Pub Date : 2024-07-05DOI: 10.1142/s1793048024300020
Alessandro Nutini, Sümeyye Tunç
The regeneration and repair of bone tissue is a multiphase process that requires a lot of attention, especially if stimulated through scaffold implantation. This review analyzes the process from both a biological and mechanical point of view through the analysis of the porosity and characteristics of the biomaterials that can provide optimal regeneration of bone tissue and functional vascularization that prevents implant failure. Particular attention is paid to the porosity of the new biomaterials and the related physiological effects and the angiogenesis process that the biomaterials themselves can stimulate, analyzing some of the works present in the literature.
{"title":"Repair and Regeneration of Bone Tissue by Scaffold Implant — A Biomechanical Review","authors":"Alessandro Nutini, Sümeyye Tunç","doi":"10.1142/s1793048024300020","DOIUrl":"https://doi.org/10.1142/s1793048024300020","url":null,"abstract":"The regeneration and repair of bone tissue is a multiphase process that requires a lot of attention, especially if stimulated through scaffold implantation. This review analyzes the process from both a biological and mechanical point of view through the analysis of the porosity and characteristics of the biomaterials that can provide optimal regeneration of bone tissue and functional vascularization that prevents implant failure. Particular attention is paid to the porosity of the new biomaterials and the related physiological effects and the angiogenesis process that the biomaterials themselves can stimulate, analyzing some of the works present in the literature.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-08DOI: 10.1142/s1793048024500012
Khaled A. Al-Utaibi, Alessandro Nutini, Sadiq M. Sait, S. Iqbal
The immune response is essential for the human body to function well and to survive against the sudden and chronic diseases such as viral & bacterial infections and cancers. In the immunosurveillance process, Natural Killer (NK) cells are one of the main elements in controlling the development of such infections and, for this reason, they have become the subject of “in-depth” studies especially for the application of new forms of immunotherapy. NK cells can rapidly destroy both autologous and tumor cells in vitro and for this reason the interest in their function is increasingly growing. Their presence in the tumor micro-environment (TME) also assumes prognostic value since the repertoire of NK cell receptors has been linked to anti-tumor function. In this work, a Markov chain modeling approach is proposed to analyze the network of interactions that NK cells carry out with other immune elements in the defense against cancer such as CD4+ cells and CD8+ cells and dendritic cells (DCs) that activate and enhance immune responses. The probabilistic approach used is promising since it helps to understand the balance and the communication in the micro-environment, in a realistic manner. The advantage of discrete time Markov chain approach is that, it can be further extended to complex networks using the state-of-the-art algorithms and can also be translated for the novel AI tools for the cytokines and protein databases.
人体要想在病毒和细菌感染以及癌症等突发性和慢性疾病面前保持良好的机能和生存能力，免疫反应是必不可少的。在免疫监视过程中，自然杀伤（NK）细胞是控制此类感染发展的主要因素之一，因此，它们已成为 "深入 "研究的主题，特别是在应用新型免疫疗法方面。NK 细胞能在体外迅速消灭自体细胞和肿瘤细胞，因此，人们对其功能的兴趣与日俱增。它们在肿瘤微环境（TME）中的存在也具有预后价值，因为 NK 细胞受体的排列与抗肿瘤功能有关。这项研究提出了一种马尔可夫链建模方法，用于分析 NK 细胞与其他免疫元素（如 CD4+ 细胞、CD8+ 细胞和树突状细胞 (DC)）在抗癌过程中的相互作用网络，这些免疫元素可激活和增强免疫反应。所采用的概率方法很有前途，因为它有助于以现实的方式了解微环境中的平衡和交流。离散时间马尔可夫链方法的优势在于，它可以利用最先进的算法进一步扩展到复杂网络，还可以转化为细胞因子和蛋白质数据库的新型人工智能工具。
{"title":"Markov Chains to Explore the Nanosystems for the Biophysical Studies of Cancers","authors":"Khaled A. Al-Utaibi, Alessandro Nutini, Sadiq M. Sait, S. Iqbal","doi":"10.1142/s1793048024500012","DOIUrl":"https://doi.org/10.1142/s1793048024500012","url":null,"abstract":"The immune response is essential for the human body to function well and to survive against the sudden and chronic diseases such as viral & bacterial infections and cancers. In the immunosurveillance process, Natural Killer (NK) cells are one of the main elements in controlling the development of such infections and, for this reason, they have become the subject of “in-depth” studies especially for the application of new forms of immunotherapy. NK cells can rapidly destroy both autologous and tumor cells in vitro and for this reason the interest in their function is increasingly growing. Their presence in the tumor micro-environment (TME) also assumes prognostic value since the repertoire of NK cell receptors has been linked to anti-tumor function. In this work, a Markov chain modeling approach is proposed to analyze the network of interactions that NK cells carry out with other immune elements in the defense against cancer such as CD4+ cells and CD8+ cells and dendritic cells (DCs) that activate and enhance immune responses. The probabilistic approach used is promising since it helps to understand the balance and the communication in the micro-environment, in a realistic manner. The advantage of discrete time Markov chain approach is that, it can be further extended to complex networks using the state-of-the-art algorithms and can also be translated for the novel AI tools for the cytokines and protein databases.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-08DOI: 10.1142/s1793048024500036
K. Das, Anirban Patra, Seema Sarkar, Rajinder Pal Kaur, Biswadip Pal, Md Firoj Ali, Sayantari Ghosh, Somnath Sikari
This paper explores a predator–prey system featuring fear and disease within the predator population,utilizing the Rosenzweig–MacArthur model with Holling type-II functional response. The primary focus lies in investigating the impact of a fear factor, wherein the prey’s growth rate is hindered due to predator-induced fear. Additionally, the model accounts for the spread of disease among predators,leading to a division between susceptible and infected predator subpopulations. The inclusion of an Allee effect in the susceptible predator further enriches the model. The study involves a thorough examination, encompassing local and global stability analysis as well as Hopf bifurcation analysis around the interior equilibrium point. Numerical simulations underscore a noteworthy observation: an escalation in interaction force propels the system into chaotic dynamics,marked by stable focus, limit cycles and period-doubling phenomena. A noteworthy finding pertains to the influence of the Allee parameter ([Formula: see text]) on chaotic dynamics. As the Allee parameter values increase, the system tends to stable focus through a sequence of chaotic states, period-doubling and limit cycles. Subsequently, the paper introduces the role of another pivotal parameter, the fear factor, into the chaotic dynamics. Intriguingly, chaos transforms into stable focus through diverse nonlinear phenomena, including period-doubling and limit cycles. This nuanced exploration of parameters sheds light on the intricate dynamics governing the predator–prey system, offering a comprehensive understanding of the interplay between fear, disease and ecological factors. So our observation throughout this paper that how chaos behaves here after one by one injection of our new features: fear factor and Allee parameter?
本文利用霍林 II 型功能反应的罗森茨韦格-麦克阿瑟模型，探讨了捕食者-猎物系统中捕食者种群的恐惧和疾病问题。主要重点是研究恐惧因素的影响，即捕食者引起的恐惧会阻碍猎物的生长速度。此外，该模型还考虑了疾病在捕食者之间的传播，从而导致易感和受感染的捕食者亚群的划分。在易感捕食者中加入阿利效应进一步丰富了模型。该研究进行了全面审查，包括局部和全局稳定性分析，以及内部平衡点周围的霍普夫分岔分析。数值模拟强调了一个值得注意的观察结果：相互作用力的升级推动系统进入混沌动力学，其特点是稳定焦点、极限循环和周期加倍现象。一个值得注意的发现是阿利参数（[公式：见正文]）对混沌动力学的影响。随着阿利参数值的增加，系统通过一系列混沌状态、周期加倍和极限循环趋于稳定聚焦。随后，论文在混沌动力学中引入了另一个关键参数--恐惧因子的作用。耐人寻味的是，通过各种非线性现象，包括周期加倍和极限循环，混沌状态转化为稳定焦点。这种对参数的细微探索揭示了捕食者-猎物系统错综复杂的动力学规律，让我们对恐惧、疾病和生态因素之间的相互作用有了全面的了解。因此，我们在本文中观察到，在逐一注入我们的新特征：恐惧因子和阿利参数后，混沌在这里是如何表现的？
{"title":"Role of Allee and Fear for Controlling Chaos in a Predator–Prey System with Circulation of Disease in Predator","authors":"K. Das, Anirban Patra, Seema Sarkar, Rajinder Pal Kaur, Biswadip Pal, Md Firoj Ali, Sayantari Ghosh, Somnath Sikari","doi":"10.1142/s1793048024500036","DOIUrl":"https://doi.org/10.1142/s1793048024500036","url":null,"abstract":"This paper explores a predator–prey system featuring fear and disease within the predator population,utilizing the Rosenzweig–MacArthur model with Holling type-II functional response. The primary focus lies in investigating the impact of a fear factor, wherein the prey’s growth rate is hindered due to predator-induced fear. Additionally, the model accounts for the spread of disease among predators,leading to a division between susceptible and infected predator subpopulations. The inclusion of an Allee effect in the susceptible predator further enriches the model. The study involves a thorough examination, encompassing local and global stability analysis as well as Hopf bifurcation analysis around the interior equilibrium point. Numerical simulations underscore a noteworthy observation: an escalation in interaction force propels the system into chaotic dynamics,marked by stable focus, limit cycles and period-doubling phenomena. A noteworthy finding pertains to the influence of the Allee parameter ([Formula: see text]) on chaotic dynamics. As the Allee parameter values increase, the system tends to stable focus through a sequence of chaotic states, period-doubling and limit cycles. Subsequently, the paper introduces the role of another pivotal parameter, the fear factor, into the chaotic dynamics. Intriguingly, chaos transforms into stable focus through diverse nonlinear phenomena, including period-doubling and limit cycles. This nuanced exploration of parameters sheds light on the intricate dynamics governing the predator–prey system, offering a comprehensive understanding of the interplay between fear, disease and ecological factors. So our observation throughout this paper that how chaos behaves here after one by one injection of our new features: fear factor and Allee parameter?","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-16DOI: 10.1142/s1793048023500066
E. Pankratov
A model to describe tumor development was introduced in the paper. The model takes into account division, nutrition and dying of the considered cells. An analytical approach for analysis of the introduced model has also been introduced. The approach gives a possibility of taking into account changes of conditions of the considered processes. The possibility of changing of their rates is being considered.
{"title":"On Influence of Several Factors on Development of Tumors","authors":"E. Pankratov","doi":"10.1142/s1793048023500066","DOIUrl":"https://doi.org/10.1142/s1793048023500066","url":null,"abstract":"A model to describe tumor development was introduced in the paper. The model takes into account division, nutrition and dying of the considered cells. An analytical approach for analysis of the introduced model has also been introduced. The approach gives a possibility of taking into account changes of conditions of the considered processes. The possibility of changing of their rates is being considered.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"84 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140236416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.1142/s1793048023500054
Abhishek Sarkar, K. Das, Kulbhushan Agnihotri
This paper explores a predator–prey system with disease in the predator population, focusing on the impact of alternative food sources. Investigating the eco-epidemiological systems with strong Allee effects in prey populations, the study analyzes local stability, introduces ecological and disease basic reproduction numbers, and observes the community structure. Extensive numerical simulations reveal varied global behaviors, including stable focus, limit cycles, period-doubling, and chaos in response to changes in the infection levels. The research emphasizes the role of alternative food in mitigating chaotic dynamics, noting that increased availability promotes stability, while decreased availability leads to a shift from chaos to a stable focus. Overall, the study underscores the significance of incorporating alternative food sources in conservation efforts for ecosystems with predator populations experiencing strong Allee effects, offering insights into the complex dynamics of eco-epidemiological systems and their implications for biodiversity conservation and disease management.
{"title":"Role of Alternative Food in Controlling Chaotic Dynamics in an Eco-Epidemiological Model with Strong Allee Effects in Prey Populations","authors":"Abhishek Sarkar, K. Das, Kulbhushan Agnihotri","doi":"10.1142/s1793048023500054","DOIUrl":"https://doi.org/10.1142/s1793048023500054","url":null,"abstract":"This paper explores a predator–prey system with disease in the predator population, focusing on the impact of alternative food sources. Investigating the eco-epidemiological systems with strong Allee effects in prey populations, the study analyzes local stability, introduces ecological and disease basic reproduction numbers, and observes the community structure. Extensive numerical simulations reveal varied global behaviors, including stable focus, limit cycles, period-doubling, and chaos in response to changes in the infection levels. The research emphasizes the role of alternative food in mitigating chaotic dynamics, noting that increased availability promotes stability, while decreased availability leads to a shift from chaos to a stable focus. Overall, the study underscores the significance of incorporating alternative food sources in conservation efforts for ecosystems with predator populations experiencing strong Allee effects, offering insights into the complex dynamics of eco-epidemiological systems and their implications for biodiversity conservation and disease management.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"13 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.1142/s1793048023500042
José S. González-García
The power stroke mechanism for ribosome translocation is evaluated through calculations of basic aspects of the mechanics, hydrodynamics, and elasticity involved in such a process. The results show that reported power stroke magnitudes would generate several physical problems for ribosome translocation: unwanted or disproportionate displacements of the ribosome and mRNA, destabilizing competing forces and high tension on the mRNA in the ribosome tunnel. All these issues can be related to the ribosome losing its correct reading frame during mRNA translation. To improve ribosome translocation models, the suggestion is to change the focus from tRNA–mRNA forced displacement to ribosome motion along the mRNA. Also, to consider that the mRNA is essentially different from cytoskeleton fibers and that the ribosome does not work alone during mRNA translation.
{"title":"Theoretical Estimates of Physical Aspects Involved in the Power Stroke Hypothesis for Ribosome Translocation","authors":"José S. González-García","doi":"10.1142/s1793048023500042","DOIUrl":"https://doi.org/10.1142/s1793048023500042","url":null,"abstract":"The power stroke mechanism for ribosome translocation is evaluated through calculations of basic aspects of the mechanics, hydrodynamics, and elasticity involved in such a process. The results show that reported power stroke magnitudes would generate several physical problems for ribosome translocation: unwanted or disproportionate displacements of the ribosome and mRNA, destabilizing competing forces and high tension on the mRNA in the ribosome tunnel. All these issues can be related to the ribosome losing its correct reading frame during mRNA translation. To improve ribosome translocation models, the suggestion is to change the focus from tRNA–mRNA forced displacement to ribosome motion along the mRNA. Also, to consider that the mRNA is essentially different from cytoskeleton fibers and that the ribosome does not work alone during mRNA translation.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"48 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.1142/s1793048023310021
Afam Uzorka, David Kibirige, David Makumbi
In the search for novel treatments for various diseases, the nexus of biophysics and drug discovery represents a dynamic and transformational paradigm. A new age in pharmaceutical research has begun thanks to biophysics and its thorough grasp of the structural and physical characteristics of biological molecules. This study explores how advances in biophysical approaches, including allosteric modulation, intrinsically disordered proteins (IDPs), and protein phase transitions, have transformed the drug development process. Protein phase transitions, supported by the biophysical principles, have provided crucial insights into disorders like ALS and Alzheimer’s, opening up new avenues for therapeutic intervention. Targeting IDPs and their role in liquid–liquid phase separation has produced creative approaches to diseases that were formerly thought to be resistant to therapy. In order to reduce the complexity of complicated diseases, the concept of allosteric modulation, made possible by biophysical understanding, offers a precise and selective approach to medication creation. This review highlights the significant influence of biophysics on the development of therapeutics and the discovery of new drugs. Biophysics is advancing the discipline toward the creation of more precise, efficient, and tailored medications by illuminating the biophysical properties of proteins, the complexities of phase transitions, and the dynamics of drug interactions. Biophysical insights are poised to alter healthcare by bringing together several fields and sustained innovation, giving people dealing with a variety of ailments fresh hope.
{"title":"Biophysical Insights into Drug Discovery: Leveraging Phase Transitions and Protein Behavior for Therapeutic Innovation","authors":"Afam Uzorka, David Kibirige, David Makumbi","doi":"10.1142/s1793048023310021","DOIUrl":"https://doi.org/10.1142/s1793048023310021","url":null,"abstract":"In the search for novel treatments for various diseases, the nexus of biophysics and drug discovery represents a dynamic and transformational paradigm. A new age in pharmaceutical research has begun thanks to biophysics and its thorough grasp of the structural and physical characteristics of biological molecules. This study explores how advances in biophysical approaches, including allosteric modulation, intrinsically disordered proteins (IDPs), and protein phase transitions, have transformed the drug development process. Protein phase transitions, supported by the biophysical principles, have provided crucial insights into disorders like ALS and Alzheimer’s, opening up new avenues for therapeutic intervention. Targeting IDPs and their role in liquid–liquid phase separation has produced creative approaches to diseases that were formerly thought to be resistant to therapy. In order to reduce the complexity of complicated diseases, the concept of allosteric modulation, made possible by biophysical understanding, offers a precise and selective approach to medication creation. This review highlights the significant influence of biophysics on the development of therapeutics and the discovery of new drugs. Biophysics is advancing the discipline toward the creation of more precise, efficient, and tailored medications by illuminating the biophysical properties of proteins, the complexities of phase transitions, and the dynamics of drug interactions. Biophysical insights are poised to alter healthcare by bringing together several fields and sustained innovation, giving people dealing with a variety of ailments fresh hope.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"30 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139148284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1142/s1793048023500030
Yuri K. Shestopaloff
The present view of biological phenomena is based on a biochemical paradigm that the development of living organisms is defined by information stored in a molecular form as some genetic code. However, new facts and discoveries indicate that biological phenomena cannot be confined to a biochemical realm alone, but are also influenced by physical mechanisms. One such discovered mechanism works at cellular, organ and whole organism spatial levels. It imposes uniquely defined constraints on the distribution of nutrients between biomass synthesis and maintenance of existing biomass. The relative (to the total consumed nutrients) amount of produced biomass, which decreases during the growth, accordingly changes the composition of biochemical reactions and secures their irreversibility during the organismal life cycle. Mathematically, this growth mechanism is represented by a growth equation. Using this equation, we introduce growth models for unicellular organisms Amoeba, Schizosaccharomyces pombe, Escherichia coli, Bacillus subtilis, Staphylococcus, show their adequacy to experimental data, and present two types of possible division mechanisms. Also, on the basis of the growth equation, we find different metabolic characteristics of these organisms. For instance, it was shown that in logarithmic coordinates the values of their metabolic allometric exponents are located on a straight line. This fact has important implications with regard to evolutionary process of organisms within a food chain, considered as a single system. High adequateness of obtained results to experimental data, from different perspectives, as well as excellent compliance with previously proven more particular knowledge, and with general criteria for validation of scientific truths, proves the validity of the introduced growth equation and of the discovered growth mechanism (which has all indications to be a real physical mechanism presenting in Nature). Taken together, the obtained results set solid grounds for the introduction of a more comprehensive physical–biochemical paradigm of Life origin, development and evolution.
{"title":"Physical Mechanisms Influencing Life Origin and Development. Physical–Biochemical Paradigm of Life","authors":"Yuri K. Shestopaloff","doi":"10.1142/s1793048023500030","DOIUrl":"https://doi.org/10.1142/s1793048023500030","url":null,"abstract":"The present view of biological phenomena is based on a biochemical paradigm that the development of living organisms is defined by information stored in a molecular form as some genetic code. However, new facts and discoveries indicate that biological phenomena cannot be confined to a biochemical realm alone, but are also influenced by physical mechanisms. One such discovered mechanism works at cellular, organ and whole organism spatial levels. It imposes uniquely defined constraints on the distribution of nutrients between biomass synthesis and maintenance of existing biomass. The relative (to the total consumed nutrients) amount of produced biomass, which decreases during the growth, accordingly changes the composition of biochemical reactions and secures their irreversibility during the organismal life cycle. Mathematically, this growth mechanism is represented by a growth equation. Using this equation, we introduce growth models for unicellular organisms Amoeba, Schizosaccharomyces pombe, Escherichia coli, Bacillus subtilis, Staphylococcus, show their adequacy to experimental data, and present two types of possible division mechanisms. Also, on the basis of the growth equation, we find different metabolic characteristics of these organisms. For instance, it was shown that in logarithmic coordinates the values of their metabolic allometric exponents are located on a straight line. This fact has important implications with regard to evolutionary process of organisms within a food chain, considered as a single system. High adequateness of obtained results to experimental data, from different perspectives, as well as excellent compliance with previously proven more particular knowledge, and with general criteria for validation of scientific truths, proves the validity of the introduced growth equation and of the discovered growth mechanism (which has all indications to be a real physical mechanism presenting in Nature). Taken together, the obtained results set solid grounds for the introduction of a more comprehensive physical–biochemical paradigm of Life origin, development and evolution.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"93 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-21DOI: 10.1142/s1793048023410059
S. Çelik, Serpil Deniz, Ali Mahir Gündüz, Leyla Turgut Çoban, Zehra İlik Akman, A. Sohail, Serhat Güneş, Barzin Tajani, M. Ç. Kotan
Research motivation: Staging esophageal cancer is of paramount importance for treatment. With conventional methods, accuracy of staging is low, we aimed to improve the accuracy of the “T” stage of esophageal cancer by using deep learning techniques. Method/Material: Clinically diagnosed esophageal cancer patients were prospectively observed and their data were collected. jpeg images were collected from the Computed Tomography of patients. 80% of the data were used for training and 20% for tests. Pathology results were used as the gold standard in the training of deep learning algorithms. EfficientNetB7 and ResNet152V2 models were used in the study. Both architectures with convolutional neural networks have Convolutional layers, pool layers, and fully connected layers. Results: A total of 477 images of 50 patients were analyzed. EfficientNetB7 makes predictions with a total of 64,107,931 parameters, and ResNet152V2 58,339,844 parameters within seconds (2[Formula: see text]s) at rates close to the accuracy offered by humans. With the EfficientNetB7 architecture, one of the Convolutional Neural Networks used in this study, 90% accuracy was achieved in the “T” staging of esophageal cancer. Conclusion: Despite the very limited dataset, deep learning algorithms can perform effective and reliable staging under the supervision of an experienced radiologist. With more datasets, the precision of the estimation can increase.
{"title":"Forecasting the “T” Stage of Esophageal Cancer by Deep Learning Methods: A Pilot Study","authors":"S. Çelik, Serpil Deniz, Ali Mahir Gündüz, Leyla Turgut Çoban, Zehra İlik Akman, A. Sohail, Serhat Güneş, Barzin Tajani, M. Ç. Kotan","doi":"10.1142/s1793048023410059","DOIUrl":"https://doi.org/10.1142/s1793048023410059","url":null,"abstract":"Research motivation: Staging esophageal cancer is of paramount importance for treatment. With conventional methods, accuracy of staging is low, we aimed to improve the accuracy of the “T” stage of esophageal cancer by using deep learning techniques. Method/Material: Clinically diagnosed esophageal cancer patients were prospectively observed and their data were collected. jpeg images were collected from the Computed Tomography of patients. 80% of the data were used for training and 20% for tests. Pathology results were used as the gold standard in the training of deep learning algorithms. EfficientNetB7 and ResNet152V2 models were used in the study. Both architectures with convolutional neural networks have Convolutional layers, pool layers, and fully connected layers. Results: A total of 477 images of 50 patients were analyzed. EfficientNetB7 makes predictions with a total of 64,107,931 parameters, and ResNet152V2 58,339,844 parameters within seconds (2[Formula: see text]s) at rates close to the accuracy offered by humans. With the EfficientNetB7 architecture, one of the Convolutional Neural Networks used in this study, 90% accuracy was achieved in the “T” staging of esophageal cancer. Conclusion: Despite the very limited dataset, deep learning algorithms can perform effective and reliable staging under the supervision of an experienced radiologist. With more datasets, the precision of the estimation can increase.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44392765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 10.1142/s1793048023410035
Muhammad Jalal, Muhammad Kamal, Andleeb Zafar
The conventional method for determining whether a drug is carcinogenic involves subjecting rodents to a 2-year bioassay, but this approach is both time-consuming and expensive, not to mention unethical. Consequently, machine learning techniques have emerged as a popular alternative. One such technique is ensemble learning, which aims to create more accurate and robust models. In this particular study, the LightGBM model was utilized to predict the carcinogenicity of chemicals using its fingerprints. Molecular fingerprints were generated from the Simplified Molecular-Input Line-Entry System (SMILES) of 1003 chemicals from the Carcinogenic Potency Database (CPDB) dataset. The performance of the LightGBM model was found to be superior to other machine learning models reported in previous research. To further validate the model, it was tested on a database related to humans from the International Agency for Research on Cancer (IARC), as well as on chemicals that were withdrawn from the market between 1950 and 2014. The results showed that the LightGBM model was effective in identifying carcinogenic chemicals, suggesting that this approach could potentially replace traditional methods of carcinogenicity testing in the future.
{"title":"ChemCarcinoPred: Carcinogenicity Prediction of Small Drug-Like Molecules Using LightGBM and Molecular Fingerprints","authors":"Muhammad Jalal, Muhammad Kamal, Andleeb Zafar","doi":"10.1142/s1793048023410035","DOIUrl":"https://doi.org/10.1142/s1793048023410035","url":null,"abstract":"The conventional method for determining whether a drug is carcinogenic involves subjecting rodents to a 2-year bioassay, but this approach is both time-consuming and expensive, not to mention unethical. Consequently, machine learning techniques have emerged as a popular alternative. One such technique is ensemble learning, which aims to create more accurate and robust models. In this particular study, the LightGBM model was utilized to predict the carcinogenicity of chemicals using its fingerprints. Molecular fingerprints were generated from the Simplified Molecular-Input Line-Entry System (SMILES) of 1003 chemicals from the Carcinogenic Potency Database (CPDB) dataset. The performance of the LightGBM model was found to be superior to other machine learning models reported in previous research. To further validate the model, it was tested on a database related to humans from the International Agency for Research on Cancer (IARC), as well as on chemicals that were withdrawn from the market between 1950 and 2014. The results showed that the LightGBM model was effective in identifying carcinogenic chemicals, suggesting that this approach could potentially replace traditional methods of carcinogenicity testing in the future.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49225897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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