Doença Mineral e Óssea (DMO) num Transplantado Renal

A 50-year-old post-menopausal recipient of a kidney allograft with bone pain, osteoporosis, persistent hypercalcaemia and elevated parathormone (PTH) levels, despite a satisfactory graft function, was treated with bisphosphonates and cinacalcet starting, respectively, 5 and 6 months after renal transplantation (RT). Sixteen months after treatment, there was improvement of bone mineral density (BMD) measured by dualenergy X-ray absorptiometry (DEXA). A bone biopsy was taken, unveiling a surprising and worrisome result. Post-RT bone disease is different from classic CKD-MBD and should be managed distinctly, including, in some difficult cases, an invasive evaluation through the performance of a bone biopsy, as suggested in the KDIGO guidelines

Cytosolic NADPH balancing in Penicillium chrysogenum cultivated on mixtures of glucose and ethanol

The in vivo flux through the oxidative branch of the pentose phosphate pathway (oxPPP) in Penicillium chrysogenum was determined during growth in glucose/ethanol carbon-limited chemostat cultures, at the same growth rate. Non-stationary 13C flux analysis was used to measure the oxPPP flux. A nearly constant oxPPP flux was found for all glucose/ethanol ratios studied. This indicates that the cytosolic NADPH supply is independent of the amount of assimilated ethanol. The cofactor assignment in the model of van Gulik et al. (Biotechnol Bioeng 68(6):602–618, 2000) was supported using the published genome annotation of P. chrysogenum. Metabolic flux analysis showed that NADPH requirements in the cytosol remain nearly the same in these experiments due to constant biomass growth. Based on the cytosolic NADPH balance, it is known that the cytosolic aldehyde dehydrogenase in P. chrysogenum is NAD +  dependent. Metabolic modeling shows that changing the NAD + -aldehyde dehydrogenase to NADP + -aldehyde dehydrogenase can increase the penicillin yield on substrate

Red flags for the early detection of spinal infection in back pain patients

© 2019 The Author(s). Background: Red flags are signs and symptoms that are possible indicators of serious spinal pathology. There is limited evidence or guidance on how red flags should be used in practice. Due to the lack of robust evidence for many red flags their use has been questioned. The aim was to conduct a systematic review specifically reporting on studies that evaluated the diagnostic accuracy of red flags for Spinal Infection in patients with low back pain. Methods: Searches were carried out to identify the literature from inception to March 2019. The databases searched were Medline, CINHAL Plus, Web of Science, Embase, Cochrane, Pedro, OpenGrey and Grey Literature Report. Two reviewers screened article texts, one reviewer extracted data and details of each study, a second reviewer independently checked a random sample of the data extracted. Results: Forty papers met the eligibility criteria. A total of 2224 cases of spinal infection were identified, of which 1385 (62%) were men and 773 (38%) were women mean age of 55 (± 8) years. In total there were 46 items, 23 determinants and 23 clinical features. Spinal pain (72%) and fever (55%) were the most common clinical features, Diabetes (18%) and IV drug use (9%) were the most occurring determinants. MRI was the most used radiological test and Staphylococcus aureus (27%), Mycobacterium tuberculosis (12%) were the most common microorganisms detected in cases. Conclusion: The current evidence surrounding red flags for spinal infection remains small, it was not possible to assess the diagnostic accuracy of red flags for spinal infection, as such, a descriptive review reporting the characteristics of those presenting with spinal infection was carried out. In our review, spinal infection was common in those who had conditions associated with immunosuppression. Additionally, the most frequently reported clinical feature was the classic triad of spinal pain, fever and neurological dysfunction. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Association between diabetes mellitus and active tuberculosis: A systematic review and meta-analysis.

The burgeoning epidemic of diabetes mellitus (DM) is one of the major global health challenges. We systematically reviewed the published literature to provide a summary estimate of the association between DM and active tuberculosis (TB). We searched Medline and EMBASE databases for studies reporting adjusted estimates on the TB-DM association published before December 22, 2015, with no restrictions on region and language. In the meta-analysis, adjusted estimates were pooled using a DerSimonian-Laird random-effects model, according to study design. Risk of bias assessment and sensitivity analyses were conducted. 44 eligible studies were included, which consisted of 58,468,404 subjects from 16 countries. Compared with non-DM patients, DM patients had 3.59-fold (95% confidence interval (CI) 2.25-5.73), 1.55-fold (95% CI 1.39-1.72), and 2.09-fold (95% CI 1.71-2.55) increased risk of active TB in four prospective, 16 retrospective, and 17 case-control studies, respectively. Country income level (3.16-fold in low/middle-vs. 1.73-fold in high-income countries), background TB incidence (2.05-fold in countries with >50 vs. 1.89-fold in countries with ≤50 TB cases per 100,000 person-year), and geographical region (2.44-fold in Asia vs. 1.71-fold in Europe and 1.73-fold in USA/Canada) affected appreciably the estimated association, but potential risk of bias, type of population (general versus clinical), and potential for duplicate data, did not. Microbiological ascertainment for TB (3.03-fold) and/or blood testing for DM (3.10-fold), as well as uncontrolled DM (3.30-fold), resulted in stronger estimated association. DM is associated with a two- to four-fold increased risk of active TB. The association was stronger when ascertainment was based on biological testing rather than medical records or self-report. The burgeoning DM epidemic could impact upon the achievements of the WHO "End TB Strategy" for reducing TB incidence

Q-Learning-based financial trading: some results and comparisons

In this paper, we consider different financial trading systems (FTSs) based on a Reinforcement Learning (RL) methodology known as Q-Learning (QL). QL is a machine learning method which real-time optimizes its behavior in relation to the responses it gets from the environment as a consequence of its acting. In the paper, first we introduce the essential aspects of RL and QL which are of interest for our purposes, then we present some original and differently configurated FTSs based on QL, finally we apply such FTSs to eight time series of daily closing stock returns from the Italian stock market