Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.

The clinical high-risk (CHR) state for psychosis demonstrates considerable clinical heterogeneity, presenting challenges for clinicians and researchers alike. Basic symptoms, to date, have largely been ignored in explorations of clinical profiles.

We examined clinical profiles by using a broader spectrum of CHR symptoms, including not only (attenuated) psychotic, but also basic symptoms.

Patients (N = 875) of specialised early intervention centres for psychosis in Germany and Switzerland were assessed with the Schizophrenia Proneness Instruments and the Structured Interview for Psychosis-Risk Syndromes. Latent class analysis was applied to CHR symptoms to identify clinical profiles. Additionally, demographics, other symptoms, current non-psychotic DSM-IV axis I disorders and neurocognitive variables were assessed to further describe and compare the profiles.

A three-class model was best fitting the data, whereby basic symptoms best differentiated between the profiles (η2 = 0.08–0.52). Class 1 had a low probability of CHR symptoms, the highest functioning and lowest other psychopathology, neurocognitive deficits and transition-to-psychosis rate. Class 2 had the highest probability of basic and (attenuated) positive symptoms (excluding hallucinations), lowest functioning, highest symptom load, most neurocognitive deficits and highest transition rate (55.1%). Class 3 was mostly characterised by attenuated hallucination, and was otherwise intermediate between the other two classes. Comorbidity rates were comparable across classes, with some class differences in diagnostic categories.

Our profiles based on basic and (attenuated) psychotic symptoms provide clinically useful entities by parsing out heterogeneity in clinical presentation. In future, they could guide class-specific intervention.

Community-acquired pneumonia (CAP) is a leading cause of hospitalizations and mortality in the US. Overuse of extended spectrum antibiotics (ESA) for CAP contributes to antimicrobial resistance. The 2019 Infectious Diseases Society of America/American Thoracic Society CAP guidelines emphasize de-escalation of ESA following negative cultures, early switch to oral (PO) antibiotics, and limited duration of therapy (DOT). This study describes clinicians’ acceptance of an infectious diseases-trained (ID) pharmacist-led stewardship recommendations in hospitalized patients with CAP.

This prospective, single-arm, cohort study included adults admitted with a diagnosis of pneumonia to six Cleveland Clinic hospitals receiving ID pharmacist-led stewardship recommendations. The ID pharmacist provided recommendations for ESA de-escalation, DOT, intravenous (IV) to PO transition, and antimicrobial discontinuation. Descriptive statistics were used to describe clinician acceptance rates.

From November 1, 2022, to January 31, 2024, the ID pharmacist made recommendations for 685 patient encounters to 327 clinicians. Of these patients, 52% received an ESA and 15% had severe CAP. There were 959 recommendations: ESA de-escalation (19%), DOT (46%), IV to PO transition (19%), antimicrobial discontinuation (13%), and other (3%). Clinicians accepted 693 recommendations (72%): IV to PO transition (148/184, 80%), ESA de-escalation (141/181 78%), antimicrobial discontinuation (94/128, 73%), DOT (286/437, 65%), and other (24/29, 83%).

Clinicians were generally receptive to ID pharmacist-led CAP recommendations with an overall acceptance rate of 72%. Prescribers were most receptive to recommendations for IV to PO conversion and least receptive to limiting DOT.

Early exposure to neighborhood social fragmentation has been shown to be associated with schizophrenia. The impact of social fragmentation and friendships on distressing psychotic-like experiences (PLE) remains unknown. We investigate the relationships between neighborhood social fragmentation, number of friends, and distressing PLE among early adolescents.

Data were collected from the Adolescent Brain Cognitive Development Study. Generalized linear mixed models tested associations between social fragmentation and distressing PLE, as well as the moderating role of the number of total and close friends.

Participants included 11 133 adolescents aged 9 to 10, with 52.3% being males. Greater neighborhood social fragmentation was associated with higher levels of distressing PLE (adjusted β = 0.05; 95% CI: 0.01–0.09). The number of close but not total friends significantly interacted with social fragmentation to predict distressing PLE (adjusted β = −0.02; 95% CI: −0.04 to <−0.01). Among those with fewer close friends, the association between neighborhood social fragmentation and distressing PLE was significant (adjusted β = 0.07; 95% CI: 0.03–0.11). However, among those with more close friends, the association was non-significant (adjusted β = 0.03; 95% CI: −0.01 to 0.07).

Greater neighborhood social fragmentation is associated with higher levels of distressing PLE, particularly among those with fewer close friends. Further research is needed to disentangle aspects of the interaction between neighborhood characteristics and the quality of social interactions that may contribute to psychosis, which would have implications for developing effective interventions at the individual and community levels.

This study builds on the work by Rehman et al (2022) who argued that transcranial magnetic stimulation (TMS) treatment not only helps treat depression but also decreases sleep problems such as difficulty falling asleep,staying asleep, and waking too early. The present study further explores differences in sleep onset latency, meaning the time it takes to fall asleep, and duration of sleep per night in the pre and post treatment phases of rTMS. The information regarding major attributes of sleep is critical because recent research shows that about 90% of patients with major depressive disorder (MDD) also struggle with sleep disorders (Li et al., 2022), and sleeping for less than seven hours may eventually lead to sleep deprivation (Hirshkowitz et al., 2015), with increased risk of physical and mental health problems (Sheehan et al, 2019). Sleep onset latency estimates vary from individual to individual but typical sleep latency is considered between 10 to 20 minutes (Jung et al, 2013). As it has been shown that overall sleep problems improve with rTMS, we hypothesized that self-reported sleep onset latency will decrease, and sleep duration will increase.

All participants met inclusion criteria for MDD diagnosis and completed a full course of TMS treatment (N=470; Mean age=53.45, SD=13.73). The sample was mostly male (81%) and ethnically diverse: 77.7% non-Hispanic White, 13.3% Black Americans, 1.9% Asian, 0.2 % Asian Indian, and 1.9% other ethnicities. Sleep problems were assessed using the following questions at the pre and post treatment stages: the number of minutes it takes to fall asleep and duration of sleep each night.

A Wilcoxon matched-pairs signed-rank test was conducted to determine whether there was a difference in sleep onset latency and hours of sleep per night between pre and post intervention. The results indicated a significant difference in time to fall asleep between pre and post treatment (pre-treatment M = 1.19, SD = 0.99, post-treatment M = 0.93, SD = 0.91; z = -5.01, p < .001. In addition, there was a significant increase in the minutes of sleep per night in pre (M = 6.11, SD = 2.07) compared to the post treatment (M = 6.32, SD = 1.77), z = -2.56, p = .010.

Reduced sleep is known to negatively impact mood, cognitive ability, work performance, and immune function (Besedovsky et al., 2012; Killgore, 2010; Massar et al, 2019; Vandekerckhove & Wang, 2018). Similarly, longer sleep onset latency can cause an individual to enter the first sleep stage later than expected and complete fewer sleep cycles. The results of the present study show the effectiveness of rTMS in decreasing sleep onset latency and increasing the duration of sleep. Given the comorbidity and bidirectionality between sleep disturbances and mood disorders (Fang et al., 2019; Palagini et al., 2019), further researching treatments such as rTMS to improve sleep as a means to also improve mood is crucial. We propose acquiring knowledge about sleep attributes as an essential part of clinicians’ work early on in the rTMS treatment in order to monitor an individual’s global functioning level in light of improved sleep.

There is currently little consensus as to how burnout is best defined and measured, and whether the syndrome should be afforded clinical status. The latter issue would be advanced by determining whether burnout is a singular dimensional construct varying only by severity (and with some level of severity perhaps indicating clinical status), or whether a categorical model is superior, presumably reflecting differing ‘sub-clinical’ versus ‘clinical’ or ‘burning out’ vs ‘burnt out’ sub-groups. This study sought to determine whether self-diagnosed burnout was best modelled dimensionally or categorically.

We recently developed a new measure of burnout which includes symptoms of exhaustion, cognitive impairment, social withdrawal, insularity, and other psychological symptoms. Mixture modelling was utilised to determine if scores from 622 participants on the measure were best modelled dimensionally or categorically.

A categorical model was supported, with the suggestion of a sub-syndromal class and, after excluding such putative members of that class, two other classes. Analyses indicated that the latter bimodal pattern was not likely related to current working status or differences in depression symptomatology between participants, but reflected subsets of participants with and without a previous diagnosis of a mental health condition.

Findings indicated that sub-categories of self-identified burnout experienced by the lay population may exist. A previous diagnosis of a mental illness from a mental health professional, and therefore potentially a psychological vulnerability factor, was the most likely determinant of the bimodal data, a finding which has theoretical implications relating to how best to model burnout.