Biological Classification Research Articles

Superficial ALK-rearranged myxoid spindle cell neoplasms: Clinicopathologic and molecular analysis of two cases and a review of the literature

Superficial anaplastic lymphoma kinase (ALK)-rearranged myxoid spindle cell neoplasms are a recently identified subtype of cutaneous soft tissue tumors, distinct for their co-expression of CD34 and S100 and characterized by ALK gene rearrangements. Although 72 cases have been reported primarily as isolated case reports, this tumor subtype has yet to be included in the WHO classification of soft tissue tumors, underscoring the need for further study. In this study, we diagnosed two additional cases, both arising in the dermis and subcutaneous tissue. These tumors exhibited characteristic pathological features, including linear or concentric whorl patterns, prominent myxoid and collagenized stroma, mild cellular atypia, and rare mitotic activity. The presence of infiltrative margins and the potential for recurrence after surgery suggest at least locally aggressive clinical behavior. Immunohistochemically, the tumors diffusely expressed S100 and CD34, with strong ALK-D5F3 positivity, confirmed by ALK gene rearrangement. These findings further expand the clinical and pathological spectrum of ALK-rearranged neoplasms and highlight the need for continued research on their biological behavior and classification.

Elliptic geometry-based kernel matrix for improved biological sequence classification

Protein sequence classification plays a pivotal role in bioinformatics as it enables the comprehension of protein functions and their involvement in diverse biological processes. While numerous machine learning models have been proposed to tackle this challenge, traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences. These limitations stem from operating within high-dimensional non-Euclidean spaces. To address this issue, we introduce the application of the elliptic geometry-based approach for protein sequence classification. First, we transform the problem in elliptic geometry and integrate it with the Gaussian kernel to map the problem into the Mercer kernel. The Gaussian-Elliptic approach allows for the implicit mapping of data into a higher-dimensional feature space, enabling the capture of complex nonlinear relationships. This feature becomes particularly advantageous when dealing with hierarchical or tree-like structures commonly encountered in biological sequences. Experimental results highlight the effectiveness of the proposed model in protein sequence classification, showcasing the advantages of utilizing elliptic geometry in bioinformatics analyses. It outperforms state-of-the-art methods by achieving 76% and 84% accuracies for DNA and Protein datasets, respectively. Furthermore, we provide theoretical justifications for the proposed model. This study contributes to the burgeoning field of geometric deep learning, offering insights into the potential applications of elliptic representations in the analysis of biological data.

Conceptualizations of ‘race’: surveys of Polish academics on the race concept

AbstractRecent studies suggest that race is no longer viewed as a biological category by most anthropologists in the United States, but less empirical work has been carried out in other countries. In this study, we engaged the Polish academic community in anthropology (biological and cultural) and biology by conducting surveys to assess how its members approach and conceptualize race in these disciplines. We surveyed participants (a total of 270 respondents) on their views on the existence of races (i.e. whether humans may be subdivided into biological races), whether race is a concept that is needed in science, what the term ‘race’ should symbolize, and whether the respondents were familiar with the term ‘social race’. The results demonstrate that although the view on the reality of races is still generally shared by the majority of the studied academic community, an awareness of the non‐existence of races in our species has emerged among a group of biological anthropologists. Both biologists and cultural anthropologists in Poland associated race mostly with a category of biological classification, while for biological anthropologists, race primarily symbolized a relic of the past. The article emphasizes the importance of becoming acquainted with the debate over race in academic discourse as well as the role of education in shaping attitudes towards race.

Follicular lymphoma comprises germinal center–like and memory-like molecular subtypes with prognostic significance

AbstractA robust prognostic and biological classification for newly diagnosed follicular lymphoma (FL) using molecular profiling remains challenging. FL tumors from patients treated in the RELEVANCE trial with rituximab-chemotherapy (R-chemo) or rituximab-lenalidomide (R2) were analyzed using RNA sequencing, DNA sequencing, immunohistochemistry (IHC), and/or fluorescence in situ hybridization. Unsupervised gene clustering identified 2 gene expression signatures (GSs) enriched in normal memory (MEM) B cells and germinal center (GC) B-cell signals, respectively. These 2 GSs were combined into a 20-gene predictor (FL20) to classify patients into MEM-like (n = 160) or GC-like (n = 164) subtypes, which also displayed different mutational profiles. In the R-chemo arm, patients with MEM-like FL had significantly shorter progression-free survival (PFS) than patients with GC-like FL (hazard ratio [HR], 2.13; P = .0023), and this prognostic correlation remained significant in a multivariable model that included the FL-International Prognostic Index (P = .005). In the R2 arm, both subtypes had comparable PFS, demonstrating that R2 has a benefit over R-chemo for patients with MEM-like FL (HR, 0.54; P = .011). The prognostic value of FL20 was validated in an independent FL cohort with R-chemo treatment (GSE119214 [n = 137]). An IHC algorithm (FLCM) that used FOXP1, LMO2, CD22, and MUM1 antibodies was developed with significant prognostic correlation with FL20 in a training set of patients from the RELEVANCE trial (n = 264), which was then validated in a different set of patients (n = 116). These data indicate that FL tumors can be classified into MEM-like and GC-like subtypes that are biologically distinct and clinically different in their risk profile. The FLCM assay can be used in routine clinical practice to identify patients with MEM-like FL who might benefit from therapies other than R-chemo, such as the R2 combination. This trial was registered at www.clinicaltrials.gov as #NCT01476787 and #NCT01650701.

Designing the First Trials for Parkinson's Prevention.

For decades the greatest goal of Parkinson's disease (PD) research has often been distilled to the discovery of treatments that prevent the disease or its progression. However, until recently only the latter has been realistically pursued through randomized clinical trials of candidate disease-modifying therapy (DMT) conducted on individuals after they received traditional clinical diagnosis of PD (i.e., tertiary prevention trials). Now, in light of major advances in our understanding of the prodromal stages of PD, as well as its genetics and biomarkers, the first secondary prevention trials for PD are beginning. In this review, we take stock of DMT trials to date, summarize the breakthroughs that allow the identification of cohorts at high risk of developing a traditional diagnosis of PD, and describe key design elements of secondary prevention trials and how they depend on the prodromal stage being targeted. These elements address whom to enroll, what interventions to test, and how to measure secondary prevention (i.e., slowed progression during the prodromal stages of PD). Although these design strategies, along with the biological definition, subtype classification, and staging of the disease are evolving, all are driven by continued progress in the underlying science and integrated by a broad motivated community of stakeholders. While considerable methodological challenges remain, opportunities to move clinical trials of DMT to earlier points in the disease process than ever before have begun to unfold, and the prospects for PD prevention are nowtangible.

Computer-aided diagnosis of breast cancer from mammogram images using deep learning algorithms

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. Computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper.

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

How Phenograms and Cladograms Became Molecular Phylogenetic Trees

Tree diagrams are the prevailing form of visualization in biological classification and phylogenetics. Already during the time of the so-called Systematist Wars from the mid-1960s until the 1980s most journal articles and textbooks published by systematists contained tree diagrams. Although this episode of systematics is well studied by historians and philosophers of biology, most analyses prioritize scientific theories over practices and tend to emphasize conflicting theoretical assumptions. In this article, I offer an alternative perspective by viewing the conflict through the lens of representational practices with a case study on tree diagrams that were used by numerical taxonomists (phenograms) and cladists (cladograms). I argue that the current state of molecular phylogenetics should not be interpreted as the result of a competition of views within systematics. Instead, molecular phylogenetics arose independently of systematics and elements of cladistics and phenetics were integrated into the framework of molecular phylogenetics, facilitated by the compatibility of phenetic and cladistic practices with the quantitative approach of molecular phylogenetics. My study suggests that this episode of scientific change is more complex than common narratives of battles and winners or conflicts and compromises. Today, cladograms are still used and interpreted as specific types of molecular phylogenetic trees. While phenograms and cladograms represented different forms of knowledge during the time of the Systematist Wars, today they are both used to represent evolutionary relationships. This indicates that diagrams are versatile elements of scientific practice that can change their meaning, depending on the context of use within theoretical frameworks.

Selection of reference data significantly influences biological maturity timing classification in national youth soccer players

This study investigated biological maturity timing (BMT) in 116 Irish U13 and 958 Swedish U15 male national soccer players using various growth reference datasets. BMT was expressed as a Z-score comparing each player's percentage of predicted adult height (%PAH) to age-specific means and standard deviations reported in the: Berkeley Growth Longitudinal (US), UK 1990 growth reference (UK), and Swedish 2000s growth reference (SWE) studies. Multinomial logistic regression was used to compare BMT classification (Early, On-time, Late) between datasets. One-sampled means t-tests were used to examine BMT-related maturity biases per cohort and reference dataset. For both Swedish U15 ( p < 0.001) and Irish U13 ( p = 0.003) players, BMT classification was significantly influenced by the selection of the growth reference dataset. Only 61% of players had the same BMT category across all reference datasets. Significant maturity biases existed in favour of early maturing Swedish players (mean = 96.5%PAH) in all datasets, although moderate using SWE ( d = 0.7), large using UK ( d = 1.02), and very large using US ( d = 1.36) (all p < 0.001). Small significant maturity biases existed in favour of early maturing Irish players (mean = 87.1%PAH) using UK ( p < 0.001, d = 0.48) and US ( p < 0.001, d = 0.33), but not SWE. We conclude that maturity Z-scores must be interpreted with significant caution, given the large variations in BMT classification between reference datasets.

MycoAI: Fast and accurate taxonomic classification for fungal ITS sequences.

Efficient and accurate classification of DNA barcode data is crucial for large-scale fungal biodiversity studies. However, existing methods are either computationally expensive or lack accuracy. Previous research has demonstrated the potential of deep learning in this domain, successfully training neural networks for biological sequence classification. We introduce the MycoAI Python package, featuring various deep learning models such as BERT and CNN tailored for fungal Internal Transcribed Spacer (ITS) sequences. We explore different neural architecture designs and encoding methods to identify optimal models. By employing a multi-head output architecture and multi-level hierarchical label smoothing, MycoAI effectively generalizes across the taxonomic hierarchy. Using over 5 million labelled sequences from the UNITE database, we develop two models: MycoAI-BERT and MycoAI-CNN. While we emphasize the necessity of verifying classification results by AI models due to insufficient reference data, MycoAI still exhibits substantial potential. When benchmarked against existing classifiers such as DNABarcoder and RDP on two independent test sets with labels present in the training dataset, MycoAI models demonstrate high accuracy at the genus and higher taxonomic levels, with MycoAI-CNN being the fastest and most accurate. In terms of efficiency, MycoAI models can classify over 300,000 sequences within 5 min. We publicly release the MycoAI models, enabling mycologists to classify their ITS barcode data efficiently. Additionally, MycoAI serves as a platform for developing further deep learning-based classification methods. The source code for MycoAI is available under the MIT Licence at https://github.com/MycoAI/MycoAI.

Genetics of Parkinson's Disease: From Causes to Treatment.

The genetic architecture of Parkinson's disease (PD) comprises five autosomal dominantly inherited forms with a clinical picture overall resembling idiopathic disease (PARK-SNCA, PARK-LRRK2, PARK-VPS35, PARK-CHCHD2, and PARK-RAB32) and three recessive types (PARK-PRKN, PARK-PINK1, and PARK-PARK7), several monogenic forms causing atypical parkinsonism, as well as a plethora of known genetic risk factors, most notably SNCA and GBA1 including a recently discovered risk variant unique to individuals of African descent, as well as polygenic scores. The Movement Disorder Society Genetic mutation database (MDSGene) (www.mdsgene.org) provides PD genotype-phenotype relationships, whereas global PD genetics networks, such as the Global Parkinson's Genetics Program (www.gp2.org) elucidate PD genetic factors at an unprecedented scale. Two large studies in relatively unselected, multicenter PD samples estimate the frequency of genetic forms, including PARK-GBA1, at ∼15%. PD genetics are becoming increasingly actionable, with the first gene-targeted clinical trials underway. Furthermore, PD genetics has recently been incorporated into a new biological classification of PD.

An improved efficientnet B7 underwater marine organism image classification model based on the spatial group enhancement attention mechanisms

In this paper, we adopt the Efficientnet B7 model, which is an excellent performer in deep learning algorithms, and improve it by combining with the Spatial Group Enhancement attention mechanism to provide an innovative method for underwater biological image classification. After a reasonable division of the dataset, we introduced the improved Efficientnet B7 model for training. By observing the change of the loss curve on the training set, it can be seen that the loss value of the model gradually decreases from 0.6 to 0.05 and tends to be stable, which indicates that the classification prediction of the model is gradually close to the real result, and it can classify the underwater marine organisms images effectively. The accuracy on the training set gradually increases from the initial 80.12% to 98.31%, and the recall also grows from 75.82% to 98.93%, showing that the model predicts well. The performance on the test set shows an accuracy of 84.86%, a recall of 82.40%, and an F1 value of 80.2%, indicating that the model has good generalisation ability. This means that our proposed improved methods and ideas have achieved significant results in the task of underwater biological image classification, providing a useful reference for research in related fields. By comparing the experimental results, it can be seen that the optimisation of the Efficientnet B7 model by introducing the Spatial Group Enhancement attention mechanism achieves better performance in the underwater marine organisms image classification task, which lays the foundation for the future application of deep learning in underwater environments. This study is of positive significance for the promotion of underwater biology research, marine protection and other fields, and provides new ideas and methods for carrying out more in-depth related research.

Expanding the spectrum of low-grade sinonasal adenocarcinoma with biphasic seromucinous differentiation and activating HRAS/AKT1 mutations.

Low-grade non-intestinal-type sinonasal adenocarcinoma (LGSNAC) is a rare heterogeneous and poorly characterised group of tumours, distinct from intestinal- and salivary-type neoplasms. Therefore, further characterisation is needed for clearer biological understanding and classification. Clinical, histological and molecular characterisation of four cases of biphasic, low-grade adenocarcinomas of the sinonasal tract was performed. All patients were male, aged between 48 and 78 years, who presented with polypoid masses in the nasal cavity. Microscopically, virtually all tumours were dominated by tubulo-glandular biphasic patterns, microcystic, focal (micro)papillary, oncocytic or basaloid features. Immunohistochemical staining confirmed biphasic differentiation with an outer layer of myoepithelial cells. Molecular profiling revealed HRAS (p.G13R, p.Q61R) mutations, and concomitant AKT1 (p.E17K, p.Q79R) mutations in two cases. Two cases showed potential in-situ/precursor lesions adjacent to the tumour. Follow-up periods ranged from 1 to 30 months, with one case relapsing locally after 12 and > 20 years. This study further corroborates a distinct biphasic low-grade neoplasm of the sinonasal tract with seromucinous differentiation. Although morphological and molecular features overlap with salivary gland epithelial-myoepithelial carcinoma, several arguments favour categorising these tumours within the spectrum of LGSNAC.

Chemical, biological, radiological and nuclear event detection and classification using ontology interrogation and social media data

In an era where chemical, biological, radiological, and nuclear (CBRN) incidents present a grave threat to public safety, timely and accurate information is paramount. The complexity of the CBRN concept encompasses a range of incidents, each with unique and overlapping symptoms, related substances, and event descriptions. This study introduces an innovative approach to the development of a CBRN-specific ontology, uniting diverse data sources and domain expertise to construct a comprehensive repository of CBRN events, sub-events, their causes, symptoms, and toxic substances. Unlike prior methodologies reliant on keyword searches and predefined categories, our approach enables a holistic analysis of textual data by capturing intricate relationships between symptoms and toxic substances. We leverage this ontology in conjunction with a tailored interrogation algorithm to detect potential CBRN incidents through social media data. The algorithm was then tested on datasets of three actual CBRN incidents, one fictional incident (TV show) that simulated a nuclear incident and one non-CBRN. The interrogation algorithm was able to detect the five CBRN incidents accurately. However, the study showcased the need to extend the algorithm to distinguish between real and fictional CBRN incidents. These findings underscore the potential of this approach to deliver timely information on potential CBRN incidents. Nevertheless, the study acknowledged the inherent challenges and limitations in utilizing social media data, including the risk of misinformation, fictional events, fake news, and interference from malicious actors, all of which can affect the accuracy and reliability of the information collected.

Differential transcriptome study on the damage of testicular tissues caused by chronic infection of T. gondii in mice

BackgroundToxoplasma gondii is an intracellular protozoan parasite that is widely distributed in humans and warm-blooded animals. T. gondii chronic infections can cause toxoplasmic encephalopathy, adverse pregnancy, and male reproductive disorders. In male reproduction, the main function of the testis is to provide a stable place for spermatogenesis and immunological protection. The disorders affecting testis tissue encompass abnormalities in the germ cell cycle, spermatogenic retardation, or complete cessation of sperm development. However, the mechanisms of interaction between T. gondii and the reproductive system is unclear. The aims were to study the expression levels of genes related to spermatogenesis, following T. gondii infection, in mouse testicular tissue.MethodsRNA-seq sequencing was carried out on mouse testicular tissues from mice infected or uninfected with the T. gondii type II Prugniaud (PRU) strain and validated in combination with real-time quantitative PCR and immunofluorescence assays.ResultsThe results showed that there were 250 significant differentially expressed genes (DEGs) (P < 0.05, |log2fold change| ≧ 1). Bioinformatics analysis showed that 101 DEGs were annotated to the 1696 gene ontology (GO) term. While there was a higher number of DEGs in the biological process classification as a whole, the GO enrichment revealed a significant presence of DEGs in the cellular component classification. The Arhgap18 and Syne1 genes undergo regulatory changes following T. gondii infection, and both were involved in shaping the cytoskeleton of the blood–testis barrier (BTB). The number of DEGs enriched in the MAPK signaling pathway, the ERK1/2 signaling pathway, and the JNK signaling pathway were significant. The PTGDS gene is located in the Arachidonic acid metabolism pathway, which plays an important role in the formation and maintenance of BTB in the testis. The expression of PTGDS is downregulated subsequent to T. gondii infection, potentially exerting deleterious effects on the integrity of the BTB and the spermatogenic microenvironment within the testes.ConclusionsOverall, our research provides in-depth insights into how chronic T. gondii infection might affect testicular tissue and potentially impact male fertility. These findings offer a new perspective on the impact of T. gondii infection on the male reproductive system.Graphical

A systematic classification and typological assessment method for mortise and tenon joints

The classification of Mortise and Tenon (MT) joints is vital, as it enables standardized terminology, facilitates comparative analysis, and enhances understanding of construction techniques across a variety of applications including the design, manufacturing, and management of wood products. Although the classification of MT joints is crucial, current research in this area lacks a systematic approach. The study adopts a morphological composition paradigm to investigate MT joints. This study introduces a 6-level classification index hierarchy for MT morphology, employing methods from biological classification and arithmetic cross-method coding. By encoding joint features and morphological composition, the study delineates 352 possible joint types and 1056 theoretical compositions across dimensions, elucidating diverse structural logics and aiding comprehension. Next, a feasibility typicality assessment identifies 198 typical and 310 atypical morphological types, presented clearly in graphical form. Validations are conducted through analysis of 2654 research cases, which are encoded according to the index hierarchy, thereby affirming the scientific validity and practical utility of the classification system.

Abstract PO5-01-01: The comparison of HER2 low and HER2 0 in ductal carcinoma in situ (DCIS) for breast cancer

Abstract Background: Trastuzumab deruxtecan is a standard of care after prior chemotherapy for metastatic breast cancer with HER2 low. HER2 low recognized as a new category of breast cancer. The definition of HER2 low is IHC 1+ or IHC 2+/ISH- in invasive ductal components. We have already reported the frequency and prognosis of HER2 positive and low in invasive cancer (Breast Cancer. 2022;29:234-241), but the distribution and characteristics of HER2 low in ductal carcinoma in situ (DCIS) is unknown. It is also well known that HER2 expression is higher in DCIS than in invasive cancer. We focused on the frequency of HER2 low in DCIS, performed an analysis of its frequency and characteristics, and compared to DCIS with HER2 0. Methods: We retrospectively examined breast cancer patients with DCIS and invasive ductal carcinoma (IDC) who underwent surgery at our institution between 2005 and 2015. Regarding the expression status of HER2, we classified cases into three groups: HER2 0, HER2-low (defined as IHC 1+ or IHC 2+/ISH-), and HER2-positive (IHC 2+/ISH+ or IHC 3+), and investigated their frequency. We analyzed the relationship between HER2 expression and Van Nuys classification, as well as the relationship between HER2 expression and ER expression, using the chi-square test. Results: A total of 726 DCIS patients were included in this analysis. The patient demographics showed a median age of 52 years (range: 20-84), with 413 patients (56.9%) undergoing total mastectomy and 311 patients (42.8%) undergoing breast-conserving surgery (two cases had unknown surgical procedures). The expression of HER2 in DCIS patients was observed as HER2-0, low and positive in 100 (13.8%), 474 (65.3%) and 152 patients (20.9%), respectively. The expression of HER2 in IDC was observed as HER2 0, low and positive in 838 (17.1%), 3169 patients (64.4%) and 911 patients (18.5%) (Table 1). DCIS tended to have higher HER2 expression(P=0.048). Then we compared the characteristics of HER2 0 and HER2 low in DCIS. Table 2 shows the relation between Van Nuys classification and HER2 expression. Van Nuys classification was evaluated in 346 cases. Van Nuy grade 3 accounted for 4.8% of the HER2 0 cases and 15.8% of the HER2 low cases. HER2 low cases tend to have higher grades(P &amp;lt; 0.01). Table 3 shows the relation between ER expression and HER2 expression. ER negative accounted for 5% of HER2 0 cases and 8.4% of HER2 low cases. ER negatives tended to be more common in Her2 low cases(P &amp;lt; 0.01). Conclusion: HER2 low is not considered a molecular biological classification, but rather a classification of the target of treatment for Trastuzumab deruxtecan. In our study, comparing HER2 0 and HER2 low, HER2 low tended to have higher grade and lower ER expression. Further investigation including basic research is needed to clarify the biological meaning of her2 low. Table1: The frequency of HER2 expression in DCIS and IDC Table2: The relationship between Van Nuys classification and HER2 expression Table3: The relationship between ER status and HER2 expression Citation Format: Nari Kureyama, Maho Kusudo, Akira Nakakami, Rie Komaki, Yuka Endo, Kazuki Nozawa, Ayumi Kataoka, Haruru Kotani, Akiyo Yoshimura, Masaya Hattori, Masataka Sawaki, Hiroji Iwata. The comparison of HER2 low and HER2 0 in ductal carcinoma in situ (DCIS) for breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-01.

Structural Elucidation of Glycosaminoglycans in the Tissue of Flounder and Isolation of Chondroitin Sulfate C.

Glycosaminoglycans (GAGs) are valuable bioactive polysaccharides with promising biomedical and pharmaceutical applications. In this study, we analyzed GAGs using HPLC-MS/MS from the bone (B), muscle (M), skin (S), and viscera (V) of Scophthalmus maximus (SM), Paralichthysi (P), Limanda ferruginea (LF), Cleisthenes herzensteini (G), Platichthys bicoloratus (PB), Pleuronichthys cornutus (PC), and Cleisthenes herzensteini (CH). Unsaturated disaccharide products were obtained by enzymatic hydrolysis of the GAGs and subjected to compositional analysis of chondroitin sulfate (CS), heparin sulfate (HS), and hyaluronic acid (HA), including the sulfation degree of CS and HS, as well as the content of each GAG. The contents of GAGs in the tissues and the sulfation degree differed significantly among the fish. The bone of S. maximus contained more than 12 μg of CS per mg of dry tissue. Although the fish typically contained high levels of CSA (CS-4S), some fish bone tissue exhibited elevated levels of CSC (CS-6S). The HS content was found to range from 10-150 ug/g, primarily distributed in viscera, with a predominant non-sulfated structure (HS-0S). The structure of HA is well-defined without sulfation modification. These analytical results are independent of biological classification. We provide a high-throughput rapid detection method for tissue samples using HPLC-MS/MS to rapidly screen ideal sources of GAG. On this basis, four kinds of CS were prepared and purified from flounder bone, and their molecular weight was determined to be 23-28 kDa by HPGPC-MALLS, and the disaccharide component unit was dominated by CS-6S, which is a potential substitute for CSC derived from shark cartilage.

A Review on Plant Taxonomy

The Identification and naming of plants has attracted a lot of attention throughout history. The science that investigates, identifies, describes, naming of the plant and classifies all plants. Plant taxonomy includes the description of the variation of plants, the observation of the characters like morphological, anatomical and phylogenic characters of the plants, variation, comparison and the manipulation of the data obtained from the previous information/ data is useful to arrange plants in a system of classification. The 3 aspects of the taxonomy are Identification, Nomenclature, classification are the main activates of the taxonomy. Taxonomy means law full arrangement of plants and it is a process of grouping the plants and other organisms. In ancient days plants the classification of plants was very easier and the classification of made by taking consideration of one or two morphological characters which is known as artificial classification but in course of time the classification of plants underwent many medications were taken place because of technological development. But end the end of 19th century the system of classification based on external morphological characters especially based on floral characters because floral characters are stable when compare to morphological characters while growth of the plants, this system of classification is classical/ alpha taxonomy. In course of time the classification of organisms/plants taken consideration of the multiple allied disciplines like the evidences from Anatomy, Embryology, Palynology, Cytology and Genetics. This type of classification is used to resolve taxonomic problems in addition to morphology this classification special significance which is called as biosystematics. Of course an insufficient number of experts, and the instability of phenotypes, which are easily affected by environmental factors, made this classification meet difficulties. With the advent evidences of the fields of phylogenetic, cladistics, experimental botanical gardens, field study, herbariums, keys, museums, computers and systematics, the artificial classification have progressed to a system of modern/ Phylogenic system of biological classification based on consideration of the evolutionary relationships between organisms and both biotic and abiotic factors. Here, I discussed the history of Identification, nomenclature classification and its different systems, for the identification of plants, the development of various molecular techniques that generate molecular markers has made it possible to accurately identify plants. Here I referred the reviews which gives information of the modern techniques and traditional methods that can be used for identification plant species through genotypic studies based on DNA barcoding.

On Love and Death

"On Love and Death" is a short story recounting the brief meetings of a personified Love and Death. The two meet on three occasions, from the beginning to the end of time. Without dialogue or biological classification, the story tells of life beyond the confines of humankind's perspective and implies a destructive evolutionary process by which the two suffer.