Explore the vast range of titles available.

This paper deals with a stochastic Susceptible-Infective-Vaccinated-Susceptible (SIVS) model with infection reintroduction. Health policies depend on vaccine coverage, v 0 , that guarantees herd immunity levels in the population. Vaccine failures occur when an organism develops a disease despite of being vaccinated against it. After vaccination, a proportion of healthy individuals unsuccessfully tries to increase antibody levels and, consequently these individuals are not immune to the vaccine preventable disease. When an infectious process is in progress, the initial vaccine coverage drops down and herd immunity will be lost. Our objective was to introduce a warning vaccination level and define random measures quantifying the time until the number of vaccinated descends to a warning vaccination level (i.e., the so-called sleeping period), and the epidemic size. A sensitivity analysis was performed to assess the influence of the model parameters on the variation and robustness of the sleeping period and the number of infections observed within it.

This paper deals with an infective process of type SIS, taking place in a closed population of moderate size that is inspected periodically. Our aim is to study the number of inspections that find the epidemic process still in progress. As the underlying mathematical model involves a discrete time Markov chain (DTMC) with a single absorbing state, the number of inspections in an outbreak is a first-passage time into this absorbing state. Cumulative probabilities are numerically determined from a recursive algorithm and expected values came from explicit expressions.

We consider the classical deterministic susceptible-infective-susceptible epidemic model, where the infection and recovery rates depend on a background environmental process that is modeled by a continuous time Markov chain. This framework is able to capture several important characteristics that appear in the evolution of real epidemics in large populations, such as seasonality effects and environmental influences. We propose computational approaches for the determination of various distributions that quantify the evolution of the number of infectives in the population.

In this paper, we revisit level-dependent quasi-birth-death processes with finitely many possible values of the level and phase variables by complementing the work of Gaver, Jacobs, and Latouche (Adv. Appl. Probab. 1984), where the emphasis is upon obtaining numerical methods for evaluating stationary probabilities and moments of first-passage times to higher and lower levels. We provide a matrix-analytic scheme for numerically computing hitting probabilities, the number of upcrossings, sojourn time analysis, and the random area under the level trajectory. Our algorithmic solution is inspired from Gaussian elimination, which is applicable in all our descriptors since the underlying rate matrices have a block-structured form. Using the results obtained, numerical examples are given in the context of varicella-zoster virus infections.

IntroductionPalliative WBRT is the main treatment for multiple BMs. Recent studies report no benefit in survival after WBRT compared to palliative supportive care in patients (pts) with poor prognosis. A new era of systemic treatment strategies based on targeted therapies are improving the prognosis of patients with BMs. The purpose of this study is to develop a prognostic score in palliative pts with BMs who undergo WBRT in this new setting.Methods239 pts with BMs who received palliative WBRT between 2013–2022 in our center were analyzed retrospectively. The score was designed according to the value of the β coefficient of each variable with statistical significance in the multivariate model using Cox regression. Once the score was established, a comparison was performed according to Kaplan–Meier and was analyzed by log-rank test.Results149 pts (62.3%) were male and median (m) age was 60 years. 139 (58,2%) were lung cancer and 35 (14,6%) breast cancer. All patients received 30Gys in 10 sessions. m overall survival (OS) was 3,74 months (ms). 37 pts (15,5%) had a specific target mutation. We found that 62 pts were in group < 4 points with mOS 6,89 ms (CI 95% 3,18–10,62), 84 in group 4–7 points with mOS 4,01 ms (CI 95% 3,40–4,62) and 92 pts in group > 7 points with mOS 2,72 ms (CI 95% 1,93–3,52) (p < 0,001).ConclusionsMETASNCore items are associated with OS and they could be useful to select palliative pts to receive WBRT. More studies are necessary to corroborate our findings.

This paper is concerned with the M/G/1 queue with repeated attempts where a customer who finds the server busy leaves the service area and repeats his request after a random amount of time. The study of the waiting time process is concentrated upon. Its analysis in terms of Laplace transforms has been discussed in the literature. However, this solution has important limitations in practice. For instance, the first moments of the waiting time, W, cannot be calculated by direct differentiation. This paper supplements the existing work and provides a direct method of computation for the second moment of W. Then the maximum entropy approach is used to estimate the true waiting time distribution.

This paper deals with the long-term behaviour and incidence of a vaccine-preventable contact disease, under the assumption that both vaccine protection and immunity after recovery are not lifelong. The mathematical model is developed in a stochastic markovian framework. The evolution of the disease in a finite population is thus represented by a three-dimensional continuous-time Markov chain, which is versatile enough to be able to compensate for the loss of protection by including vaccination before the onset of the outbreak and also during the course of the epidemics.

To evaluate the influence of delirium on the functional and cognitive capacity of patients included in the DELIPRECAS study, as well as on their quality of life, in the 3–4 years after cardiac surgery. Prospective observational study. Assessment of cognitive and functional status from hospital discharge to the present, 3 years after cardiac surgery. 313 patients undergoing cardiac surgery consecutively, aged 18 years or over. The primary outcome measure was the cognitive and functional status of the patients 3 years after cardiac surgery, evaluated by telephone interview, and the possible influence on them of delirium diagnosed by the Confusion Assessment Method in Intensive Care Units (CAM-ICU) during their stay in the intensive care unit after cardiac surgery. Postoperative delirium acts as an independent risk factor for the long-term development of memory problems (OR 6.11, 95% CI 2.54 to 14.68, p < 0.001), concentration (OR 11.20, 95% CI 3.58 to 35.09, p > 0.001), confusion/disorientation (OR 10.93, 95% CI 3.61 to 33.12, p > 0.001), sleep problems (OR 5.21, 95% CI 2 0.29 to 11.84, p < 0.001), nightmares (OR 8.99, 95% CI 1.98 to 40.90, p = 0.004), emotional problems (OR 4.30, 95% CI 1.87 to 9.91, p = 0.001) and poorer mobility after hospital discharge (OR 2.436, 95% CI 1.06 to 5.61, p = 0.037). The number of hospital readmissions was also significantly higher in those patients who developed delirium after cardiac surgery (27% vs 13.8%, p = 0.022). Postoperative delirium is a risk factor for decreased quality of life in patients 3 years after heart surgery, being associated with greater cognitive and functional deterioration, as well as greater risk of hospital readmission. Therefore, emphasis should be placed on both prevention and early recognition and treatment of delirium to improve long-term outcomes for patients after cardiac surgery. •Postoperative delirium is a risk factor for impaired quality of life in patients 3 years after cardiac surgery.•Postoperative delirium is independently associated with greater cognitive impairment, and greater risk of hospital readmission.•Both prevention and early recognition and treatment of delirium are essential to improve long-term outcomes

Background Diagnosing septic shock promptly is essential but challenging, especially due to its clinical similarity to non-septic shock. Extracellular vesicle-derived miRNAs may serve as biomarkers to distinguish septic shock from non-septic shock, providing a more accurate diagnostic tool for postsurgical patients. This study aims to identify extracellular vesicle-derived miRNA signatures that differentiate septic shock from non-septic shock in postsurgical patients, potentially improving diagnostic accuracy and clinical decision-making. Methods A multicentre, prospective study was conducted on miRNA profiles in shock patients. Two cohorts were recruited from the Intensive Care Units of two Spanish hospitals: a discovery cohort with 109 patients and a validation cohort with 52 patients. Plasma samples were collected within 24 h of shock diagnosis and subjected to miRNA sequencing. High-throughput sequencing data from the discovery cohort were analysed to identify differentially expressed miRNAs. These findings were validated via qPCR in the validation cohort. Results Thirty miRNAs were identified as significantly differentially expressed between septic and non-septic shock patients. Among these, six miRNAs—miR-100-5p, miR-484, miR-10a-5p, miR-148a-3p, miR-342-3p, and miR-451a—demonstrated strong diagnostic capabilities for septic shock. A combination of miR-100-5p, miR-148a-3p, and miR-451a achieved an area under the curve of 0.894, with qPCR validation in the validation cohort yielding an area under the curve of 0.960. Conclusions This study highlights extracellular vesicle-derived miRNAs as promising biomarkers for differentiating septic from non-septic shock. The identified three-miRNA signature has significant potential to enhance septic shock diagnosis, thereby aiding in timely and appropriate treatment for postsurgical patients.

Purpose Molecular subtyping based on gene expression profiling (i.e., PAM50 assay) aids in determining the prognosis and treatment of breast cancer (BC), particularly in hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative tumors, where luminal A and B subtypes have different prognoses and treatments. Several surrogate classifications have been proposed for distinguishing between the luminal A and B subtypes. This study determines the accuracy of local immunohistochemistry (IHC) techniques for classifying HR-positive/HER2-negative (HR+/HER2−) tumors according to intrinsic subtypes using the nCOUNTER PAM50 assay as reference and the HR status definition according the ASCO/CAP recommendations. Methods Molecular subtypes resulting from nCOUNTER PAM50 performed in our laboratory between 2014 and 2020 were correlated with three different proxy surrogates proposed in the literature based on ER, PR, HER2, and Ki67 expression with different cut-off values. Concordance was measured using the level of agreement and kappa statistics. Results From 1049 samples with the nCOUNTER test, 679 and 350 were luminal A and B subtypes, respectively. Only a poor-to-fair correlation was observed between the three proxy surrogates and real genomic subtypes as determined by nCOUNTER PAM50. Moreover, 5–11% and 18–36% of the nCOUNTER PAM50 luminal B and A tumors were classified as luminal A and B, respectively, by these surrogates. Conclusion The concordance between luminal subtypes determined by three different IHC-based classifiers and the nCOUNTER PAM50 assay was suboptimal. Thus, a significant proportion of luminal A and B tumors as determined by the surrogate classifiers could be undertreated or over-treated.