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Background and Objectives: Recurrent pregnancy loss (RPL) is a multifactorial condition, encompassing genetic, anatomical, immunological, endocrine, as well as infectious and environmental factors; however, the etiology remains elusive in a substantial number of cases. Genetic factors linked to RPL include parental karyotype abnormalities (e.g., translocations, inversions, copy number variants), an increase in sperm aneuploidy, fetal microchimerism, severe skewing of X chromosome inactivation, and various gene polymorphisms. Our study aims to explore the value of routine conventional parental karyotyping in couples with RPL. Materials and Methods: A total of 213 couples (426 individuals) with a history of RPL were enrolled in this retrospective study. The peripheral blood samples included in this study were referred to the Human Genomics Laboratory of the University of Medicine and Pharmacy in Craiova, Romania, for conventional cytogenetic analysis between January 2013 and December 2023, by the Outpatient Medical Genetics Clinic of the Emergency Clinical County Hospital of Craiova. Chromosome analysis was performed using standard protocols and karyotypes were reported according to ISCN. Results: Out of 426 patients provided with conventional G-banded chromosome analysis, 410 had a normal karyotype (96.2%) and 16 had chromosome abnormalities (3.8%). The most common chromosomal abnormalities were reciprocal and Robertsonian translocations, with chromosomes 8, 11, 14, and 21 being most frequently involved. A single numerical anomaly was detected (47,XYY). One or multiple chromosomal polymorphisms were identified in 104 subjects (24.4%). In addition, we conducted a stratified analysis of the unselected group and detected chromosome abnormalities in only four cases (0.94%). Conclusions: Our results are consistent with recommendations for paternal karyotyping after an individual risk assessment in instances such as a previous live birth with congenital anomalies and/or the detection of unbalanced chromosomes or a translocation in product of conception or chorionic villi/amniotic fluid samples. In the absence of a positive history, blindly karyotyping couples may prove too expensive and labor intensive, while providing no information on fertility status or live birth rates.

Oral microbiota have shown a higher bacterial diversity in patients with cancers of the digestive tract, with higher levels of periopathogens. Recent studies have shown that Fusobacterium links to gastro-intestinal neoplastic tissue and accelerates its progression, as well as worsening patient outcome. The present pilot study was carried out between February and December 2020 to evaluate the possible association between the abundance of some periopathogens (Fusobacterium nucleatum, Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, Treponema denticola and Tannerella forsythia) in subgingival plaque and periodontal status with characteristics of gastric cancer. The study was performed on a sample of 24 patients with gastric cancer from the 1st Department of Surgery and Department of Gastroenterology within the Clinical County Hospital of Emergency of Craiova, Romania. The patients’ oral cavity was examined, gingival crevicular samples were collected, and signs of periodontal disease were recorded. On the histopathological exam, the differentiation grade and size of the tumour were registered. Our results showed that, from the periopathogens studied, the most abundant bacteria were F. nucleatum followed by T. forsythia in all groups. In our present study, the strong correlation between tumour dimension and all periodontal parameters but also between tumour dimension and F. nucleatum could suggest a positive association between periodontal disease, tumoral growth and periopathogens implication in this process.

Measurement(s) Natural Language • lexical semantic change Technology Type(s) Programming Language • word embeddings • Cosine Distance Method Factor Type(s) time period Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16438863

Abstract Clustering is an essential technique for network analysis, with applications in a diverse range of fields. Although spectral clustering is a popular and effective method, it fails to consider higher-order structure and can perform poorly on directed networks. One approach is to capture and cluster higher-order structures using motif adjacency matrices. However, current formulations fail to take edge weights into account, and thus are somewhat limited when weight is a key component of the network under study.We address these shortcomings by exploring motif-based weighted spectral clustering methods. We present new and computationally useful matrix formulae for motif adjacency matrices on weighted networks, which can be used to construct efficient algorithms for any anchored or non-anchored motif on three nodes. In a very sparse regime, our proposed method can handle graphs with a million nodes and tens of millions of edges. We further use our framework to construct a motif-based approach for clustering bipartite networks.We provide comprehensive experimental results, demonstrating (i) the scalability of our approach, (ii) advantages of higher-order clustering on synthetic examples, and (iii) the effectiveness of our techniques on a variety of real world data sets; and compare against several techniques from the literature. We conclude that motif-based spectral clustering is a valuable tool for analysis of directed and bipartite weighted networks, which is also scalable and easy to implement.