Intrauterine hormonal contraception and risk of breast cancer

Intrauterine hormonal contraception devices are widely used among Danish women. Cases of breast cancer have been reported in women using the devices and studies have found evidence of an increased risk, but a new meta-analysis did not find an increased risk. The reported relative risks in some of the studies are numerically substantial as quoted in this review, but it is very important that physicians take absolute numbers into account when interpreting risk in order to provide the best guidance of patients with regard to contraception and associated risks

Intrauterine hormonal contraception and risk of breast cancer

Intrauterine hormonal contraception devices are widely used among Danish women. Cases of breast cancer have been reported in women using the devices and studies have found evidence of an increased risk, but a new meta-analysis did not find an increased risk. The reported relative risks in some of the studies are numerically substantial as quoted in this review, but it is very important that physicians take absolute numbers into account when interpreting risk in order to provide the best guidance of patients with regard to contraception and associated risks

Intrauterine hormonal contraception and risk of breast cancer

Intrauterine hormonal contraception devices are widely used among Danish women. Cases of breast cancer have been reported in women using the devices and studies have found evidence of an increased risk, but a new meta-analysis did not find an increased risk. The reported relative risks in some of the studies are numerically substantial as quoted in this review, but it is very important that physicians take absolute numbers into account when interpreting risk in order to provide the best guidance of patients with regard to contraception and associated risks

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Direct observation of multiple conformational states in Cytochrome P450 oxidoreductase and their modulation by membrane environment and ionic strength

AbstractCytochrome P450 oxidoreductase (POR) is the primary electron donor in eukaryotic cytochrome P450 (CYP) containing systems. A wealth of ensemble biophysical studies of Cytochrome P450 oxidoreductase (POR) has reported a binary model of the conformational equilibrium directing its catalytic efficiency and biomolecular recognition. In this study, full length POR from the crop plant Sorghum bicolor was site-specifically labeled with Cy3 (donor) and Cy5 (acceptor) fluorophores and reconstituted in nanodiscs. Our single molecule fluorescence resonance energy transfer (smFRET) burst analyses of POR allowed the direct observation and quantification of at least three dominant conformational sub-populations, their distribution and occupancies. Moreover, the state occupancies were remodeled significantly by ionic strength and the nature of reconstitution environment, i.e. phospholipid bilayers (nanodiscs) composed of different lipid head group charges vs. detergent micelles. The existence of conformational heterogeneity in POR may mediate selective activation of multiple downstream electron acceptors and association in complexes in the ER membrane.</jats:p

Oral capsules of tetra-hydro-cannabinol (THC), cannabidiol (CBD) and their combination in peripheral neuropathic pain treatment

Background: Cannabinoids are often prescribed for neuropathic pain, but the evidence-based recommendation is ‘weak against’. Objectives: The aim was to examine the effect of two cannabinoids and their combination in peripheral neuropathic pain. Methods: This was a randomized, double-blind, trial with treatment arms for cannabidiol (CBD), tetra-hydro-cannabinol (THC), CBD and THC combination (CBD/THC), and placebo in a 1:1:1:1 ratio and flexible drug doses (CBD 5–50 mg, THC 2.5–25 mg, and CBD/THC 5 mg/2.5 mg–50 mg/25 mg). Treatment periods of 8-week duration were proceeded by 1 week for baseline observations. Patients with painful polyneuropathy, post-herpetic neuralgia and peripheral nerve injury (traumatic or surgical) failing at least one previous evidence-based pharmacological treatment were eligible for inclusion. The primary outcome was the change in weekly average of daily pain measured with a numeric rating scale (NRS). Trail Making Test (TMT) was used as one of the tests of mental functioning. Results: In all, 145 patients were included in the study of which 118 were randomized and 115 included in the intention-to-treat analysis. None of the treatments reduced pain compared to placebo (p = 0.04–0.60). Effect sizes as estimated in week 8 (positive values worse and negative better than placebo) were CBD mean 1.14 NRS points (95% CI 0.11–2.19), THC 0.38 (CI −0.65 to 1.4) and CBD/THC −0.12 (−1.13 to 0.89). Conclusions: CBD, THC and their combination did not relieve peripheral neuropathic pain in patients failing at least one previous evidence-based treatment for neuropathic pain.</p