Gram negative pyomyositis: Two case reports and a review of the literature.

Pyomyositis due to Gram negative bacteria is rare. Here we describe two cases in immunocompromised hosts. Both were bacteremic with a Gram-negative bacterium and had impaired immunity related to prolonged and ongoing chemotherapy for hematologic malignancies. Both eventually cleared the infection with a combination of local drainage and systemic antibiotics. This unusual diagnosis should be considered in an immunocompromised patient with muscle pain and fever.

Development and Implementation of a Virtual Clinical Skills Examination in General Psychiatry.

OBJECTIVE: During the COVID-19 pandemic, psychiatry programs have administered the Clinical Skills Evaluation (CSE) through videoconferencing. The authors evaluated the feasibility and appropriateness of administering virtual CSEs. METHODS: Virtual CSEs were administered to 11 general psychiatry residents on March 16, 2021. Teleconference software was used to connect faculty at work sites, residents at a simulation center, and volunteer patients at home. Before and after the CSE, residents and faculty were surveyed with Likert scale questions to evaluate their perceptions and experience. RESULTS: All virtual CSEs were completed successfully. Nine residents (82%) and 12 faculty (92%) responded to both surveys. Most participants (range, 67-83%) indicated that the virtual CSE was appropriate for assessing patient health and resident skills. Most participants (range, 56-100%) reported that the opening and closing of the interview, informational and affective cues, and rapport were adequately assessed. All participants agreed that suicidal and homicidal risks could be adequately assessed. Most faculty and residents (76%) believed that unique skills were required for telehealth interviews. Before the CSE, more faculty than residents believed that they received adequate training for the virtual CSE (P=.02); afterward, most participants thought that training was adequate (P=.46). More faculty than residents reported increased convenience with virtual assessments (both surveys, P<.01). CONCLUSION: Virtual CSEs were deemed feasible and appropriate. Further research is needed to identify the specific skills required to perform a virtual CSE and to clarify the potential limitations and benefits of this format.

Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS' and CO-MED's escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS' escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.