Effect of recombinant bovine granulocyte colony-stimulating factor treatment during the peripartum period on postpartum diseases, reproductive performance, and milk production in Holstein cattle.

To boost the immune function around parturition, recombinant bovine granulocyte colony-stimulating factor (rbG-CSF) has been used to increase the number of neutrophils. Therefore, the aim of this study was to quantify the effect of rbG-CSF administration on the incidence of postpartum pathologies, reproductive performance, and milk production during the first three months of lactation. A total of 199 Holstein cows from one herd were included and were randomly allocated into two groups: Control (n = 103) and rbG-CSF (n = 96). Cows in the rbG-CSF group received 2 doses of a rbG-CSF injectable formulation, one 7 days before the expected date of calving and the other within 24 h after calving. For 6 weeks following calving, animals were examined weekly to assess the presence of postpartum pathologies. Milk production, protein and fat content, and somatic cell count were determined monthly by the regional dairy herd improvement association. Data about the reproductive performance were collected from on-farm software. To analyse the effect of treatment on the incidence of postpartum pathologies, Pearson's χ2 test and multivariable logistic regressions were performed. The effect on reproductive performance was analysed using Cox proportional hazard regression analysis for days open, binary logistic regression for first service conception rate and Oneway ANOVA test for the number of artificial inseminations. The effects of treatment on milk yield and milk composition were checked using GLM repeated measures analysis. No statistically significant differences were observed between treatment groups for any of the parameters evaluated. Only parity had a significant effect on days open and milk production (p < 0.05). In conclusion, in the present study no evidence was found that rbG-CSF could have an effect on the reproductive and productive parameters evaluated.

Prevalence, Risk Factors, and Relationship between Reproductive Performance and the Presence of Antibodies against Coxiellosis in Dairy Farm Milk Tanks in the Northwest of Spain.

Q fever is a zoonotic disease that has been associated with reproductive problems in animals. As there is little epidemiological data regarding the distribution and risk factors of this disorder in cattle, the objective of this study was to evaluate the prevalence of Coxiella burnetii among dairy herds in the northwest of Spain, and to determine the on-farm risk factors associated with the disease and its effects on reproductive performance. Bulk tank milk (BTM) samples were collected from 262 commercial dairy herds from A Coruña, Lugo, and Pontevedra provinces. Data about location, mean age, and herd management features were obtained. A commercial indirect ELISA kit was used to determine the presence of antibodies against C. burnetii in BTM samples. The relationship between seropositivity to C. burnetii and the risk factors was checked using a Pearson's χ2 test and a classification tree analysis. In addition, a one-way ANOVA test and the Mann-Whitney U test were used to check the impact of seropositivity to C. burnetii on reproductive performance. A total of 60.1% of the farms tested positive for coxiellosis, the herd size, the external purchase of livestock, and the geographical area were identified as the main risk factors. Conception rate and first-service conception rate were significantly lower (p < 0.05) in positive farms (37.1 and 32.9%) compared to negative farms (39.8 and 36.1%). Similarly, positive farms had significant higher incidence of endometritis (13.7% vs. 11.2%, p < 0.05). Consequently, a high seropositivity and slightly negative effects of coxiellosis on reproductive performance were observed, which intensifies the need for further research, including the identification an active infection in positive herds and the characterization of the genotype.

Impact of Balloon Guide Catheters in Elderly Patients Treated with Mechanical Thrombectomy: Insights from the ROSSETTI Registry.

BACKGROUND AND PURPOSE: Several nonrandomized studies have demonstrated the effectiveness of balloon guide catheters in treating patients with anterior circulation large-vessel occlusion. However, their impact on the elderly populations has been underreported. We aimed to analyze the effect of balloon guide catheters in a cohort of elderly patients (80 years of age or older) with anterior circulation large-vessel occlusion. MATERIALS AND METHODS: Consecutive patients from June 2019 to June 2022 were collected from the ROSSETTI Registry. Demographic and clinical data, angiographic endovascular technique, and clinical outcome were compared between balloon guide catheter and non-balloon guide catheter groups. We studied the association between balloon guide catheters and the rate of complete recanalization after a single first-pass effect modified TICI 2c-3, as well as their association with functional independence at 3 months. RESULTS: A total of 808 patients were included during this period, 465 (57.5%) of whom were treated with balloon guide catheters. Patients treated with balloon guide catheters were older, had more neurologic severity at admission and lower baseline ASPECTS, and were less likely to receive IV fibrinolytics. No differences were observed in terms of the modified first-pass effect between groups (45.8 versus 39.9%, P = .096). In the multivariable regression analysis, balloon guide catheter use was not independently associated with a modified first-pass effect or the final modified TICI 2c-3, or with functional independence at 3 months. CONCLUSIONS: In our study, balloon guide catheter use during endovascular treatment of anterior circulation large-vessel occlusion in elderly patients did not predict the first-pass effect, near-complete final recanalization, or functional independence at 3 months. Further studies, including randomized clinical trials, are needed to confirm these results.

CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study.

Colorectal cancer (CRC) is one of the most common types of cancer worldwide. The KRAS mutation is present in 30-50% of CRC patients. This mutation confers resistance to treatment with anti-EGFR therapy. This article aims at proving that computer tomography (CT)-based radiomics can predict the KRAS mutation in CRC patients. The piece is a retrospective study with 56 CRC patients from the Hospital of Santiago de Compostela, Spain. All patients had a confirmatory pathological analysis of the KRAS status. Radiomics features were obtained using an abdominal contrast enhancement CT (CECT) before applying any treatments. We used several classifiers, including AdaBoost, neural network, decision tree, support vector machine, and random forest, to predict the presence or absence of KRAS mutation. The most reliable prediction was achieved using the AdaBoost ensemble on clinical patient data, with a kappa and accuracy of 53.7% and 76.8%, respectively. The sensitivity and specificity were 73.3% and 80.8%. Using texture descriptors, the best accuracy and kappa were 73.2% and 46%, respectively, with sensitivity and specificity of 76.7% and 69.2%, also showing a correlation between texture patterns on CT images and KRAS mutation. Radiomics could help manage CRC patients, and in the future, it could have a crucial role in diagnosing CRC patients ahead of invasive methods.

Testing for an ignorable sampling bias under random double truncation.

In clinical and epidemiological research doubly truncated data often appear. This is the case, for instance, when the data registry is formed by interval sampling. Double truncation generally induces a sampling bias on the target variable, so proper corrections of ordinary estimation and inference procedures must be used. Unfortunately, the nonparametric maximum likelihood estimator of a doubly truncated distribution has several drawbacks, like potential nonexistence and nonuniqueness issues, or large estimation variance. Interestingly, no correction for double truncation is needed when the sampling bias is ignorable, which may occur with interval sampling and other sampling designs. In such a case the ordinary empirical distribution function is a consistent and fully efficient estimator that generally brings remarkable variance improvements compared to the nonparametric maximum likelihood estimator. Thus, identification of such situations is critical for the simple and efficient estimation of the target distribution. In this article, we introduce for the first time formal testing procedures for the null hypothesis of ignorable sampling bias with doubly truncated data. The asymptotic properties of the proposed test statistic are investigated. A bootstrap algorithm to approximate the null distribution of the test in practice is introduced. The finite sample performance of the method is studied in simulated scenarios. Finally, applications to data on onset for childhood cancer and Parkinson's disease are given. Variance improvements in estimation are discussed and illustrated.

Equalden.HD: An R Package for testing the equality of a high dimensional set of densities.

BACKGROUND AND OBJECTIVE: Nowadays the "low sample size, large dimension" scenario is often encountered in genetics and in the omic sciences, where the microarray data is typically formed by a large number of possibly dependent small samples. Standard methods to solve the k-sample problem in such a setting are of limited applicability due to lack of theoretical validation for large k, lengthy computational times, missing software solutions, or inability to deal with statistical dependence among the samples. This paper presents the R package Equalden.HD to overcome the referred limitations. METHODS: The package implements several tests for the null hypothesis that a large number of samples follow a common density. These methods are particularly well suited to the "low sample size, large dimension" setting. The implemented procedures allow for dependent samples. For each method Equalden.HD reports, among other things, the standardized value of the test statistic and the corresponding p-value. The package also includes two high-dimensional genetic data sets, Hedenfalk and Rat, which are used in this paper for illustration purposes. RESULTS: The usage of Equalden.HD has been illustrated through the analysis of Hedenfalk and Rat genetic data. Statistical dependence among the samples was found for both genetic data sets. The application of an appropriate k-sample test within Equalden.HD rejected the null hypothesis of inter-samples homogeneity. The methods were used to test for the within groups homogeneity in cluster analysis too, which is usually performed when the k samples are found to be significantly different. Equalden.HD helped to identify the individuals which are responsible for the lack of homogeneity of the samples. The limitations of the standard Kruskal-Wallis test for the identification of homogeneous clusters have been highlighted. CONCLUSIONS: The methods implemented by Equalden.HD are the unique omnibus nonparametric k-sample tests that have been validated as k grows. Furthermore, the package provides suitable corrections for possibly dependent samples, which is another distinctive feature. Thus, the package opens new doors for the statistical analysis of omic data. Limitations of standard methods (e.g. Anderson-Darling and Kruskal-Wallis) and existing software solutions in the setting with a large k have been emphasized.

Assessment of assumptions of statistical analysis methods in randomised clinical trials: the what and how.

When analysing and presenting results of randomised clinical trials, trialists rarely report if or how underlying statistical assumptions were validated. To avoid data-driven biased trial results, it should be common practice to prospectively describe the assessments of underlying assumptions. In existing literature, there is no consensus on how trialists should assess and report underlying assumptions for the analyses of randomised clinical trials. With this study, we developed suggestions on how to test and validate underlying assumptions behind logistic regression, linear regression, and Cox regression when analysing results of randomised clinical trials.Two investigators compiled an initial draftbased on a review of the literature. Experienced statisticians and trialists from eight different research centres and trial units then participated in a anonymised consensus process, where we reached agreement on the suggestions presented in this paper.This paper provides detailed suggestions on 1) which underlying statistical assumptions behind logistic regression, multiple linear regression and Cox regression each should be assessed; 2) how these underlying assumptions may be assessed; and 3) what to do if these assumptions are violated.We believe that the validity of randomised clinical trial results will increase if our recommendations for assessing and dealing with violations of the underlying statistical assumptions are followed.

Nonparametric estimation of the cumulative incidences of competing risks under double truncation.

Registry data typically report incident cases within a certain calendar time interval. Such interval sampling induces double truncation on the incidence times, which may result in an observational bias. In this paper, we introduce nonparametric estimation for the cumulative incidences of competing risks when the incidence time is doubly truncated. Two different estimators are proposed depending on whether the truncation limits are independent of the competing events or not. The asymptotic properties of the estimators are established, and their finite sample performance is investigated through simulations. For illustration purposes, the estimators are applied to childhood cancer registry data, where the target population is peculiarly defined conditional on future cancer development. Then, in our application, the cumulative incidences inform on the distribution by age of the different types of cancer.

Assessing assumptions for statistical analyses in randomised clinical trials.

In order to ensure the validity of results of randomised clinical trials and under some circumstances to optimise statistical power, most statistical methods require validation of underlying statistical assumptions. The present paper describes how trialists in major medical journals report tests of underlying statistical assumptions when analysing results of randomised clinical trials. We also consider possible solutions how to improve current practice by adequate reporting of tests of underlying statistical assumptions. We conclude that there is a need to reach consensus on which underlying assumptions should be assessed, how these underlying assumptions should be assessed and what should be done if the underlying assumptions are violated.

Goodness-of-fit tests for disorder detection in NGS experiments.

Next-generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high-dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experiments, that is, when the focus is the detection of deletion and duplication disorders for homozygosity and heterozygosity in DNA sequencing. A statistical model to cope with guanine/cytosine bias and phasing and prephasing phenomena at base level is proposed, and a goodness-of-fit procedure for disorder detection is derived. The method combines the proper evaluation of local p-values (one for each DNA base) with suitable corrections for multiple comparisons and the discrete nature of the p-values. A global test for the detection of disorders in the whole DNA region is proposed too. The performance of the introduced procedures is investigated through simulations. A real data illustration is provided.

Nonparametric estimation of transition probabilities for a general progressive multi-state model under cross-sectional sampling.

Nonparametric estimation of the transition probability matrix of a progressive multi-state model is considered under cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are proposed. The estimators require full retrospective information before the truncation time, which, when exploited, increases efficiency. They are obtained as differences between two survival functions constructed for sub-samples of subjects occupying specific states at a certain time point. Both estimators correct the oversampling of relatively large survival times by using the left-truncation times associated with the cross-sectional observation. Asymptotic results are established, and finite sample performance is investigated through simulations. One of the proposed estimators performs better when there is no censoring, while the second one is strongly recommended with censored data. The new estimators are applied to data on patients in intensive care units (ICUs).

Inverse probability weighted Cox regression for doubly truncated data.

Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease.

Pillar[5]arene-Based Supramolecular Plasmonic Thin Films for Label-Free, Quantitative and Multiplex SERS Detection.

Novel plasmonic thin films based on electrostatic layer-by-layer (LbL) deposition of citrate-stabilized Au nanoparticles (NPs) and ammonium pillar[5]arene (AP[5]A) have been developed. The supramolecular-induced LbL assembly of the plasmonic nanoparticles yields the formation of controlled hot spots with uniform interparticle distances. At the same time, this strategy allows modulating the density and dimensions of the Au aggregates, and therefore the optical response, on the thin film with the number of AuNP-AP[5]A deposition cycles. Characterization of the AuNP-AP[5]A hybrid platforms as a function of the deposition cycles was performed by means of visible-NIR absorption spectroscopy, and scanning electron and atomic force microscopies, showing larger aggregates with the number of cycles. Additionally, the surface enhanced Raman scattering efficiency of the resulting AuNP-AP[5]A thin films has been investigated for three different laser excitations (633, 785, and 830 nm) and using pyrene as Raman probe. The best performance was shown by the AuNP-AP[5]A film obtained with two deposition cycles ((AuNP-AP[5]A)2) when excited with a 785 laser line. The optical response and SERS efficiency of the thin films were also simulated using the M3 solver and employing computer aided design models built based on SEM images of the different films. The use of host molecules as building blocks to fabricate (AuNP-AP[5]A)2) films has enabled the ultradetection, in liquid and gas phase, of low molecular weight polyaromatic hydrocarbons, PAHs, with no affinity for gold but toward the hydrophobic AP[5]A cavity. Besides, these plasmonic platforms allowed achieving quantitative detection within certain concentration regimes. Finally, the multiplex sensing capabilities of the AuNP-AP[5]A)2 were evaluated for their ability to detect in liquid and gas phase three different PAHs.

Direct modeling of regression effects for transition probabilities in the progressive illness-death model.

In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness-death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score equations that are able to remove the bias due to censoring are introduced. By solving these equations, one can estimate the possibly time-varying regression coefficients, which have an immediate interpretation as covariate effects on the transition probabilities. The performance of the proposed estimator is investigated through simulations. We apply the method to data from the Registry of Systematic Lupus Erythematosus RELESSER, a multicenter registry created by the Spanish Society of Rheumatology. Specifically, we investigate the effect of age at Lupus diagnosis, sex, and ethnicity on the probability of damage and death along time. Copyright © 2017 John Wiley & Sons, Ltd.

An extended sequential goodness-of-fit multiple testing method for discrete data.

The sequential goodness-of-fit (SGoF) multiple testing method has recently been proposed as an alternative to the familywise error rate- and the false discovery rate-controlling procedures in high-dimensional problems. For discrete data, the SGoF method may be very conservative. In this paper, we introduce an alternative SGoF-type procedure that takes into account the discreteness of the test statistics. Like the original SGoF, our new method provides weak control of the false discovery rate/familywise error rate but attains false discovery rate levels closer to the desired nominal level, and thus it is more powerful. We study the performance of this method in a simulation study and illustrate its application to a real pharmacovigilance data set.

Towards a Real-Time Embedded System for Water Monitoring Installed in a Robotic Sailboat.

Problems related to quality (and quantity) of water in natural resources or in artificial reservoirs are frequently arising and are at the center of attention of authorities and governments around the world. Many times the monitoring is not performed in an efficient time frame and a precise manner, whereas the adoption of fast and punctual solutions would undoubtedly improve the water quality and consequently enhance the life of people. To minimize or diminish such kinds of problems, we propose an architecture for sensors installed in a robotic platform, an autonomous sail boat, able to acquire raw data relative to water quality, to process and make them available to people that might be interested in such information. The main contributions are the sensors architecture itself, which uses low cost sensors, with practical experimentation done with a prototype. Results show data collected for points in lakes and rivers in the northeast of Brazil. This embedded system is fixed in the sailboat robot with the intention to facilitate the study of water quality for long endurance missions. This robot can help monitoring water bodies in a more consistent manner. Nonetheless the system can also be used with fixed vases or buoys in strategic points.

Relationship between damage clustering and mortality in systemic lupus erythematosus in early and late stages of the disease: cluster analyses in a large cohort from the Spanish Society of Rheumatology Lupus Registry.

OBJECTIVES: To identify patterns (clusters) of damage manifestations within a large cohort of SLE patients and evaluate the potential association of these clusters with a higher risk of mortality. METHODS: This is a multicentre, descriptive, cross-sectional study of a cohort of 3656 SLE patients from the Spanish Society of Rheumatology Lupus Registry. Organ damage was ascertained using the Systemic Lupus International Collaborating Clinics Damage Index. Using cluster analysis, groups of patients with similar patterns of damage manifestations were identified. Then, overall clusters were compared as well as the subgroup of patients within every cluster with disease duration shorter than 5 years. RESULTS: Three damage clusters were identified. Cluster 1 (80.6% of patients) presented a lower amount of individuals with damage (23.2 vs 100% in clusters 2 and 3, P < 0.001). Cluster 2 (11.4% of patients) was characterized by musculoskeletal damage in all patients. Cluster 3 (8.0% of patients) was the only group with cardiovascular damage, and this was present in all patients. The overall mortality rate of patients in clusters 2 and 3 was higher than that in cluster 1 (P < 0.001 for both comparisons) and in patients with disease duration shorter than 5 years as well. CONCLUSION: In a large cohort of SLE patients, cardiovascular and musculoskeletal damage manifestations were the two dominant forms of damage to sort patients into clinically meaningful clusters. Both in early and late stages of the disease, there was a significant association of these clusters with an increased risk of mortality. Physicians should pay special attention to the early prevention of damage in these two systems.

Methods for testing the Markov condition in the illness-death model: a comparative study.

Markov three-state progressive and illness-death models are often used in biomedicine for describing survival data when an intermediate event of interest may be observed during the follow-up. However, the usual estimators for Markov models (e.g., Aalen-Johansen transition probabilities) may be systematically biased in non-Markovian situations. On the other hand, despite non-Markovian estimators for transition probabilities and related curves are available, including the Markov information in the construction of the estimators allows for variance reduction. Therefore, testing for the Markov condition is a relevant issue in practice. In this paper, we discuss several characterizations of the Markov condition, with special focus on its equivalence with the quasi-independence between left truncation and survival times in standard survival analysis. New methods for testing the Markovianity of an illness-death model are proposed and compared with existing ones by means of an intensive simulation study. We illustrate our findings through the analysis of a data set from stem cell transplant in leukemia. Copyright © 2016 John Wiley & Sons, Ltd.

Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study.

Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov. Several non-Markov estimators have been proposed in the recent literature, and their superiority with respect to the Aalen-Johansen estimator has been proved in situations in which the Markov condition is strongly violated. However, the existing estimators have the drawback of requiring that the support of the censoring distribution contains the support of the lifetime distribution, which is not often the case. In this article, we propose two new methods for estimating the transition probabilities in the progressive illness-death model. Some asymptotic results are derived. The proposed estimators are consistent regardless the Markov condition and the referred assumption about the censoring support. We explore the finite sample behavior of the estimators through simulations. The main conclusion of this piece of research is that the proposed estimators are much more efficient than the existing non-Markov estimators in most cases. An application to a clinical trial on colon cancer is included. Extensions to progressive processes beyond the three-state illness-death model are discussed.