Obstructive sleep apnea (OSA) is a common condition, and if not treated can be a significant risk factor for multiple comorbidities like hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF). The underlying pathophysiology involves coagulation and inflammatory pathways, including an overactive sympathetic nervous system. This ultimately causes hemodynamic changes and subclinical myocardial injuries. We reviewed the published literature about the impact of continuous positive airway pressure (CPAP) when used as a mode of treatment to reduce the OSA effects on cardiomyocytes. We found that the results were mixed, including both ill and good effects. The cardiac markers like N-terminal pro-brain natriuretic peptide (NT-proBNP) and atrial natriuretic peptide (ANP) were reduced, implying the decrease in the incidence of heart failure with CPAP treatment in a few of the studies. They also proved a significant decrease in harmful cardiovascular (CV) outcomes, while others concluded that CPAP therapy might be stressful on the heart, causing an elevation in cardiac troponin T levels. However, the impact on inflammatory markers is still indeterminate and needs more research in future.
Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource (https://drmz.shinyapps.io/mds_latent).
BackgroundGenomic mutations drive the pathogenesis of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). While morphological and clinical features, complemented by cytogenetics, have dominated the classical criteria for diagnosis and classi cation, incorporation of molecular mutational data can illuminate functional pathobiology. MethodsWe combined cytogenetic and molecular features from a multicenter cohort of 3588 MDS, MDS/ myeloproliferative neoplasm (including chronic myelomonocytic leukemia [CMML]), and secondary AML patients to generate a molecular-based scheme using machine learning methods and then externally validated the model on 412 patients. Molecular signatures driving each cluster were identi ed and used for genomic subclassi cation. FindingsUnsupervised analyses identi ed 14 distinctive and clinically heterogenous molecular clusters (MCs) with unique pathobiological associations, treatment responses, and prognosis. Normal karyotype (NK) was enriched in MC2, MC4, MC6, MC9, MC10, and MC12 with different distributions of TET2, SF3B1, ASXL1, DNMT3A, and RAS mutations. Complex karyotype and trisomy 8 were enriched in MC13 and MC1, respectively. We then identi ed ve risk groups to re ect the biological differences between clusters. Our clustering model was able to highlight the signi cant survival differences among patients assigned to the similar IPSS-R risk group but with heterogenous molecular con gurations. Different response rates to hypomethylating agents (e.g., MC9 and MC13 [OR: 2.2 and 0.6, respectively]) re ected the biological differences between the clusters. Interestingly, our clusters continued to show survival differences regardless of the bone marrow blast percentage. InterpretationDespite the complexity of the molecular alterations in myeloid neoplasia, our model recognized functional objective clusters, irrespective of anamnestic clinico-morphological features, that re ected disease evolution and informed classi cation, prognostication, and molecular interactions. Our subclassi cation model is available via a web-based open-access resource as well (https://drmz.shinyapps.io/mds_latent).
Emphysematous gastritis is a rare medical condition characterized by the presence of intra-mural air in the stomach associated with portal venous air tracking to a variable degree. There are no established guidelines favoring surgery over medical management. We present a case of a 64-year-old Caucasian male with a history of stage four colon adenocarcinoma with peritoneal carcinomatosis, malignant ascites, and liver metastasis status post-three cycles of chemotherapy who presented to the emergency room with complaints of generalized abdominal pain, nausea, non-bilious vomiting, and melena stools. He was managed conservatively as a case of sepsis secondary to emphysematous gastritis and made a full recovery. To our knowledge, this is the first reported case of emphysematous gastritis in an adult with colon cancer. Although we cannot establish a causal link between his chemotherapy regimen and emphysematous gastritis, the combined effect of an immunosuppressive state caused by advanced malignancy and cytotoxic effects of chemotherapy are the probable risk factors in our patient. We described the possible mechanisms of mucosal disruption by fluorouracil and bevacizumab in our case. Despite historically having a poor prognosis, emphysematous gastritis can be managed conservatively on a case-by-case basis. Clinicians should be aware that chemotherapy can be a predisposing factor to developing this rare condition.
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