Hasil untuk "Biology (General)"

Christina V. Theodoris, Ling Xiao, Anant Chopra et al.

Office on Smoking

Geoffrey B. West, James H. Brown, B. Enquist

S. Timmermans, Jolien Souffriau, C. Libert

Glucocorticoids (GCs) are steroid hormones widely used for the treatment of inflammation, autoimmune diseases, and cancer. To exert their broad physiological and therapeutic effects, GCs bind to the GC receptor (GR) which belongs to the nuclear receptor superfamily of transcription factors. Despite their success, GCs are hindered by the occurrence of side effects and glucocorticoid resistance (GCR). Increased knowledge on GC and GR biology together with a better understanding of the molecular mechanisms underlying the GC side effects and GCR are necessary for improved GC therapy development. We here provide a general overview on the current insights in GC biology with a focus on GC synthesis, regulation and physiology, role in inflammation inhibition, and on GR function and plasticity. Furthermore, novel and selective therapeutic strategies are proposed based on recently recognized distinct molecular mechanisms of the GR. We will explain the SEDIGRAM concept, which was launched based on our research results.

P. Behzadi, H. García‐Perdomo, T. Karpiński

Background/Aim Toll-like receptors (TLRs) are pivotal biomolecules in the immune system. Today, we are all aware of the importance of TLRs in bridging innate and adaptive immune system to each other. The TLRs are activated through binding to damage/danger-associated molecular patterns (DAMPs), microbial/microbe-associated molecular patterns (MAMPs), pathogen-associated molecular patterns (PAMPs), and xenobiotic-associated molecular patterns (XAMPs). The immunogenetic molecules of TLRs have their own functions, structures, coreceptors, and ligands which make them unique. These properties of TLRs give us an opportunity to find out how we can employ this knowledge for ligand-drug discovery strategies to control TLRs functions and contribution, signaling pathways, and indirect activities. Hence, the authors of this paper have a deep observation on the molecular and structural biology of human TLRs (hTLRs). Methods and Materials To prepare this paper and fulfill our goals, different search engines (e.g., GOOGLE SCHOLAR), Databases (e.g., MEDLINE), and websites (e.g., SCOPUS) were recruited to search and find effective papers and investigations. To reach this purpose, we tried with papers published in the English language with no limitation in time. The iCite bibliometrics was exploited to check the quality of the collected publications. Results Each TLR molecule has its own molecular and structural biology, coreceptor(s), and abilities which make them unique or a complementary portion of the others. These immunogenetic molecules have remarkable roles and are much more important in different sections of immune and nonimmune systems rather than that we understand to date. Conclusion TLRs are suitable targets for ligand-drug discovery strategies to establish new therapeutics in the fields of infectious and autoimmune diseases, cancers, and other inflammatory diseases and disorders.

Emily Dunn, Renfu Shao

The mitochondrial (mt) genomes of animals, including humans, are typically a single circular chromosome containing all mt genes. In several animal lineages, however, mt genomes have become fragmented, with genes distributed on multiple minichromosomes. How fragmented mt genomes are transcribed is still poorly understood. In this study, we investigated the transcription of the extensively fragmented mt genomes of the human head louse (<i>Pediculus humanus capitis</i>) and the human body louse (<i>Pediculus humanus corporis</i>). RNA-seq reads of both subspecies were retrieved from the NCBI Sequence Read Archive database and mapped to their mt genomes. The transcription level of each mt gene, minichromosome, motif, coding region and non-coding region, measured by RPKM (Reads Per Kilobase of transcript per Million mapped reads), TPM (Transcripts Per Million) or read coverage, was analysed statistically. In both subspecies, mt minichromosomes were transcribed entirely, with coding regions transcribed at much higher levels than non-coding regions. The 37 mt genes are transcribed unevenly, with <i>rrnL</i>, <i>cox1</i>, <i>cox2</i>, <i>cox3</i> and <i>atp6</i> transcribed at significantly higher levels than several other genes. Many transcription events terminate near a GC-rich motif in the non-coding regions; however, some transcription events pass this motif, leading to the transcription of entire non-coding regions. Despite the drastic difference in mt genome organisation, the human lice share several transcriptional features with humans, but also have unique features related to their fragmented mt genome organisation. The current study represents the first effort into the transcription of fragmented mt genomes. As more RNA-seq data become available, further studies on other animals with fragmented mt genomes are necessary to fully understand how genome fragmentation affects transcription.

Stuart A. Newman, Sahotra Sarkar

This article frames the relation between biology and physics by characterizing the former as a subdiscipline rather than a special case of the latter. To do this, we posit biological physics as the science of living matter in contrast to classic biophysics, the study of organismal properties by physical techniques. At the scale of the individual cell, living matter is nonunitary, i.e., not composed of aggregated subunits, and has features (e.g., intracellular organizational arrangements and biomolecular condensates) that are unlike any materials of the nonliving world. In transiently or constitutively multicellular forms (social microorganisms, animals, plants), living matter sustains physical processes that are generic (shared with nonliving matter, e.g., subunit communication by molecular diffusion in cellular slime molds), biogeneric (analogous to nonliving matter but realized through cellular activities, e.g., subunit demixing in animal embryos) or nongeneric (pertaining to sui generis materials, e.g., budding of active solids in plants). This "forms of matter" perspective is philosophically situated in the dialectical materialism of Engels and Hessen and the multilevel physicalism of Neurath and the logical empiricists. We counterpose this view to informationism and to genetic and other hierarchically reductionist physical theories of biological systems and highlight open questions regarding incompletely characterized and enigmatic forms of living matter.

Baskar Venkidasamy, Ashok Kumar Balaraman, Muthu Thiruvengadan

Jigar Joshi, Bhavin Dudhia, Dhaval Mehta et al.

Introduction: Fully detect risks of nerve damage, which can lead to temporary or permanent issues. Cone-beam computed tomography (CBCT) offers a three-dimensional (3D) view, providing more detailed visualisation of anatomical structures and their spatial relationships, which improves the accuracy of predicting nerve exposure. The study aims to evaluate and compare these imaging techniques’ effectiveness in categorising the relationship between third molars and the inferior alveolar canal, emphasising the importance of precise imaging for safer surgical outcomes. Materials and Methods: A pilot study involving 20 patients, representing 10% of the total sample size of 200, was conducted at Ahmedabad Dental College’s Department of Oral Medicine and Radiology. Investigators, trained to interpret radiological images from orthopantomography (OPG) and CBCT, compared their interpretations with those of two experts. A high inter-rater reliability was confirmed with a kappa statistic of 0.98. Following ethical approval, data were retrospectively collected from 20 cases, with digital OPG and CBCT images analysed and classified according to established criteria. Results: The results revealed a significant association between the results diagnosed through OPG and CBCT indicating similarity in their diagnosis. It was also seen that there was no bias towards the gender and the distribution was similar in case of diagnosis through OPG or CBCT. Conclusion: CBCT demands an in-depth understanding of anatomy and pathology, coupled with proficiency in operating imaging software and the ability to identify abnormalities in cross-sectional images. When executed and interpreted accurately, CBCT proves to be an exceptionally valuable tool in clinical dental practice. Its detailed 3D imaging capabilities enhance the assessment of complex cases, such as those involving intricate anatomical structures and pathologies. By providing comprehensive views that surpass traditional two-dimensional imaging, CBCT aids in precise diagnosis and treatment planning, making it an indispensable resource for addressing various dental conditions effectively.

Jose Ignacio Arroyo, Pablo A. Marquet, Christopher P. Kempes et al.

We developed a theory showing that under appropriate normalizations and rescalings, temperature response curves show a remarkably regular behavior and follow a general, universal law. The impressive universality of temperature response curves remained hidden due to various curve-fitting models not well-grounded in first principles. In addition, this framework has the potential to explain the origin of different scaling relationships in thermal performance in biology, from molecules to ecosystems. Here, we summarize the background, principles and assumptions, predictions, implications, and possible extensions of this theory.

Alexis Chevalier, Soumya Ghosh, Urvi Awasthi et al.

Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existing foundation models only modestly improve over task-specific models in downstream applications. Here, we explored two avenues for improving single-cell foundation models. First, we scaled the pre-training data to a diverse collection of 116 million cells, which is larger than those used by previous models. Second, we leveraged the availability of large-scale biological annotations as a form of supervision during pre-training. We trained the \model family of models comprising six transformer-based state-of-the-art single-cell foundation models with 70 million, 160 million, and 400 million parameters. We vetted our models on several downstream evaluation tasks, including identifying the underlying disease state of held-out donors not seen during training, distinguishing between diseased and healthy cells for disease conditions and donors not seen during training, and probing the learned representations for known biology. Our models showed substantial improvement over existing works, and scaling experiments showed that performance improved predictably with both data volume and parameter count.

José Ignacio Arroyo, B. Díez, Christopher P. Kempes et al.

Significance One of the most fundamental physical constraints on living systems is temperature. Despite its importance, a simple, mechanistic, and general theory that fully predicts the response to temperature across all scales has not yet been derived. Here we develop such a theory based on the fundamental chemical kinetics and statistical physics governing the biochemical reactions that support life. Our mathematical framework includes an explanation for why temperature response curves have a maximum or minimum value and the derivation of a single universal curve onto which data for the temperature dependence of diverse biological quantities covering all levels of organization, collapse. The theory has multiple potential applications including predicting responses to global warming, yields of industrial processes, and epidemic outbreaks.

Lei Chen, Mengna Peng, Wei He et al.

Abstract The presence of bacterial biofilms and the occurrence of excessive inflammatory response greatly imped the healing process of chronic wounds in diabetic patients. However, effective strategies to simultaneously address these issues are still lacking. Here, a microenvironment‐adaptive nanodecoy (GC@Pd) is constructed via the coordination and in situ reduction of palladium ions on gallic acid‐modified chitosan (GC) to promote wound healing by synergistic biofilm eradication, inflammation alleviation, and immunoregulation. During the weakly acidic conditions of the biofilm infection stage, GC@Pd serves as a nanodecoy to induce bacterial aggregation. Subsequently, through its oxidase‐like activity generating reactive oxygen species and the hyperthermia from photothermal effects, it effectively eliminates the biofilm. As the local microenvironment of diabetic wounds transitions to an alkaline inflammatory state, the enzyme‐like activity of GC@Pd adapts to catalase‐like activity, effectively eliminating reactive oxygen species at the site of inflammation. Additionally, GC@Pd could selectively capture pro‐inflammatory cytokines through Michael addition reactions. In vivo experiments and transcriptomic analysis confirmed that GC@Pd could accelerate the wound transition from inflammatory to proliferative phase by eliminating biofilm infection and reducing the inflammatory response, thus promoting diabetic chronic wound healing. The nanodecoy provides a potential therapeutic strategy for treating biofilm‐infected diabetic chronic wounds.

Pavel Pereslavtsev, Christian Bachmann, Joelle Elbez-Uzan et al.

There is widespread use of nuclear radiation for medical imagery and treatments. Worldwide, almost 40 million treatments are performed per year. There are also applications of radiation sources in other commercial fields, e.g., for weld inspection or steelmaking processes, in consumer products, in the food industry, and in agriculture. The large number of neutrons generated in a fusion reactor such as DEMO could potentially contribute to the production of the required radioactive isotopes. The associated commercial value of these isotopes could mitigate the capital investments and operating costs of a large fusion plant. The potential of producing various radioactive isotopes was studied from material pieces arranged inside a DEMO equatorial port plug. In this location, they are exposed to an intensive neutron spectrum suitable for a high isotope production rate. For this purpose, the full 3D geometry of one DEMO toroidal sector with an irradiation chamber in the equatorial port plug was modeled with an MCNP code to perform neutron transport simulations. Subsequent activation calculations provide detailed information on the quality and composition of the produced radioactive isotopes. The technical feasibility and the commercial potential of the production of various isotopes in the DEMO port are reported.

Xinyue Wang, Weifan Lin, Weiting Zhang et al.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.

Sylvain Thierry, Sarah Maadadi, Aurore Berton et al.

Toll-like receptor 3 (TLR3) is an innate immune receptor that recognizes double-stranded RNA (dsRNA) and induces inflammation in immune and normal cells to initiate anti-microbial responses. TLR3 acts also as a death receptor only in cancer cells but not in their normal counterparts, making it an attractive target for cancer therapies. To date, all of the TLR3-activating dsRNAs used at preclinical or clinical stages have major drawbacks such as structural heterogeneity, toxicity, and lack of specificity and/or efficacy. We conducted the discovery process of a new family of TLR3 agonists that are chemically manufactured on solid-phase support and perfectly defined in terms of sequence and size. A stepwise discovery process was performed leading to the identification of TL-532, a 70 base pair dsRNA that is potent without transfection reagent and is highly specific for TLR3 without activating other innate nucleic sensors such as RIG-I/MDA5, TLR7, TLR8, and TLR9. TL-532 induces inflammation in murine RAW264.7 myeloid macrophages, in human NCI-H292 lung cancer cells, and it promotes immunogenic apoptosis in tumor cells in vitro and ex vivo without toxicity towards normal primary cells. In conclusion, we identified a novel TLR3 agonist called TL-532 that has promising anticancer properties.

Vincent D. Zaballa, Elliot E. Hui

Systems biology relies on mathematical models that often involve complex and intractable likelihood functions, posing challenges for efficient inference and model selection. Generative models, such as normalizing flows, have shown remarkable ability in approximating complex distributions in various domains. However, their application in systems biology for approximating intractable likelihood functions remains unexplored. Here, we elucidate a framework for leveraging normalizing flows to approximate complex likelihood functions inherent to systems biology models. By using normalizing flows in the Simulation-based inference setting, we demonstrate a method that not only approximates a likelihood function but also allows for model inference in the model selection setting. We showcase the effectiveness of this approach on real-world systems biology problems, providing practical guidance for implementation and highlighting its advantages over traditional computational methods.

Stephen D. Williams, Tunde M. Smith, LaMonica V. Stewart et al.

Physiological changes such as hypoxia in the tumor microenvironment (TME) endow cancer cells with malignant properties, leading to tumor recurrence and rapid progression. Here, we assessed the effect of hypoxia (1% Oxygen) on the tumor suppressor Annexin A6 (AnxA6) and the response of triple-negative breast cancer (TNBC) cells to epidermal growth factor receptor (EGFR) and androgen receptor (AR) targeted therapies. We demonstrate that brief exposure of TNBC cells to hypoxia (within 24 h) is associated with down regulation of AnxA6 while > 24 h exposure cell type dependently stimulated the expression of AnxA6. Hypoxia depicted by the expression and stability of HIF-1/2α led to up regulation of the HIF target genes <i>SLC2A1</i>, <i>PGK1</i> as well as AR and the AR target genes <i>FABP-4</i> and <i>PPAR-γ</i>, but the cellular levels of AnxA6 protein decreased under prolonged hypoxia. Down regulation of AnxA6 in TNBC cells inhibited, while AnxA6 over expression enhanced the expression and cellular levels of HIF-1/2α, <i>SLC2A1</i> and <i>PGK1</i>. RNAi mediated inhibition of hypoxia induced AnxA6 expression also strongly inhibited glucose uptake and ROS production in AnxA6 expressing TNBC cells. Using a luciferase reporter assay, we confirm that short-term exposure of cells to hypoxia inhibits while prolonged exposure of cells to hypoxia enhances AnxA6 promoter activity in HEK293T cells. Compared to cells cultured under normoxia, TNBC cells were more resistant to lapatinib under hypoxic conditions, and the downregulation of AnxA6 sensitized the cells to EGFR as well as AR antagonists. These data suggest that AnxA6 is a hypoxia inducible gene and that targeting AnxA6 upregulation may be beneficial in overcoming TNBC resistance to EGFR and/or AR targeted therapies.

Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Yukio Mizuno et al.

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates.

Ibrahim Aldulijan, Jacob Beal, Sonja Billerbeck et al.

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost.