The hybridoma 192 was used to produce a monoclonal antibody (MAb) against 17-hydroxyprogesterone (17-OHP), for possible use in screening for congenital adrenal hyperplasia (CAH). The factors influencing the MAb production were screened and optimized in a 2 L stirred bioreactor. The production was then scaled up to a 20 L bioreactor. All of the screened factors (aeration rate, stirring speed, dissolved oxygen concentration, pH, and temperature) were found to significantly affect production. Optimization using the response surface methodology identified the following optimal production conditions: 36.8°C, pH 7.4, stirring speed of 100 rpm, 30% dissolved oxygen concentration, and an aeration rate of 0.09 vvm. Under these conditions, the maximum viable cell density achieved was 1.34 ± 0.21 × 10(6) cells mL(-1) and the specific growth rate was 0.036 ± 0.004 h(-1) . The maximum MAb titer was 11.94 ± 4.81 μg mL(-1) with an average specific MAb production rate of 0.273 ± 0.135 pg cell(-1) h(-1) . A constant impeller tip speed criterion was used for the scale-up. The specific growth rate (0.040 h(-1) ) and the maximum viable cell density (1.89 × 10(6) cells mL(-1) ) at the larger scale were better than the values achieved at the small scale, but the MAb titer in the 20 L bioreactor was 18% lower than in the smaller bioreactor. A change in the culture environment from the static conditions of a T-flask to the stirred bioreactor culture did not affect the specificity of the MAb toward its antigen (17-OHP) and did not compromise the structural integrity of the MAb.
Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.
Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.
The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.