Chemotherapy in the Treatment of Ovarian Psammocarcinoma: A Case Report and Review of the Literature
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Abstract:
Introduction: Psammocarcinoma is a rare form of epithelial serous ovarian carcinoma characterized by extensive formation of psammoma bodies, invasion of ovarian stroma, peritoneum or intraperitoneal viscera, and moderate cytological atypia. These tumors represent a real problem of the diagnostic, and the role of chemotherapy is not yet clearly demonstrated.
Case presentation: We herein report a case of psammocarcinoma of ovary with peritoneal carcinosis in a forty year old Moroccan female. The patient underwent optimal surgical debulking and nine courses of chemotherapy with carboplatinum and paclitaxel with a complete response. The prognosis for this type of ovarian cancer is unclear, but it appears to be better than other forms of epithelial ovarian cancer.
Conclusion: The psammocarcinome is a rare entity, majority of patients are diagnosed at an advanced stage. The role of chemotherapy is poorly defined; some authors have their patients treated by neoadjuvant or adjuvant chemotherapy.
We currently lack evidence of increasing the benefits that can bring chemotherapy in the management of advanced ovarian psammocarcinomas. Only trials in wide yard can answer this question.Keywords:
Omics
Abstract Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.
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Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multi-omics data.
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Various animal and cell culture models of diabetes mellitus (DM) have been established and utilized to study diabetic peripheral neuropathy (DPN). The divergence of metabolic abnormalities among these models makes their etiology complicated despite some similarities regarding the pathological and neurological features of DPN. Thus, this study aimed to review the omics approaches toward DPN, especially on the metabolic states in diabetic rats and mice induced by chemicals (streptozotocin and alloxan) as type 1 DM models and by genetic mutations (MKR, db/db and ob/ob) and high-fat diet as type 2 DM models. Omics approaches revealed that the pathways associated with lipid metabolism and inflammation in dorsal root ganglia and sciatic nerves were enriched and controlled in the levels of gene expression among these animal models. Additionally, these pathways were conserved in human DPN, indicating the pivotal pathogeneses of DPN. Omics approaches are beneficial tools to better understand the association of metabolic changes with morphological and functional abnormalities in DPN.
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Abstract Background Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. Methods We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. Results All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. Conclusions Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.
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During the COVID-19 pandemic, it became apparent that precision medicine relies heavily on biological multi-omics discoveries. High throughput omics technologies, such as host genomics, transcriptomics, proteomics, epigenomics, metabolomics/lipidomics, and microbiomics, have become an integral part of precision diagnostics. The large number of data generated by omics technologies allows for the identification of vulnerable demographic populations that are susceptible to poor disease outcomes. Additionally, these data help to pinpoint the omics-based biomarkers that are currently driving advancements in precision and preventive medicine, such as early diagnosis and disease prognosis, individualized treatments, and vaccination. This report summarizes COVID-19-omic studies, highlights the results of completed and ongoing omics investigations in individuals who have experienced severe disease outcomes, and examines the impact that repurposed/novel antiviral drugs, targeted immunotherapeutics, and vaccines have had on individual and public health.
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Polycystic ovary syndrome (PCOS) is the most common gynecological endocrine disease, involving multiple genes, multiple pathways, and complex hormone secretion processes. Hence, the pathogenesis of PCOS cannot be explained by a single factor. Omics analysis includes genomics, transcriptomics, and proteomics, which are fast and effective methods for studying the pathogenesis of diseases. PCOS is primarily characterized by androgen excess, and reproductive and metabolic dysfunctions. The application of omics analysis in the body fluids, blood, cells or tissues of women with PCOS offers the potential for unexpected molecular advantages in explaining new mechanisms of PCOS etiology and pathophysiology, and provides new perspectives for identifying potential biomarkers and developing new therapeutic targets. At present, several omics analyses have been applied to produce complex datasets. In this manuscript, the recent advances in omics research on PCOS are summarized, aiming at an important and parallel review of the newly published research.
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Purpose of review The purpose of this review is to recapture recent advances in cachexia-related diseases, mainly cancer cachexia, and treatment using genomic, transcriptomics, proteomic, and metabolomics-related techniques. Recent findings From recent studies in the cancer cachexia field it is clear that the tumor has a direct effect on distant organs via its secretome. The affected pathways on the other hand were largely known from earlier studies with changes in energy-related pathways (mainly lipid metabolism) and the protein degradation pathways. Treatment-oriented studies use mostly rodent models and in-vivo cultures and it is too early for human studies. Summary Omics tools are powerful if used in the right way. Omics research has identified the tumor as an important player in cancer cachexia and some interesting novel treatments have been found in experimental models.
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