暂无摘要。

The European and global dairy breeding industry has benefited enormously from collaboration and sharing of data. The new era of genomics has disrupted the information flow due to the requirement to protect commercial investments. New trait phenotypes, evaluation models, and breeding goals continue to evolve and will impact the way national and proprietary data are shared and presented to the dairy industry. The global nature of cattle breeding will, however, continue to require some form of collaboration, even under the new ways of working.

Down syndrome is the most frequently occurring genetic condition, with a substantial escalation in risk associated with advanced maternal age. The syndrome is characterized by a diverse range of phenotypes, affecting to some extent all levels of organization, and its progeroid nature - early manifestation of aspects of the senile phenotype. Despite extensive investigations, many aspects and mechanisms of the disease remain unexplored. The current review aims to provide an overview of the main causes and manifestations of Down syndrome, while also examining the phenomenon of accelerated aging and exploring potential therapeutic strategies.

BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention.
METHODS: In this study, we applied k-means and latent profile analysis (LPA) to identify patient phenotypes, using aggregated data from three major DCM trials. The combination of Nurick score, NDI (neck disability index), neck pain, as well as motor and sensory scores facilitated clustering. Goodness-of-fit indices were used to determine the optimal cluster number. ANOVA and post hoc Tukey\'s test assessed outcome differences, while multinomial logistic regression identified significant predictors of group membership.
RESULTS: A total of 1047 patients with DCM (mean [SD] age: 56.80 [11.39] years, 411 [39%] females) had complete one year outcome assessment post-surgery. Latent profile analysis identified four DCM phenotypes: \"severe multimodal impairment\" (n = 286), \"minimal impairment\" (n = 116), \"motor-dominant\" (n = 88) and \"pain-dominant\" (n = 557) groups. Each phenotype exhibited a unique symptom profile and distinct functional recovery trajectories. The \"severe multimodal impairment group\", comprising frail elderly patients, demonstrated the worst overall outcomes at one year (SF-36 PCS mean [SD]: 40.01 [9.75]; SF-36 MCS mean [SD], 46.08 [11.50]) but experienced substantial neurological recovery post-surgery (ΔmJOA mean [SD]: 3.83 [2.98]). Applying the k-means algorithm yielded a similar four-class solution. A higher frailty score and positive smoking status predicted membership in the \"severe multimodal impairment\" group (OR 1.47 [95% CI 1.07-2.02] and 1.58 [95% CI 1.25-1.99, respectively]), while undergoing anterior surgery and a longer symptom duration were associated with the \"pain-dominant\" group (OR 2.0 [95% CI 1.06-3.80] and 3.1 [95% CI 1.38-6.89], respectively).
CONCLUSIONS: Unsupervised learning on multiple clinical metrics predicted distinct patient phenotypes. Symptom clustering offers a valuable framework to identify DCM subpopulations, surpassing single patient reported outcome measures like the mJOA.
BACKGROUND: No funding was received for the present work. The original studies were funded by AO Spine North America.

As veterinary practitioners serve modern dairies and ranches, genetic consultation is an area that many practitioners are implementing to bring value to their clients. As an unbiased professional with intimate knowledge of the herd\'s health and management practices and vision for the future, veterinarians are uniquely positioned to provide this consultative service.

BACKGROUND: In highly multiracial populations with inadequate newborn screening, knowledge of the various phenotypic presentations of Cystic Fibrosis (CF) can help reach an early diagnosis. This study aims to describe phenotypes and genotypes at the time of CF diagnosis in a state in the Northeast Region of Brazil.
METHODS: Retrospective cross-sectional study. Clinical data were extracted from the medical records of CF patients. Clinical, laboratory, and genotypic characteristics were described for patients admitted to a tertiary referral center between 2007 and 2021.
RESULTS: Fifty-eight (58) patients were included in the study, 53.5% of whom were diagnosed through clinical suspicion. The median age at diagnosis was 4.7 months (IQR: 1.5-14.8 months). Five patients had false-negative results in the newborn screening. Faltering growth was the most frequent clinical manifestation. Bronchiectasis and a history of pneumonia predominated in those older than ten, while thinness, underweight, and electrolyte imbalances were more frequent in children under two. Sequencing of the CFTR gene identified 27 genotypes, with at least one class I-III variant in all patients, and nine variants that are rare, previously undescribed, or have uncertain significance (619delA, T12991, K162Q, 3195del6, 1678del > T, 124del123bp, 3121-3113 A > T). The most frequent alleles were p.Phe508del, p.Gly542*, p.Arg334Trp, and p.Ser549Arg.
CONCLUSIONS: Malnutrition and electrolyte imbalances were the most frequent phenotypes for children < 2 years and were associated with genotypes including 2 class I-III variants. Rare and previously undescribed variants were identified. The p.Gly542*, p.Arg334Trp, and p.Ser549Arg alleles were among the most frequent variants in this population.

Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future. Here, we present a novel genome-wide survival analysis and use the emergence of drug resistance in Mycobacterium tuberculosis as an example to demonstrate the potential and utility of the technique.

BACKGROUND: Right ventricular (RV) imaging has not a definite role in risk stratification of pulmonary arterial hypertension (PAH) patients. We tested the hypothesis that echocardiography-derived phenotypes, depicting different degrees of RV remodeling and dysfunction, may provide additional prognostic information to current risk stratification tools.
METHODS: Consecutive incident PAH patients aged ≥18 years, diagnosed between January 2005 and December 2021, underwent clinical assessment, right heart catheterization, standard echocardiography. Simple echocardiographic variables were combined in order to define a priori four phenotypes representing different degrees of RV dilatation and RV-pulmonary arterial (PA) coupling: Phenotype 1 with mildy dilated right ventricle and preserved RV-PA coupling (n = 152 patients); phenotype 2 with mildly dilated right ventricle and poor RV-PA coupling (n = 143 patients); phenotype 3 with severely dilated right ventricle and preserved RV-PA coupling (n = 201 patients); phenotype 4 with severely dilated right ventricle and poor RV-PA coupling, with or without severe tricuspid regurgitation (n = 519 patients). Risk stratification was based on the European Society of Cardiology/European Respiratory Society (ESC/ERS) 3-strata model and Registry to Evaluate Early and Long-Term PAH disease Management (REVEAL) 2.0 score.
RESULTS: These phenotypes were present in all risk groups. Notably, regardless of the ESC/ERS risk stratum assigned to the patient, phenotype 4 was associated with a 2-fold increase of the odds of death (HR 2.1, 95% CI 1.6-2.8, p < 0.001), while phenotype 1 was associated with a 71% reduction in the odds of dying (HR 0.29, 95% CI 0.18-0.47, p < 0.001).
CONCLUSIONS: Echocardiography-derived phenotypes describing RV remodeling and dysfunction may provide prognostic information which is independent of and additional to the clinically defined risk in incident PAH patients.

Cardiogenic shock (CS) is a heterogeneous syndrome reflecting a broad spectrum of shock severity, diverse etiologies, variable cardiac function, different hemodynamic trajectories, and concomitant organ dysfunction. These factors influence the clinical presentation, management, response to therapy, and outcomes of CS patients, necessitating a tailored approach to care. To better understand the variability inherent to CS populations, recent algorithms for staging the severity of CS have been described and validated. This paper is part 1 of a 2-part state-of-the-art review. In this first article, we consider the context for clinical staging and stratification in CS with a focus on established severity staging systems for CS and their use for risk stratification and clinical care. We describe the use of staging for predicting outcomes in populations with or at risk for CS, including risk modifiers that provide more nuanced risk stratification, and highlight how these approaches may allow individualized care.

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.