Assessment of human microbiota stability across longitudinal samples using iteratively growing-partitioned clustering.

Microbiome research is advancing rapidly, and every new study should definitively be based on updated methods, trends and milestones in this field to avoid the wrong interpretation of results. Most human microbiota surveys rely on data captured from snapshots-single data points from subjects-and have permitted uncovering the recognized interindividual variability and major covariates of such microbial communities. Currently, changes in individualized microbiota profiles are under the spotlight to serve as robust predictors of clinical outcomes (e.g. weight loss via dietary interventions) and disease anticipation. Therefore, novel methods are needed to provide robust evaluation of longitudinal series of microbiota data with the aim of assessing intrapersonally short-term to long-term microbiota changes likely linked to health and disease states. Consequently, we developed microbiota STability ASsessment via Iterative cluStering (µSTASIS)-a multifunction R package to evaluate individual-centered microbiota stability. µSTASIS targets the recognized interindividual variability inherent to microbiota data to stress the tight relationships observed among and characteristic of longitudinal samples derived from a single individual via iteratively growing-partitioned clustering. The algorithms and functions implemented in this framework deal properly with the sparse and compositional nature of microbiota data. Moreover, the resulting metric is intuitive and independent of beta diversity distance methods and correlation coefficients, thus estimating stability for each microbiota sample rather than giving nonconsensus magnitudes that are difficult to interpret within and between datasets. Our method is freely available under GPL-3 licensing. We demonstrate its utility by assessing gut microbiota stability from three independent studies published previously with multiple longitudinal series of multivariate data and respective metadata.

Artificial intelligence for dysplasia detection during surveillance colonoscopy in patients with ulcerative colitis: A cross-sectional, non-inferiority, diagnostic test comparison study.

BACKGROUND AND STUDY AIM: High-definition virtual chromoendoscopy, along with targeted biopsies, is recommended for dysplasia surveillance in ulcerative colitis patients at risk for colorectal cancer. Computer-aided detection (CADe) systems aim to improve colonic adenoma detection, however their efficacy in detecting polyps and adenomas in this context remains unclear. This study evaluates the CADe Discovery™ system's effectiveness in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer. PATIENTS AND METHODS: A prospective cross-sectional, non-inferiority, diagnostic test comparison study was conducted on ulcerative colitis patients undergoing colorectal cancer surveillance colonoscopy between January 2021 and April 2021. Patients underwent virtual chromoendoscopy (VCE) with iSCAN 1 and 3 with optical enhancement. One endoscopist, blinded to CADe Discovery™ system results, examined colon sections, while a second endoscopist concurrently reviewed CADe images. Suspicious areas detected by both techniques underwent resection. Proportions of dysplastic lesions and patients with dysplasia detected by VCE or CADe were calculated. RESULTS: Fifty-two patients were included, and 48 lesions analyzed. VCE and CADe each detected 9 cases of dysplasia (21.4% and 20.0%, respectively; p=0.629) in 8 patients and 7 patients (15.4% vs. 13.5%, respectively; p=0.713). Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy for dysplasia detection using VCE or CADe were 90% and 90%, 13% and 5%, 21% and 2%, 83% and 67%, and 29.2% and 22.9%, respectively. CONCLUSIONS: The CADe Discovery™ system shows similar diagnostic performance to VCE with iSCAN in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer.

Low miR200c expression in tumor budding of invasive front predicts worse survival in patients with localized colon cancer and is related to PD-L1 overexpression.

At the histological level, tumor budding in colon cancer is the result of cells undergoing at least partial epithelial-to-mesenchymal transition. The microRNA 200 family is an important epigenetic driver of this process, mainly by downregulating zinc-finger E-box binding homeobox (ZEB) and transforming growth factor beta (TGF-ß) expression. We retrospectively explored the expression of the miR200 family, and ZEB1 and ZEB2, and their relationship with immune resistance mediated through PD-L1 overexpression. For this purpose, we analyzed a series of 125 colon cancer cases and took samples from two different tumor sites: the area of tumor budding at the invasive front and from the tumor center. We found significant ZEB overexpression and a reduction in miR200 in budding areas, a profile compatible with epithelial-to-mesenchymal transition. In multivariate analysis of the cases with localized disease, low miR200c expression in budding areas, but not at the tumor center, was an adverse tumor-specific survival factor (hazard ratio: 0.12; 95% confidence interval: 0.03-0.81; p = 0.02) independent of the clinical stage of the disease. PD-L1 was significantly overexpressed in the budding areas and its levels correlated with a mesenchymal transition profile. These results highlight the importance of including budding areas among the samples used for biomarker evaluation and provides relevant data on the influence of mesenchymal transition in the immune resistance mediated by PD-L1 overexpression.

RpeakChrom: Novel R package for the automated characterization and optimization of column efficiency in high-performance liquid chromatography analysis.

Characterization of chromatographic columns using the traditional van Deemter method is limited by the necessity of calculating extra-column variance, issue particularly relevant when modeling asymmetrical peaks eluted from monolithic columns. A novel R package that implements Parabolic Variance Modified Gaussian approach for accurate peak modeling, van Deemter equation and two alternatives approaches, based on van Deemter, has been developed to calculate the height equivalent to a theoretical plate (HETP). To assess package capabilities conventional packed reverse-phase and monolithic HPLC columns were characterized. Peaks eluted from the monolithic column showed a high value of factor asymmetry due, in part, to the contribution of extra-column factors. Such deviation can be circumvented by the two alternatives approaches implemented in the R-package. Furthermore, increased values of eddy diffusion and mass transfer kinetics terms in HETP were observed for the packed column, while accuracy was below 9% in all cases. These results showed the usefulness of the R-package for both modeling chromatographic peaks and assessing column efficiency. The RpeakChrom package could become a helpful tool for testing new stationary phases during column development and to evaluate column during its lifetime. This R tool is freely available from CRAN (https://CRAN.R-project.org/package=RpeakChrom).

Optimizing strategies for meningococcal C disease vaccination in Valencia (Spain).

BACKGROUND: Meningococcal C (MenC) conjugate vaccines have controlled invasive diseases associated with this serogroup in countries where they are included in National Immunization Programs and also in an extensive catch-up program involving subjects up to 20 years of age. Catch-up was important, not only because it prevented disease in adolescents and young adults at risk, but also because it decreased transmission of the bacteria, since it was in this age group where the organism was circulating. Our objective is to develop a new vaccination schedule to achieve maximum seroprotection in these groups. METHODS: A recent study has provided detailed age-structured information on the seroprotection levels against MenC in Valencia (Spain), where vaccination is routinely scheduled at 2 months and 6 months, with a booster dose at 18 months of age. A complementary catch-up campaign was also carried out in n for children from 12 months to 19 years of age. Statistical analyses of these data have provided an accurate picture on the evolution of seroprotection in the last few years. RESULTS: An agent-based model has been developed to study the future evolution of the seroprotection histogram. We have shown that the optimum strategy for achieving high protection levels in all infants, toddlers and adolescents is a change to a 2 months, 12 months and 12 years of age vaccination pattern. If the new schedule were implemented in January 2014, high-risk subjects between 15-19 years of age would have very low seroprotection for the next 6 years, thereby threatening the program. CONCLUSIONS: High protection levels and a low incidence of meningococcal C disease can be achieved in the future by means of a cost-free change in vaccination program. However, we recommend a new catch-up program simultaneous to the change in regular vaccination program.

A Bayesian SIRS model for the analysis of respiratory syncytial virus in the region of Valencia, Spain.

We present a Bayesian stochastic susceptible-infected-recovered-susceptible (SIRS) model in discrete time to understand respiratory syncytial virus dynamics in the region of Valencia, Spain. A SIRS model based on ordinary differential equations has also been proposed to describe RSV dynamics in the region of Valencia. However, this continuous-time deterministic model is not suitable when the initial number of infected individuals is small. Stochastic epidemic models based on a probability of disease transmission provide a more natural description of the spread of infectious diseases. In addition, by allowing the transmission rate to vary stochastically over time, the proposed model provides an improved description of RSV dynamics. The Bayesian analysis of the model allows us to calculate both the posterior distribution of the model parameters and the posterior predictive distribution, which facilitates the computation of point forecasts and prediction intervals for future observations.

Potential impact of nirsevimab and bivalent maternal vaccine against RSV bronchiolitis in infants: A population-based modelling study.

BACKGROUND: A new monoclonal antibody (nirsevimab; Beyfortus®) and a bivalent prefusion RSV vaccine (Abrysvo®) for maternal immunization have been approved recently. This is a modelling study to estimate the potential impact of different immunization programs with these products on RSV-bronchiolitis. METHODS: Population-based real-world data from primary care and hospitalizations were considered. RSV bronchiolitis dynamics in absence of these immunization scenarios were explained by a multivariate age-structured Bayesian model. Then, the potential impact was simulated under different assumptions including the most recent clinical trial data. Differences in endpoints, populations, and timeframes between trials make the two products' efficacy difficult to compare. RESULTS: A seasonal with catch-up program, assuming a constant effectiveness of 79.5 % during the first 5 months followed by a linear decay to 0 by month 10 with nirsevimab, would prevent between 5121 and 8846 RSV bronchiolitis per 100,000 infants-years. Assuming 77.3 % effectiveness with the same decay, between 976 and 1686 RSV-hospitalizations per 100,000 infants-years could be prevented depending on the uptake. A year-round maternal immunization program, with 51 % of effectiveness during the first 6 months followed by a linear decay to 0 by month 10 would prevent between 3246 and 5606 RSV bronchiolitis cases per 100,000 infants-years. Assuming 56.9 % effectiveness with the same decay, between 713 and 1231 RSV-hospitalizations per 100,000 infants-years could be prevented. CONCLUSIONS: Our results suggest that each strategy would effectively reduce RSV-bronchiolitis.

Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment.

Gut microbiota plays a key role in host health under normal conditions. However, bacterial composition can be altered by external factors such as antibiotic (AB) intake. While there are many descriptive publications about the effects of AB on gut microbiota composition after treatment, the dynamics and interactions among the bacterial taxa are still poorly understood. In this work, we performed a longitudinal study of gut microbiome dynamics in B. germanica treated with kanamycin. The AB was supplied in three separate periods, giving the microbiota time to recover between each antibiotic intake. We applied two new statistical models, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa and how some groups affect a single taxon. The first model provides information on the importance of a given genus, and the rest of the community, to define the abundance of that genus. The second model, on the other hand, provides details about the relationship between groups of bacteria, focusing on which community groups affect the taxa. These models help us to identify which bacteria are community-dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic. In addition, these models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions, suggesting that when the environment is more hostile (as it is under antibiotic treatment), the whole community tends to work together.

Effectiveness of a videoconference training course on implementing pharmacy services.

OBJECTIVE: The aim of this research was to assess the effects of a series of four training courses comprised of 13 synchronous videoconferences on the implementation of cognitive services in Spanish community pharmacies. Setting A phone survey to continuing training course attendants. METHODS: A random sample of 225 pharmacists registered in a 2004 videoconference course was selected. The phone-survey questionnaire included quality perception elements rated on a 5-point Likert scale, and a series of questions used to identify position in the Rogers 5-step innovation-decision model. An algorithm was used to translate the questions into Rogers' categories. To discover determinants of attendants position in these categories, bivariate analysis, simple correspondence analysis, and logistic regressions were performed. MAIN OUTCOME MEASURE: Position in Rogers' diffusion of innovation steps regarding the adoption of pharmacotherapy follow-up. RESULTS: The perception of the course quality rated between good and very good for the majority of respondents. A significant association between having attended two or more of these four courses and the Persuasion/Decision step in Rogers's model appeared. No association was found between course attendance and the Implementation/Confirmation step of patient follow-up. Fifty percent of those who indicated they implemented the service reported following-up with less than 10 patients, and only 25% reported following up with more than 20 patients. CONCLUSIONS: Although participation in these courses was associated with higher steps in Rogers' model, significant association appeared only with Persuasion/Decision steps and not with the Implementation/Confirmation step, reflecting an attitude but not a performance change.

Preoperative inspiratory muscular training to prevent postoperative hypoxemia in morbidly obese patients undergoing laparoscopic bariatric surgery. A randomized clinical trial.

BACKGROUND: Morbidly obese patients show an increased risk of postoperative hypoxemia and pulmonary complications when compared with normal weight subjects. The purpose of this study was to determine the effects of preoperative inspiratory muscular training (IMT) on postoperative arterial oxygenation in morbidly obese patients submitted to laparoscopic bariatric surgery. METHODS: Forty-four morbidly obese patients were randomly assigned to receive either preoperative usual care (control group, n = 21) or preoperative IMT (trained group, n = 23) for a month prior to the date of surgery. Data on oxygenation (PaO2/FiO2 ratio) were obtained at 1 h and at 12 h after surgery (PACU 1 h and PACU 12 h, respectively). Data on maximum static inspiratory pressure (MIP) were obtained before and after the training period, and at postanesthesia care unit (PACU) 12-h time point. RESULTS: PaO2/FiO2 was significantly higher in the trained group than in the control group, both at PACU 1 h (305.2 ± 77.6 vs. 248.8 ± 53.8, P = 0.008) and at PACU 12 h (333.5 ± 59.6 vs. 289.7 ± 79.6, P = 0.044). As a consequence, the percentage of patients with relative hypoxemia (PaO2/FiO2 lower than 300 mmHg) at the time of PACU discharge was higher in the control group (57 vs. 17 %, P: 0.006). MIP was significantly higher in the trained group compared with the control group at the preoperative time point (89.87 ± 19.00 vs. 77.00 ± 21.20 cm H2O, P = 0.04). CONCLUSIONS: Preoperative IMT improved postoperative oxygenation and increased inspiratory muscular strength in morbidly obese patients submitted to laparoscopic bariatric surgery.

Impact of pharmacists' participation in a pharmacotherapy follow-up program.

OBJECTIVE: To evaluate the impact of a continuing pharmacy education (CPE) course on Spanish community pharmacists' participation in a pharmacotherapy follow-up program. DESIGN: Participation in a CPE course offered 4 times over a 4-year period via satellite teleconferencing was monitored and the data analyzed to determine the course's impact on community pharmacists' participation in a pharmacotherapy follow-up program. ASSESSMENT: Community pharmacists' participation in the pharmaceutical care CPE course had a slightly positive impact on their participation in the pharmacotherapy follow-up program. In the best profiles, there was a probability of 7.3% that participants would participate in the pharmacotherapy follow-up program. CONCLUSIONS: Completion of pharmaceutical care CPE courses did not have a significant impact on pharmacists' participation in a pharmacotherapy follow-up program.

Analysing the Spanish smoke-free legislation of 2006: a new method to quantify its impact using a dynamic model.

BACKGROUND: There are many models that study aspects of smoking habits: the influence of price, tax, relapse time, and the effects of prohibition. There are also studies examining the effects of the Spanish smoke-free law. We wanted to build a model able to separate the effect of the law from the pre-law evolution of smoking habits. METHODS: Using data from the Spanish Ministry of Health and Social Policy, we developed a dynamic model of tobacco use. The model projects the evolution over time of the number of non-smokers, smokers and ex-smokers before 2006. Then, we compared the predictions of the model with data for the years after the law came into force, 2006 and 2009. RESULTS: We show that smoke-free law has had a significant impact on different sub-populations. The number of ex-smokers increased significantly in 2006 and this increase was maintained in 2009. The number of smokers also decreased significantly in 2006, but in 2009 this returned to its value before the law. Simultaneously, the number of non-smokers decreased in 2009. CONCLUSIONS: When the law came into force (2006), its restriction on smoking in public and work places made many smokers decide to give up smoking, decreasing the number of smokers and increasing the number of ex-smokers. In 2009, the majority of those who succeeded in giving up smoking did not return to the habit. However, the smoke-free law had no effect on new smokers and the number of smokers returned to previous levels, whereas the number of non-smokers decreased. Therefore, we can conclude that the law had a very positive effect in the first few years but this has dissipated over time, with the exception of ex-smokers, whose number is still higher than before the law.

epiModel: a system to build automatically systems of differential equations of compartmental type-epidemiological models.

In this paper we describe epiModel, a code developed in Mathematica that facilitates the building of systems of differential equations corresponding to type-epidemiological linear or quadratic models whose characteristics are defined in text files following an easy syntax. It includes the possibility of obtaining the equations of models involving age and/or sex groups.