Approximately 95% of patients with a beta-lactam allergy noted in their medical record are not truly allergic when tested. These patients may unnecessarily avoid first-line antibiotics, resulting in increased treatment failure, higher costs, and antibiotic resistance. Bone marrow transplant (BMT) patients may be at higher risk for these adverse outcomes due to weakened immune systems and high risk for severe infections. Our objective was to evaluate beta-lactam allergy labels and their influence on BMT patient outcomes.

We conducted a retrospective cohort study of adult inpatients undergoing BMT during April 2018-March 2020. Eligibility for penicillin allergy testing/de-labeling was evaluated. Multivariable logistic regression was performed to measure independent effects of beta-lactam allergy labels on 100-day outcomes: mortality, ICU admission, rehospitalization, and intravenous antibiotic use.

Among 358 BMT patients, 75 (21%) had a beta-lactam allergy label at baseline. Mortality was higher in patients with an allergy label (14.7% vs 7.8%, P = 0.067). In multivariable analysis, patients with allergy labels were not at a significantly greater risk of mortality (OR = 1.60; 95% CI = 0.68 – 3.78) but were significantly more likely to receive carbapenems (OR = 6.27; 95% CI = 2.81–13.98). All patients with penicillin-class allergy labels were eligible for allergy testing/de-labeling.

We did not observe a significant increased risk of mortality in BMT patients with beta-lactam allergy labels; however, increased carbapenem use was observed. Penicillin allergy de-labeling programs may help optimize antibiotic prescribing in BMT patients. Larger studies are needed to quantify the impact of beta-lactam allergy labels on BMT patient outcomes.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

In UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Pediatric cancer and cancer-related treatments may disrupt brain development and place survivors at risk for long term problems with cognitive functions. Processing efficiency has been operationalized as a nuanced cognitive skill that reflects both processing speed (PS) and working memory (WM) abilities and is sensitive to neurobiological disruption. Pediatric cancer survivors are at risk for processing efficiency deficits; however, a thorough characterization of processing efficiency skills across pediatric primary central nervous system (CNS) tumor and non-CNS cancer survivors has not yet been reported.

Participants were selected from a mixed retrospective clinical database of pediatric cancer survivors (Total n=160; primary CNS tumor n=33; Non-CNS n=127). Univariate analyses were conducted to examine differences in processing efficiency mean scores (t-tests) and percent impairment (scores >1 SD below mean; chi-squared tests) between the total sample and normative sample, and across groups (CNS vs. Non-CNS). Multiple linear regressions were utilized to evaluate the relationships between additional risk factors, including biological sex, age at diagnosis, time since treatment, and socioeconomic status, and processing efficiency outcomes.

The total sample obtained lower scores on WM (M=90.83, SD=13.35) and PS (M=88.86, SD=14.38) measures than normative samples (M=100, SD=15), p < 0.001. Greater percentage of pediatric cancer survivors demonstrated impairment across all processing efficiency measures (24.8-38.1%) than normative samples (15.9%), p < 0.001. Regarding group differences, the CNS group obtained lower mean WM (M=84.85, SD =11.77) and PS (M=80, SD=14.18) scores than the Non-CNS group (WM M=92.39, SD=13.32; PS M=91.16, SD=13.56), p < 0.001. Rates of impairment between groups only differed for PS scores, with 63.6% of the CNS group and 31.5% of the non-CNS group demonstrating impairment, p < 0.001. Primary CNS tumor cancer type and male biological sex emerged as the only significant risk factors that predicted processing efficiency skills, with male sex predicting lower scores on PS (ß=8.91 p<.001) and semantic fluency (ß=7.59, p=.007).

These findings indicate that both pediatric primary CNS tumor and non-CNS cancer survivors exhibit substantial weaknesses in processing efficiency skills after treatment. While both groups demonstrated deficits compared to normative samples, the CNS group was more susceptible to PS impairments than non-CNS group. A basic initial study of the relationships between risk factors and processing efficiency skills revealed that primary CNS cancer was a predictor of lower performance on working memory and processing speed measures, while male biological sex was a significant risk factor for worse performance on processing speed and semantic fluency measures. Continued focus on the construct of processing efficiency in pediatric cancer survivors is warranted. Applying a standardized approach to assessing and communicating this nuanced cognitive skill could contribute to advancing both clinical practice and outcomes research of pediatric cancer survivors.

Digit Span has been a core Working Memory task, with extensive research conducted on the Forward and Backward components. The latest revision of the WAIS-IV introduced the Sequencing component, designed to increase the working memory and mental manipulation demands. However, relatively little research has been done to understand how Sequencing can be interpreted in clinical settings, as compared to Forward and Backward. The purpose of this study was to investigate how effectively individual components of the Digit Span task predict performance on four independent neuropsychological measures with high working memory demands.

Subjects included 148 adults (Age: M= 39.22, SD= 13.61; Handedness= 130 right, 10 left and 8 mixed; Males = 88) with refractory epilepsy. Two subjects had primary generalized seizures while 146 subjects had complex partial seizures (EEG Localization: 44 right temporal; 60 left temporal; 24 independent bitemporal; 1 left extratemporal; 17 indeterminant). Dependent variables included the 2.4 second ISI trial of the Paced Auditory Serial Addition Task (PASAT); the sum of correct responses on Trial 1 and List B of the California Verbal Learning Test (CVLT); the DKEFS Tower Test raw score; and completion time on Part B of the Trail Making Test. The independent variables included the individual raw scores for the Forward, Backward and Sequencing components of the WAIS-IV. Hierarchical linear regression was conducted to determine the variance accounted for by each component of the Digit Span and if that variance was redundant or unique. The four dependent variables were analyzed separately with Digits Forward, Backward and Sequencing entered in a single block.

PASAT: The overall model was significant, R2= 0.36. When examining the individual components, Sequencing was the only significant predictor (ß = 0.422, p < 0.001). CVLT: The overall model was significant, R2 = 0.203. When examining the individual components, Sequencing was the only significant predictor (ß = 0.410, p < 0.001). Tower Test: The overall model was significant, R2 = 0.176. When examining the individual components, Sequencing was the only significant predictor (ß = 0.373, p = 0.004). Trail Making: The overall model was significant R2 = 0.315. When examining the individual components both Forward (ß = -0.287, p =0.005) and Sequencing (ß= -0.364, p < 0.001) accounted for a significant amount of the variance.

The combined model for Digit Span accounted for significant amounts of variance in performance on all dependent measures, ranging from 17.6% to 36%. Sequencing accounted for substantially more variance across all examined tasks. On the PASAT, CVLT and Tower Test, the variance accounted for by the components of Digit Span appears to be redundant. However, on Trail Making, both Forward and Sequencing accounted for significant amounts of variance that appear to be independent of one another. What specific task requirement(s) of the Trail Making Test versus the other measures analyzed are accounted for by Forward span is not clear. But this suggests that the individual components of the Digit Span test may measure different things across different tasks.

Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs.

We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data.

The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = −0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = −0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD.

We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.

Depression is strongly associated with chronic disease; yet, the direction of this relationship is poorly understood. Allostatic load (AL) provides a framework for elucidating depression-disease pathways. We aimed to investigate bidirectional, longitudinal associations of baseline depressive symptoms or AL with 5-year AL or depressive symptoms, respectively.

Data were from baseline, 2-year, and 5-year visits of 620 adults (45–75 years) enrolled in the Boston Puerto Rican Health Study. The Center for Epidemiology Studies Depression (CES-D) scale (0–60) captured depressive symptoms, which were categorized at baseline as low (<8), subthreshold (8–15), or depression-likely (⩾16) symptoms. AL was calculated from 11 parameters of biological functioning, representing five physiological systems. Baseline AL scores were categorized by the number of dysregulated parameters: low (0–2), moderate (3–5), or high (⩾6) AL. Multivariable, multilevel random intercept and slope linear regression models were used to examine associations between 3-category baseline CES-D score and 5-year continuous AL score, and between baseline 3-category AL and 5-year continuous CES-D score.

Baseline subthreshold depressive symptoms [(mean (95% CI)): 4.8 (4.5–5.2)], but not depression-likely symptoms [4.5 (4.2–4.9)], was significantly associated with higher 5-year AL scores, compared to low depressive symptoms [4.3 (3.9–4.7)]. Baseline high AL [19.4 (17.6–21.2)], but not low AL [18.5 (16.5–20.6)], was significantly associated with higher 5-year CES-D score, compared to baseline moderate AL [16.9 (15.3–18.5)].

Depressive symptoms and AL had a bi-directional relationship over time, indicating a nuanced pathway linking depression with chronic diseases among a minority population.