Pub Date : 2024-10-05DOI: 10.1016/j.csbj.2024.10.006
The influence of biomechanics on cell function has become increasingly defined over recent years. Biomechanical changes are known to affect oncogenesis; however, these effects are not yet fully understood. Atomic force microscopy (AFM) is the gold standard method for measuring tissue mechanics on the micro- or nano-scale. Due to its complexity, however, AFM has yet to become integrated in routine clinical diagnosis. Artificial intelligence (AI) and machine learning (ML) have the potential to make AFM more accessible, principally through automation of analysis. In this review, AFM and its use for the assessment of cell and tissue mechanics in cancer is described. Research relating to the application of artificial intelligence and machine learning in the analysis of AFM topography and force spectroscopy of cancer tissue and cells are reviewed. The application of machine learning and artificial intelligence to AFM has the potential to enable the widespread use of nanoscale morphologic and biomechanical features as diagnostic and prognostic biomarkers in cancer treatment.
{"title":"Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis","authors":"","doi":"10.1016/j.csbj.2024.10.006","DOIUrl":"10.1016/j.csbj.2024.10.006","url":null,"abstract":"<div><div>The influence of biomechanics on cell function has become increasingly defined over recent years. Biomechanical changes are known to affect oncogenesis; however, these effects are not yet fully understood. Atomic force microscopy (AFM) is the gold standard method for measuring tissue mechanics on the micro- or nano-scale. Due to its complexity, however, AFM has yet to become integrated in routine clinical diagnosis. Artificial intelligence (AI) and machine learning (ML) have the potential to make AFM more accessible, principally through automation of analysis. In this review, AFM and its use for the assessment of cell and tissue mechanics in cancer is described. Research relating to the application of artificial intelligence and machine learning in the analysis of AFM topography and force spectroscopy of cancer tissue and cells are reviewed. The application of machine learning and artificial intelligence to AFM has the potential to enable the widespread use of nanoscale morphologic and biomechanical features as diagnostic and prognostic biomarkers in cancer treatment.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-05DOI: 10.1016/j.csbj.2024.10.007
Aims
This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.
Methods and Results
We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443–0.526 vs kappa=0.87; 95 % CI, 0.852–0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance.
Conclusions
This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.
目的本研究旨在开发一种人工智能算法，用于对尿路上皮膀胱癌（UBCa）进行自动 HER2 评分，并根据乳腺癌标准使用人工和人工智能辅助方法评估观察者之间的一致性。方法与结果我们利用两家机构的 330 张切片进行了初步的人工智能开发，并选择了 200 张切片进行环形研究，共有 6 名病理学家（3 名高级病理学家，3 名初级病理学家）参与。我们的人工智能算法在两项独立测试中取得了很高的准确率，分别为 0.94 和 0.92。在环形研究中，与人工评分相比，人工智能辅助方法提高了准确性（0.66 vs 0.94）和一致性（kappa=0.48；95 % CI，0.443-0.526 vs kappa=0.87；95 % CI，0.852-0.885），尤其是在 HER2 低的病例中（F1 分数：0.63 vs 0.92）。此外，在 62.3% 的异质性 HER2 阳性病例中，判读准确性也有显著提高（0.49 vs 0.93）。在人工智能的帮助下，病理学家，尤其是初级病理学家，提高了准确性和一致性。结论这是第一项为 UBCa 中的 HER2 评分提供量化算法的研究，可帮助病理学家进行诊断。环形研究表明，基于乳腺癌标准的 HER2 评分可有效应用于 UBCa。此外，即使是在异质性病例中，人工智能的辅助也能大大提高经验水平不同的病理学家解释的准确性和一致性。
{"title":"Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study","authors":"","doi":"10.1016/j.csbj.2024.10.007","DOIUrl":"10.1016/j.csbj.2024.10.007","url":null,"abstract":"<div><h3>Aims</h3><div>This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.</div></div><div><h3>Methods and Results</h3><div>We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443–0.526 vs kappa=0.87; 95 % CI, 0.852–0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance.</div></div><div><h3>Conclusions</h3><div>This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.csbj.2024.09.021
Background and objective
This article uses three different probabilistic convolutional architectures applied to ultrasound image analysis for grading Fatty Liver Content (FLC) in Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) patients. Steatosis is a new silent epidemic and its accurate measurement is an impelling clinical need, not only for hepatologists, but also for experts in metabolic and cardiovascular diseases. This paper aims to provide a robust comparison between different uncertainty quantification strategies to identify advantages and drawbacks in a real clinical setting.
Methods
We used a classical Convolutional Neural Network, a Monte Carlo Dropout, and a Bayesian Convolutional Neural Network with the goal of not only comparing the goodness of the predictions, but also to have access to an evaluation of the uncertainty associated with the outputs.
Results
We found that even if the prediction based on a single ultrasound view is reliable (relative RMSE [5.93%-12.04%]), networks based on two ultrasound views outperform them (relative RMSE [5.35%-5.87%]). In addition, the results show that the introduction of a “not confident” category contributes to increase the percentage of correctly predicted cases and to decrease the percentage of mispredicted cases, especially for semi-intrusive methods.
Conclusions
The possibility of having access to information about the confidence with which the network produces its outputs is a great advantage, both from the point of view of physicians who want to use neural networks as computer-aided diagnosis, and for developers who want to limit overfitting and obtain information about dataset problems in terms of out-of-distribution detection.
{"title":"ANN uncertainty estimates in assessing fatty liver content from ultrasound data","authors":"","doi":"10.1016/j.csbj.2024.09.021","DOIUrl":"10.1016/j.csbj.2024.09.021","url":null,"abstract":"<div><h3>Background and objective</h3><div>This article uses three different probabilistic convolutional architectures applied to ultrasound image analysis for grading Fatty Liver Content (FLC) in Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) patients. Steatosis is a new silent epidemic and its accurate measurement is an impelling clinical need, not only for hepatologists, but also for experts in metabolic and cardiovascular diseases. This paper aims to provide a robust comparison between different uncertainty quantification strategies to identify advantages and drawbacks in a real clinical setting.</div></div><div><h3>Methods</h3><div>We used a classical Convolutional Neural Network, a Monte Carlo Dropout, and a Bayesian Convolutional Neural Network with the goal of not only comparing the goodness of the predictions, but also to have access to an evaluation of the uncertainty associated with the outputs.</div></div><div><h3>Results</h3><div>We found that even if the prediction based on a single ultrasound view is reliable (relative RMSE [5.93%-12.04%]), networks based on two ultrasound views outperform them (relative RMSE [5.35%-5.87%]). In addition, the results show that the introduction of a “not confident” category contributes to increase the percentage of correctly predicted cases and to decrease the percentage of mispredicted cases, especially for semi-intrusive methods.</div></div><div><h3>Conclusions</h3><div>The possibility of having access to information about the confidence with which the network produces its outputs is a great advantage, both from the point of view of physicians who want to use neural networks as computer-aided diagnosis, and for developers who want to limit overfitting and obtain information about dataset problems in terms of out-of-distribution detection.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.csbj.2024.09.019
Introduction
The quest to develop efficient, sustainable materials from non-critical, non-toxic resources is one of today's most formidable challenges in the current context of energy, transport, digital or healthcare transitions. In response, France launched the pioneering Priority Equipment and Research Program (PEPR) DIADEM in 2022. This innovative initiative, focused on DIscovery Acceleration for the Deployment of Emerging Materials (DIADEM), leverages Artificial Intelligence (AI) to accelerate the innovation chain from conception to realization, revolutionizing Materials Science sustainably. With a strategic emphasis on scientific synergy, PEPR DIADEM aims to expedite the discovery and development of novel materials essential for contemporary and future societal challenges. To achieve this, the program seeks to catalyze breakthroughs in areas ranging from energy efficiency to transportation, digitalization, and healthcare, covering a broad spectrum of materials from metallic alloys to functional nanostructures. Aligned with the Green Deal framework's ambitious targets, PEPR DIADEM addresses the urgent need for accelerated sustainable materials research. By utilizing cutting-edge technologies like rapid synthesis and characterization tools, automation, digital simulations, data management, AI, additive manufacturing, and thin film engineering, the program is set to significantly reshape the materials science landscape. As PEPR DIADEM embarks on its journey of innovation, it not only advances scientific knowledge but also holds the promise of addressing current global challenges and paving the way for a more sustainable and prosperous future.
{"title":"PEPR DIADEM: Priority equipment and research program on the development of innovative materials using artificial intelligence","authors":"","doi":"10.1016/j.csbj.2024.09.019","DOIUrl":"10.1016/j.csbj.2024.09.019","url":null,"abstract":"<div><h3>Introduction</h3><div>The quest to develop efficient, sustainable materials from non-critical, non-toxic resources is one of today's most formidable challenges in the current context of energy, transport, digital or healthcare transitions. In response, France launched the pioneering Priority Equipment and Research Program (PEPR) DIADEM in 2022. This innovative initiative, focused on DIscovery Acceleration for the Deployment of Emerging Materials (DIADEM), leverages Artificial Intelligence (AI) to accelerate the innovation chain from conception to realization, revolutionizing Materials Science sustainably. With a strategic emphasis on scientific synergy, PEPR DIADEM aims to expedite the discovery and development of novel materials essential for contemporary and future societal challenges. To achieve this, the program seeks to catalyze breakthroughs in areas ranging from energy efficiency to transportation, digitalization, and healthcare, covering a broad spectrum of materials from metallic alloys to functional nanostructures. Aligned with the Green Deal framework's ambitious targets, PEPR DIADEM addresses the urgent need for accelerated sustainable materials research. By utilizing cutting-edge technologies like rapid synthesis and characterization tools, automation, digital simulations, data management, AI, additive manufacturing, and thin film engineering, the program is set to significantly reshape the materials science landscape. As PEPR DIADEM embarks on its journey of innovation, it not only advances scientific knowledge but also holds the promise of addressing current global challenges and paving the way for a more sustainable and prosperous future.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1016/j.csbj.2024.09.012
We present a meta-analytics approach to quantify NSCLC disease burden by integrative survival models. Aggregated survival data from public sources were used to parameterize the models for early as well as advanced NSCLC stages incorporating chemotherapies, targeted therapies, and immunotherapies. Overall survival (OS) was predicted in a heterogeneous patient cohort based on various stratifications and initial conditions. Pharmacoeconomic metrics (life years gained (LYG) and quality-adjusted life years (QALY) gained), were evaluated to quantify the benefits of specialized treatments and improved early detection of NSCLC. Simulations showed that the introduction of novel therapies for the advanced NSCLC sub-group increased median survival by 8.1 months (95 % CI: 5.9, 10.0), with corresponding gains of 2.9 months (95 % CI: 2.2, 3.6) in LYG and 1.65 months (95 % CI: 1.2, 2.0) in QALY. Scenarios representing improved detection of early cancer in the whole patient cohort, revealed up to 17.6 (95 % CI: 16.5, 19.0) and 15.7 months (95 % CI: 14.8, 16.6) increase in median survival, with respective gains of 6.2 months (95 % CI: 5.9, 6.4) and 5.2 months (95 % CI: 4.9, 5.4) in LYG and 6.6 months (95 % CI: 6.4, 6.7) and 6.0 months (95 % CI: 5.9, 6.2) in QALY for conventional and optimal treatment. This integrative modeling platform, aimed at characterizing cancer burden, allows to precisely quantify the cumulative benefits of introducing specialized therapies into the treatment schemes and survival prolongation upon early detection of the disease.
{"title":"Assessment of NSCLC disease burden: A survival model-based meta-analysis study","authors":"","doi":"10.1016/j.csbj.2024.09.012","DOIUrl":"10.1016/j.csbj.2024.09.012","url":null,"abstract":"<div><div>We present a meta-analytics approach to quantify NSCLC disease burden by integrative survival models. Aggregated survival data from public sources were used to parameterize the models for early as well as advanced NSCLC stages incorporating chemotherapies, targeted therapies, and immunotherapies. Overall survival (OS) was predicted in a heterogeneous patient cohort based on various stratifications and initial conditions. Pharmacoeconomic metrics (life years gained (LYG) and quality-adjusted life years (QALY) gained), were evaluated to quantify the benefits of specialized treatments and improved early detection of NSCLC. Simulations showed that the introduction of novel therapies for the advanced NSCLC sub-group increased median survival by 8.1 months (95 % CI: 5.9, 10.0), with corresponding gains of 2.9 months (95 % CI: 2.2, 3.6) in LYG and 1.65 months (95 % CI: 1.2, 2.0) in QALY. Scenarios representing improved detection of early cancer in the whole patient cohort, revealed up to 17.6 (95 % CI: 16.5, 19.0) and 15.7 months (95 % CI: 14.8, 16.6) increase in median survival, with respective gains of 6.2 months (95 % CI: 5.9, 6.4) and 5.2 months (95 % CI: 4.9, 5.4) in LYG and 6.6 months (95 % CI: 6.4, 6.7) and 6.0 months (95 % CI: 5.9, 6.2) in QALY for conventional and optimal treatment. This integrative modeling platform, aimed at characterizing cancer burden, allows to precisely quantify the cumulative benefits of introducing specialized therapies into the treatment schemes and survival prolongation upon early detection of the disease.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1016/j.csbj.2024.09.010
The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of plant stress resilience. The proliferation of experimental and analytical methods used in biology has resulted in a situation where plentiful data exists, but the volume and heterogeneity of this data has made analysis a significant challenge. Current advanced deep-learning models have displayed an unprecedented level of comprehension and problem-solving ability, and have been used to predict gene structure, function and expression based on DNA or protein sequence, and prominently also their use in high-throughput phenomics in agriculture. However, the application of deep-learning models to understand gene regulatory and signalling behaviour is still in its infancy. We discuss in this review the availability of data resources and bioinformatic tools, and several applications of these advanced ML/AI models in the context of plant stress response, and demonstrate the use of a publicly available LLM (ChatGPT) to derive a knowledge graph of various experimental and computational methods used in the study of plant stress. We hope this will stimulate further interest in collaboration between computer scientists, computational biologists and plant scientists to distil the deluge of genomic, transcriptomic, proteomic, metabolomic and phenomic data into meaningful knowledge that can be used for the benefit of humanity.
基因组学时代的到来使高通量数据和计算方法得以产生，它们成为强大的假设生成工具，用于了解植物抗逆性的基因组和基因功能基础。生物学中使用的实验和分析方法层出不穷，导致出现了大量数据的情况，但这些数据的数量和异质性给分析带来了巨大挑战。目前先进的深度学习模型已经展现出前所未有的理解力和解决问题的能力，并已被用于根据 DNA 或蛋白质序列预测基因结构、功能和表达，在农业领域的高通量表型组学中也有突出应用。然而，深度学习模型在理解基因调控和信号行为方面的应用仍处于起步阶段。我们将在这篇综述中讨论数据资源和生物信息学工具的可用性，以及这些先进的 ML/AI 模型在植物胁迫响应方面的几种应用，并演示如何使用公开可用的 LLM（ChatGPT）来得出植物胁迫研究中使用的各种实验和计算方法的知识图谱。我们希望这将进一步激发计算机科学家、计算生物学家和植物科学家之间的合作兴趣，将大量的基因组学、转录组学、蛋白质组学、代谢组学和表观组学数据提炼成可用于造福人类的有意义的知识。
{"title":"Confronting the data deluge: How artificial intelligence can be used in the study of plant stress","authors":"","doi":"10.1016/j.csbj.2024.09.010","DOIUrl":"10.1016/j.csbj.2024.09.010","url":null,"abstract":"<div><div>The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of plant stress resilience. The proliferation of experimental and analytical methods used in biology has resulted in a situation where plentiful data exists, but the volume and heterogeneity of this data has made analysis a significant challenge. Current advanced deep-learning models have displayed an unprecedented level of comprehension and problem-solving ability, and have been used to predict gene structure, function and expression based on DNA or protein sequence, and prominently also their use in high-throughput phenomics in agriculture. However, the application of deep-learning models to understand gene regulatory and signalling behaviour is still in its infancy. We discuss in this review the availability of data resources and bioinformatic tools, and several applications of these advanced ML/AI models in the context of plant stress response, and demonstrate the use of a publicly available LLM (ChatGPT) to derive a knowledge graph of various experimental and computational methods used in the study of plant stress. We hope this will stimulate further interest in collaboration between computer scientists, computational biologists and plant scientists to distil the deluge of genomic, transcriptomic, proteomic, metabolomic and phenomic data into meaningful knowledge that can be used for the benefit of humanity.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1016/j.csbj.2024.09.009
The advent of advanced coatings has transformed material functionalities, extending their roles from basic coverage and visual appeal to include unique properties such as self-healing, superior hydrophobicity, and antimicrobial action. However, the traditional dependency on petrochemical-derived materials for these coatings raises environmental concerns. This study proposes the use of renewable and alternative materials for coating development. We present the use of bioengineered spider silk-inspired protein (SSIP), produced through recombinant technology, as a viable, eco-friendly alternative due to their ease of processing under ambient pressure and the utilization of water as a solvent, alongside their exceptional physicochemical properties. Our research investigates the effects of different thermal treatments and protein concentrations on the mechanical strength and surface water repellency of coatings on silica bases. Our findings reveal a direct correlation between the temperature of heat treatment and the enhancements in surface hydrophobicity and mechanical strength, where elevated temperatures facilitate increased resistance to water and improved mechanical integrity. Consequently, we advocate SSIPs present a promising, sustainable choice for advanced coatings, providing a pathway to fine-tune coating recipes for better mechanical and hydrophobic properties with a reduced ecological footprint, finding potential uses in various fields such as electronics.
{"title":"Tailoring hydrophobicity and strength in spider silk-inspired coatings via thermal treatments","authors":"","doi":"10.1016/j.csbj.2024.09.009","DOIUrl":"10.1016/j.csbj.2024.09.009","url":null,"abstract":"<div><p>The advent of advanced coatings has transformed material functionalities, extending their roles from basic coverage and visual appeal to include unique properties such as self-healing, superior hydrophobicity, and antimicrobial action. However, the traditional dependency on petrochemical-derived materials for these coatings raises environmental concerns. This study proposes the use of renewable and alternative materials for coating development. We present the use of bioengineered spider silk-inspired protein (SSIP), produced through recombinant technology, as a viable, eco-friendly alternative due to their ease of processing under ambient pressure and the utilization of water as a solvent, alongside their exceptional physicochemical properties. Our research investigates the effects of different thermal treatments and protein concentrations on the mechanical strength and surface water repellency of coatings on silica bases. Our findings reveal a direct correlation between the temperature of heat treatment and the enhancements in surface hydrophobicity and mechanical strength, where elevated temperatures facilitate increased resistance to water and improved mechanical integrity. Consequently, we advocate SSIPs present a promising, sustainable choice for advanced coatings, providing a pathway to fine-tune coating recipes for better mechanical and hydrophobic properties with a reduced ecological footprint, finding potential uses in various fields such as electronics.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002976/pdfft?md5=e578139e728a4a4cd27b5e029c98b8b7&pid=1-s2.0-S2001037024002976-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.csbj.2024.09.004
Jędrzej Kukułowicz, Marek Bajda
NTT4 is one of the neutral amino acid transporters that regulate neural concentration of precursors for glutamate biosynthesis. Here, we provide insight into the structure of NTT4 and rationalize substrate selectivity. Furthermore, we demonstrate how the mutations associated with mental disabilities imply malfunction of the transporter at the molecular level. We also compared the structures of NTT4 and BAT2 (SLC6A15), which is a close homolog, sharing 66 % of the common amino acids. Our analyses may be useful in the search for compounds that inhibit substrate transport. Moreover, they allow a better understanding of the function of these transporters.
{"title":"In silico structural studies on the vesicular neutral amino acid transporter NTT4 (SLC6A17)","authors":"Jędrzej Kukułowicz, Marek Bajda","doi":"10.1016/j.csbj.2024.09.004","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.09.004","url":null,"abstract":"NTT4 is one of the neutral amino acid transporters that regulate neural concentration of precursors for glutamate biosynthesis. Here, we provide insight into the structure of NTT4 and rationalize substrate selectivity. Furthermore, we demonstrate how the mutations associated with mental disabilities imply malfunction of the transporter at the molecular level. We also compared the structures of NTT4 and BAT2 (SLC6A15), which is a close homolog, sharing 66 % of the common amino acids. Our analyses may be useful in the search for compounds that inhibit substrate transport. Moreover, they allow a better understanding of the function of these transporters.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.csbj.2024.09.002
Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope optical aberrations remains a critical challenge. This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy. By simulating different types of aberrations, including astigmatism, coma, spherical aberration, trefoil, and mixed aberrations, we conduct a thorough evaluation of various cell instance segmentation models using the DynamicNuclearNet (DNN) and LIVECell datasets, representing fluorescence and bright field microscopy cell datasets, respectively. We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads (FPN, C3) and backbones (ResNet, VGG, Swin Transformer), under aberrated conditions. Additionally, we provide usage recommendations for the Cellpose 2.0 Toolbox on complex cell degradation images. The results indicate that the combination of FPN and SwinS demonstrates superior robustness in handling simple cell images affected by minor aberrations. In contrast, Cellpose 2.0 proves effective for complex cell images under similar conditions. Furthermore, we innovatively propose the Point Spread Function Image Label Classification Model (PLCM). This model can quickly and accurately identify aberration types and amplitudes from PSF images, assisting researchers without optical training. Through PLCM, researchers can better apply our proposed cell segmentation guidelines. This study aims to provide guidance for the effective utilization of cell segmentation models in the presence of minor optical aberrations and pave the way for future research directions.
{"title":"Practical guidelines for cell segmentation models under optical aberrations in microscopy","authors":"","doi":"10.1016/j.csbj.2024.09.002","DOIUrl":"10.1016/j.csbj.2024.09.002","url":null,"abstract":"<div><div>Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope optical aberrations remains a critical challenge. This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy. By simulating different types of aberrations, including astigmatism, coma, spherical aberration, trefoil, and mixed aberrations, we conduct a thorough evaluation of various cell instance segmentation models using the DynamicNuclearNet (DNN) and LIVECell datasets, representing fluorescence and bright field microscopy cell datasets, respectively. We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads (FPN, C3) and backbones (ResNet, VGG, Swin Transformer), under aberrated conditions. Additionally, we provide usage recommendations for the Cellpose 2.0 Toolbox on complex cell degradation images. The results indicate that the combination of FPN and SwinS demonstrates superior robustness in handling simple cell images affected by minor aberrations. In contrast, Cellpose 2.0 proves effective for complex cell images under similar conditions. Furthermore, we innovatively propose the Point Spread Function Image Label Classification Model (PLCM). This model can quickly and accurately identify aberration types and amplitudes from PSF images, assisting researchers without optical training. Through PLCM, researchers can better apply our proposed cell segmentation guidelines. This study aims to provide guidance for the effective utilization of cell segmentation models in the presence of minor optical aberrations and pave the way for future research directions.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.csbj.2024.09.005
Rui Han, Xu Wang, Xuan Wang, Yadong Wang, Junyi Li
Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.
{"title":"AFSC: A self-supervised augmentation-free spatial clustering method based on contrastive learning for identifying spatial domains","authors":"Rui Han, Xu Wang, Xuan Wang, Yadong Wang, Junyi Li","doi":"10.1016/j.csbj.2024.09.005","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.09.005","url":null,"abstract":"Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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