Search Results (25)

Systematic reviews are a cornerstone of evidence-based research, providing comprehensive summaries of existing studies to answer specific research questions. This article offers a detailed guide to conducting high-quality systematic reviews in biology, health and social sciences. It outlines key steps, including developing and registering a protocol, designing comprehensive search strategies, and selecting studies through a screening process. The article emphasizes the importance of accurate data extraction and the use of validated tools to assess the risk of bias across different study designs. Both meta-analysis (quantitative approach) and narrative synthesis (qualitative approach) are discussed in detail. The guide also highlights the use of frameworks, such as GRADE, to assess the certainty of evidence and provides recommendations for clear and transparent reporting in line with the PRISMA 2020 guidelines. This paper aims to adapt and translate evidence-based review principles, commonly applied in clinical research, into the context of biological sciences. By highlighting domain-specific methodologies, challenges, and resources, we provide tailored guidance for researchers in ecology, molecular biology, evolutionary biology, and related fields in order to conduct transparent and reproducible evidence syntheses. Full article

Kleptoplastidy is a nutrition mode in which cells of protists and some multicellular organisms acquire, maintain, and exploit chloroplasts of prey algae cells as photosynthesis reactors. It is an important aspect of the mixotrophic feeding strategy, which plays a role in the formation of harmful algae blooms (HABs). We developed a new mathematical model, in which kleptoplastidy is regarded as a mechanism of enhancing mixotrophy of protists. The model is constructed using three thought (theoretical) experiments and the concept of biological time. We propose to measure the contribution of kleptoplastidy to mixotrophy using a new ecological indicator: the kleptoplastidy index. This index is a function of two dimensionless variables, one representing the ratio of photosynthetic production of acquired chloroplasts versus native chloroplasts, and the other representing the balance between autotrophic and heterotrophic feeding modes. The index is tested by data for the globally distributed, bloom-forming potentially toxic mixotrophic dinoflagellates Prorocentrum cordatum. The model supports our hypothesis that kleptoplastidy can increase the division rate of algae significantly (by 40%), thus boosting their population growth and promoting blooms. The proposed model can contribute to advancements in ecological modeling aimed at forecasting and management of HABs that deteriorate marine coastal environments worldwide. Full article

For a highly beneficial mutation A at locus 1 spreading in a very large population, we have analyzed the scenario that at a closely linked locus 2 a second beneficial mutant B arises before A has fixed. Under the assumptions that the fitness of B is greater than that of A and that A- and B-carrying chromosomes can recombine at some rate $r$, recombinants AB may form and eventually fix. We present explicit formulas for the fixation time of AB under additive fitness of the mutants as a function of the frequency $X_2\left(0\right)$ of A at the time when B is introduced. Our analysis suggests that the effect of interference between the beneficial mutations is most pronounced for small values of $X_2\left(0\right)<0.1.$ Furthermore, we identify a threshold value for $r$, above which recombination speeds up fixation. Using published simulation data, we also describe the genomic footprint of competing beneficial mutations. At neutral sites between the two linked selected loci, an excess of intermediate-frequency variants may occur when interference is strong, i.e.,$X_2\left(0\right)$ small. Finally, we discuss under which circumstances this scenario may be encountered in real sequences from recombining genomic regions. Full article

Mammalian renal intercalated cells are known for their role in acid secretion and maintaining acid–base balance. Herein, we discuss the theoretical reasons behind their development based on published data, focusing on the unique characteristics of renal intercalated cell biology that distinguish them from other mammalian cell types, while simultaneously attempting to explain the persistence of cells similar to intercalated cells throughout evolution. In addition, we traced these characteristics phylogenetically back to the simplest organisms. Intercalated cells have several functions and attributes. First, they contribute to kidney defense mechanisms in response to both infectious and non-infectious kidney damage. Second, intercalated cells are energized by V-ATPases in a manner similar to that of protozoa. Third, they possess T-antigens, which are commonly found in embryonic and cancer cells and which confer invasive abilities to these cells. Fourth, their plasticity enables the regeneration of other epithelial cells. These observations indicate that the origins of renal intercalated cells may be traceable back to amoeboid cells that originated from an evolutionary lineage including protists, or even to the last eukaryote common ancestor. The theoretical framework presented herein supports two predictions: first, that sponge amoebocytes possess membrane V-ATPase and are sensitive to bafilomycin, but not to ouabain; and second, that sponge amoebocytes—along with cells from diploblasts (such as Xenacoelomorpha), cnidarians, worms, fish and mollusk ionocytes, and the entire cell lineage containing V-ATPase, carbonic anhydrase, and anion exchangers (HCO₃⁻/Cl⁻)—have innate immunity, cellular dedifferentiation, and regeneration capabilities. Full article

This paper aims to investigate the dielectric properties of blood for tumor detection and staging. The application of complex thermodynamic models and the study of the trend over time of some thermodynamic functions have allowed us to highlight the generation of displacement currents caused by changes in charge, i.e., by the activation and consequent accumulation of platelets on migrating tumor cells. Although few studies exist to date in this regard, the technique used has provided promising results, especially in terms of building a database. In this context, the evaluation of the dielectric parameters of healthy and cancerous blood can be exploited for the staging of cancer. The main advantages of this method include easy application, non-invasiveness, low cost, and online monitoring. Full article

(1) Background: Previous theoretical studies have provided arguments for the existence of a circular or hairpin RNA that could have served as a primitive informational and functional molecule at the origin of life. The present article consists of searching in current genomes for RNAs closest to this primitive RNA in terms of the occurrence of similar nucleotide motifs. (2) Methods: In searching for the smallest possible RNA capable of interacting with amino acids in the construction of the peptides of the primitive living world, we found a circular docosamer RNA molecule (length 22), which we called AL (for ALpha or Archetypal Loop). Then, we started to systematically track AL relics in current genomes in the form of motifs like pentamers or pairs of consecutive codons in common with AL. (3) Results: The sequence correspondence between AL and RNA sequences of organisms from different kingdoms of life (Archaea, Bacteria, and Eukarya) was found with high statistical significance, with a frequency gradient depending on both the antiquity of the species and the functional necessity of the genes. (4) Conclusions: Considering the suitability of AL as a candidate for being a primitive sequence, and the evolution of the different species considered, we can consider the AL RNA as a possible actor that favored the appearance of life on Earth. Full article

The Constrained Disorder Principle (CDP) offers a new framework for understanding how biological systems use and manage noise to maintain optimal functionality. This review explores the relationship between noise and biological systems at various scales, including genetic, cellular, and organ levels, and its implications for system malfunctions. According to the CDP, all systems require an optimal range of noise to function appropriately, and disease states can arise when these noise levels are disrupted. This review presents evidence supporting this principle across different biological contexts, such as genetic variability, cellular behavior, brain functions, human behavior, aging, evolution, and drug administration. For accurate clinical assessments, it is essential to distinguish between technical variability and intrinsic biological variability. When noise is adequately constrained, it serves as a fundamental mechanism for system adaptation and optimal functioning rather than simply a source of disruption. These findings have important implications for developing more effective therapeutic strategies and understanding biological systems’ dynamics. CDP-based second-generation artificial intelligence systems can help regulate noise levels to address malfunctions. These systems have improved clinical outcomes in various conditions by incorporating controlled randomness. Understanding these patterns of variability has significant implications for diagnosis, treatment monitoring, and the development of more effective therapeutic strategies across various medical conditions. Full article

The constrained disorder principle (CDP) defines complex biological systems based on inherent variability. Allostasis refers to the physiological processes that help maintain stability in response to changing environmental demands. Allostatic load describes the cumulative wear and tear on the body resulting from prolonged exposure to stress, and it has been suggested to mediate the relationship between stress and disease. This study presents the concepts of CDP and allostasis while discussing their similarities and differences. We reviewed the current literature on the potential benefits of introducing controlled doses of biological noise into interventions, which may enhance the effectiveness of therapies. The paper highlights the promising role of variability provided by a CDP-based second-generation artificial intelligence system in improving health outcomes. Full article

Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches, based on similarity determination, to the plantdisease classification domain remains limited. This study evaluates different loss functions used in similarity learning, including Contrastive, Triplet, Quadruplet, SphereFace, CosFace, and ArcFace, alongside various backbone networks, such as MobileNet, EfficientNet, ConvNeXt, and ResNeXt. Custom datasets of real-life images, comprising over 4000 samples across 68 classes of plant diseases, pests, and their effects, were utilized. The experiments evaluate standard transfer learning approaches alongside similarity learning methods based on two classes of loss function. Results demonstrate the superiority of cosine-based methods over Siamese networks in embedding extraction for disease classification. Effective approaches for model organization and training are determined. Additionally, the impact of data normalization is tested, and the generalization ability of the models is assessed using a special dataset consisting of 400 images of difficult-to-identify plant disease cases. Full article

Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability, on the other hand, is measured by the frequency distribution of observed data. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. According to the CDP, systems exhibit a degree of variability necessary for their proper function, allowing them to adapt to changes in their environments. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper explores the various aspects of un-certainties in biology. It focuses on using CDP-based platforms for refining fuzzy algorithms to address some of the challenges associated with biological and medical uncertainties. Developing a fuzzy decision tree that considers the natural variability of systems can help minimize uncertainty. This method can reveal previously unidentified classes, reduce the number of unknowns, improve the accuracy of modeling results, and generate algorithm outputs that are more biologically and clinically relevant. Full article

The oscillation synchronization analysis in biological systems will expand our knowledge about the response of living systems to changes in environmental conditions. This knowledge can be used in medicine (diagnosis, therapy, monitoring) and agriculture (increasing productivity, resistance to adverse effects). Currently, the search is underway for an informative, accurate and sensitive method for analyzing the synchronization of oscillatory processes in cell biology. It is especially pronounced in analyzing the concentration oscillations of intracellular signaling molecules in electrically nonexcitable cells. The bispectral analysis method could be applied to assess the characteristics of synchronized oscillations of intracellular mediators. We chose endothelial cells from mouse microvessels as model cells. Concentrations of well-studied calcium and nitric oxide (NO) were selected for study in control conditions and well-described stress: heating to 40 °C and hyperglycemia. The bispectral analysis allows us to accurately evaluate the proportion of synchronized cells, their synchronization degree, and the amplitude and frequency of synchronized calcium and NO oscillations. Heating to 40 °C increased cell synchronization for calcium but decreased for NO oscillations. Hyperglycemia abolished this effect. Heating to 40 °C changed the frequencies and increased the amplitudes of synchronized oscillations of calcium concentration and the NO synthesis rate. The first part of this paper describes the principles of the bispectral analysis method and equations and modifications of the method we propose. In the second part of this paper, specific examples of the application of bispectral analysis to assess the synchronization of living cells in vitro are presented. The discussion compares the capabilities of bispectral analysis with other analytical methods in this field. Full article

The three most important commercial bacterial insecticides are all derived from subspecies of Bacillus thuringiensis (Bt). Specifically, Bt subsp. kurstaki (Btk) and Bt subsp. aizawai (Bta) are used to control larval lepidopteran pests. The third, Bt subsp. israelensis (Bti), is primarily used to control mosquito and blackfly larvae. All three subspecies produce a parasporal body (PB) during sporulation. The PB is composed of insecticidal proteins that damage the midgut epithelium, initiating a complex process that results in the death of the insect. Among these three subspecies of Bt, Bti is unique as it produces the most complex PB consisting of three compartments. Each compartment is bound by a multilaminar fibrous matrix (MFM). Two compartments contain one protein each, Cry11Aa1 and Cyt1Aa1, while the third contains two, Cry4Aa1/Cry4Ba1. Each compartment is packaged independently before coalescing into the mature spherical PB held together by additional layers of the MFM. This distinctive packaging process is unparalleled among known bacterial organelles, although the underlying molecular biology is yet to be determined. Here, we present structural and molecular evidence that the MFM has a hexagonal pattern to which Bti proteins Bt152 and Bt075 bind. Bt152 binds to a defined spot on the MFM during the development of each compartment, yet its function remains unknown. Bt075 appears to be derived from a bacteriophage major capsid protein (MCP), and though its sequence has markedly diverged, it shares striking 3-D structural similarity to the Escherichia coli phage HK97 Head 1 capsid protein. Both proteins are encoded on Bti’s pBtoxis plasmid. Additionally, we have also identified a six-amino acid motif that appears to be part of a novel molecular process responsible for targeting the Cry and Cyt proteins to their cytoplasmic compartments. This paper describes several previously unknown features of the Bti organelle, representing a first step to understanding the biology of a unique process of sorting and packaging of proteins into PBs. The insights from this research suggest a potential for future applications in nanotechnology. Full article

(1) Background: It is our purpose to identify the differences in the changes in Complement C5a receptor 1 (C5aR1) levels showing the degree of inflammation at onset and Immunoglobulin G (IgG) levels showing the extent of survival of the virus fragments after recovery between coronavirus disease 2019 (COVID-19) and pneumonia coronavirus disease (non-COVID-19) for saving patients’ lives. (2) Methods: First, the studies showing these markers’ levels in individual patients before and after the passage of time were selected from the PubMed Central^® databases with the keywords (((COVID-19) AND individual) NOT review) AND C5a/IgG. Then, no changes in these markers’ levels with conventional analyses were selected from the studies. Finally, the no changes were reexamined with our new two-tailed t-test using the values on the regression line between initial levels and changed levels instead of the mean or median of changed levels as the expected values of changed levels. (3) Results: Not conventional analyses but our new t-test suggested a greater increase in C5aR1-levels at onset and a smaller decrease in IgG-levels after recovery in COVID-19 patients than non-COVID-19 patients. (4) Conclusion: Our new t-test also should be used in clinics for COVID-19 patients. Full article

Regression models are extensively used to explore the relationship between a dependent variable and its covariates. These models work well when the dependent variable is categorical and the data are supposedly independent, as is the case with generalized linear models (GLMs). However, trait data from related species do not operate under these conditions due to their shared common ancestry, leading to dependence that can be illustrated through a phylogenetic tree. In response to the analytical challenges of count-dependent variables in phylogenetically related species, we have developed a novel phylogenetic negative binomial regression model that allows for overdispersion, a limitation present in the phylogenetic Poisson regression model in the literature. This model overcomes limitations of conventional GLMs, which overlook the inherent dependence arising from shared lineage. Instead, our proposed model acknowledges this factor and uses the generalized estimating equation (GEE) framework for precise parameter estimation. The effectiveness of the proposed model was corroborated by a rigorous simulation study, which, despite the need for careful convergence monitoring, demonstrated its reasonable efficacy. The empirical application of the model to lizard egg-laying count and mammalian litter size data further highlighted its practical relevance. In particular, our results identified negative correlations between increases in egg mass, litter size, ovulation rate, and gestation length with respective yearly counts, while a positive correlation was observed with species lifespan. This study underscores the importance of our proposed model in providing nuanced and accurate analyses of count-dependent variables in related species, highlighting the often overlooked impact of shared ancestry. The model represents a critical advance in research methodologies, opening new avenues for interpretation of related species data in the field. Full article

Predictive models based on empirical similarity are instrumental in biology and data science, where the premise is to measure the likeness of one observation with others in the same dataset. Biological datasets often encompass data that can be categorized. When using empirical similarity-based predictive models, two strategies for handling categorical covariates exist. The first strategy retains categorical covariates in their original form, applying distance measures and allocating weights to each covariate. In contrast, the second strategy creates binary variables, representing each variable level independently, and computes similarity measures solely through the Euclidean distance. This study performs a sensitivity analysis of these two strategies using computational simulations, and applies the results to a biological context. We use a linear regression model as a reference point, and consider two methods for estimating the model parameters, alongside exponential and fractional inverse similarity functions. The sensitivity is evaluated by determining the coefficient of variation of the parameter estimators across the three models as a measure of relative variability. Our results suggest that the first strategy excels over the second one in effectively dealing with categorical variables, and offers greater parsimony due to the use of fewer parameters. Full article