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General General

STAGETOOL, a novel automated approach for mouse testis histological analysis.

In Endocrinology ; h5-index 58.0

Spermatogenesis is a complex differentiation process that takes place in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium enables a synchronous progress of germ cells at certain steps of differentiation on the spermatogenic pathway. This can be observed in testis cross-sections where seminiferous tubules can be classified into distinct stages of constant cellular composition (twelve stages in the mouse). For a detailed analysis of spermatogenesis, these stages have to be individually observed from testis cross-sections. However, the recognition of stages requires special training and expertise. Furthermore, the manual scoring is laborious considering the high number of tubule cross-sections that have to be analyzed. To facilitate the analysis of spermatogenesis, we have developed a convolutional deep neural network-based approach named "STAGETOOL". STAGETOOL analyses histological images of DAPI-stained mouse testis cross-sections at ×400 magnification, and very accurately classifies tubule cross-sections into five stage classes and cells into nine categories. STAGETOOL classification accuracy for stage classes of seminiferous tubules of a whole-testis cross-section is 99.1%. For cellular level analysis the F1 score for nine seminiferous epithelial cell types ranges 0.80-0.98. Furthermore, we show that STAGETOOL can be applied for the analysis of knockout mouse models with spermatogenic defects, as well as for automated profiling of protein expression patterns. STAGETOOL is the first fluorescent labeling-based automated method for mouse testis histological analysis that enables both stage and cell-type recognition. While STAGETOOL qualitatively parallels an experienced human histologist, it outperforms human time-wise, therefore representing a major advancement in male reproductive biology research.

Meikar Oliver, Majoral Daniel, Heikkinen Olli, Valkama Eero, Leskinen Sini, Rebane Ana, Ruusuvuori Pekka, Toppari Jorma, Mäkelä Juho-Antti, Kotaja Noora


DAPI staining, Mouse testis histology, automated analysis, deep learning, seminiferous epithelial cycle, spermatogenesis

General General

Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives.

In Journal of clinical pharmacology

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

Liu Jing, Barrett Jeffrey S, Leonardi Efthimia T, Lee Lucy, Roychoudhury Satrajit, Chen Yong, Trifillis Panayiota


disease progression modeling, natural history, rare diseases, real-world data, real-world evidence

General General

AlphaFold2: A versatile tool to predict the appearance of functional adaptations in evolution: Profilin interactions in uncultured Asgard archaea: Profilin interactions in uncultured Asgard archaea.

In BioEssays : news and reviews in molecular, cellular and developmental biology

The release of AlphaFold2 (AF2), a deep-learning-aided, open-source protein structure prediction program, from DeepMind, opened a new era of molecular biology. The astonishing improvement in the accuracy of the structure predictions provides the opportunity to characterize protein systems from uncultured Asgard archaea, key organisms in evolutionary biology. Despite the accumulation in metagenomics-derived Asgard archaea eukaryotic-like protein sequences, limited structural and biochemical information have restricted the insight in their potential functions. In this review, we focus on profilin, an actin-dynamics regulating protein, which in eukaryotes, modulates actin polymerization through (1) direct actin interaction, (2) polyproline binding, and (3) phospholipid binding. We assess AF2-predicted profilin structures in their potential abilities to participate in these activities. We demonstrate that AF2 is a powerful new tool for understanding the emergence of biological functional traits in evolution.

Ponlachantra Khongpon, Suginta Wipa, Robinson Robert C, Kitaoku Yoshihito


AlphaFold2, Asgard archaea, actin cytoskeleton, eukaryogenesis, evolution, pofilin

General General

AMP-BERT: Prediction of Antimicrobial Peptide Function Based on a BERT Model.

In Protein science : a publication of the Protein Society

Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by non-specific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to traditional antimicrobial drugs. In this study, we developed an improved AMP classification model, called AMP-BERT. We propose a deep learning model with a fine-tuned BERT architecture designed to extract structural/functional information from input peptides and identify each input as AMP or non-AMP. We compared the performance of our proposed model and other machine/deep learning-based methods. Our model, AMP-BERT, yielded the best prediction results among all models evaluated with our curated external dataset. In addition, we utilized the attention mechanism in BERT to implement an interpretable feature analysis and determine the specific residues in known AMPs that contribute to peptide structure and antimicrobial function. The results show that AMP-BERT can capture the structural properties of peptides for model learning, enabling the prediction of AMPs or non-AMPs from input sequences. AMP-BERT is expected to contribute to the identification of candidate AMPs for functional validation and drug development. The code and dataset for the fine-tuning of AMP-BERT is publicly available at This article is protected by copyright. All rights reserved.

Lee Hansol, Lee Songyeon, Lee Ingoo, Nam Hojung


Antimicrobial peptides, BERT, Transformer, antimicrobial resistance, deep learning, drug discovery, machine learning, sequence classification

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PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.

In Scientific reports ; h5-index 158.0

Infectious diseases are known to cause a wide variety of post-infection complications. However, it's been challenging to identify which diseases are most associated with a given pathogen infection. Using the recently developed LeMeDISCO approach that predicts comorbid diseases associated with a given set of putative mode of action (MOA) proteins and pathogen-human protein interactomes, we developed PHEVIR, an algorithm which predicts the corresponding human disease comorbidities of 312 viruses and 57 bacteria. These predictions provide an understanding of the molecular bases of complications and means of identifying appropriate drug targets to treat them. As an illustration of its power, PHEVIR is applied to identify putative driver pathogens and corresponding human MOA proteins for Type 2 diabetes, atherosclerosis, Alzheimer's disease, and inflammatory bowel disease. Additionally, we explore the origins of the oncogenicity/oncolyticity of certain pathogens and the relationship between heart disease and influenza. The full PHEVIR database is available at .

Zhou Hongyi, Astore Courtney, Skolnick Jeffrey


General General

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.

In Communications biology

Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensitive processes in living cells and animals. The low photon emission of known luciferases, however, demands long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. We apply our method to image subcellular dynamics in mouse embryonic stem cells, epithelial morphology during zebrafish development, and DAF-16 FoxO transcription factor shuttling from the cytoplasm to the nucleus under external stress. Finally, we concatenate neural networks for denoising and light-field deconvolution to resolve intracellular calcium dynamics in three dimensions of freely moving Caenorhabditis elegans.

Morales-Curiel Luis Felipe, Gonzalez Adriana Carolina, Castro-Olvera Gustavo, Lin Li-Chun Lynn, El-Quessny Malak, Porta-de-la-Riva Montserrat, Severino Jacqueline, Morera Laura Battle, Venturini Valeria, Ruprecht Verena, Ramallo Diego, Loza-Alvarez Pablo, Krieg Michael