Progress in Biophysics & Molecular Biology最新文献

The widespread usage of smartphones has made accessing vast troves of data easier for everyone. Smartphones are powerful, handy, and easy to operate, making them a valuable tool for improving public health through diagnostics. When combined with other devices and sensors, smartphones have shown potential for detecting, visualizing, collecting, and transferring data, enabling rapid disease diagnosis. In resource-limited settings, the user-friendly operating system of smartphones allows them to function as a point-of-care platform for healthcare and disease diagnosis. Herein, we critically reviewed the smartphone-based biosensors for the diagnosis and detection of diseases caused by infectious human pathogens, such as deadly viruses, bacteria, and fungi. These biosensors use several analytical sensing methods, including microscopic imaging, instrumental interface, colorimetric, fluorescence, and electrochemical biosensors. We have discussed the diverse diagnosis strategies and analytical performances of smartphone-based detection systems in identifying infectious human pathogens, along with future perspectives.

Over the last four decades, methodological innovations have continuously changed transcriptome profiling. It is now feasible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples using RNA sequencing (RNA-seq). These transcriptomes serve as a connection between cellular behaviors and their underlying molecular mechanisms, such as mutations. This relationship, in the context of cancer, provides a chance to unravel tumor complexity and heterogeneity and uncover novel biomarkers or treatment options. Since colon cancer is one of the most frequent malignancies, its prognosis and diagnosis seem to be critical. The transcriptome technology is developing for an earlier and more accurate diagnosis of cancer which can provide better protectivity and prognostic utility to medical teams and patients. A transcriptome is a whole set of expressed coding and non-coding RNAs in an individual or cell population. The cancer transcriptome includes RNA-based changes. The combined genome and transcriptome of a patient may provide a comprehensive picture of their cancer, and this information is beginning to affect treatment decision-making in real-time. A full assessment of the transcriptome of colon (colorectal) cancer has been assessed in this review paper based on risk factors such as age, obesity, gender, alcohol use, race, and also different stages of cancer, as well as non-coding RNAs like circRNAs, miRNAs, lncRNAs, and siRNAs. Similarly, they have been examined independently in the transcriptome study of colon cancer.

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Autophagy is a highly conserved intracellular degradation system in eukaryotes that maintains cellular and tissue homeostasis. Upon autophagy induction, cytoplasmic components are engulfed by a double-membrane organelle called the autophagosome that fuses with a lysosome to degrade its contents. In recent years, it has become clear that autophagy becomes dysregulated with aging, which leads to age-related diseases. Kidney function is particularly prone to age-related decline, and aging is the most significant risk factor for chronic kidney disease. This review first discuss the relationship between autophagy and kidney aging. Second, we describe how age-related dysregulation of autophagy occurs. Finally, we discuss the potential of autophagy-targeting drugs to ameliorate human kidney aging and the approaches necessary to discover such agents.

This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.

Glycometabolism is well known for its roles as the main source of energy, which mainly includes three metabolic pathways: oxidative phosphorylation, glycolysis and pentose phosphate pathway. The orderly progress of glycometabolism is the basis for the maintenance of cardiovascular function. However, upon exposure to harmful stimuli, the intracellular glycometabolism changes or tends to shift toward another glycometabolism pathway more suitable for its own development and adaptation. This shift away from the normal glycometabolism is also known as glycometabolism reprogramming, which is commonly related to the occurrence and aggravation of cardiovascular diseases. In this review, we elucidate the physiological role of glycometabolism in the cardiovascular system and summarize the mechanisms by which glycometabolism drives cardiovascular diseases, including diabetes, cardiac hypertrophy, heart failure, atherosclerosis, and pulmonary hypertension. Collectively, directing GMR back to normal glycometabolism might provide a therapeutic strategy for the prevention and treatment of related cardiovascular diseases.

Malignancies are the leading human health threat worldwide. Despite rapidly developing treatments, poor prognosis and outcome are still common. Magnetic fields have shown good anti-tumoral effects both in vitro and in vivo, and represent a potential non-invasive treatment; however, the specific underlying molecular mechanisms remain unclear. We here review recent studies on magnetic fields and their effect on tumors at three different levels: organismal, cellular, and molecular. At the organismal level, magnetic fields suppress tumor angiogenesis, microcirculation, and enhance the immune response. At the cellular level, magnetic fields affect tumor cell growth and biological functions by affecting cell morphology, cell membrane structure, cell cycle, and mitochondrial function. At the molecular level, magnetic fields suppress tumors by interfering with DNA synthesis, reactive oxygen species level, second messenger molecule delivery, and orientation of epidermal growth factor receptors. At present, scientific experimental evidence is still lacking; therefore, systematic studies on the biological mechanisms involved are urgently needed for the future application of magnetic fields to tumor treatment.

I argue that Baverstock's demotion of the gene in favour of the cellular phenotype is still too limited, and that phenotypes must be considered at multiple irreducible levels. I emphasise process thinking and the significance of agency in living systems.

An increasing body of data are revealing key roles of epigenetics in evolutionary processes. The scope of this manuscript is to assemble in a coherent frame experimental evidence supporting a role of epigenetic factors and networks, active during embryogenesis, in orchestrating variation-inducing phenomena underlying evolution, seen as a global process. This process unfolds over two crucial levels: i) a flow of RNA-based information - predominantly small regulatory RNAs released from somatic cells exposed to environmental stimuli – taken up by spermatozoa and delivered to oocytes at fertilization and ii) the highly permissive and variation-prone environments offered by zygotes and totipotent early embryos. Totipotent embryos provide a variety of biological tools favouring the emergence of evolutionarily significant phenotypic novelties driven by RNA information.

Under this light, neither random genomic mutations, nor the sieving role of natural selection are required, as the sperm-delivered RNA cargo conveys specific information and acts as “phenotypic-inducer” of defined environmentally acquired traits.

Osteoporosis is a kind of bone diseases characterized by dynamic imbalance of bone formation and bone absorption, which is prone to fracture, and seriously endangers human health. At present, there is a lack of highly effective drugs for it, and the existing measures all have some side effects. In recent years, mesenchymal stem cell therapy has brought a certain hope for osteoporosis, while shortcomings such as homing difficulty and unstable therapeutic effects limit its application widely. Therefore, it is extremely urgent to find effective and reliable means/drugs for adjuvant stem cell therapy or develop new research techniques. It has been reported that static magnetic fields(SMFs) has a certain alleviating and therapeutic effect on varieties of bone diseases, also promotes the proliferation and osteogenic differentiation of mesenchymal stem cells derived from different tissues to a certain extent. Basing on the above background, this article focuses on the key words “static/constant magnetic field, mesenchymal stem cell, osteoporosis”, combined literature and relevant contents were studied to look forward that SMFs has unique advantages in the treatment of osteoporosis with mesenchymal stem cells, which can be used as an application tool to promote the progress of stem cell therapy in clinical application.