A Markov Chain Approach for Forecasting Progression of Opioid Addiction

The U.S. is currently facing an opioid crisis. Naltrexone is a common treatment for drug addiction; it reduces the desire to take opiates. However, addicts often stop treatment or continue to use opioids while in treatment. This results in increased fatalities and associated costs. A Markov-chain model is presented to analyze the progression of opioid addiction to assist the medical community in developing appropriate treatments. The model includes patients who continue opiate use while on naltrexone (blocked patients) and those who use opiates after missing naltrexone doses (unblocked patients). The other types of patients are abstinent (the best-case scenario) and dropout (the worst-case scenario). The Markov-chain model is built on probability estimates of transitions from one stage to another; the model predicts the proportion of patients in the different stages for a given rate of intervention on dropouts. Many factors, including psychological, environmental, sociodemographic, and access-to-healthcare, impact transition probabilities and thereby the observational data used for constructing the Markov-chain model. Markov chains have been used successfully in predicting the progression of HIV (Human Immunodeficiency Virus) and other diseases. Modeling statistically provides an offline method, based on existing data, to develop successful strategies for addressing this public-health crisis

Slow epidemic extinction in populations with heterogeneous infection rates

We explore how heterogeneity in the intensity of interactions between people affects epidemic spreading. For that, we study the susceptible-infected-susceptible model on a complex network, where a link connecting individuals $i$ and $j$ is endowed with an infection rate $\beta_{ij} = \lambda w_{ij}$ proportional to the intensity of their contact $w_{ij}$, with a distribution $P(w_{ij})$ taken from face-to-face experiments analyzed in Cattuto $et\;al.$ (PLoS ONE 5, e11596, 2010). We find an extremely slow decay of the fraction of infected individuals, for a wide range of the control parameter $\lambda$. Using a distribution of width $a$ we identify two large regions in the $a-\lambda$ space with anomalous behaviors, which are reminiscent of rare region effects (Griffiths phases) found in models with quenched disorder. We show that the slow approach to extinction is caused by isolated small groups of highly interacting individuals, which keep epidemic alive for very long times. A mean-field approximation and a percolation approach capture with very good accuracy the absorbing-active transition line for weak (small $a$) and strong (large $a$) disorder, respectively

Epidemiological evaluation of subclinical mastitis of dairy cows in Greece

ΔΕΝ ΔΙΑΤΙΘΕΤΑΙ ΠΕΡΙΛΗΨΗSubclinical mastitis, diagnosed by elevated somatic cell count (SCC) in milk, is an important monitoring parameter of dairy cows’ udder health, related to their productivity and welfare. The present retrospective study aims to evaluate the epidemiology of subclinical mastitis (SCM) among the 37 herds of the Holstein Association of Greece participating in the milk quality recording system “ΙΩ”, from the start of 2015 until the end of 2018. The herds’ inclusion criterion was the consistency of monthly SCC recording throughout at least one full year between 2015 and 2018, with a maximum interval of 61 days between two consecutive monthly SCC recordings. Twenty-six herds (8630 cows) in 2015, thirty herds (10763 cows) in 2016, thirty herds (10945 cows) in 2017 and twenty-six herds (9597 cows) in 2018 were included. The prevalence of SCM and chronic SCM, the incidence rate of new cases of SCM, as well as the average somatic cell score and bulk tank milk SCC were determined for each of the four years. The results indicate a progressive deterioration of udder health from the onset of the cow’s productive life until culling. A year-over-year increase in the number of cows with subclinical mastitis led to an overall SCM prevalence of 34.5%, chronic SCM prevalence of 26.9% and a bulk tank milk SCC of 463000 cells/mL, in 2018. The average somatic cell score, a base 2logarithm of individual cow’s SCC, was found persistently above the subclinical mastitis indicative cut-off in all four years, with a peak in 2018. At herd level, the incidence rate of new SCM cases was 12 new cases / 100 cows / month; the highest incidence rate was observed in the early lactation stage group (1-60 days-in-milk), in all four years, reaching a peak of 31 new cases / 100 cows / month, in 2018. In 2018, prevalence of heifers’ SCM and chronic SCM was23.4% and 16.9%, respectively. Despite the adequate average 305-days milk yield (9608 kg in 2018), the results were indicative of poor udder health status, pointed out by reduced duration of cows’ productive life (less than 3 lactations)and lower milk quality (elevated SCC). The severity and wide spreading of subclinical mastitis in Greek dairy herds highlights the necessity of a national mastitis control program, aiming to improve the productive efficacy, management decisions accuracy and quality of produced milk

Tail risk and the cross-section of mutual fund expected returns

We test for the presence of a tail risk premium in the cross-section of mutual fund returns and find that the top tail risk quintile of funds outperforms the bottom by 4.4% per annum. This premium is not simply a reward for market risk, nor do commonly used risk factors offer an adequate explanation. Our findings hold across double-sorted portfolios formed on tail risk and a number of fund characteristics. We also find that funds susceptible to tail risk tend to be small, young, have high management fees, and have managers who do not risk their own capital

The effect of antihypertensive drugs on arterial stiffness and central hemodynamics: Not all fingers are made the same

Arterial stiffness and central hemodynamics attract increasing scientific interest within the hypertensive community during the last decade. Accumulating evidence indicates that aortic stiffness is a strong and independent predictor of cardiovascular events and all-cause mortality in hypertensive patients, and its predictive value extends beyond traditional risk factors. The role of central hemodynamics and augmentation index (a marker of reflected waves), remains less established and requires further investigation. Several lines of evidence indicate that antihypertensive therapy results in significant reductions of pulse wave velocity and central hemodynamics. However, beta-blockers seem to be the only exception with significant within-class differences. Conventional beta-blockers, although equally effective in reducing pulse wave velocity, seem to be less beneficial on central hemodynamics and augmentation index than the other antihypertensive drug categories, whereas the newer vasodilating beta-blockers seem to share the benefits of the other antihypertensive drugs. In conclusion, aortic stiffness seems ready for ‘prime-time’ in the management of essential hypertension, while further research is needed for central hemodynamics and augmentation index

Endothelin receptor antagonists (ERA) in hypertension and chronic kidney disease: A rose with many thorns

The discovery of endothelin created a lot of enthusiasm and paved new therapeutic avenues for the treatment of arterial hypertension. Endothelin plays a significant role in blood pressure regulation through pronounced vasoconstriction and modulation of sodium and water reabsorption in the kidneys. Endothelin receptor antagonists have been tested in many clinical trials in patients with arterial hypertension, heart failure, pulmonary arterial hypertension, systemic sclerosis, chronic kidney disease, and diabetic nephropathy. However, the results were usually disappointing, except in pulmonary hypertension and scleroderma digital ulcers. The future of ERAs for the treatment of arterial hypertension and chronic kidney disease does not seem bright, and only the combination with other classes of antihypertensive drugs might offer a way out

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines

A multi-factorial genetic model for prognostic assessment of high risk melanoma patients receiving adjuvant interferon

Purpose: IFNa was the first cytokine to demonstrate anti-tumor activity in advanced melanoma. Despite the ability of high-dose IFNa reducing relapse and mortality by up to 33%, large majority of patients experience side effects and toxicity which outweigh the benefits. The current study attempts to identify genetic markers likely to be associated with benefit from IFN-a2b treatment and predictive for survival. Experimental design: We tested the association of variants in FOXP3 microsatellites, CTLA4 SNPs and HLA genotype in 284 melanoma patients and their association with prognosis and survival of melanoma patients who received IFNa adjuvant therapy. Results: Univariate survival analysis suggested that patients bearing either the DRB1*15 or HLA-Cw7 allele suffered worse OS while patients bearing either HLA-Cw6 or HLA-B44 enjoyed better OS. DRB1*15 positive patients suffered also worse RFS and conversely HLA-Cw6 positive patients had better RFS. Multivariate analysis revealed that a five-marker genotyping signature was prognostic of OS independent of disease stage. In the multivariate Cox regression model, HLA-B38 (p = 0.021), HLA-C15 (p = 0.025), HLA-C3 (p = 0.014), DRB1*15 (p = 0.005) and CT60*G/G (0.081) were significantly associated with OS with risk ratio of 0.097 (95% CI, 0.013-0.709), 0.387 (95% CI, 0.169-0.889), 0.449 (95% CI, 0.237-0.851), 1.948 (95% CI, 1.221-3.109) and 1.484 (95% IC, 0.953-2.312) respectively. Conclusion: These results suggest that gene polymorphisms relevant to a biological occurrence are more likely to be informative when studied in concert to address potential redundant or conflicting functions that may limit each gene individual contribution. The five markers identified here exemplify this concept though prospective validation in independent cohorts is needed