Biological Classification Research Articles

Abstract 4561: Mechanism of histone H3K27me3 demethylase therapy for restoring cisplatin sensitivity in refractory testicular cancer

Abstract Testicular germ cell tumors (TGCTs) can be cured at a high rate with standard cisplatin-based chemotherapy, but 15% of metastatic patients are cisplatin resistant/refractory and die from progressive disease. In prior work, we used de novo pharmacogenetic approaches leveraging a series of unique cisplatin resistant cell models that uncovered an important role for polycomb and histone H3K27me3 methylation in mediating TGCT sensitivity and resistance to cisplatin. Importantly, we demonstrated that pharmacological inhibition of H3K27 methylation with the EZH2 inhibitor GSK-126 conferred cisplatin resistance to parental cells while induction of H3K27 methylation with the histone lysine demethylase inhibitor GSK-J4 resulted in a high degree of cisplatin sensitization of TGCT tumors including cisplatin resistant TGCT cells. In order to elucidate mechanisms that account for the remarkable cisplatin sensitization effects of GSKJ4 in TGCTs, we used two distinct cisplatin resistant TGCT cell lines 2102EP-C1 and NT2D1-A4. Cells were pretreated for three days with either vehicle control or 1µM GSK-J4 to modulate H3K27me3 followed by treatment with vehicle control or 0.5µM cisplatin for 6 hours. RNA was extracted from cells after 24 hours of cisplatin removal. This treatment protocol was designed to capture “early” transcriptional responses to GSKJ4 sensitization before the onset of cell death. RNA-seq transcriptomic profiling and differential gene expression analysis revealed as expected a severely attenuated transcriptional response to cisplatin in cisplatin refractory cells that was restored with GSKJ4 pretreatment. Interestingly GSKJ4 alone resulted in a robust transcriptional response with a large overlap with gene changed with cisplatin alone, despite the fact that GSKJ4 alone did not affect cell proliferation or survival. However, GSKJ4 uniquely regulated a subset of genes and uniquely restored cisplatin response to a subset of genes which are candidates for driving the remarkable cisplatin sensitizing effects of GSKJ4 in TGCTs. Further enrichment and biological classification analysis of these distinct gene subsets are ongoing and will be presented. In summary, our data suggests mechanisms to account for why directly targeting H3K27 methylation with GSKJ4 appear highly effective in treating cisplatin resistant/refractory TGCTs and may provide biomarkers for future clinical investigation of this strategy. Citation Format: Doha Naguib Ahmed Mohamed Shokry, Ratnakar Singh, Mehwish Khan, Zeeshan Fazal, Raya Boyd, Christine Powell, Sarah J. Freemantle, Michael J. Spinella. Mechanism of histone H3K27me3 demethylase therapy for restoring cisplatin sensitivity in refractory testicular cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4561.

WGDPool: A broad scope extraction for weighted graph data

Graph pooling is a commonly used operation in graph neural networks to reduce the size of graph representation. To extract key information, pooling and representation need to be coupled. We propose a graph pooling method for weighted graphs called WGDPool (Weighted Graph Dual Pooling). Unlike traditional graph representation learning methods, the weight information of edges is also fed into convolutional graph neural networks (ConvGNN) to obtain graph representations. Dual branch convolutional graph neural networks is designed to learn the nodes’ and edges’ embeddings independently, and they are fused into a comprehensive representation of graph data. Pooling, as a tool of feature extraction and scale reduction of graph representation, adopts a differentiable version of k-means clustering and a multi-item parameterized loss function. Cut loss, orthogonality loss, clustering loss, and reconstruction loss are simultaneously considered. By parameterization, WGDPool is competent for diverse graph tasks. WGDPool outperformed other graph pooling methods in such common supervised and unsupervised tasks as biological or chemical classification, bibliography clustering and integrated circuit partition, demonstrating the effectiveness of our proposed pooling method.

Hypoxia-induced circRNAs encoded by PPARA are highly expressed in human cardiomyocytes and are potential clinical biomarkers of acute myocardial infarction

BackgroundAcute myocardial infarction (AMI) is a serious cardiovascular disease that adversely affects human health. Circular RNAs (circRNAs) are involved in the pathological and physiological processes of AMI, but the biological mechanism of their involvement and their clinical significance remain unknown. We aimed to identify circRNAs that are significantly associated with morbidity in the peripheral blood of patients with AMI and evaluate their diagnostic utility.MethodsHigh-throughput sequencing was used to screen for differentially expressed circRNAs in peripheral blood samples obtained from five patients with AMI and five sex- and age-matched healthy controls. A series of bioinformatics tools and databases were used to determine the biological functional classification and pathway enrichment of the circRNAs based on data obtained from sequencing. A hypoxia model was established and used to evaluate the effect of hypoxia on circRNA expression in human cardiomyocytes. A cytoplasmic separation assay and enzyme resistance assay were employed to identify the biological characteristics of circRNA. Polymerase chain reaction validity testing and receiver operating characteristic (ROC) curve analysis were used to evaluate the utility of circRNA assessments in the diagnosis of AMI.ResultsA large number of circRNAs were found to be differentially expressed in the peripheral blood of patients with AMI, and significantly more of these circRNAs were highly expressed than lowly expressed. The genes encoding these circRNAs have a wide range of effects on various functions in the body. A hypoxic environment promoted the upregulation of circRNA expression in human cardiomyocytes, and hsa_circ_0116795 encoded by PPARA was highly expressed in the peripheral blood of the patients with AMI. In terms of biological characteristics, under physiological conditions, hsa_circ_0116795 (circ_PPARA) was mainly located in the cytoplasm of cardiomyocytes and found to be resistant to exonuclease. The ROC curve analysis showed that the expression levels of circ_PPARA in the peripheral blood of patients with AMI were significantly different from those in the peripheral blood of healthy controls.ConclusionA large number of abnormally expressed circRNAs are detectable in the peripheral blood of patients with AMI. In particular, circ_PPARA is highly expressed in human myocardial cells under hypoxic conditions, and its biological characteristics indicate that it could be employed as a biomarker for the early diagnosis of AMI.

Nkrabea and Hyɛbrɛ in African Philosophy of Life

The concepts of nkrabea and hyɛbrɛ in Africa’s philosophy of life present very distinctive nomenclature and epistemology of how it applies within the socioreligiocultural cosmology. As a challenge to normative definition, the epistemology of the African philosophical context engenders new nomenclatures that hitherto were rejected by the hegemonic frameworks of Western paradigmatic nomenclature. The African philosophy of life which is formed by a very distinct socioreligioculture and framed by fascination and experience of the supernatural does not fail to recognise the existence of vast dimensions and active spiritual cosmology. If there is anything conceived with such an aura of reverence, it is life, beyond biological classification and, experiences such as the ability to think, feel, or be aware of one’s existence. Life is “energy” (spiritual force) which provides every element its function or purpose and nkrabea and hyɛbrɛ are the function or purpose predetermined for every element. The objective of the study was to find out the meaning of nkrabea and hyɛbrɛ and how they apply to the African philosophy of life. The study used a qualitative research method. Data was gathered from journals, articles, and library materials. Ethical responsibilities defined a set of principles and protocols that guided the study. The study concluded that the concepts of nkrabea, and hyɛbrɛ are conceptualised within the African philosophy of life and present critical nomenclatures on the function, or purpose of life within the context of human destiny and inform new epistemology. Its belief provides a perspective that informs the idea that nothing is purposeless. Keywords: Nkrabea, Hyɛbrɛ, Cosmology, Spirituality, Philosophy

Tumor immunity: A brief overview of tumor‑infiltrating immune cells and research advances into tumor‑infiltrating lymphocytes in gynecological malignancies (Review).

Tumor immunity is a promising topic in the area of cancer therapy. The 'soil' function of the tumor microenvironment (TME) for tumor growth has attracted wide attention from scientists. Tumor-infiltrating immune cells in the TME, especially the tumor-infiltrating lymphocytes (TILs), serve a key role in cancer. Firstly, relevant literature was searched in the PubMed and Web of Science databases with the following key words: 'Tumor microenvironment'; 'TME'; 'tumor-infiltrating immunity cells'; 'gynecologic malignancies'; 'the adoptive cell therapy (ACT) of TILs'; and 'TIL-ACT' (https://pubmed.ncbi.nlm.nih.gov/). According to the title and abstract of the articles, relevant items were screened out in the preliminary screening. The most relevant selected items were of two types: All kinds of tumor-infiltrating immune cells; and advanced research on TILs in gynecological malignancies. The results showed that the subsets of TILs were various and complex, while each subpopulation influenced each other and their effects on tumor prognosis were diverse. Moreover, the related research and clinical trials on TILs were mostly concentrated in melanoma and breast cancer, but relatively few focused on gynecological tumors. In conclusion, the present review summarized the biological classification of TILs and the mechanisms of their involvement in the regulation of the immune microenvironment, and subsequently analyzed the development of tumor immunotherapy for TILs. Collectively, the present review provides ideas for the current treatment dilemma of gynecological tumor immune checkpoints, such as adverse reactions, safety, personal specificity and efficacy.

Deep centroid: a general deep cascade classifier for biomedical omics data classification.

Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets. To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications-cancer early diagnosis, cancer prognosis, and drug sensitivity prediction-using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine. Deep Centroid is available at both github (github.com/xiexiexiekuan/DeepCentroid) and Figshare (https://figshare.com/articles/software/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification/24993516).

Predicting Changes in and Future Distributions of Plant Habitats of Climate-Sensitive Biological Indicator Species in South Korea

Climate change has been progressing rapidly in recent years; consequently, current plant habitats are expected to change. Therefore, to monitor plant movement caused by changed habitat environments, certain plants are designated as bioindicators and managed accordingly. Monitoring changes in plant habitats is important for protecting vulnerable plant species and establishing suitable measures for vegetation environments with suitable plant species under future climates. As part of this task, South Korea manages climate-sensitive plant species for each biological classification group, including plants. Accordingly, in this study, possible current habitats were identified and future habitats were predicted for nine climate-sensitive species in South Korea under climate change scenarios (representative concentration pathways RCP 4.5 and RCP 8.5) using a species distribution model (SDM) and based on national data acquired through field surveys. The MaxEnt algorithm, with high accuracy, was used for the SDM analysis. The MaxEnt algorithm is a powerful tool that analyzes the effects of environmental variables based on occurrence data and indicates possible habitats. To obtain precise results, environmental variables were utilized by collecting comprehensive climatic and topographic data for South Korea. Based on a current habitat analysis, the model accuracy of nine species yielded a high value of more than 0.9, on average, which indicates the extremely high performance of the model. Under climate change scenarios, evergreen coniferous and deciduous broadleaf plant habitats were predicted to expand inland and to the north of South Korea. The results of this study provide valuable data for establishing future conservation and management strategies for climate-sensitive plant species in South Korea. In addition, the detailed environment variable construction method and SDM analysis method used in this study could be applied to the analysis of changes in comprehensive plant habitats caused by climate change in other countries.

Mengidentifikasi keanekaragaman Hewan Invertebrata di Pesisir Pantai Sarae Me’e Desa Tolotangga Kecematan Monta

Invertebrates are a large group of living creatures that do not have backbones. In biological classification, these animals include various groups such as arthropoda, mollusca, annelida, and echinodermata. Even without backbones, invertebrates have various body suppart systems, such as exoskeletons or internal skeletons. The study of invertebrtes not only illustrates their morphological divetsity, but also shows their crucial role in ecosystems as pollinators, decomposers, and food sources.and 97% of species in the world are invertebrates. This research activity is an activity that aims to identify data about the types of invertebrate animals that exist on Sarae Me’e beach, Sarae Me’e Beach is an open sea type beach, causing the waves on the beach to be large. This research was carried out on January 3 2024. The method used in this research is the exploration and identification method using exploration techniques along the coastline. The research objects are Invertebrate animals found along the coast of sarae me’e, Tolotangga village, Monta subdistrict. The data analysis technique used in this research is descriptive analysis technique. The results of the research faund four Invertebrate animals including the phylum Mollusca, Gastropoda, Athropoda and Annelida.and the salinity condition of the waterthat we tested using a salinity tool was 30% salt content on Sarae Me’e when we conducted the research.

Species

This article discusses the many usages and meanings of the term species in biology, as currently applied (1) to named taxa such as Homo sapiens and Panthera tigris, (2) to a rank (usually but not necessarily the lowest and/or the most fundamental one) of the biological classification, and (3) as a variegated set of notions, the most important among them being the morphospecies, the biospecies, the ecospecies, the evolutionary species, the agamospecies and the taxonomic species. The debatable relationship between the Aristotelian “definition by species and genus” and the historical roots of the usage of genus and species as basic ranks in biological classification are outlined, as are the historical roots of the scientific nomenclature applied to biological species.

Comparative Study of Molluscan Diversity of Kelve and Shirgaon Beach, Palghar, Maharashtra (India)

Coastal ecosystems, encompassing various habitats such as mangroves, coral reefs, beaches, cliffs, lagoons, and estuaries, are vital for the diversity of life. Mollusks, belonging to the phylum Mollusca in the animal kingdom, play a significant role in coastal ecosystems. They serve as important members of the food chain and exhibit remarkable ecological adaptations to various habitats. This research aims to investigate the diversity of mollusks at Kelwa and Shirgaon beaches in Palghar district, Maharashtra. The study will reveal the molluscan diversity at these specific locations, providing insights into their biological classification through systematic arrangement and accompanying shell photographs.

Research on the construction of marine creatures classification and identification model based on ResNet50

There are many kinds of Marine organisms and their biological forms differ greatly, so it is difficult to guarantee the accuracy of artificial species identification, which brings great challenges to the work of Marine species identification. In this paper, we propose a recognition method of Marine biological image classification using residual neural network, redefining convolution layer and using batch regularization to avoid gradient parameter disorder. The bottleneck layer is realized by the residual connection in the neural network, and the residual network ResNet50 is constructed by the transfer learning method. The classification training was conducted on 19 common Marine animal data sets, and the experimental results showed that the recognition accuracy of ResNet50 reached about 90%. Compared with the traditional convolutional neural network VGG19, the results showed that the recognition efficiency of ResNet50 was better, thus verifying the effectiveness of the Marine animal classification and recognition model proposed in this paper.

Unveiling the role of MGMT and DAPK hypermethylation in response to anti-EGFR agents: Molecular insights for advancing HNSCC treatment.

Epidermal growth factor receptor (EGFR) is frequently activated in head and neck squamous cell carcinoma (HNSCC) and serves as a valuable target for therapy. Despite the availability of the EGFR inhibitors Cetuximab, Afatinib, and Allitinib, there are limited predictive markers for their response. Understanding molecular aberrations in HNSCC could facilitate the identification of new strategies for patient clinical and biological classification, offering novel therapeutic avenues. We assessed CCNA1, DCC, MGMT, CDKN2A/p16, and DAPK methylation status in HNSCC cell lines and their association with anti-EGFR treatment response. MGMT methylation status displayed high sensitivity and specificity in distinguishing sensitive and resistant HNSCC cell lines to Afatinib (AUC = 0.955) and Allitinib (AUC = 0.935). Moreover, DAPK methylation status predicted response to Allitinib with high accuracy (AUC = 0.852), indicating their putative predictive biomarker roles. These findings hold promise for the development of more personalized and effective treatment approaches for HNSCC patients.

A Statement of the MDS on Biological Definition, Staging, and Classification of Parkinson's Disease.

A Statement of the MDS on Biological Definition, Staging, and Classification of Parkinson's Disease.

Which cortical regions affect the prediction of cerebrospinal fluid amyloid beta from PET images by novel residual neural network (ArcheD)

AbstractBackgroundAmyloid‐β (Aβ) aggregation, as a biological hallmark of Alzheimer’s disease (AD), is a key component of biological classification of AD in the AT(N) framework. This research aimed to develop a new deep learning model for predicting Aβ cerebrospinal fluid (CSF) measures from amyloid PET scans and explore which cortical regions are most important contributors in the model’s prediction.MethodWe developed a Residual Network model (ArcheD) to predict Aβ CSF from amyloid PET images, and trained it on 1,870 PET scans from the Alzheimer’s Disease Neuroimaging Initiative. We registered PET scans to MNI152 template and used the Neuromorphometrics atlas to derive cortical gray matter (GM) regions. We extracted brain regions’ contributions to model decision‐making by guided backpropagation, and visualized these contributions as relevance heatmaps. We used bootstrapping, Welch’s t‐test and FDR correction in testing significance of brain regions’ relevance to predictions in the whole cohort, and separately in clinical diagnostic classes of AD, mild cognitive impairment (MCI), and cognitively normal (CN) cases.ResultArcheD showed high accuracy in predicting Aβ CSF (r = 0.81; p<0.00001). According to the relevance analysis for all samples (n = 1,390) temporal (mean 0.106; 95%CI [0.105, 0.108]) and parietal (0.105; 95%CI [0.104, 0.107]) lobes were the most important GM regions for the model decision‐making. Occipital (0.098; 95%CI [0.098, 0.100]) and frontal lobes (0.091; 95%CI [0.089, 0.92]) showed noticeably fewer relevance values. Based on between‐classes comparison, these regions contribute significantly more to CSF prediction in AD and MCI individuals compared to CN (p<0.05). Analysis of 49 smaller regions showed an almost 7‐fold difference between the most and least contributing regions.ConclusionWe developed a deep residual neural network model that predicted Aβ CSF from amyloid PET scans with high accuracy, and found that temporal and parietal lobes have the highest influence on Aβ CSF predictions. In the next step, we will validate the model in independent data and aim to develop the model further for clinical use.

Which cortical regions affect the prediction of cerebrospinal fluid amyloid beta from PET images by novel residual neural network (ArcheD)

AbstractBackgroundAmyloid‐ß (Aß) aggregation, as a biological hallmark of Alzheimer’s disease (AD), is a key component of biological classification of AD in the AT(N) framework. This research aimed to develop a new deep learning model for predicting Aß cerebrospinal fluid (CSF) measures from amyloid PET scans and explore which cortical regions are most important contributors in the model’s prediction.MethodWe developed a Residual Network model (ArcheD) to predict Aß CSF from amyloid PET images, and trained it on 1,870 PET scans from the Alzheimer’s Disease Neuroimaging Initiative. We registered PET scans to MNI152 template and used the Neuromorphometrics atlas to derive cortical gray matter (GM) regions. We extracted brain regions’ contributions to model decision‐making by guided backpropagation, and visualized these contributions as relevance heatmaps. We used bootstrapping, Welch’s t‐test and FDR correction in testing significance of brain regions’ relevance to predictions in the whole cohort, and separately in clinical diagnostic classes of AD, mild cognitive impairment (MCI), and cognitively normal (CN) cases.ResultArcheD showed high accuracy in predicting Aß CSF (r = 0.81; p<0.00001). According to the relevance analysis for all samples (n = 1,390) temporal (mean 0.106; 95%CI [0.105, 0.108]) and parietal (0.105; 95%CI [0.104, 0.107]) lobes were the most important GM regions for the model decision‐making. Occipital (0.098; 95%CI [0.098, 0.100]) and frontal lobes (0.091; 95%CI [0.089, 0.92]) showed noticeably fewer relevance values. Based on between‐classes comparison, these regions contribute significantly more to CSF prediction in AD and MCI individuals compared to CN (p<0.05). Analysis of 49 smaller regions showed an almost 7‐fold difference between the most and least contributing regions.ConclusionWe developed a deep residual neural network model that predicted Aß CSF from amyloid PET scans with high accuracy, and found that temporal and parietal lobes have the highest influence on Aß CSF predictions. In the next step, we will validate the model in independent data and aim to develop the model further for clinical use.

A biological classification of Parkinson's disease: Its time has come

A biological classification of Parkinson's disease: Its time has come

Bio-Heuristics Inspired by Fauna (Review)

In recent years, there has been a noticeable increase in publications devoted to new metaheuristics for the effective solution of optimization problems for which classical methods do not provide acceptable results with reasonable time and resources. The review focuses on bio-heuristics inspired by fauna, which account for about half of the publications of meta-heuristics in the last ten years, and which model patterns of behavior of individuals of the animal world. The review is organized taking into account the biological classification of fauna and the simulated mechanisms of animal behavior. The analysis of the mechanisms used to ensure a balance between the convergence rate of the algorithm and the diversification of the solution search space is also given.

A new biological classification for Parkinson disease.

A new biological classification for Parkinson disease.

On Taxonomy. Reflections on the Usefulness of a ‘Non-Modern’ Science

Taxonomy is a science whose roots go back to the dawn of human curiosity. Its evergrowing body of knowledge, laid down in the biological classification, provides the framework within which every biologist operates. Taxonomists have always used the oldest scientific tools for their research — their natural capabilities of observation, gestalt perception, categorization, and classification — as well as the most advanced technologies of the day. Today, taxonomy is firmly anchored in evolutionary theory, spearheading on topics such as the species problem and biodiversity and its development.

Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

A new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet‐18 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF‐7) and normal human mammary epithelial cells (MCF‐10A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. This approach is shown to be superior to that of using conventional 2D‐rotation augmentation, and can be used to decrease substantially the number of cell examples needed for training the classification model without impairing the results. This novel concept has great potential to be incorporated into label‐free imaging flow cytometry and improve cell classification, and be used to detect various types of medical conditions and diseases.