Progress in Biophysics & Molecular Biology最新文献

In 2021 I noted that in all information-based systems we understand, Cognition creates Code, which controls Chemical reactions. Known agents write software which controls hardware, and not the other way around. I proposed the same is true in all of biology. Though the textbook description of cause and effect in biology proposes the reverse, that Chemical reactions produce Code from which Cognition emerges, there are no examples in the literature demonstrating either step. A mathematical proof for the first step, cognition generating code, is based on Turing's halting problem. The second step, code controlling chemical reactions, is the role of the genetic code. Thus a central question in biology: What is the nature and source of cognition? In this paper I propose a relationship between biology and Quantum Mechanics (QM), hypothesizing that the same principle that enables an observer to collapse a wave function also grants biology its agency: the organism's ability to act on the world instead of merely being a passive recipient. Just as all living cells are cognitive (Shapiro 2021, 2007; McClintock 1984; Lyon 2015; Levin 2019; Pascal and Pross, 2022), I propose humans are quantum observers because we are made of cells and all cells are observers. This supports the century-old view that in QM, the observer does not merely record the event but plays a fundamental role in its outcome.The classical world is driven by laws, which are deductive; the quantum world is driven by choices, which are inductive. When the two are combined, they form the master feedback loop of perception and action for all biology. In this paper I apply basic definitions of induction, deduction and computation to known properties of QM to show that the organism altering itself (and its environment) is a whole shaping its parts. It is not merely parts comprising a whole. I propose that an observer collapsing the wave function is the physical mechanism for producing negentropy. The way forward in solving the information problem in biology is understanding the relationship between cognition and QM.

Natural substances are increasingly being used as cancer treatments. Scutellarin, as a flavonoid, recently has been identified in a Chinese herbal extract called Erigeron breviscapus (Vant.). Scutellarin is being researched for its potential benefits due to the discovery that it possesses a variety of biological effects, such as neuroprotective, anti-bacterial, and anti-viral properties. In addition to these biological functions, scutellarin has also been found to have anti-tumor properties. The underlying mechanisms of scutellarin's anticancer activity involve its ability to inhibit various signaling pathways, such as Jak/STAT, ERK/AMPK, and Wnt/β-catenin. Additionally, scutellarin activates intrinsic and extrinsic apoptotic pathways, which causes the death of tumor cells, interrupts the cell cycle, and promotes its arrest. By limiting metastasis, angiogenesis, drug resistance, and other tumorigenic processes, scutellarin also reduces the aggressiveness of tumors. Despite its promising anticancer activity, scutellarin faces several challenges in its clinical development, including poor solubility, bioavailability, and pharmacokinetic properties. Therefore, it has been suggested that certain modifications can enhance the pharmacogenetic capabilities of scutellarin to decrease its limited water solubility. In conclusion, scutellarin represents a potential candidate for cancer treatment and further studies are needed to explore its clinical utility and optimize its therapeutic potential.

Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB. Despite being relatively fast and low cost, it can be exhausting because it depends on manually counting TB bacilli (Mycobacterium tuberculosis) in microscope images. In this context, different Deep Learning (DL) techniques are proposed in the literature to assist in performing smear microscopy. This article presents a systematic review based on the PRISMA procedure, which investigates which DL techniques can contribute to classifying TB bacilli in microscopic images of sputum smears using the Ziehl-Nielsen method. After an extensive search and a careful inclusion/exclusion procedure, 28 papers were selected from a total of 400 papers retrieved from nine databases. Based on these articles, the DL techniques are presented as possible solutions to improve smear microscopy. The main concepts necessary to understand how such techniques are proposed and used are also presented. In addition, replication work is also carried out, verifying reproducibility and comparing different works in the literature. In this review, we look at how DL techniques can be a partner to make sputum smear microscopy faster and more efficient. We also identify some gaps in the literature that can guide which issues can be addressed in other works to contribute to the practical use of these methods in laboratories.

Cellular and biochemical studies of nonhomologous DNA end joining (NHEJ) have long established that nuclease and polymerase action are necessary for the repair of a very large fraction of naturally-arising double-strand breaks (DSBs). This conclusion is derived from NHEJ studies ranging from yeast to humans and all genetically-tractable model organisms. Biochemical models derived from recent real-time and structural studies have yet to incorporate physical space or timing for DNA end processing. In real-time single molecule FRET (smFRET) studies, we analyzed NHEJ synapsis of DNA ends in a defined biochemical system. We described a Flexible Synapsis (FS) state in which the DNA ends were in proximity via only Ku and XRCC4:DNA ligase 4 (X4L4), and in an orientation that would not yet permit ligation until base pairing between one or more nucleotides of microhomology (MH) occurred, thereby allowing an in-line Close Synapsis (CS) state. If no MH was achievable, then XLF was critical for ligation. Neither FS or CS required DNA-PKcs, unless Artemis activation was necessary to permit local resection and subsequent base pairing between the two DNA ends being joined. Here we conjecture on possible 3D configurations for this FS state, which would spatially accommodate the nuclease and polymerase processing steps in an iterative manner. The FS model permits repeated attempts at ligation of at least one strand at the DSB after each round of nuclease or polymerase action. In addition to activation of Artemis, other possible roles for DNA-PKcs are discussed.

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.

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.

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.

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.