Anhedonia and depression symptoms have been linked to potential deficits in reward learning. However, how anhedonia impacts the ability to adjust and learn about the effort required to obtain rewards remains unclear.

We examined young people (N = 155, 16–25 years) with a range of depression and anhedonia symptoms using a probabilistic instrumental reward and effort learning task. Participants were asked to learn which options to choose to maximize reward or minimize effort for reward. We compared the exerted effort (button pressing speed) for high (puppy images) vs low (dog images) rewards and collected subjective reports of “liking,” “wanting,” and “willingness to exert effort.” Computational models were fit to the learning data and estimated parameter values were correlated with depression and anhedonia symptoms.

As depression symptoms and consummatory anhedonia increased, reward liking decreased, and as anticipatory anhedonia increased, liking, wanting, and willingness to exert effort for reward decreased.

Participants exerted more effort for high rewards than for low rewards, but anticipatory anhedonia diminished this difference.

Higher consummatory anhedonia was associated with poorer reward and effort learning, and with increased temperature parameter values for both learning types, indicating a higher tendency to make exploratory choices. Higher depression symptoms were associated with lower reward learning accuracy.

We provide novel evidence that anhedonia is associated with difficulties in modulating effort as a function of reward value and with the underexploitation of low effort and high reward options. We suggest that addressing these impairments could be a novel target for intervention in anhedonic young people.

Recipients of hematopoietic cell transplantation (HCT) are at increased risk for Clostridioides difficile infection (CDI) and its recurrence, which are associated with significant morbidity. We aimed to characterize risk factors for primary and recurrent CDI in a large cohort of HCT recipients.

We conducted a retrospective cohort study of 2725 adults who underwent HCT from 2010–2023 to evaluate the epidemiology, timing, and risk factors for CDI. We compared patients who developed CDI with those who did not, adjusting for patient demographics, comorbidities, transplant factors, medications, and laboratory values. Among patients who developed CDI, we investigated risk factors for recurrent CDI.

The cumulative 1-year incidence of CDI was 17.8% among allogeneic HCT recipients (181/1016) and 4.1% among autologous recipients (71/1709). Overall CDI incidence increased by 1.4% annually during the study period (95%CI: 1.24–1.53%). Independent risk factors for primary CDI included penicillin antibiotics (aOR 1.51; 95%CI: 1.13–2.02), prior chemotherapy (aOR 8.36; 95%CI: 2.95–23.69 for 1–3 regimens), and umbilical cord blood stem cells (aOR 1.95; 95%CI: 1.07–3.57). Autologous HCT was associated with decreased risk. Among 252 patients with primary CDI, 22 (8.7%) developed recurrence. Macrolide use before index CDI (aOR 7.25; 95%CI: 1.80–29.2) and allogeneic HCT (aOR 31.04; 95%CI: 1.37–731.58) were associated with recurrence.

CDI is a common, early complication of HCT particularly among allogeneic recipients. Penicillin exposure, prior chemotherapy, and cord blood stem cell source are key risk factors, while macrolide use is associated with recurrence. Our findings highlight potential targets for risk-stratified prevention strategies.

Neuroticism, a personality trait linked to both cardiovascular and psychiatric disorders, has been associated with cognitive decline and increased dementia risk, though the underlying neural mechanisms remain unclear. Mapping its relationship with brain structure could provide valuable insights into neural pathways and targets for early intervention.

We examined brain-wide associations between neuroticism and structural neuroimaging metrics derived from T1-, T2-weighted, and diffusion MRI in 36,901 dementia-free UK Biobank participants. Bonferroni-significant associations underwent bidirectional two-sample Mendelian randomization to evaluate the evidence for a causal relationship. Given that neuroticism is generally stable across adulthood and challenging to modify, we assessed whether these associations were mediated by health conditions (depression, anxiety, hypertension, ischemic heart disease [IHD], and diabetes) that are both consequences of neuroticism and known risk factors for dementia, and also modifiable through widely available and efficacious therapeutic interventions.

Higher neuroticism was found to be associated with reduced grey matter volumes in the frontal and limbic regions, as well as widespread differences in white matter microstructure, particularly in thalamic radiations. Genetic analyses supported a potential causal effect of neuroticism on increased diffusivity in thalamic radiations. Hypertension mediated the associations between neuroticism and both grey and white matter measures, while depression and anxiety primarily mediated associations with white matter microstructure. Contributions from IHD and diabetes were minimal.

Neuroticism is linked to widespread structural brain differences that contribute to poorer brain health, and targeting vascular and mental health may help mitigate its impact.

This study examined the changes in intimacy and sexuality amongst cancer patients at the end of life, including those in the final stage, and the distress they raised while experiencing those changes.

A phenomenological qualitative study, based on interviews with 35 dying cancer patients. The results were analyzed by Constant Comparison Analysis method.

Some of the dying patients reported absence of essential change in their sexual needs and ability. while others reported about changes. The changes caused seven various forms of distress to the majority of patients, for example grief due to diminution of sexuality, impact on the partner due to lack of sex and distress resulting from consciousness of the end of life. A minority did not experience distress from the sexuality changes. About a third of the interviewees were interested in sexual counseling during their dying period, and about 80% considered it important for the palliative care team to raise the issue of sexuality.

End of life patients and even those in final stages may have needs related to their intimate and sexual life. As long as the person breathes, even towards death, he can continue to live, even in the intimate aspect, so the palliative team has an important role in answering the specific and complex needs related to sexuality at the end of life. Recommendations were formulated specifically based on this research, for professional intervention regarding sexuality at the end of life, by a PASSION model.

This study examined the health-related issues and proposed solutions among residents who returned or migrated to Namie in Fukushima Prefecture, Japan, after the evacuation order following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident was lifted. Moreover, this study assessed vulnerable groups comprising individuals who faced difficulties obtaining health services.

This study conducted semi-structured interviews with 16 participants who lived or worked in Namie. A thematic analysis was employed to analyze the interview data.

The results revealed four themes regarding prioritized health-related issues: poor lifestyle habits cause disease, mental health, inadequate medical and nursing care systems, and social isolation and issues with social relationships in the community. Likewise, four themes were identified for solutions: measures to improve lifestyle habits, enhancement of medical and nursing services, revitalization of community and social relationships, and provision of personalized care and support. Additionally, seven groups that had difficulty accessing health services were identified.

The findings suggest that comprehensive individual support is required during the long-term recovery phase after nuclear disasters to meet the health needs of vulnerable groups.

Most children recover from mild traumatic brain injury (mTBI), but some experience persistent neurocognitive effects. Understanding is limited due to methodological differences and a lack of pre-injury data. The study aimed to assess changes in neurocognitive outcomes in children following mTBI compared to orthopedic injury (OI) and non-injured (NI) controls, while accounting for pre-injury functioning.

Data were drawn from the Adolescent Brain and Cognitive Development (ABCD) study, a prospective longitudinal cohort. The sample included children with mTBI between the 1-year and 2-year follow-ups (n = 83), identified by parent report of head injury with memory loss or loss of consciousness, compared to children who experienced OI within the same period (n = 231) and an NI control group (n = 218). Changes in neurocognitive outcomes from baseline to the 2-year follow-up between groups (mTBI vs. OI; mTBI vs. NI) were estimated using linear mixed-effects models, accounting for demographic, behavioral, genetic, and white matter microstructural covariates.

At baseline prior to injury, the mTBI group demonstrated better performance on picture vocabulary and crystallized composite scores than the OI group. At post-injury, after adjusting for pre-injury baseline differences, children who sustained an mTBI were no different in any measure of neurocognitive outcomes compared to OI and NI controls.

The findings highlight the importance of accounting for pre-injury differences when evaluating neurocognitive outcomes following pediatric mTBI. Neurocognitive differences within a year post-injury may be more related to pre-existing individual factors rather than the injury itself, underscoring the need for a comprehensive approach in studying pediatric mTBI.

Psychotic-like experiences (PLEs) are considered a subclinical component of psychosis continuum. Studies indicate that PLEs arise from multimodal factors, yet research comprehensively examining these factors together remains scarce. Using a large youth sample, we present the first model that simultaneously examines multimodal factors related to PLEs. As a secondary aim, we evaluate the model’s ability to explain psychosis in an external validation cohort that included individuals experiencing psychosis.

After applying variable selection including generalized estimating equations, correlation filtering, Least Absolute Shrinkage and Selection Operator model to 741 variables (i.e., environmental factors, cognitive appraisals, clinical variables, cognitive functioning, and structural brain connectome measures), obtained PLEs predictors (N = 27) and covariates (i.e., age, sex, IQ) were included in the classification model based on Elastic Net algorithm for predicting high/low PLEs in 396 healthy participants aged 14–24 (Mage = 19.72 ± 2.5). We externally validated PLE-related predictors in a clinical sample comprising first-episode psychosis patients (n = 19), their siblings (n = 20), and healthy controls (n = 19).

Eleven factors, including environmental and cognitive appraisals, along with 16 structural network properties spanning frontal, temporal, occipital, and parietal regions, were identified as important predictors of PLEs. The model’s performance was moderate in predicting low versus high PLEs (accuracy = 75%, AUC = 0.750). Specificity was high (84.2%) in distinguishing siblings from patients.

Multimodal features, including environmental burden, cognitive schemas, and brain network alterations, predict PLEs and partially generalize to clinical psychosis. These variables may reflect intermediate phenotypes across the psychosis spectrum, offering insights into both vulnerability and resilience.

Patients’ involvement in the decision-making process is essential for shared decision-making and optimal patient-centered care. However, when there are concerns about a patient’s cognition and judgmen, the complexity of providing patient-centered care increases. It is often necessary to evaluate patients’ decision-making capacity (DMC) to determine whether they are able to make a particular decision or whether to rely on their previously expressed wishes or the patient’s caregivers.

In this article, we present a case of an older adult with colon cancer who presented to the emergency room.

We describe how multidisciplinary care can enhance the evaluation of DMC and improve quality of care for older patients with advanced cancer.

Multidisciplinary discussions and good communication skills are essential for navigating these complex situations, reducing potential harm and maximizimizing quality of life.

Outbreak investigation and control are critical for preventing the spread of infectious diseases in healthcare settings. Traditional methods rely on manual processes, which are time-consuming and limited in scope. Whole genome sequencing (WGS) surveillance improves outbreak detection but still requires extensive manual chart reviews to identify transmission routes. Integrating artificial intelligence (AI) may enhance the efficiency and accuracy of these investigations.

We evaluated an AI tool developed to streamline healthcare outbreak investigations detected by the Enhanced Detection System for Healthcare-associated Transmission (EDS-HAT). For outbreaks detected between November 2021 and November 2023, multiple data elements were extracted from electronic health records (EHR) for all patients. The AI algorithm was applied to identify transmission routes, and its performance was assessed against expert manual reviews. Key measures included additional transmission routes identified and sensitivity.

Data from 172 outbreaks involving 476 case patients were analyzed. The AI tool identified 37 transmission routes that were missed by manual review, including procedures and provider routes. The algorithm achieved a sensitivity of 76.0% (95% confidence interval [CI] 71.1%–81.1%) compared to manual review, increasing to 91.7% (95% CI 87.7%–94.7%) after accounting for transmission at other facilities and downstream exposures.

The EDS-HAT AI tool significantly improved outbreak investigations by automating the identification of transmission routes, both with concordant findings of manual review as well as finding additional routes of transmission missed by traditional chart review. AI with genomic surveillance has the potential to optimize outbreak detection and investigation to streamline interventions in healthcare settings.

Given substantial comorbidity among, and considerable heterogeneity within, psychiatric diagnoses, researchers have suggested alternative systems for classifying psychopathology. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a recently proposed framework for understanding mental disorders based on how symptoms and diagnoses tend to cluster across individuals. While the model is grounded in existing research and supported by recent meta-analytic evidence, its structure has not yet been directly tested using large, representative clinical datasets. In this study, we used electronic health record (EHR) data to examine the overall organization of mental disorders as proposed by HiTOP, with the goal of informing future research on biological and environmental risk factors as well as important life outcomes.

Data were drawn from the All of Us Research Program, a landmark nationwide US biobank initiative designed to advance population-scale health research, and included participants’ psychiatric diagnoses and sociodemographic correlates as documented in their EHRs. A total of 127,963 participants and 39 primary diagnoses were identified. We analyzed patterns of co-occurrence among psychiatric diagnoses to identify broader psychopathology dimensions, assess the overall structure of mental disorders, and clarify the placement of conditions that have been inconsistently categorized in past research. Several competing dimensional models were compared based on their statistical fit and complementary assessments of factor strength, specificity, and reproducibility.

A model identifying six broad and correlated dimensions – Fear, Distress, Externalizing, Substance Use, Thought Problems, and Neurodevelopmental Disorders – provided the best fit to the data. This structure was highly consistent across analyses and showed strong split-half replicability and meaningful associations with relevant clinical and demographic characteristics.

These findings support a 6-factor model of psychopathology that broadly resembles major dimensions in the HiTOP framework. By addressing key gaps in the literature, this study advances our understanding of the structure and correlates of mental disorders. The results offer a foundation for more nuanced investigations into the etiology, progression, and treatment of mental health conditions.