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Recombinant Proteins

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Interpreting IGF-1 in children treated with recombinant growth hormone: challenges during early puberty.

Frontiers in endocrinology
OBJECTIVE: It can be challenging to determine the correct dosage of recombinant growth hormone (GH) in children with GH deficiency, leading to highly variable treatment responses. Insulin-like growth factor-1 (IGF-1) is a tool for monitoring GH treat...

High-Yield Preparation of American Oyster Defensin (AOD) via a Small and Acidic Fusion Tag and Its Functional Characterization.

Marine drugs
The marine peptide, American oyster defensin (AOD), is derived from and exhibits a potent bactericidal effect. However, recombinant preparation has not been achieved due to the high charge and hydrophobicity. Although the traditional fusion tags suc...

Clinical cure induced by pegylated interferon α-2b in the advantaged population of chronic hepatitis B virus infection: a retrospective cohort study.

Frontiers in cellular and infection microbiology
BACKGROUND: Among the advantaged population with clinical cure of chronic hepatitis B, chronic inactive hepatitis B virus carriers (IHCs) and nucleoside analog-experienced patients have similar serological manifestations. This study established non-i...

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor.

Biotechnology and bioengineering
As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amo...

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling.

Biotechnology and applied biochemistry
Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such ...

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods.

International journal of biological macromolecules
Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key rol...

Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.

Biotechnology and bioengineering
Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by variou...

Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment.

International journal of medical informatics
OBJECTIVE: Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an important drug for this purpose,but even in the hepatitis B e antigen(HBeAg)-negative population,there is still a p...

A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.

Nucleic acids research
Species-specific differences in protein translation can affect the design of protein-based drugs. Consequently, efficient expression of recombinant proteins often requires codon optimization. Publicly available optimization tools do not always result...

Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models.

Scientific reports
This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms. Various models, including AdaBoost, Decision Tree Regression (D...