Reliable Exclusion of Acute Coronary Syndrome Among Hospitalized Patients With Elevated Troponin

Background Elevated cardiac troponin I ( cTnI ) occurs in acute coronary syndrome ( ACS ) as well as various scenarios not associated with ACS . Hypothesis Simple clinical criteria can reliably exclude ACS among hospitalized patients with elevated cTnI. Methods Records for patients hospitalized from January to April 2011 with elevated cTnI (>0.29 ng/ dL ) and an available echocardiogram were retrospectively reviewed. Patients with ST ‐segment elevation myocardial infarction were excluded. Based on available clinical data, patients were classified as having ACS or elevation of cTnI unrelated to ACS (non‐ ACS ). Median follow‐up was 365 days. Results Of 265 records meeting inclusion criteria, 82 (31%) had ACS and 183 (69%) had non‐ ACS . In multivariable analysis, odds ratios for non‐ ACS were 7.6 (95% confidence interval [ CI ]: 3.8‐15.3) for peak cTnI <2 ng/ dL , 6.3 (95% CI : 3.1‐13.0) for absent wall‐motion abnormality, and 4.4 (95% CI : 2.2‐8.6) for no prior coronary artery disease history. The area under the receiver operating curve for a model using these 3 variables was 0.86, with a 98% negative predictive value for excluding ACS . Patients who met these 3 criteria had no ACS ‐related deaths over 1‐year follow‐up. Conclusions Hospitalized patients with peak Tn level <2 ng/ dL , no prior history of coronary artery disease, and no new echocardiographic wall‐motion abnormality appear to have a very low likelihood of ACS . Prospective validation of these results is needed to determine whether additional diagnostic testing could be safely avoided in hospitalized patients meeting these simple clinical criteria.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108093/1/clc22263.pd

Effects of preset sequential administrations of sunitinib and everolimus on tumour differentiation in Caki-1 renal cell carcinoma.

BACKGROUND: Sunitinib (VEGFR/PDGFR inhibitor) and everolimus (mTOR inhibitor) are both approved for advanced renal cell carcinoma (RCC) as first-line and second-line therapy, respectively. In the clinics, sunitinib treatment is limited by the emergence of acquired resistance, leading to a switch to second-line treatment at progression, often based on everolimus. No data have been yet generated on programmed alternating sequential strategies combining alternative use of sunitinib and everolimus before progression. Such strategy is expected to delay the emergence of acquired resistance and improve tumour control. The aim of our study was to assess the changes in tumours induced by three different sequences administration of sunitinib and everolimus. METHODS: In human Caki-1 RCC xenograft model, sunitinib was alternated with everolimus every week, every 2 weeks, or every 3 weeks. Effects on necrosis, hypoxia, angiogenesis, and EMT status were assessed by immunohisochemistry and immunofluorescence. RESULTS: Sunitinib and everolimus programmed sequential regimens before progression yielded longer median time to tumour progression than sunitinib and everolimus monotherapies. In each group of treatment, tumour growth control was associated with inhibition of mTOR pathway and changes from a mesenchymal towards an epithelial phenotype, with a decrease in vimentin and an increase in E-cadherin expression. The sequential combinations of these two agents in a RCC mouse clinical trial induced antiangiogenic effects, leading to tumour necrosis. CONCLUSIONS: In summary, our study showed that alternate sequence of sunitinib and everolimus mitigated the development of mesenchymal phenotype compared with sunitinib as single agent

Seroprevalence of 34 Human Papillomavirus Types in the German General Population

The natural history of infections with many human papillomavirus (HPV) types is poorly understood. Here, we describe for the first time the age- and sex-dependent antibody prevalence for 29 cutaneous and five mucosal HPV types from 15 species within five phylogenetic genera (alpha, beta, gamma, mu, nu) in a general population. Sera from 1,797 German adults and children (758 males and 1,039 females) between 1 and 82 years (median 37 years) were analysed for antibodies to the major capsid protein L1 by Luminex-based multiplex serology. The first substantial HPV antibody reactions observed already in children and young adults are those to cutaneous types of the genera nu (HPV 41) and mu (HPV 1, 63). The antibody prevalence to mucosal high-risk types, most prominently HPV 16, was elevated after puberty in women but not in men and peaked between 25 and 34 years. Antibodies to beta and gamma papillomaviruses (PV) were rare in children and increased homogeneously with age, with prevalence peaks at 40 and 60 years in women and 50 and 70 years in men. Antibodies to cutaneous alpha PV showed a heterogeneous age distribution. In summary, these data suggest three major seroprevalence patterns for HPV of phylogenetically distinct genera: antibodies to mu and nu skin PV appear early in life, those to mucosal alpha PV in women after puberty, and antibodies to beta as well as to gamma skin PV accumulate later in life

High-sensitive Troponin I in acute cardiac conditions: Implications of baseline and sequential measurements for diagnosis of myocardial infarction

BACKGROUND: High-sensitive Troponin I (hsTnI) facilitates the early diagnosis of myocardial infarction (MI). However, since hsTnI has not been well characterized in non-ischemic cardiac conditions, the predictive value of hsTnI for MI remains unclear. METHODS: hsTnI (ADVIA Centaur, Siemens) on admission was analyzed in 929 patients with acute cardiac condition and invasive ascertainment of coronary status by catheterization. RESULTS: Hs-TnI upon presentation was higher in patients with STEMI (median 1.27 ng/mL, IQR 0.13 - 14.5 ng/mL) as compared to patients with Non-STEMI (0.66 ng/mL, IQR 0.10 – 4.0 ng/mL, p < 0.001) whereas it did not differ from STEMI in Tako-Tsubo cardiomyopathy (2.57 ng/mL, IQR 0.17 - 8.4 ng/mL) and myocarditis (9.76 ng/mL, IQR 2.0 - 27.0 ng/mL). In patients with resuscitation of non-ischemic cause (0.31 ng/mL, IQR 0.06 - 1.3 ng/mL), acute heart failure (0.088 ng/mL, IQR 0.035 - 0.30 ng/mL) and hypertensive emergency (0.066 ng/mL, IQR 0.032 - 0.34 ng/mL), hs-TnI was elevated above the recommended threshold of 0.04 ng/mL. At this cutpoint of 0.04 ng/mL, hsTnI indicated acute MI (STEMI or Non-STEMI) with a sensitivity of 88% and a specificity of 45% (ROC-AUC 0.748). When patients with STEMI were excluded, hsTnI indicated Non-STEMI with a sensitivity of 87% and a specificity of 45% (ROC-AUC 0.725). When sequential measurements were taken into account in a restricted cohort, a maximum hsTnI of ≥ 0.40 ng/mL provided a sensitivity of 89% and a specificity of 85% (ROC-AUC 0.909) for Non-STEMI. CONCLUSIONS: HsTnI is a sensitive, albeit unspecific marker of MI. In patients with mildly elevated hsTnI and without evidence for STEMI, we suggest serial assessment of hsTnI and a tenfold higher cutpoint of 0.40 ng/mL before Non-STEMI is assumed

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently