate in iPSC-based studies on RPE-related diseases, there are some challenges, i.e., teratoma formation potential of iPSCs, an expensive procedure of iPSC-based regeneration of RPEs, lack of a universal protocol or cellular product applicable in all patients, etc. This article reviews the iPSC-based RPE generation and their therapeutic applications, studies on RPE-related molecular and cellular pathophysiologic features of RDD in the iPSC-based models, future perspectives, and the challenges ahead.

Taheri-Saramad x-ray detector (TSXD): a novel high spatial resolution x-ray imager based on ZnO nano scintillator wires in polycarbonate membrane

A novel x-ray imager based on ZnO nanowires is designed and fabricated. The proposed architecture is based on scintillation properties of ZnO nanostructures in a polycarbonate track-etched membrane. Because of higher refractive index of ZnO nanowire compared to the membrane, the nanowire acts as an optical fiber that prevents the generated optical photons to spread inside the detector. This effect improves the spatial resolution of the imager. The detection quantum efficiency and spatial resolution of the fabricated imager are 11% and <6.8 μm, respectively.

A Novel Scheme for Optimal Control of a Nonlinear Delay Differential Equations Model to Determine Effective and Optimal Administrating Chemotherapy Agents in Breast Cancer

BACKGROUND

Determining the optimal and effective scheme for administrating the chemotherapy agents in breast cancer is the main goal of this scientific research. The most important issue here is the amount of drug or radiation administrated in chemotherapy and radiotherapy for increasing patient's survival. This is because in these cases, the therapy not only kills the tumor cells, but also kills some of the healthy tissues and causes serious damages. In this paper we investigate optimal drug scheduling effect for breast cancer model which consist of nonlinear ordinary differential time-delay equations.

METHODS

In this paper, a mathematical model of breast cancer tumors is discussed and then optimal control theory is applied to find out the optimal drug adjustment as an input control of system. Finally we use Sensitivity Approach (SA) to solve the optimal control problem.

RESULTS

The goal of this paper is to determine optimal and effective scheme for administering the chemotherapy agent, so that the tumor is eradicated, while the immune systems remains above a suitable level. Simulation results confirm the effectiveness of our proposed procedure.

CONCLUSION

In this paper a new scheme is proposed to design a therapy protocol for chemotherapy in Breast Cancer. In contrast to traditional pulse drug delivery, a continuous process is offered and optimized, according to the optimal control theory for time-delay systems.

Classification of restrictive and obstructive pulmonary diseases using spirometry data

Spirometry provides useful information for diagnosis purposes in the pulmonary system. A decision support system could help in using this useful noninvasive test more reliably. Bayesian reasoning could aggregate the results of a group of neural network classifiers to improve the diagnosis of pulmonary diseases and discriminating between its restrictive and obstructive patterns.

Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network

A new method based on adaptive linear neuron (Adaline) is used to estimate the radon concentration in soil associated with the environmental parameters. Analysis of the data obtained from a site in Thailand indicates that our proposed method is able to differentiate temporal variation of radon concentration related to the environmental parameters from those caused by phenomena in the earth (e.g. earthquake). The result also shows agreement between our method and another method based on impulse responses from multivariable time series (complex mathematical equations).

Neutron spectra unfolding in Bonner spheres spectrometry using neural networks

The neural network method has been used for the unfolding of neutron spectra in neutron spectrometry by Bonner spheres. A back propagation algorithm was used for training of neural networks. 4 mm x 4 mm bare LiI (Eu) and in a polyethylene sphere set: 2, 3, 4, 5, 6, 7, 8, 10, 12, 18 inch diameter have been used for unfolding of neutron spectra. Neural networks were trained by 199 sets of neutron spectra, which were subdivided into 6, 8, 10, 12, 15 and 20 energy bins and for each of them an appropriate neural network was designed and trained. The validation was performed by the 21 sets of neutron spectra. A neural network with 10 energy bins which had a mean value of error of 6% for dose equivalent estimation of spectra in the validation set showed the best results. The obtained results show that neural networks can be applied as an effective method for unfolding neutron spectra especially when the main target is neutron dosimetry.

Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction

A layered neural network (LNN) has been employed to estimate the radon concentration in soil related to the environmental parameters. This technique can find any functional relationship between the radon concentration and the environmental parameters. Analysis of the data obtained from a site in Thailand indicates that this approach is able to differentiate time variation of radon concentration caused by environmental parameters from those arising by anomaly phenomena in the earth (e.g. earthquake). This method is compared with a linear computational technique based on impulse responses from multivariable time series. It is indicated that the proposed method can give a better estimation of radon variations related to environmental parameters that may have a non-linear effect on the radon concentration in soil, such as rainfall.