The concurrent incidence of autoimmune comorbidities in obsessive–compulsive disorder (OCD) is known. However, the association between OCD and related autoimmune skin diseases (ASDs) has not been well studied.

This study aimed to investigate the association between OCD and the risk of ASDs.

To assess the risk of developing ASDs, we recruited 44 324 patients with OCD and 177 296 matched controls from the National Health Insurance Research Database in Taiwan. A Cox regression model was used for the analyses.

After adjusting for confounders, an increased risk of ASDs among the patients with OCD (adjusted hazard ratio [aHR]: 6.36; 95% confidence interval [CI]: 5.43-7.45) was found when compared to the controls. Statistically significant associations were found between OCD and seven individual ASDs, including psoriasis (aHR: 12.52; 95% CI: 8.78-17.85), lichen planus (aHR: 27.22; 95% CI: 13.09-56.60), alopecia areata (aHR: 13.69; 95% CI: 9.38-19.98), autoimmune bullous diseases (aHR: 4.30; 95% CI: 2.03-9.11), hidradenitis suppurativa (aHR: 29.95; 95% CI: 3.35-267.62), vitiligo (aHR: 9.35; 95% CI: 5.35-16.32), and lupus erythematosus (aHR: 2.10; 95% CI: 1.52-2.91).

Patients with OCD had an increased risk of developing ASDs compared to matched controls. Further studies are required to clarify the underlying mechanisms.

Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy.

The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18–30 years), and older (31–50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400).

We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240.

The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

Cognitive impairment is common in late-life depression, which may increase Alzheimer disease (AD) risk. Therefore, we aimed to investigate whether late-life major depressive disorder (MDD) has worse cognition and increases the characteristic AD neuropathology. Furthermore, we carried out a comparison between treatment-resistant depression (TRD) and non-TRD. We hypothesized that patients with late-life depression and TRD may have increased β-amyloid (Aβ) deposits in brain regions responsible for global cognition.

We recruited 81 subjects, including 54 MDD patients (27 TRD and 27 non-TRD) and 27 matched healthy controls (HCs). Neurocognitive tasks were examined, including Mini-Mental State Examination and Montreal Cognitive Assessment to detect global cognitive functions. PET with Pittsburgh compound-B and fluorodeoxyglucose were used to capture brain Aβ pathology and glucose use, respectively, in some patients.

MDD patients performed worse in Montreal Cognitive Assessment (p = 0.003) and had more Aβ deposits than HCs across the brain (family-wise error-corrected p < 0.001), with the most significant finding in the left middle frontal gyrus. Significant negative correlations between global cognition and prefrontal Aβ deposits existed in MDD patients, whereas positive correlations were noted in HCs. TRD patients had significantly more deposits in the left-sided brain regions (corrected p < 0.001). The findings were not explained by APOE genotypes. No between-group fluorodeoxyglucose difference was detected.

Late-life depression, particularly TRD, had increased brain Aβ deposits and showed vulnerability to Aβ deposits. A detrimental role of Aβ deposits in global cognition in patients with late-onset or non-late-onset MDD supported the theory that late-life MDD could be a risk factor for AD.

Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks.

We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls).

We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05).

The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

The Clinical and Translational Science Awards (CTSA) Consortium, a network of academic health care institutions with CTSA hubs, is charged with improving the national clinical and translational research enterprise. The CTSA Consortium and the NIH National Center for Advancing Translational Sciences implemented the Common Metrics Initiative comprised of standardized metrics and a shared performance improvement framework. This article summarizes hubs’ perspectives on its value during the initial implementation.

The value was assessed across 58 hubs. Survey items assessed change in perceived ability to manage performance and advance clinical and translational science. Semi-structured interviews elicited hubs’ perspectives on meaningfulness and value-added of the Common Metrics Initiative and hubs’ recommendations.

Hubs considered their abilities to manage performance to have improved, but there was no change in perceived ability to advance clinical and translational science. The initiative added value by providing a formal structured process, enabling strategic conversations, facilitating improvements in processes, providing an external impetus for improvement, and providing justification for funds invested. Hubs were concerned about the usefulness of the metrics chosen and whether the value-added was sufficient relative to the effort required. Hubs recommended useful benchmarking, disseminating best practices and promoting peer-to-peer learning, and expanding the use of data to inform the initiative.

Implementing Common Metrics and a performance improvement framework yielded concrete short-term benefits, but concerns about usefulness remained, particularly considering the effort required. The Common Metrics Initiative should focus on facilitating cross-hub collaboration around metrics that address high-priority impact areas for individual hubs and the Consortium.

Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR).

This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders.

The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity.

These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

We calculated the human resources required for an antimicrobial stewardship program (ASP) in Korean hospitals.

Multicenter retrospective study.

Eight Korean hospitals ranging in size from 295 to 1,337 beds.

The time required for performing ASP activities for all hospitalized patients under antibiotic therapy was estimated and converted into hours per week. The actual time spent on patient reviews of each ASP activity was measured with a small number of cases, then the total time was estimated by applying the determined times to a larger number of cases. Full-time equivalents (FTEs) were measured according to labor laws in Korea (52 hours per week).

In total, 225 cases were reviewed to measure time spent on patient reviews. The median time spent per patient review for ASP activities ranged from 10 to 16 minutes. The total time spent on the review for all hospitalized patients was estimated using the observed number of ASP activities for 1,534 patients who underwent antibiotic therapy on surveillance days. The most commonly observed ASP activity was ‘review of surgical prophylactic antibiotics’ (32.7%), followed by ‘appropriate antibiotics recommendations for patients with suspected infection without a proven site of infection but without causative pathogens’ (28.6%). The personnel requirement was calculated as 1.20 FTEs (interquartile range [IQR], 1.02–1.38) per 100 beds and 2.28 FTEs (IQR, 1.93–2.62) per 100 patients who underwent antibiotic therapy, respectively.

The estimated time required for human resources performing extensive ASP activities on all hospitalized patients undergoing antibiotic therapy in Korean hospitals was ~1.20 FTEs (IQR, 1.02–1.38) per 100 beds.

No studies have reported on how to relieve distress or relax in medical health workers while wearing medical protective equipment in coronavirus disease 2019 (COVID-19) pandemic. The study aimed to establish which relaxation technique, among six, is the most feasible in first-line medical health workers wearing medical protective equipment.

This was a two-step study collecting data with online surveys. Step 1: 15 first-line medical health workers were trained to use six different relaxation techniques and reported the two most feasible techniques while wearing medical protective equipment. Step 2: the most two feasible relaxation techniques revealed by step 1 were quantitatively tested in a sample of 65 medical health workers in terms of efficacy, no space limitation, no time limitation, no body position requirement, no environment limitation to be done, easiness to learn, simplicity, convenience, practicality, and acceptance.

Kegel exercise and autogenic relaxation were the most feasible techniques according to step 1. In step 2, Kegel exercise outperformed autogenic relaxation on all the 10 dimensions among the 65 participants while wearing medical protective equipment (efficacy: 24 v. 15, no space limitation: 30 v. 4, no time limitation: 31 v. 4, no body position requirement: 26 v. 4, no environment limitation: 30 v. 11, easiness to learn: 28 v. 5, simplicity: 29 v. 7, convenience: 29 v. 4, practicality: 30 v. 14, acceptance: 32 v. 6).

Kegel exercise seems a promising self-relaxation technique for first-line medical health workers while wearing medical protective equipment among COVID-19 pandemic.

The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework.

Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited.

Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying out performance improvement plans. Seven key factors affected progress: hub size and resources, hub prior experience with performance management, alignment of local context with needs of the Common Metrics implementation, hub authority in the local institutional structure, hub engagement (including CTSA Principal Investigator involvement), stakeholder engagement, and attending training and coaching.

Implementing Common Metrics and performance improvement in a large network of research-focused organizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.