Current development of molecular classifications of gastric cancer based on omics (Review).

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next­generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.

CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.

BACKGROUND: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. RESULTS: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. CONCLUSIONS: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Classification of l-DOPA pharmacokinetics shapes and creating a predictive model.

BACKGROUND: It is known that the pharmacokinetics (PK) of levodopa (LD) varies considerably. Difference in PK shapes is expected to affect drug efficacy and development of dyskinesia. In this study, the authors aimed to explore correlations between PK series data of LD and clinical characteristics and dyskinesia in patients with Parkinson's disease (PD). METHODS: We studied 270 PD patients who underwent PK assessment after administration of LD/carbidopa (100/10 mg) in non-compartmental analysis. The patients were grouped according to similarities in time series data of blood LD concentration. Each group was analyzed with respect to clinical characteristics and PK parameters. We created a model to predict PK patterns based on these findings. RESULTS: PD patients were divided into three groups by clustering analysis: blood LD concentration of the patients in Groups 1 (n = 129), 3 (n = 44) and 2 (n = 97) rose rapidly, relatively slowly and at an intermediate rate, respectively. There were no statistically significant differences in patient characteristics except age among the three groups (one-way ANOVA). Multivariate analysis showed that frequency of dyskinesias in Group 1 was significantly higher than that in Group 2. We successfully created a PK pattern prediction model based on body weight and blood LD concentration at 15 and 30 min after administration. CONCLUSIONS: The PK series data of LD was classified into three patterns. The rapid absorption was associated with dyskinesias. Patients' PK patterns were successfully predicted based on their body weight and two-point LD concentration.

Five cluster classifications of long COVID and their background factors: A cross-sectional study in Japan.

PURPOSE: The long-term symptoms of coronavirus disease 2019 (COVID-19), i.e., long COVID, have drawn research attention. Evaluating its subjective symptoms is difficult, and no established pathophysiology or treatment exists. Although there are several reports of long COVID classifications, there are no reports comparing classifications that include patient characteristics, such as autonomic dysfunction and work status. We aimed to classify patients into clusters based on their subjective symptoms during their first outpatient visit and evaluate their background for these clusters. METHODS: Included patients visited our outpatient clinic between January 18, 2021, and May 30, 2022. They were aged ≥ 15 years and confirmed to have SARS-CoV-2 infection and residual symptoms lasting at least 2 months post-infection. Patients were evaluated using a 3-point scale for 23 symptoms and classified into five clusters (1. fatigue only; 2. fatigue, dyspnea, chest pain, palpitations, and forgetfulness; 3. fatigue, headache, insomnia, anxiety, motivation loss, low mood, and forgetfulness; 4. hair loss; and 5. taste and smell disorders) using CLUSTER. For continuous variables, each cluster was compared using the Kruskal-Wallis test. Multiple comparison tests were performed using the Dunn's test for significant results. For nominal variables, a Chi-square test was performed; for significant results, a residual analysis was conducted with the adjusted residuals. RESULTS: Compared to patients in other cluster categories, those in cluster categories 2 and 3 had higher proportions of autonomic nervous system disorders and leaves of absence, respectively. CONCLUSIONS: Long COVID cluster classification provided an overall assessment of COVID-19. Different treatment strategies must be used based on physical and psychiatric symptoms and employment factors.

Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library.

Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question "who is speaking?" On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are designed with the prerequisite that only one speaker is present in the analyzed audio recording. However, some applications involve the need to identify multiple speakers that interact freely in an audio recording. This paper proposes two architectures of speaker identification systems based on a combination of diarization and identification methods, which operate on the basis of segment-level or group-level classification. The open-source PyAnnote framework was used to develop the system. The performance of the speaker identification system was verified through the application of the AMI Corpus open-source audio database, which contains 100 h of annotated and transcribed audio and video data. The research method consisted of four experiments to select the best-performing supervised diarization algorithms on the basis of PyAnnote. The first experiment was designed to investigate how the selection of the distance function between vector embedding affects the reliability of identification of a speaker's utterance in a segment-level classification architecture. The second experiment examines the architecture of cluster-centroid (group-level) classification, i.e., the selection of the best clustering and classification methods. The third experiment investigates the impact of different segmentation algorithms on the accuracy of identifying speaker utterances, and the fourth examines embedding window sizes. Experimental results demonstrated that the group-level approach offered better identification results were compared to the segment-level approach, and the latter had the advantage of real-time processing.

Personality profiles in young adults with orthorexic eating behaviors.

PURPOSE: Orthorexia nervosa (ON), defined as an excessive preoccupation with healthy eating, has gained more interest in the literature over these past few years. However, little is known about its risk and protective factors, in particular with regards to personality. METHODS: A total of 3235 college students (10.32% men, 89.67% women) with a mean age of 21.13 (SD = 2.23) answered self-administered questionnaires assessing ON, psychopathological symptoms, and personality disorders including schizotypal, borderline, paranoid, obsessive-compulsive, and narcissistic personality. A subsample of 106 participants (91.51% women, mean age = 20.91, SD = 2.31) was selected based on the DOS cutoff score, and was then considered as the "orthorexic subsample". RESULTS: Hierarchical cluster analysis was performed in the orthorexic subsample and led to the identification of four groups: 1-a cluster with a low level of traits (L); 2-a cluster with moderate traits and low narcissistic traits (MD); 3-a cluster with a low level of traits and moderate narcissistic traits (MN); 4-a cluster with high paranoid and narcissistic traits (PN) and a moderate level of schizotypal and borderline traits. Levels of anxiety, obsessional-compulsive, and depressive symptoms were higher in the PN and MD clusters than in the L and MN clusters. Social phobia was higher and self-esteem lower in the MD cluster and hypochondriasis was higher in the PN and MN clusters. CONCLUSIONS: This study suggests that ON can be associated with different personality profiles, some of them displaying significant psychopathological levels. It also emphasizes the importance of taking into account personality disorder traits of young adults with orthorexic eating behaviors. LEVEL OF EVIDENCE: Descriptive (cross-sectional) study, Level V.

A novel chromosome cluster types identification method using ResNeXt WSL model.

Chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, due to the complicated morphological characteristics of various types of chromosome clusters, chromosome instance segmentation is the most challenging stage of chromosome karyotyping analysis, leading chromosome karyotyping analysis to highly dependent on skilled clinical analysts. Since most of the chromosome instance segmentation efforts are currently devoted to segmenting chromosome instances from different types of chromosome clusters, type identification of chromosome clusters is a vital anterior task for chromosome instance segmentation. Firstly, this paper proposes an automatic approach for chromosome cluster identification using recent transfer learning techniques. The proposed framework is based on ResNeXt weakly-supervised learning (WSL) pre-trained backbone and a task-specific network header. Secondly, this paper proposes a fast training methodology that tunes our framework from coarse-to-fine gradually. Extensive evaluations on a clinical dataset consisting of 6592 clinical chromosome samples show that the proposed framework achieves 94.09%accuracy, 92.79%sensitivity, and 98.03%specificity. Such performance is superior to the best baseline model that we obtain 92.17%accuracy, 89.1%sensitivity, and 97.42%specificity. To foster research and application in the chromosome cluster type identification, we make our clinical dataset and code available via GitHub.

Using Persistent Homology as a New Approach for Super-Resolution Localization Microscopy Data Analysis and Classification of γH2AX Foci/Clusters.

DNA double strand breaks (DSB) are the most severe damages in chromatin induced by ionizing radiation. In response to such environmentally determined stress situations, cells have developed repair mechanisms. Although many investigations have contributed to a detailed understanding of repair processes, e.g., homologous recombination repair or non-homologous end-joining, the question is not sufficiently answered, how a cell decides to apply a certain repair process at a certain damage site, since all different repair pathways could simultaneously occur in the same cell nucleus. One of the first processes after DSB induction is phosphorylation of the histone variant H2AX to γH2AX in the given surroundings of the damaged locus. Since the spatial organization of chromatin is not random, it may be conclusive that the spatial organization of γH2AX foci is also not random, and rather, contributes to accessibility of special repair proteins to the damaged site, and thus, to the following repair pathway at this given site. The aim of this article is to demonstrate a new approach to analyze repair foci by their topology in order to obtain a cell independent method of categorization. During the last decade, novel super-resolution fluorescence light microscopic techniques have enabled new insights into genome structure and spatial organization on the nano-scale in the order of 10 nm. One of these techniques is single molecule localization microscopy (SMLM) with which the spatial coordinates of single fluorescence molecules can precisely be determined and density and distance distributions can be calculated. This method is an appropriate tool to quantify complex changes of chromatin and to describe repair foci on the single molecule level. Based on the pointillist information obtained by SMLM from specifically labeled heterochromatin and γH2AX foci reflecting the chromatin morphology and repair foci topology, we have developed a new analytical methodology of foci or foci cluster characterization, respectively, by means of persistence homology. This method allows, for the first time, a cell independent comparison of two point distributions (here the point distributions of two γH2AX clusters) with each other of a selected ensample and to give a mathematical measure of their similarity. In order to demonstrate the feasibility of this approach, cells were irradiated by low LET (linear energy transfer) radiation with different doses and the heterochromatin and γH2AX foci were fluorescently labeled by antibodies for SMLM. By means of our new analysis method, we were able to show that the topology of clusters of γH2AX foci can be categorized depending on the distance to heterochromatin. This method opens up new possibilities to categorize spatial organization of point patterns by parameterization of topological similarity.

Personality profiles in young adults with disordered eating behavior.

Personality traits are closely related to eating disorders (ED) and might be involved in their development and maintenance. Nevertheless little is known regarding the association between personality traits and disordered eating in subclinical populations. College students answered questionnaires assessing disordered eating behaviors (DEB) and the following personality disorder (PD) traits: schizotypal, autistic, obsessional, borderline and cyclothymic. Participants with DEB (n=101, 87% women) displayed significantly higher scores for several variables including schizotypy, cyclothymic, borderline and obsessional traits compared to other participants (n=378). Cluster analysis in the DEB subsample led to the identification of three groups: 1) a cluster with a high level of traits (HT); 2) a cluster scoring high on schizotypal, borderline and cyclothymic traits (SBC); 3) a cluster with a low level of traits (LT). Symptoms of depression, suicidal ideations, trait anger and obsessive-compulsive symptoms were higher in the HT and the SBC clusters compared to the LT cluster. Given that two thirds of participants suffering from DEB appeared to display a morbid personality profile, it appears of prime importance to take into account PD traits of individuals with DEB.