Over the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods.
Malnutrition is prevalent among patients hospitalized in Intensive Care Units (ICUs) and causes various complications. Dietary supplementation to provide appropriate nutritional support may reduce the malnutrition and complications through improvement in nutritional status. This study was carried out to assess the association between dietary supplementation and malnutrition among patients in ICUs.
This clinical trial aimed to discover the effects of probiotic soy milk and soy milk on MLH1 and MSH2 promoter methylation, and oxidative stress among type II diabetic patients. Forty patients with type II diabetes mellitus aged 35-68 years were assigned to two groups in this randomized, double-blind, controlled clinical trial. Patients in the intervention group consumed 200 ml/day of probiotic soy milk containing Lactobacillus plantarum A7, while those in the control group consumed 200 ml/d of conventional soy milk for 8 weeks. Fasting blood samples, anthropometric measurements, and 24-h dietary recalls were collected at the baseline and at the end of the study, respectively. Probiotic soy milk significantly decreased promoter methylation in proximal and distal MLH1 promoter region (P 0.05). The consumption of probiotic soy milk improved antioxidant status in type II diabetic patients and may decrease promoter methylation among these patients, indicating that probiotic soy milk is a promising agent for diabetes management.
Recombinant protein production is a significant biotechnological process as it allows researchers to produce a specific protein in desired quantities. Escherichia coli (E. coli) is the most popular heterologous expression host for the production of recombinant proteins due to its advantages such as low cost, high-productivity, well-characterized genetics, simple growth requirements and rapid growth. There are a number of factors that influence the expression level of a recombinant protein in E. coli which are the gene to be expressed, the expression vector, the expression host, and the culture condition. The major motivation to develop our database, EcoliOverExpressionDB, is to provide a means for researchers to quickly locate key factors in the overexpression of certain proteins. Such information would be a useful guide for the overexpression of similar proteins in E. coli. To the best of the present researchers' knowledge, in general and specifically in E. coli, EcoliOverExpressionDB is the first database of recombinant protein expression experiments which gathers the influential parameters on protein overexpression and the results in one place.
Primary dysmenorrhea is a womanhood problem around the world and negatively affects quality of life. This study was designed to investigate the prevalence of primary dysmenorrhea and to determine the factors associated with its intensity. A cross-sectional study was carried out among 311 undergraduate female students aged 18 to 27 years in Isfahan University of Medical Sciences, Iran. Socio-demographic characteristics and menstrual factors were obtained through interviews with the help of a pretested questionnaire. The prevalence of primary dysmenorrhea was 89.1%. Residing at home, younger age, lower number of years of formal education for the mother, positive family history of dysmenorrhea, higher severity of bleeding, and shorter menstrual period intervals were significantly associated with the higher intensity of primary dysmenorrhea. Primary dysmenorrhea is a common health concern among young women. Being aware of the factors that are associated with its intensity makes it possible for health professionals to organize better focused programs to reduce the adverse effects of dysmenorrhea.
Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein.
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.