Defining priorities in the transition from paediatric to adult healthcare for rare bone disease patients: a dialogic approach.

The Italian patient association for Multiple Osteochondromas, Ollier Disease, and Maffucci Syndrome, Associazione Conto Alla Rovescia-ACAR Aps, conducted a mixed-methods study at its 2023 annual conference. The study included the Open Dialogue Approach and a feedback survey to identify the main priorities in the transitioning process from paediatric to adult healthcare for patients with Multiple Osteochondromas, Ollier Disease, and Maffucci Syndrome. The common needs identified by patients, families, caregivers, and healthcare professionals were coordination and continuity of care, patient empowerment and communication, social and practical support, and transition planning and support. This experience fostered a sense of collaboration and cooperation among stakeholders, helping to build trust and create a shared vision for improving the quality of care for these patients. Furthermore, it could be considered a starting point for other patient associations interested in using different approaches to identify the needs of their members and actively involve all stakeholders.

Trends in hospital acquired New Delhi metallo-beta-lactamase-producing Enterobacterales in Tuscany (Italy) from 2019 to 2021: impact of the COVID-19 pandemic.

OBJECTIVES: In Tuscany, Italy, New Delhi metallo-beta-lactamase-producing carbapenem-resistant Enterobacterales (NDM-CRE) in hospitalized patients has increasingly been observed since 2018, leading in 2019 to the implementation of enhanced control measures successfully reducing transmission. We describe the NDM-CRE epidemiology during the COVID-19 pandemic in Tuscany. METHODS: Data on NDM-CRE patients hospitalized in five Tuscan hospitals were collected from January 2019 to December 2021. Weekly rates of NDM-CRE cases on hospital days in medical and critical-care wards were calculated. In March-December 2020, NDM-CRE rates were stratified by COVID-19 diagnosis. Multi-variate regression analysis was performed to assess outcomes' differences among two periods analysed and between COVID-19 populations. RESULTS: Since March 2020, an increase in NDM-CRE cases was observed, associated with COVID-19 admissions. COVID-19 patients differed significantly from non-COVID-19 ones by several variables, including patient features (age, Charlson index) and clinical history and outcomes (NDM-CRE infection/colonization, intensive care unit stay, length of stay, mortality). During the pandemic, we observed a higher rate of NDM-CRE cases per hospital day in both non-COVID-19 patients (273/100,000) and COVID-19 patients (370/100,00) when compared with pre-pandemic period cases (187/100,00). CONCLUSIONS: Our data suggest a resurgence in NDM-CRE spread among hospitalized patients in Tuscany during the COVID-19 pandemic, as well as a change in patients' case-mix. The observed increase in hospital transmission of NDM-CRE could be related to changes in infection prevention and control procedures, aimed mainly at COVID-19 management, leading to new challenges in hospital preparedness and crisis management planning.

Are evaluations on young genotyped animals benefiting from the past generations?

Data sets of US Holsteins, Israeli Holsteins, and pigs from PIC (a Genus company, Hendersonville, TN) were used to evaluate the effect of different numbers of generations on ability to predict genomic breeding values of young genotyped animals. The influence of including only 2 generations of ancestors (A2) or all ancestors (Af) was also investigated. A total of 34,506 US Holsteins, 1,305 Israeli Holsteins, and 5,236 pigs were genotyped. The evaluations were computed by traditional BLUP and single-step genomic BLUP, and computing performance was assessed for the latter method. For the 2 Holstein data sets, coefficients of determination (R(2)) and regression (δ) of deregressed evaluations from a full data set with records up to 2011 on estimated breeding values and genomic estimated breeding values from the truncated data sets were computed. The thresholds for data deletion were set by intervals of 5 yr, based on the average generation interval in dairy cattle. For the PIC data set, correlations between corrected phenotypes and estimated or genomic estimated breeding values were used to evaluate predictive ability on young animals born in 2010 and 2011. The reduced data set contained data up to 2009, and the thresholds were set based on an average generation interval of 3 yr. The number of generations that could be deleted without a reduction in accuracy depended on data structure and trait. For US Holsteins, removing 3 and 4 generations of data did not reduce accuracy of evaluations for final score in Af and A2 scenarios, respectively. For Israeli Holsteins, the accuracies for milk, fat, and protein yields were the highest when only phenotypes recorded in 2000 and later were included and full pedigrees were applied. Of the 135 Israeli bulls with genotypes (validation set) and daughter records only in the complete data set, 38 and 97 were sons of Israeli and foreign bulls, respectively. Although more phenotypic data increased the prediction accuracy for sons of Israeli bulls, the reverse was true for sons of foreign bulls. Also, more phenotypic data caused large inflation of genomic estimated breeding values for sons of foreign bulls, whereas the opposite was true with the deletion of all but the most recent phenotypic data. Results for protein and fat percentage were different from those for milk, fat, and protein yields; however, relatively, the changes in coefficients of determination and regression were smaller for percentage traits. For PIC data set, removing data from up to 5 generations did not erode predictive ability for genotyped animals for the 2 reproductive traits used in validation. Given the data used in this study, truncating old data reduces computation requirements but does not decrease the accuracy. For small populations that include local and imported animals, truncation may be beneficial for one group of animals and detrimental to another group.

Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield.

Predictive ability of yet-to-be observed litter size (pig) grain yield (wheat) records of several reproducing kernel Hilbert spaces (RKHS) regression models combining different number of Gaussian or t kernels was evaluated. Predictive performance was assessed as the average (over 50 replicates) predictive correlation in the testing set. Predictions from these models were combined using three different types of model averaging: (i) mean of predicted phenotypes obtained in each model, (ii) weighted average using mean squared error as weight or (iii) using the marginal likelihood as weight. (ii) and (iii) were obtained in a validation set with 5% of the data. Phenotypes consisted of 2598, 1604 and 1879 average litter size records from three commercial pig lines and wheat grain yield of 599 lines evaluated in four macro-environments. SNPs from the PorcineSNP60 BeadChip and 1447 DArT markers were used as predictors for the pig and wheat data analyses, respectively. Gaussian and univariate t kernels led to same predictive performance. Multikernel RKHS regression models overcame shortcomings of single kernel models (increasing the predictive correlation of RKHS models by 0.05 where 3 Gaussian or t kernels were fitted in the RKHS models simultaneously). None of the proposed averaging strategies improved the predictive correlations attained with single models using multiple kernel fitting.

Genome-enabled methods for predicting litter size in pigs: a comparison.

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r = 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r = 0.31) when G was used in crossbreds, but the worst (r = 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

Estimativas de herdabilidade e tendências genéticas para características de crescimento e reprodutivas em bovinos da raça Nelore / Estimates of heritability and genetic trends for growth and reproduction traits in Nelore cattle

Foram estimados os coeficientes de herdabilidade e a mudança genética para peso à desmama (PD), peso ao sobreano (PS), ganho de peso do nascimento à desmama (GND), ganho de peso da desmama ao sobreano (GDS), perímetro escrotal (PE) e idade ao primeiro parto (IPP) em animais da raça Nelore. Foram utilizados dados de 128.148 animais nascidos entre 1984 e 2006. Os componentes de variância foram estimados pelo método da máxima verossimilhança restrita, e os valores genéticos foram preditos por modelos mistos aplicando-se modelo animal bicaracterística, incluindo peso à desmama em todas as análises. As tendências genéticas foram estimadas pela regressão dos valores genéticos sobre o ano de nascimento dos animais. Os coeficientes de herdabilidade do efeito direto estimados foram de 0,23 (0,07) (PD); 0,24 (0,02) (PS); 0,21 (0,01) (GND); 0,23 (0,01) (GDS); 0,46 (0,02) (PE) e 0,15 (0,01) (IPP). As tendências genéticas diretas estimadas foram de 0,171 (0,01); 0,219 (0,02); 0,186 (0,03) e 0,224 (0,02) kg/ano para PD, PS, GND e GDS, respectivamente, o que representa incrementos de 0,10; 0,08; 0,13 e 0,22 por cento nas médias das mesmas características ao ano, respectivamente. Para o PE e a IPP no período de 1984 a 1995, as tendências genéticas foram nulas, com valores de 0,011 (0,03) cm/ano e -0,003 (0,06) dias/ano, respectivamente. No segundo período considerado (1996 a 2006), as tendências genéticas para PE e IPP foram de 0,069 (0,01) cm/ano e -3,024 (0,04) dias/ano, respectivamente, indicando melhorias consideráveis em tais características. Esses valores sugerem que características produtivas e reprodutivas, quando utilizadas como critério de seleção, proporcionam progresso genético no rebanho, sendo indicadas para seleção de animais da raça Nelore.

The heritability coefficients and genetic trends for weaning weight (WW), post-yearling weight (PW), average gains from birth to weaning (GBW), average gains from weaning to post-yearling (GWP), scrotal circumference (SC), and age at first calving (AFC) were estimated in Nelore cattle. Variance components and heritability coefficients were estimated by restricted maximum likelihood and breeding values were predicted fitting bivariate animal models, always including weaning weight. Data from 128,148 animals born from 1984 to 2006 were used. Genetic trends for each trait were estimated by regression of breeding values on the animal birth year. Heritability estimates for direct effect were 0.23 (0.07) for WW; 0.24 (0.02) for PW; 0.21 (0.01) for GBW; 0.23 (0.01) for GWP; 0.46 (0.02) for SC; and 0.15 (0.01) for AFC. The direct genetic trends were 0.171 (0.01); 0.219 (0.02); 0.186 (0.03); and 0.224 (0.02) kg per year, for WW, PW, GBW, and GWP, respectively, corresponding to increases of 0.10, 0.08, 0.13, and 0.22 percent in the means of the same traits per year, respectively. In the first period (1984 to 1995), the SC and AFC genetic trends were null, with values of 0.011 (0.03) cm/year and -0.003 (0.06) days/year, respectively. Moreover, in the other period (1996 to 2006), the genetic trends of SC and AFC were 0.069 (0.01) cm/year and -3.024 (0.04) days/year, respectively. These values suggest that productive and reproductive traits, when used as selection criteria, result in genetic progress of the herd. Thus, they are indicated for selection of Nelore cattle.

Covariance functions for body weight from birth to maturity in Nellore cows.

The objective of this study was to estimate (co)variance functions using random regression models on Legendre polynomials for the analysis of repeated measures of BW from birth to adult age. A total of 82,064 records from 8,145 females were analyzed. Different models were compared. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered as random covariables. Eight models with polynomials of third to sixth order were used to describe additive direct and maternal effects, and animal and maternal permanent environmental effects. Residual effects were modeled using 1 (i.e., assuming homogeneity of variances across all ages) or 5 age classes. The model with 5 classes was the best to describe the trajectory of residuals along the growth curve. The model including fourth- and sixth-order polynomials for additive direct and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects were the best. Estimates of (co)variance obtained with the multi-trait and random regression models were similar. Direct heritability estimates obtained with the random regression models followed a trend similar to that obtained with the multi-trait model. The largest estimates of maternal heritability were those of BW taken close to 240 d of age. In general, estimates of correlation between BW from birth to 8 yr of age decreased with increasing distance between ages.

A dynamic linear model for genetic analysis of longitudinal traits.

A Bayesian model for quantitative genetic analysis of longitudinal traits is presented. It connects the model known as the Kalman filter (KF) with the standard mixed model of quantitative genetics. The KF model can be implemented easily in a Bayesian framework because, under standard prior assumptions, all fully conditional posterior distributions have closed forms. An analysis of beef cattle growth data including comparisons with a standard multivariate model was performed to assess applicability of the KF to animal breeding. Models were compared using the deviance information criterion and the Bayes factor. Models in which a KF acted on additive genetic and maternal effects were favored by the deviance information criterion, although KF did not describe residual (co)variance adequately. The Bayes factor did not provide conclusive evidence in favor of a specific model. Fitting KF to longitudinal traits provides estimates of genetic value for a whole range of time points, assuming that there are genetic differences through time between and within individuals. Different models embedding the KF in a mixed model were demonstrated to provide a more parsimonious (co)variance structure than a standard multitrait specification for the quantitative genetic analysis of longitudinal data.

Predictive ability and covariance parameters of dynamic linear models for analysis of longitudinal traits.

A set of analyses using a multiple-trait model (model 1) and dynamic models for the evaluation of beef cattle growth is presented. All models contained additive direct and maternal environmental effects, as well as contemporary groups as nuisance parameters. The predictive ability of models at different parts of the growth trajectory was compared. Body weight records of 6,856 Nelore animals taken at 6 different ages (birth to 540 d) were used. Different models embedding a Kalman filter (KF) into a mixed model representation were fitted. Model 2 assumed that additive, maternal, and residual effects changed over time according to a linear autoregressive process. Model 3 was similar to model 2, but all regression coefficients were set to 1. In model 4, KF was applied only to direct genetic and maternal environmental effects. A leave-one-out cross-validation check was used to assess the predictive ability of models. Estimates of additive variance were similar in the analysis with models 1, 3, and 4 for all ages. Posterior means of maternal components increased slightly after birth and decreased after 135 d of age. Posterior means of additive rates of change were close to 1 at almost all time points, irrespective of the model. The posterior means of residual rates of change, which varied from 0.096 to 0.529, did not support the restrictions that regression coefficients were equal to 1 imposed by model 3. Estimates of additive and maternal correlations obtained with dynamic models were larger than those from a multivariate model. Model 3 produced different phenotypic correlations. Models 2 and 4 had better predictive ability than the multivariate specification. Model 3 predicted the data very poorly, and errors increased markedly with age. The KF can be a useful tool for structuring (co)variance matrices without reducing dimensionality. This model provided accurate predictions and plausible estimates of (co)variance components. Moreover, KF is a flexible specification, because a multivariate structure can be used for some random effects, whereas a dynamic feature can be incorporated for others.

Comparison of different nonlinear functions to describe Nelore cattle growth.

This work aims to compare different nonlinear functions for describing the growth curves of Nelore females. The growth curve parameters, their (co)variance components, and environmental and genetic effects were estimated jointly through a Bayesian hierarchical model. In the first stage of the hierarchy, 4 nonlinear functions were compared: Brody, Von Bertalanffy, Gompertz, and logistic. The analyses were carried out using 3 different data sets to check goodness of fit while having animals with few records. Three different assumptions about SD of fitting errors were considered: constancy throughout the trajectory, linear increasing until 3 yr of age and constancy thereafter, and variation following the nonlinear function applied in the first stage of the hierarchy. Comparisons of the overall goodness of fit were based on Akaike information criterion, the Bayesian information criterion, and the deviance information criterion. Goodness of fit at different points of the growth curve was compared applying the Gelfand's check function. The posterior means of adult BW ranged from 531.78 to 586.89 kg. Greater estimates of adult BW were observed when the fitting error variance was considered constant along the trajectory. The models were not suitable to describe the SD of fitting errors at the beginning of the growth curve. All functions provided less accurate predictions at the beginning of growth, and predictions were more accurate after 48 mo of age. The prediction of adult BW using nonlinear functions can be accurate when growth curve parameters and their (co)variance components are estimated jointly. The hierarchical model used in the present study can be applied to the prediction of mature BW in herds in which a portion of the animals are culled before adult age. Gompertz, Von Bertalanffy, and Brody functions were adequate to establish mean growth patterns and to predict the adult BW of Nelore females. The Brody model was more accurate in predicting the birth weight of these animals and presented the best overall goodness of fit.

Analysis of beef cattle longitudinal data applying a nonlinear model.

The objective of this work was to evaluate the Nelore beef cattle, growth curve parameters using the Von Bertalanffy function in a nested Bayesian procedure that allowed estimation of the joint posterior distribution of growth curve parameters, their (co)variance components, and the environmental and additive genetic components affecting them. A hierarchical model was applied; each individual had a growth trajectory described by the nonlinear function, and each parameter of this function was considered to be affected by genetic and environmental effects that were described by an animal model. Random samples of the posterior distributions were drawn using Gibbs sampling and Metropolis-Hastings algorithms. The data set consisted of a total of 145,961 BW recorded from 15,386 animals. Even though the curve parameters were estimated for animals with few records, given that the information from related animals and the structure of systematic effects were considered in the curve fitting, all mature BW predicted were suitable. A large additive genetic variance for mature BW was observed. The parameter a of growth curves, which represents asymptotic adult BW, could be used as a selection criterion to control increases in adult BW when selecting for growth rate. The effect of maternal environment on growth was carried through to maturity and should be considered when evaluating adult BW. Other growth curve parameters showed small additive genetic and maternal effects. Mature BW and parameter k, related to the slope of the curve, presented a large, positive genetic correlation. The results indicated that selection for growth rate would increase adult BW without substantially changing the shape of the growth curve. Selection to change the slope of the growth curve without modifying adult BW would be inefficient because their genetic correlation is large. However, adult BW could be considered in a selection index with its corresponding economic weight to improve the overall efficiency of beef cattle production.

Estimates of genetic correlations between days to calving and reproductive and weight traits in Nelore cattle.

Data comprising 53,181 calving records were analyzed to estimate the genetic correlation between days to calving (DC), and days to first calving (DFC), and the following traits: scrotal circumference (SC), age at first calving (AFC), and weight adjusted for 550 d of age (W550) in a Nelore herd. (Co)variance components were estimated using the REML method fitting bivariate animal models. The fixed effects considered for DC were contemporary group, month of last calving, and age at breeding season (linear and quadratic effects). Contemporary groups were composed by herd, year, season, and management group at birth; herd and management group at weaning; herd, season, and management group at mating; and sex of calf and mating type (multiple sires, single sire, or AI). In DFC analysis, the same fixed effects were considered excluding the month of last calving. For DC, a repeatability animal model was applied. Noncalvers were not considered in analyses because an attempt to include them, attributing a penalty, did not improve the identification of genetic differences between animals. Heritability estimates ranged from 0.04 to 0.06 for DC, from 0.06 to 0.13 for DFC, from 0.42 to 0.44 for SC, from 0.06 to 0.08 for AFC, and was 0.30 for W550. The genetic correlation estimated between DC and SC was low and negative (-0.10), between DC and AFC was high and positive (0.76), and between DC and W550 was almost null (0.07). Similar results were found for genetic correlation estimates between DFC and SC (-0.14), AFC (0.94), and W550 (-0.02). The genetic correlation estimates indicate that the use of DC in the selection of beef cattle may promote favorable correlated responses to age at first mating and, consequently, higher gains in sexual precocity can be expected.

Linkage of autosomal dominant radial drusen (malattia leventinese) to chromosome 2p16-21.

OBJECTIVE: To identify the chromosomal location of the gene involved in the pathogenesis of autosomal dominant radial drusen (malattia leventinese). PATIENTS: Eighty-six members of four families affected with radial drusen; one family of American origin and three families of Swiss origin. METHODS: Family members were clinically examined for the presence of radial drusen. Affected patients and potentially informative spouses were genotyped with short tandem repeat polymorphisms distributed across the autosomal genome. The clinical and genotypic data were subjected to linkage analysis. RESULTS: Fifty-six patients were found to be clinically affected. Significant linkage was observed between the disease phenotype and markers known to lie on the short arm of chromosome 2. The maximum two-point lod score (Zmax) observed for all four families combined was 10.5 and was obtained with marker D2S378. Multipoint analysis yielded a Zmax of 12, centered on marker D2S378. The lod-1 confidence interval was 8 cM, while the disease interval defined by observed recombinants was 14 cM. CONCLUSIONS: The gene responsible for autosomal dominant radial drusen has been mapped to the short arm of chromosome 2. This is an important step toward actually isolating the disease-causing gene. In addition, this information can be used to evaluate other familial drusen phenotypes such as Doyne's macular dystrophy for a possible allelic relationship.

[Hormonal, beta-endorphin and renin activity changes in man during partial immersion, as a therapeutic method, in water at 38 degrees C]. / Modificazioni ormonali, della beta-endorfina e dell'attività reninica nell'uomo in corso di immersione parziale, con metodo terapeutico, in acqua a 38 gradi C.

This research was carried out to define the effects on men of head-out water immersion in a bath at 38.41 +/- 0.04 degrees C (mean +/- S.E.) with a method similar to that used for therapeutical rehabilitation and time of immersion of 30 minutes. Beta-endorphin, renin activity, aldosterone, cortisol, HGH, FSH, LH, TSH, T3, T4 and prolactin haematic levels were analysed. Seventeen healthy subjects (fourteen males and three females), aged 21-65 years (mean age 29.8 +/- 2.6) were studied. Water immersion caused a decrease in FSH and LH haematic concentrations; no significant changes occurred in beta-endorphin, renin activity, aldosterone, prolactin, cortisol, HGH, TSH, T3, T4 and FTI values. Thirty minutes after the end of immersion, FSH and LH levels returned to pre-immersion values. The probable pathogenesis of these observations is suggested.

Usefulness of the TRH test in the management of patients with differentiated thyroid cancer.

Thirty patients thyroidectomized for differentiated thyroid cancer were studied. Serum TSH was assayed in basal conditions and after TRH stimulation, while patients were in suppressive therapy with thyroid hormones. The basal TSH was normal in all the patients and less than 2 microU/ml in 20 patients. The TRH test was negative (no TSH response) in 27 patients and in all the cases with the basal TSH lower than 2 microU/ml.

Lipomatose pelvica. Correlacao RX, ultra-som e C.T. / Pelvic lypomatosis.Correlation radiography, ultrasonics and X-ray computed tomography

Os autores relatam um caso sobre lipomatose pelvica, salientando a importancia do diagnostico radiologico e tecendo consideracoes sobre os resultados ultrassonograficos e da tomografia computadorizada