Real-world walking cadence in people with COPD.

Introduction: The clinical validity of real-world walking cadence in people with COPD is unsettled. Our objective was to assess the levels, variability and association with clinically relevant COPD characteristics and outcomes of real-world walking cadence. Methods: We assessed walking cadence (steps per minute during walking bouts longer than 10 s) from 7 days' accelerometer data in 593 individuals with COPD from five European countries, and clinical and functional characteristics from validated questionnaires and standardised tests. Severe exacerbations during a 12-month follow-up were recorded from patient reports and medical registries. Results: Participants were mostly male (80%) and had mean±sd age of 68±8 years, post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57±19% predicted and walked 6880±3926 steps·day-1. Mean walking cadence was 88±9 steps·min-1, followed a normal distribution and was highly stable within-person (intraclass correlation coefficient 0.92, 95% CI 0.90-0.93). After adjusting for age, sex, height and number of walking bouts in fractional polynomial or linear regressions, walking cadence was positively associated with FEV1, 6-min walk distance, physical activity (steps·day-1, time in moderate-to-vigorous physical activity, vector magnitude units, walking time, intensity during locomotion), physical activity experience and health-related quality of life and negatively associated with breathlessness and depression (all p<0.05). These associations remained after further adjustment for daily steps. In negative binomial regression adjusted for multiple confounders, walking cadence related to lower number of severe exacerbations during follow-up (incidence rate ratio 0.94 per step·min-1, 95% CI 0.91-0.99, p=0.009). Conclusions: Higher real-world walking cadence is associated with better COPD status and lower severe exacerbations risk, which makes it attractive as a future prognostic marker and clinical outcome.

Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson's Disease?

BACKGROUND: Real-world walking speed (RWS) measured using wearable devices has the potential to complement the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) for motor assessment in Parkinson's disease (PD). OBJECTIVE: Explore cross-sectional and longitudinal differences in RWS between PD and older adults (OAs), and whether RWS was related to motor disease severity cross-sectionally, and if MDS-UPDRS III was related to RWS, longitudinally. METHODS: 88 PD and 111 OA participants from ICICLE-GAIT (UK) were included. RWS was evaluated using an accelerometer at four time points. RWS was aggregated within walking bout (WB) duration thresholds. Between-group-comparisons in RWS between PD and OAs were conducted cross-sectionally, and longitudinally with mixed effects models (MEMs). Cross-sectional association between RWS and MDS-UPDRS III was explored using linear regression, and longitudinal association explored with MEMs. RESULTS: RWS was significantly lower in PD (1.04 m/s) in comparison to OAs (1.10 m/s) cross-sectionally. RWS significantly decreased over time for both cohorts and decline was more rapid in PD by 0.02 m/s per year. Significant negative relationship between RWS and the MDS-UPDRS III only existed at a specific WB threshold (30 to 60 s, ß= - 3.94 points, p = 0.047). MDS-UPDRS III increased significantly by 1.84 points per year, which was not related to change in RWS. CONCLUSION: Digital mobility assessment of gait may add unique information to quantify disease progression remotely, but further validation in research and clinical settings is needed.

A five-year cohort study on testicular tumors from a population-based canine cancer registry in central Italy (Umbria).

Canine testicular tumors account for about 90 % of tumors affecting the male genitalia. Seminomas (SEM), Sertoli cell tumors (SCT), and interstitial cell tumors (ICT) are the most common histological diagnoses, but their incidence shows high variability among studies. Our aim is to report the results on the analysis of testicular tumors recorded by the population-based Umbria Canine Cancer Registry (CCR) for a 5-year time period and to assess the value of tumor major diameter, measured during trimming, in discriminating neoplastic from non-neoplastic lesions. The study was conducted on 388 testicular tumors (on 1969 total male tumors) diagnosed in 355 dogs from 2014 to 2018. The median incidence was 35 cases/100,000 dogs, with a proportion equal to 19,7 % of all tumors. The incidence on neutered male dogs was 352/100,000. Most tumors were ICTs (50 %), with fewer SEMs and SCTs (29 % and 17 %, respectively). Only 3 % of tumors were mixed germ cell-sex cord-stromal tumors (MGC-SCST). Ten percent of cases had multiple tumors in the same testicle, with SEM-ICT being prevalent (69.2 %). Tumors in cryptorchid testes were 5.9 % of the total, mostly SCT (60.9 %). Mean age at diagnosis was 10.7 ± 2.7, with similar values for different tumor types. Significant incidence ratios (IRR) were found in Golden retriever (IRR 7.18, CI95 % 4.72-10.92) and in English cocker spaniel (IRR 2.30, CI95 % 1.28-4.13) when compared with mixed breed dogs. A value of 0.3 cm (major diameter) of lesions at the moment of trimming was helpful in discriminating a final tumor histological diagnosis from a non-tumor lesion. Since the number of tumors included in this evaluation was limited, further studies to confirm the diagnostic value of this measure are recommended. Our results provided information on the incidence of canine testicular tumors in Umbria region that can be compared with future results and incidence from other geographical areas if provided with reliable data on the total population, can offer solid information on the incidence and proportion of different tumor types in specific territories, contributing also to the supervision of its inhabitants' health. Moreover, pathological data such as the major diameter of tumors can be obtained and contribute to diagnostic routine and standardization.

A regression model including fetal orbit measurements to predict parturition in Standardbred mares with normal pregnancy.

In the mare, foaling is a critical unpredictable event due to a wide range of gestational length and the absence of clear signs of impending parturition. To predict foaling, pH, inversion sodium potassium and increase of calcium concentration in mammary secretions are used. The aim of this study was to find how many days are left until parturition knowing mare's age (A) and parity (P) combined with ultrasonographic measurements of the fetal orbit in Standardbred mares with normal pregnancy. Eighty healthy Standardbred mares with normal pregnancy were hospitalized for attended delivery. Information about mare's age, parity and breeding date were recorded at admission. Transrectal ultrasonography were routinely performed at admission and every 10 days until parturition using a B-mode real time portable unit equipped with a 5-7.5 MHz linear transducer. The images of the fetal orbit were acquired when cornea, anterior and posterior chamber, vitreous body, lens and optic nerve were visible. Longitudinal diameter (LD) was considered as the distance between the two ocular poles, within the vitreous body; transverse diameter (TD), perpendicular to LD and bisecting the lens, was measured as the distance between cornea and retina. At delivery, length of pregnancy and gestational age at each exam were registered. For each ultrasound examination, days before parturition (DBP) were calculated. Seventy-eight Standardbred mares with normal pregnancies were included in the study. Mares' mean age was 9 ±â€¯5 years old (range 4-20 years) and mean gestation length was 341 ±â€¯7 days (range 327-366 days). Thirty-three mares were primiparous and 45 mares were multiparous. Data were analyzed using a regression tree: P, A, LD and TD were used as covariates. DBP was used as the variable of interest. Nine terminal nodes were identified based on the selected covariates. The first split is produced by the TD: fetuses with TD greater or equal than 2.97 cm are further split according to LD, with a threshold of 3.28 cm. The next split is dictated by A; after a further split on LD, the first terminal node is built, containing 34 fetuses with average DBP equal to 10 days. If the A is ≥ 9.5 years a further split is on P: when mares are multiparous, the TD built two different nodes. Since prediction of mare's foaling date is an important factor in stud farm management, the regression model developed may help the veterinarian to estimate the DBP in Standardbred mares with normal pregnancy.

Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data.

Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of next-generation sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a 'zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent conditional autoregressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.

Spatio-temporal model for multiple ChIP-seq experiments.

The increasing availability of ChIP-seq data demands for advanced statistical tools to analyze the results of such experiments. The inherent features of high-throughput sequencing output call for a modelling framework that can account for the spatial dependency between neighboring regions of the genome and the temporal dimension that arises from observing the protein binding process at progressing time points; also, multiple biological/technical replicates of the experiment are usually produced and methods to jointly account for them are needed. Furthermore, the antibodies used in the experiment lead to potentially different immunoprecipitation efficiencies, which can affect the capability of distinguishing between the true signal in the data and the background noise. The statistical procedure proposed consist of a discrete mixture model with an underlying latent Markov random field: the novelty of the model is to allow both spatial and temporal dependency to play a role in determining the latent state of genomic regions involved in the protein binding process, while combining all the information of the replicates available instead of treating them separately. It is also possible to take into account the different antibodies used, in order to obtain better insights of the process and exploit all the biological information available.