High-Affinity Naloxone Binding to Filamin A Prevents Mu Opioid Receptor–Gs Coupling Underlying Opioid Tolerance and Dependence

Ultra-low-dose opioid antagonists enhance opioid analgesia and reduce analgesic tolerance and dependence by preventing a G protein coupling switch (Gi/o to Gs) by the mu opioid receptor (MOR), although the binding site of such ultra-low-dose opioid antagonists was previously unknown. Here we show that with approximately 200-fold higher affinity than for the mu opioid receptor, naloxone binds a pentapeptide segment of the scaffolding protein filamin A, known to interact with the mu opioid receptor, to disrupt its chronic opioid-induced Gs coupling. Naloxone binding to filamin A is demonstrated by the absence of [3H]-and FITC-naloxone binding in the melanoma M2 cell line that does not contain filamin or MOR, contrasting with strong [3H]naloxone binding to its filamin A-transfected subclone A7 or to immunopurified filamin A. Naloxone binding to A7 cells was displaced by naltrexone but not by morphine, indicating a target distinct from opioid receptors and perhaps unique to naloxone and its analogs. The intracellular location of this binding site was confirmed by FITC-NLX binding in intact A7 cells. Overlapping peptide fragments from c-terminal filamin A revealed filamin A2561-2565 as the binding site, and an alanine scan of this pentapeptide revealed an essential mid-point lysine. Finally, in organotypic striatal slice cultures, peptide fragments containing filamin A2561-2565 abolished the prevention by 10 pM naloxone of both the chronic morphine-induced mu opioid receptor–Gs coupling and the downstream cAMP excitatory signal. These results establish filamin A as the target for ultra-low-dose opioid antagonists previously shown to enhance opioid analgesia and to prevent opioid tolerance and dependence

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Intention to have the seasonal influenza vaccination during the COVID-19 pandemic among eligible adults in the UK: a cross-sectional survey

OBJECTIVE: To investigate the likelihood of having the seasonal influenza vaccination during the COVID-19 pandemic in individuals who were eligible to receive it. DESIGN: We conducted a cross-sectional online survey in July 2020. We included predictors informed by previous research, in the following categories: sociodemographic variables; uptake of influenza vaccine last winter and beliefs about vaccination. PARTICIPANTS: 570 participants (mean age: 53.07; 56.3% female, 87.0% white) who were eligible for the free seasonal influenza vaccination in the UK. RESULTS: 59.7% of our sample indicated they were likely to have the seasonal influenza vaccination, 22.1% reported being unlikely to have the vaccination and 18.2% were unsure. We used logistic regression to investigate variables associated with intention to receive a seasonal influenza vaccine in the 2020-2021 season. A positive attitude to vaccination in general predicted intention to have the influenza vaccine in 2020-2021 (OR 1.45, 95% CI 1.19 to 1.77, p<0.001) but the strongest predictor of intention was previous influenza vaccination behaviour (OR 278.58, 95% CI 78.04 to 994.46, p<0.001). CONCLUSIONS: Previous research suggests that increasing uptake of the influenza vaccination may help contain a COVID-19 outbreak, so steps need to be taken to convert intention into behaviour and to reach those individuals who reported being unlikely or unsure about having the vaccine

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation

Adherence to self-administered tuberculosis treatment in a high HIV-prevalence setting: a cross-sectional survey in Homa Bay, Kenya.

Good adherence to treatment is crucial to control tuberculosis (TB). Efficiency and feasibility of directly observed therapy (DOT) under routine program conditions have been questioned. As an alternative, Médecins sans Frontières introduced self-administered therapy (SAT) in several TB programs. We aimed to measure adherence to TB treatment among patients receiving TB chemotherapy with fixed dose combination (FDC) under SAT at the Homa Bay district hospital (Kenya). A second objective was to compare the adherence agreement between different assessment tools

The role of the Annexin-A1/FPR2 system in the regulation of mast cell degranulation provoked by compound 48/80 and in the inhibitory action of nedocromil

Abstract1.We investigated the role of Annexin (ANX)-A1 and its receptor, ALX/FPR2, in the regulation of mast cell degranulation produced by compound 48/80.2.Both human cord-blood derived mast cells (CBDMCs) and murine bone marrow derived mast cells (BMDMCs) release phosphorylated ANX-A1 during treatment with glucocorticoids or the mast cell ‘stabilising’ drugs ketotifen and nedocromil.3.Compound 48/80 also stimulated ANX-A1 phosphorylation and release and this was also potentiated by nedocromil. Anti-ANX-A1 neutralising monoclonal antibodies (Mabs) enhanced the release of pro-inflammatory mediators in response to compound 48/80.4.Nedocromil and ketotifen potently inhibited the release of histamine, PGD2, tryptase and β-hexosaminidase from mast cells challenged with compound 48/80. Anti-ANX-A1 neutralising Mabs prevented the inhibitory effect of these drugs.5.BMDMCs derived from Anx-A1−/− mice were insensitive to the inhibitory effects of nedocromil or ketotifen but cells retained their sensitivity to the inhibitory action of hu-r-ANX-A1.6.The fpr2/3 antagonist WRW4 blocked the action of nedocromil on PGD2, but not histamine, release. BMDMCs derived from fpr2/3−/− mice were insensitive to the inhibitory effects of nedocromil on PGD2, but not histamine release.7.Compound 48/80 stimulated both p38 and JNK phosphorylation in CBDMCs and this was inhibited by nedocromil. Inhibition of p38 phosphorylation was ANX-A1 dependent.8.We conclude that ANX-A1 is an important regulator of mast cell reactivity to compound 48/80 exerting a negative feedback effect through a mechanism that depends at least partly on the FPR receptor

Incidence, mortality and survival patterns of prostate cancer among residents in Singapore from 1968 to 2002

<p>Abstract</p> <p>Background</p> <p>From 1968 to 2002, Singapore experienced an almost four-fold increase in prostate cancer incidence. This paper examines the incidence, mortality and survival patterns for prostate cancer among all residents in Singapore from 1968 to 2002.</p> <p>Methods</p> <p>This is a retrospective population-based cohort study including all prostate cancer cases aged over 20 (n = 3613) reported to the Singapore Cancer Registry from 1968 to 2002. Age-standardized incidence, mortality rates and 5-year Relative Survival Ratios (RSRs) were obtained for each 5-year period. Follow-up was ascertained by matching with the National Death Register until 2002. A weighted linear regression was performed on the log-transformed age-standardized incidence and mortality rates over period.</p> <p>Results</p> <p>The percentage increase in the age-standardized incidence rate per year was 5.0%, 5.6%, 4.0% and 1.9% for all residents, Chinese, Malays and Indians respectively. The percentage increase in age-standardized mortality rate per year was 5.7%, 6.0%, 6.6% and 2.5% for all residents, Chinese, Malays and Indians respectively. When all Singapore residents were considered, the RSRs for prostate cancer were fairly constant across the study period with slight improvement from 1995 onwards among the Chinese.</p> <p>Conclusion</p> <p>Ethnic differences in prostate cancer incidence, mortality and survival patterns were observed. There has been a substantial improvement in RSRs since the 1990s for the Chinese.</p