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Cutting-edge oncology care often depends on patients' ability to use rapidly evolving health technology. Digital health literacy (DHL; the capacity to understand health-related information with electronic media) is an emerging, yet underexplored social determinant of health in patients with cancer. We aimed to characterize sociodemographic and clinical factors associated with DHL in patients with cancer and explore whether a single-item screener could be derived from a widely-used DHL questionnaire to detect low DHL. Patients (N=105) who received systemic treatment in the past year for colorectal carcinoma (CRC) or non-Hodgkin lymphoma (NHL) were recruited through collaborating clinics. Participants self-reported DHL using the eHealth Literacy Scale (eHEALS). They also reported general health literacy and sociodemographic and clinical characteristics. Correlations and group comparisons (independent sample t tests and χ2 tests, as appropriate) were used to evaluate links between DHL and sociodemographic and clinical characteristics. Receiver operating characteristic (ROC) curve analysis was used to determine whether a single eHEALS item could effectively screen for low DHL (eHEALS score ≤20). Patients with a lower education level (Spearman ρ=0.29; P=.004) and lower general health literacy (r=0.25; P=.009) had lower DHL. Patients with NHL reported lower DHL than those with CRC (t103=2.72; P=.008). Additionally, the subset of patients who reported participation in a clinical trial (n=10) exhibited lower DHL than nonparticipants (t100=3.08; P=.003). Other sociodemographic and clinical characteristics were not significantly associated with DHL (all P>.21). The ROC curve analysis showed that eHEALS item 4 (\"I know where to find helpful health resources on the Internet\") was a strong predictor of high versus low DHL (area under the curve=0.975, 95% CI 0.949-1.00; P<.001). In this convenience sample, DHL varied based on cancer type, education level, general health literacy, and clinical trial participation. Furthermore, we found that a single item from the eHEALS has strong potential for identifying those with low DHL. These findings may inform which patients have higher need for or may benefit from DHL interventions and suggest avenues for detecting low DHL in oncology clinics.

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common diagnosed aggressive B-cell lymphoma, with poor outcomes in those who experience relapsed or refractory (R/R) disease. Landmark clinical trials have demonstrated the efficacy and safety of anti-CD19 chimeric antigen receptor (CAR) T-cell therapy for patients with R/R DLBCL, though further exploration of real-world outcomes (RWOs) and safety data is warranted. Methods: A retrospective chart review was performed to collect patient and disease characteristics from patients with R/R DLBCL receiving CAR T-cell therapy for third-line treatment or beyond at the John Theurer Cancer Center as the standard of care. Results: We report on 82 patients with R/R DLBCL that successfully completed an infusion of an anti-CD19 CAR T-cell product at our institution. Best overall and complete response rates were 74.4% (95% CI, 64.9 to 83.8) and 67.1% (95% CI, 56.9 to 77.2), respectively. From the time of CAR T-cell infusion, median PFS was 26.5 months (95% CI, 8.6 months could not be estimated) and OS was not reached. Subgroup analyses revealed no statistical differences in outcomes by use of bridging therapy, Karnofsky performance status, transformed DLBCL status, and the type of CAR T-cell product used for this study. CAR T-cell therapy was well tolerated, with 58 patients (70.7%) experiencing cytokine-release syndrome and 17 patients (20.7%) experiencing immune effector cell-associated neurotoxicity syndrome. Conclusions: These results of RWOs in third-line patients with R/R DLBCL receiving anti-CD19 CAR T-cell therapy are comparable or superior to prior clinical trials and studies of RWOs, validating the strong efficacy and manageable toxicities of CAR T-cell therapy.

Purpose Despite growing research on financial toxicity among cancer survivors, large gaps remain in understanding how to intervene to minimize financial toxicity. Uptake and efficacy of interventions mitigating cancer financial toxicity, though promising, remain limited and inconsistent. To date, survivor preferences for financial toxicity interventions are underexplored. This study aimed to evaluate survivor preferences for timing and content of a survivor-facing intervention to address financial toxicity. Methods Adult survivors ( N  = 105) of colorectal cancer ( N  = 55) or Non-Hodgkin Lymphoma ( N  = 50) from three tertiary care centers self-reported demographic and clinical characteristics, comorbidities, mental health, financial impact of cancer (Comprehensive Score for Financial Toxicity scale), and preferences for intervention timing and content. Chi-square tests examined associations between intervention timing and content preferences with financial toxicity score. ANOVAs and correlation analyses described associations between the number of intervention components survivors endorsed and survivors’ characteristics. Results Regarding intervention timing, 79% of survivors favored intervention before treatment. The most frequently endorsed content was understanding out-of-pocket costs and insurance (48.6%) and applying for aid (39%). Survivors experiencing higher financial toxicity reported greater interest in all intervention components. Survivors with colorectal cancer ( p  = .018), < 65 years ( p  = .019), higher financial toxicity ( p  < .001), greater life-altering ( p  < .001) and care-altering ( p  = .014) coping behaviors, and poorer mental health ( p  = .008) endorsed more intervention components. Conclusions Actionable insights to improve financial toxicity interventions may be to offer assistance earlier than currently provided (i.e. before treatment) and to include certain topics currently rarely offered (e.g., stress management, budget development support) in line with survivors’ preferences.

Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT.

In response to The Globe's Sept. 1 editorial, \"Resident joblessness,\" we would like to make the following comments on behalf of one of the petitioners in the case, the Massachusetts Council of Construction Employers (MCCE).

Purpose Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. Methods Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. Results Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. Conclusion Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive immature T cell cancer. Mutations in IL7R have been analyzed genetically, but downstream effector functions such as STAT5A and STAT5B hyperactivation are poorly understood. Here, we studied the most frequent and clinically challenging STAT5BN642H driver in T cell development and immature T cell cancer onset and compared it with STAT5A hyperactive variants in transgenic mice. Enhanced STAT5 activity caused disrupted T cell development and promoted an early T cell progenitor-ALL phenotype, with upregulation of genes involved in T cell receptor (TCR) signaling, even in absence of surface TCR. Importantly, TCR pathway genes were overexpressed in human T-ALL and mature T cell cancers and activation of TCR pathway kinases was STAT5 dependent. We confirmed STAT5 binding to these genes using ChIP-Seq analysis in human T-ALL cells, which were sensitive to pharmacologic inhibition by dual STAT3/5 degraders or ZAP70 tyrosine kinase blockers in vitro and in vivo. We provide genetic and biochemical proof that STAT5A and STAT5B hyperactivation can initiate T-ALL through TCR pathway hijacking and suggest similar mechanisms for other T cell cancers. Thus, STAT5 or TCR component blockade are targeted therapy options, particularly in patients with chemoresistant clones carrying STAT5BN642H.

Aedes aegypti is a primary vector of dengue, chikungunya, Zika, and urban yellow fever viruses. Indoor, ultra low volume (ULV) space spraying with pyrethroid insecticides is the main approach used for Ae. aegypti emergency control in many countries. Given the widespread use of this method, the lack of large-scale experiments or detailed evaluations of municipal spray programs is problematic. Two experimental evaluations of non-residual, indoor ULV pyrethroid spraying were conducted in Iquitos, Peru. In each, a central sprayed sector was surrounded by an unsprayed buffer sector. In 2013, spray and buffer sectors included 398 and 765 houses, respectively. Spraying reduced the mean number of adults captured per house by ~83 percent relative to the pre-spray baseline survey. In the 2014 experiment, sprayed and buffer sectors included 1,117 and 1,049 houses, respectively. Here, the sprayed sector's number of adults per house was reduced ~64 percent relative to baseline. Parity surveys in the sprayed sector during the 2014 spray period indicated an increase in the proportion of very young females. We also evaluated impacts of a 2014 citywide spray program by the local Ministry of Health, which reduced adult populations by ~60 percent. In all cases, adult densities returned to near-baseline levels within one month. Our results demonstrate that densities of adult Ae. aegypti can be reduced by experimental and municipal spraying programs. The finding that adult densities return to approximately pre-spray densities in less than a month is similar to results from previous, smaller scale experiments. Our results demonstrate that ULV spraying is best viewed as having a short-term entomological effect. The epidemiological impact of ULV spraying will need evaluation in future trials that measure capacity of insecticide spraying to reduce human infection or disease.

The study aimed to: (1) Identify distinct trajectories of change in depressive symptoms by mid-treatment during psychotherapy for late-life depression with executive dysfunction; (2) examine if nonresponse by mid-treatment predicted poor response at treatment end; and (3) identify baseline characteristics predicting an early nonresponse trajectory by mid-treatment. A sample of 221 adults 60 years and older with major depression and executive dysfunction were randomized to 12 weeks of either problem-solving therapy or supportive therapy. We used Latent Growth Mixture Models (LGMM) to detect subgroups with distinct trajectories of change in depression by mid-treatment (6th week). We conducted regression analyses with LGMM subgroups as predictors of response at treatment end. We used random forest machine learning algorithms to identify baseline predictors of LGMM trajectories. We found that ~77.5% of participants had a declining trajectory of depression in weeks 0–6, while the remaining 22.5% had a persisting depression trajectory, with no treatment differences. The LGMM trajectories predicted remission and response at treatment end. A random forests model with high prediction accuracy (80%) showed that the strongest modifiable predictors of the persisting depression trajectory were low perceived social support, followed by high neuroticism, low treatment expectancy, and low perception of the therapist as accepting. Our results suggest that modifiable risk factors of early nonresponse to psychotherapy can be identified at the outset of treatment and addressed with targeted personalized interventions. Therapists may focus on increasing meaningful social interactions, addressing concerns related to treatment benefits, and creating a positive working relationship.

Hyper-activated STAT5B variants are high value oncology targets for pharmacologic intervention. STAT5B N642H , a frequently-occurring oncogenic driver mutation, promotes aggressive T-cell leukemia/lymphoma in patient carriers, although the molecular origins remain unclear. Herein, we emphasize the aggressive nature of STAT5B N642H in driving T-cell neoplasia upon hematopoietic expression in transgenic mice, revealing evidence of multiple T-cell subset organ infiltration. Notably, we demonstrate STAT5B N642H -driven transformation of γδ T-cells in in vivo syngeneic transplant models, comparable to STAT5B N642H patient γδ T-cell entities. Importantly, we present human STAT5B and STAT5B N642H crystal structures, which propose alternative mutation-mediated SH2 domain conformations. Our biophysical data suggests STAT5B N642H can adopt a hyper-activated and hyper-inactivated state with resistance to dephosphorylation. MD simulations support sustained interchain cross-domain interactions in STAT5B N642H , conferring kinetic stability to the mutant anti-parallel dimer. This study provides a molecular explanation for the STAT5B N642H activating potential, and insights into pre-clinical models for targeted intervention of hyper-activated STAT5B. Hyper-activated STAT5B and its disease-causing variants are of interest as cancer drug targets. Here the authors combine cell based studies, X-ray crystallography, biophysical experiments and MD simulations to structurally and functionally characterize the STAT5B N642H mutant found in aggressive T-cell leukemia and lymphomas and find that it has an increased affinity for self-dimerization.