Plos One
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Tyrosine kinase inhibitors (TKIs) targeting the BCR-ABL1 fusion protein, encoded by the Philadelphia chromosome, have drastically improved the outcomes for patients with chronic myeloid leukemia (CML). Although several real-time quantitative polymerase chain reaction (RQ-PCR) kits for the detection of BCR-ABL1 transcripts are commercially available, their accuracy and efficiency in laboratory practice require reevaluation. We have developed a new in-house RQ-PCR method to detect minimal residual disease (MRD) in CML cases. ⋯ This method detected low levels of BCR-ABL1 transcripts in 14 samples (74%), but scored negative for five samples (26%) that were positive using the in-house method. From the perspective of the in-house RQ-PCR method, number of patients confirmed loss of MMR was 4. These data suggest that our new in-house RQ-PCR method is effective for monitoring MRD in CML.
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Comparative Study
Resource consumption of multi-substance users in the emergency room: A neglected patient group.
Multi-substance use is accompanied by increased morbidity and mortality and responsible for a large number of emergency department (ED) consultations. To improve the treatment for this vulnerable group of patients, it is important to quantify and break down in detail the ED resources used during the ED treatment of multi-substance users. ⋯ ED consultations of multi-substance users are expensive and resource intensive. Multi-substance users visited the ED more often and stayed longer at the ED and in-hospital. The findings of our study underline the importance of this patient group. Additional efforts should be made to improve their ED care. Special interventions should target this patient group in order to decrease the high frequency and costs of emergency consultations caused by multi-substance users.
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Exposure to air pollution particulate matter (PM) and tuberculosis (TB) are two of the leading global public health challenges affecting low and middle income countries. An estimated 4.26 million premature deaths are attributable to household air pollution and an additional 4.1 million to outdoor air pollution annually. Mycobacterium tuberculosis (M.tb) infects a large proportion of the world's population with the risk for TB development increasing during immunosuppressing conditions. ⋯ This observation coincides with the reduced expression of M.tb-induced T-bet, a transcription factor regulating IFN-γ expression in T cells. Pre-exposure to PM10 compared to PM2.5 led to greater loss of M.tb growth control. Exposure to PM2.5 and PM10 collected in different seasons differentially impairs M.tb-induced human host immunity, suggesting biological mechanisms underlying altered M.tb infection and TB treatment outcomes during air pollution exposures.
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Patients with acute-on-chronic liver failure (ACLF) precipitated by hepatic injury and extrahepatic insults had distinct clinical phenotypes, and prognosis. This study aimed to validate prognostic models for ACLF and to explore their discriminative abilities in ACLF population categorized by the etiologies of precipitating events. ⋯ The CLIF-SOFA and simpler CLIF-C OF are reliable measures of mortality risk in ACLF patients precipitated by either hepatic or extrahepatic insults. Both validated models could be used to stratify the risk of death and improve management of ACLF.
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Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. ⋯ Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.