Epidemiological and clinical characteristics of adult acute lymphoblastic leukemia patients in Chile: A single-center analysis.

Background: Acute lymphoblastic leukemia represents 20% of acute leukemias in adults. Currently, there is limited data in Chile regarding the clinical, cytogenetic, and prognostic characteristics of this condition. Methods: This is a retrospective, observational, and descriptive study of 67 patients treated for acute lymphoblastic leukemia at the Arturo Lopez Perez Foundation between 2018 and 2021. The main objective is to evaluate epidemiological and clinical characteristics, as well as identifying factors associated with improved overall survival and/or progression-free survival. Results: 88% of the cases were B-lineage, mainly the common B phenotype. Cytogenetic analysis was performed in less than 50% of the patients, with lower yield than expected according to the literature. Molecular testing was performed in 86.5% of the patients, with the most frequent alteration being BCR-ABL. No study was performed to search for Ph-like abnormalities. The rate of complete response after induction was 83.3%, the majority of patients having negative minimal residual disease. Only 12% of the patients received consolidation with allogenic bone marrow transplant. At 2 years, the overall survival was 69% and the progression-free survival was 59%. Conclusion: The results in terms of overall survival and progression-free survival are similar to those reported in the literature. Important diagnostic gaps prevent adequate prognostic characterization. Allogeneic consolidation transplantation was performed in a lower percentage than expected, highlighting the national deficit in access to this treatment.

[Update on the Diagnosis and Stratification of Patients with Acute Myeloid Leukemia: An Urgent National Need]. / Actualización en el diagnóstico y estratificación de pacientes con Leucemia Mieloide Aguda: una necesidad país imperiosa.

Acute myeloid leukemia is a neoplasm with a high lethality, with alarming results in our country, positioning it as a priority from the point of view of oncological public health. Cytology, immunophenotype, karyogram, and a few translocations/mutations by molecular biology are currently available for diagnosis and stratification. This diagnostic approach is insufficient since it allows classifying less than 50% of patients in a specific group. Therefore, consolidation therapy is selected with little biological information. The role of morphology and cytogenetics is progressively losing prognostic weight with respect to molecular biology, and next-generation sequencing has positioned itself as a key element for diagnosing our patients. In addition, the investigation of germline mutations is acquiring greater relevance, increasing its detection frequency and influencing decision-making regarding treatment and selecting a related donor for an allogeneic transplant. In this review, an update of the integrated diagnosis of patients with acute myeloid leukemia is carried out in light of the new diagnostic (WHO 2022 and ICC 2022), and prognostic classifications (ELN 2022). We propose an algorithm for integrated diagnosis to be considered for its implementation. It is imperative as a country to invest in new diagnostic technologies to improve the prognosis of our patients.

Survival and response deepening after autologous transplantation in patients with multiple myeloma in Chile.

BACKGROUND: Autologous hematopoietic stem cell transplantation (ASCT) is the standard of care in candidate patients with newly diagnosed multiple myeloma. In Chile, its indication has been expanding as have centers dedicated to this type of therapy. Here, we present the results of the first 50 patients from a Chilean reference center. METHODS: This was a retrospective analytical study of 50 patients referred to the Arturo López Pérez Foundation to receive ASCT. Patients newly diagnosed or on subsequent lines of treatment were allowed. As primary objectives, the deepening of response with ASCT and subsequent results on overall survival and progression-free survival were analyzed. RESULTS: Among 50 patients with a median follow-up of 24 months, ASCT managed to deepen responses going from at least very good partial response of 57.4%-82.5% (p = .01); complete response increased from 27.6% to 52.5% (p = .02). In turn, a median progression-free survival (PFS) of 39 months was estimated and the median overall survival was not reached. The most important factor predicting PFS is measurable residual disease. CONCLUSIONS: ASCT is an effective strategy for prolonged progression-free survival and deepening responses. Public-private collaboration is a crucial element in reducing the gaps in access to this type of complex but highly effective therapy.

Alcohol trajectories among biracial black subgroups: Testing the intermediate substance use hypothesis.

OBJECTIVE: This study tests the intermediate biracial substance use hypothesis, which suggests that the prevalence of substance use among biracial individuals fall intermediate to their corresponding mononoracial counterparts. Using National Longitudinal Study of Adolescent and Adult Health (Add Health) data, we examine alcohol-use trajectories of a de-aggregated sample of biracial Black youth and compare them with the trajectories of the corresponding monoracial counterparts. METHOD: The sample consists of 9421 adolescents and young adults who self-identified as 1 of 4 monoracial groups (i.e., Black, White, Hispanic, American Indian) or 1 of 3 biracial Black groups (i.e., Black-American Indian, Black-Hispanic, and Black-White). Study hypotheses are tested using latent growth curve modeling for first use, number of drinks, and binge drinking. RESULTS: We found partial support for the intermediate substance use hypothesis, with the alcohol use rates of biracial Blacks more closely resembling the non-Black corresponding group than the monoracial Black group. Black-American Indians face particularly high risk of problematic drinking. CONCLUSIONS: These findings demonstrate the need for additional research clarifying the onset and maintenance of alcohol use and misuse among biracial individuals and subgroups.

NCutYX: a package for clustering analysis of multilayer omics data.

SUMMARY: Multilayer omics profiling has become a major venue for understanding complex diseases. We develop NCutYX, an R package for clustering analysis of multilayer omics data. The package and methods jointly analyze multiple layers of omics measurements and effectively accommodate their regulations. They systematically conduct a series of analysis based on the normalized cut technique, including the clusterings of subjects and omics measurements and biclustering. The package can be valuable for its timely context, novel methods, and comprehensiveness. AVAILABILITY: https://cran.r-project.org/web/packages/NCutYX/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Controllable electrodeposition of binary metal films from deep eutectic solvent as an efficient and durable catalyst for the oxygen evolution reaction.

In this work, we present an easy and scalable electrodeposition protocol that operates in a deep eutectic solvent, used to prepare self-supported Ni-Fe alloy films directly grown on copper foils. Unlike electrodeposition in aqueous baths, alloy compositions deposited in deep eutectic solvent are found to be the same as in plating solution owing to the enlargement of the deposition window and secondary reaction suppression. By rationally tuning the Ni/Fe ratio in deep eutectic solvent plating solution, the best oxygen evolution reaction performance was achieved by a Ni75Fe25 catalyst, which requires only a 316 mV overpotential to reach a current density of 10 mA cm-2, while its Tafel slope is as low as 62 mV dec-1. This catalyst can operate at 10 mA cm-2 with negligible activity degradation for over 10 h, promising its potential use as a low-cost, high-performance and stable electrocatalyst in water splitting devices.

Overlapping clustering of gene expression data using penalized weighted normalized cut.

Clustering has been widely conducted in the analysis of gene expression data. For complex diseases, it has played an important role in identifying unknown functions of genes, serving as the basis of other analysis, and others. A common limitation of most existing clustering approaches is to assume that genes are separated into disjoint clusters. As genes often have multiple functions and thus can belong to more than one functional cluster, the disjoint clustering results can be unsatisfactory. In addition, due to the small sample sizes of genetic profiling studies and other factors, there may not be sufficient evidence to confirm the specific functions of some genes and cluster them definitively into disjoint clusters. In this study, we develop an effective overlapping clustering approach, which takes account into the multiplicity of gene functions and lack of certainty in practical analysis. A penalized weighted normalized cut (PWNCut) criterion is proposed based on the NCut technique and an L 2 norm constraint. It outperforms multiple competitors in simulation. The analysis of the cancer genome atlas (TCGA) data on breast cancer and cervical cancer leads to biologically sensible findings which differ from those using the alternatives. To facilitate implementation, we develop the function pwncut in the R package NCutYX.

Assisted gene expression-based clustering with AWNCut.

In the research on complex diseases, gene expression (GE) data have been extensively used for clustering samples. The clusters so generated can serve as the basis for disease subtype identification, risk stratification, and many other purposes. With the small sample sizes of genetic profiling studies and noisy nature of GE data, clustering analysis results are often unsatisfactory. In the most recent studies, a prominent trend is to conduct multidimensional profiling, which collects data on GEs and their regulators (copy number alterations, microRNAs, methylation, etc.) on the same subjects. With the regulation relationships, regulators contain important information on the properties of GEs. We develop a novel assisted clustering method, which effectively uses regulator information to improve clustering analysis using GE data. To account for the fact that not all GEs are informative, we propose a weighted strategy, where the weights are determined data-dependently and can discriminate informative GEs from noises. The proposed method is built on the NCut technique and effectively realized using a simulated annealing algorithm. Simulations demonstrate that it can well outperform multiple direct competitors. In the analysis of TCGA cutaneous melanoma and lung adenocarcinoma data, biologically sensible findings different from the alternatives are made.

Goodness-Of-Fit Test for Nonparametric Regression Models: Smoothing Spline ANOVA Models as Example.

Nonparametric regression models do not require the specification of the functional form between the outcome and the covariates. Despite their popularity, the amount of diagnostic statistics, in comparison to their parametric counter-parts, is small. We propose a goodness-of-fit test for nonparametric regression models with linear smoother form. In particular, we apply this testing framework to smoothing spline ANOVA models. The test can consider two sources of lack-of-fit: whether covariates that are not currently in the model need to be included, and whether the current model fits the data well. The proposed method derives estimated residuals from the model. Then, statistical dependence is assessed between the estimated residuals and the covariates using the HSIC. If dependence exists, the model does not capture all the variability in the outcome associated with the covariates, otherwise the model fits the data well. The bootstrap is used to obtain p-values. Application of the method is demonstrated with a neonatal mental development data analysis. We demonstrate correct type I error as well as power performance through simulations.

Clustering multilayer omics data using MuNCut.

BACKGROUND: Omics profiling is now a routine component of biomedical studies. In the analysis of omics data, clustering is an essential step and serves multiple purposes including for example revealing the unknown functionalities of omics units, assisting dimension reduction in outcome model building, and others. In the most recent omics studies, a prominent trend is to conduct multilayer profiling, which collects multiple types of genetic, genomic, epigenetic and other measurements on the same subjects. In the literature, clustering methods tailored to multilayer omics data are still limited. Directly applying the existing clustering methods to multilayer omics data and clustering each layer first and then combing across layers are both "suboptimal" in that they do not accommodate the interconnections within layers and across layers in an informative way. METHODS: In this study, we develop the MuNCut (Multilayer NCut) clustering approach. It is tailored to multilayer omics data and sufficiently accounts for both across- and within-layer connections. It is based on the novel NCut technique and also takes advantages of regularized sparse estimation. It has an intuitive formulation and is computationally very feasible. To facilitate implementation, we develop the function muncut in the R package NcutYX. RESULTS: Under a wide spectrum of simulation settings, it outperforms competitors. The analysis of TCGA (The Cancer Genome Atlas) data on breast cancer and cervical cancer shows that MuNCut generates biologically meaningful results which differ from those using the alternatives. CONCLUSIONS: We propose a more effective clustering analysis of multiple omics data. It provides a new venue for jointly analyzing genetic, genomic, epigenetic and other measurements.

Cigarette-smoking trajectories of monoracial and biracial Blacks: Testing the intermediate hypothesis.

Research on the cigarette-smoking patterns of biracial adolescents and young adults is severely limited. In this study, we tested the intermediate biracial substance-use hypothesis, which suggests that the prevalence of substance use among biracial individuals falls intermediate to their monoracial counterparts. We examined cigarette-smoking trajectories of a de-aggregated sample of biracial Black adolescents and young adults. We used longitudinal data from the National Longitudinal Study of Adolescent and Adult Health (Add Health; Harris et al., 2009). Our sample (N = 9,421) included 4 monoracial groups (Black, White, Hispanic, and American Indian [AI]) and 3 biracial groups (Black-AI, Black-Hispanic, and Black-White). Study hypotheses were tested using latent growth-curve modeling. We found some support for the intermediate biracial substance-use hypothesis for 2 of 3 biracial groups (Black-American Indian, Black-Hispanic) and 2 of 4 cigarette-use outcomes (lifetime cigarette use, number of cigarettes smoked during past month for regular smokers). The cigarette-use trajectories of biracial Blacks were significantly different from only 1 corresponding monoracial group. Black-AIs and Black Hispanics engage in lifetime cigarette use at comparable rates to monoracial Blacks. Black-Hispanic regular smokers' rate of cigarette smoking is comparable to the higher rates of Hispanics and not to the lower rates of Blacks. Knowledge of the origins, developmental course, and consequences of tobacco use among the biracial population may lead to effective intervention programs and policies for this group. (PsycINFO Database Record

Assisted clustering of gene expression data using ANCut.

BACKGROUND: In biomedical research, gene expression profiling studies have been extensively conducted. The analysis of gene expression data has led to a deeper understanding of human genetics as well as practically useful models. Clustering analysis has been a critical component of gene expression data analysis and can reveal the (previously unknown) interconnections among genes. With the high dimensionality of gene expression data, many of the existing clustering methods and results are not as satisfactory. Intuitively, this is caused by "a lack of information". In recent profiling studies, a prominent trend is to collect data on gene expressions as well as their regulators (copy number alteration, microRNA, methylation, etc.) on the same subjects, making it possible to borrow information from other types of omics measurements in gene expression analysis. METHODS: In this study, an ANCut approach is developed, which is built on the regularized estimation and NCut techniques. An effective R code that implements this approach is developed. RESULTS: Simulation shows that the proposed approach outperforms direct competitors. The analysis of TCGA (The Cancer Genome Atlas) data further demonstrates its satisfactory performance. CONCLUSIONS: We propose a more effective clustering analysis of gene expression data, with the assistance of information from regulators. It provides a new venue for analyzing gene expression data based on the assisted analysis strategy.

Similarities and differences in alcohol trajectories: Testing the catch-up effect among biracial black subgroups.

Using National Longitudinal Study of Adolescent and Adult Health (Add Health) data, we examine the alcohol-use trajectories of monoracial Black youth and biracial Black-White, Black-Hispanic, and Black-American Indian youth to assess how their trajectories differ from the alcohol-use trajectories of White youth over time. The sample consists of 9421 adolescents and young adults who self-identified as White, Black, Black-American Indian, Black-Hispanic, or Black-White. Study hypotheses are tested using latent growth curve modeling. Results indicate that a catch-up effect exists, but only for Black-American Indians whose alcohol-use rates approach the higher rates of Whites at age 29. Black-American Indians face particularly high risk of problematic drinking over the life course. Additional research is needed to understand causal factors of alcohol-use among biracial individuals particularly Black-American Indians who may be at higher risk for alcohol misuse.

Adaptation of the lateral distal femur DXA scan technique to adults with disabilities.

The technique that best addresses the challenges of assessing bone mineral density in children with neuromuscular impairments is a dual-energy X-ray absorptiometry (DXA) scan of the lateral distal femur. The purpose of this study was to adapt this technique to adults with neuromuscular impairments and to assess the reproducibility of these measurements. Thirty-one adults with cerebral palsy had both distal femurs scanned twice, with the subject removed and then repositioned between each scan (62 distal femurs, 124 scans). Each scan was independently analyzed twice by 3 different technologists of varying experience with DXA (744 analyses). Precision of duplicate analyses of the same scan was good (range: 0.4%-2.3%) and depended on both the specific region of interest and the experience of the technologist. Precision was reduced when comparing duplicate scans, ranging from 7% in the metaphyseal (cancellous) region to 2.5% in the diaphyseal (cortical) region. The least significant change was determined as recommended by the International Society for Clinical Densitometry for each technologist and each region of interest. Obtaining reliable, reproducible, and clinically relevant assessments of bone mineral density in adults with neuromuscular impairments can be challenging. The technique of obtaining DXA scans of the lateral distal femur can be successfully applied to this population but requires a commitment to developing the necessary expertise.