Taste dysfunction in patients undergoing hematopoietic stem cell transplantation: Clinical evaluation in children

The aim of this study was to determine the variability of TD in children undergoing HSCT. Cases were identified as consecutively enrolled children in the period January 2011-January 2013 among patients attending the Paediatric Department of Spedali Civili of Brescia and all candidates to HSCT. The TST was conducted in two phases: identification of threshold values and identification of perceived stimulus intensity. Sixteen sapid solutions with four flavors (sucrose, sodium chloride, citric acid, and quinine hydrochloride) at four different concentrations were administered in a random sequence. The same protocol was administered at different time intervals: before starting the conditioning therapy (T0), during the conditioning therapy (T1) (two times), and every three months (two times) after engraftment post-HSCT (T2). A p-value < 0.05 was considered statistically significant. Fifty-one children (29 female and 22 male, mean age 5.2 ± 0.7 yr) were enrolled. Threshold value means for the four flavors increased during HSCT conditioning therapy (T1) (p < 0.01); intensity of perceived stimulus decreased during HSCT conditioning therapy (p < 0.01). At six months after engraftment (T2), both parameters had returned to starting values (T0). Changes in taste perception in children undergoing HSCT seem to occur especially during the conditioning therapy and resolve in about six months after engraftment post-HSCT

A Novel t(8;14)(q24;q11) Rearranged Human Cell Line as a Model for Mechanistic and Drug Discovery Studies of NOTCH1-Independent Human T-Cell Leukemia

MYC-translocated T-lineage acute lymphoblastic leukemia (T-ALL) is a rare subgroup of T-ALL associated with CDKN2A/B deletions, PTEN inactivation, and absence of NOTCH1 or FBXW7 mutations. This subtype of T-ALL has been associated with induction failure and aggressive disease. Identification of drug targets and mechanistic insights for this disease are still limited. Here, we established a human NOTCH1-independent MYC-translocated T-ALL cell line that maintains the genetic and phenotypic characteristics of the parental leukemic clone at diagnosis. The University of Padua T-cell acute lymphoblastic leukemia 13 (UP-ALL13) cell line has all the main features of the above described MYC-translocated T-ALL. Interestingly, UP-ALL13 was found to harbor a heterozygous R882H DNMT3A mutation typically found in myeloid leukemia. Chromatin immunoprecipitation coupled with high-throughput sequencing for histone H3 lysine 27 (H3K27) acetylation revealed numerous putative super-enhancers near key transcription factors, including MYC, MYB, and LEF1. Marked cytotoxicity was found following bromodomain-containing protein 4 (BRD4) inhibition with AZD5153, suggesting a strict dependency of this particular subtype of T-ALL on the activity of super-enhancers. Altogether, this cell line may be a useful model system for dissecting the signaling pathways implicated in NOTCH1-independent T-ALL and for the screening of targeted anti-leukemia agents specific for this T-ALL subgroup

Longevity biotechnology:bridging AI, biomarkers, geroscience and clinical applications for healthy longevity

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)