CT Imaging Findings in Patients with Ovarian Cancer and Acute Abdominal Symptoms: Experience at a Tertiary Cancer Center.

PURPOSE: To evaluate computed tomography (CT) findings in patients with ovarian cancer presenting to a comprehensive cancer center's urgent care unit with acute abdominal symptoms. METHODS: This retrospective study included consecutive patients with ovarian cancer who underwent abdominal CT at a comprehensive cancer center's urgent care unit between January 1, 2018, and January 14, 2020, due to acute abdominal symptoms. Two abdominal radiologists reviewed the abdominal CT reports, categorizing imaging findings as follows: (a) no new or acute finding, (b) new or increased bowel or gastric obstruction, (c) new or increased ascites, (d) new or increased peritoneal carcinomatosis, (e) new or increased nonperitoneal metastases, (f) new inflammatory or infectious changes, (g) new or increased hydronephrosis, (h) new or increased biliary dilatation, (i) new vascular complications, or (j) new bowel perforation. RESULTS: A total of 200 patients (mean age, 59 years; range, 22-87) underwent a total of 259 abdominal CT scans, of which 217/259 (83.8%) scans were found to have new or increased findings. A total of 115/259 (44.4%) scans had only one finding while 102/259 (39.4%) scans had 2 or more findings. Altogether, 382 new or increased findings were detected: findings were most commonly related to bowel or gastric obstruction (92/382, 24.1%) with small bowel obstruction being the most common finding (80/382, 20.9%); ascites (78/382, 20.4%); peritoneal carcinomatosis (62/382, 16.2%); and nonperitoneal metastases (62/382, 16.2%). Inflammatory or infectious findings accounted for 30/382 (7.9%) findings. CONCLUSION: Most patients with ovarian cancer presenting with acute abdominal had relevant positive findings on abdominal CT, with small bowel obstruction being the most common finding.

Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports.

The development of digital cancer twins relies on the capture of high-resolution representations of individual cancer patients throughout the course of their treatment. Our research aims to improve the detection of metastatic disease over time from structured radiology reports by exposing prediction models to historical information. We demonstrate that Natural language processing (NLP) can generate better weak labels for semi-supervised classification of computed tomography (CT) reports when it is exposed to consecutive reports through a patient's treatment history. Around 714,454 structured radiology reports from Memorial Sloan Kettering Cancer Center adhering to a standardized departmental structured template were used for model development with a subset of the reports included for validation. To develop the models, a subset of the reports was curated for ground-truth: 7,732 total reports in the lung metastases dataset from 867 individual patients; 2,777 reports in the liver metastases dataset from 315 patients; and 4,107 reports in the adrenal metastases dataset from 404 patients. We use NLP to extract and encode important features from the structured text reports, which are then used to develop, train, and validate models. Three models-a simple convolutional neural network (CNN), a CNN augmented with an attention layer, and a recurrent neural network (RNN)-were developed to classify the type of metastatic disease and validated against the ground truth labels. The models use features from consecutive structured text radiology reports of a patient to predict the presence of metastatic disease in the reports. A single-report model, previously developed to analyze one report instead of multiple past reports, is included and the results from all four models are compared based on accuracy, precision, recall, and F1-score. The best model is used to label all 714,454 reports to generate metastases maps. Our results suggest that NLP models can extract cancer progression patterns from multiple consecutive reports and predict the presence of metastatic disease in multiple organs with higher performance when compared with a single-report-based prediction. It demonstrates a promising automated approach to label large numbers of radiology reports without involving human experts in a time- and cost-effective manner and enables tracking of cancer progression over time.

A Continuum-Based Framework as a Practice Assessment Tool for Integration of General Health in Behavioral Health Care.

OBJECTIVE: General medical conditions among patients with mental and substance use disorders are often not adequately detected and managed in behavioral health settings. The project described in this study sought to investigate how behavioral health clinics used a new general health integration (GHI) framework to assess integration efforts. METHODS: Eleven community behavioral health clinics were introduced to a new continuum-based framework for use in GHI assessment. A multidisciplinary team in each clinic was tasked with identifying current GHI interventions according to several framework stages (preliminary, intermediate 1, intermediate 2, and advanced) among eight domains and 15 related subdomains. The clinics provided feedback on the framework's utility for GHI planning and advancement. RESULTS: The clinics could readily identify distinct integration interventions within each domain and subdomain. Clinics reported strengths in the domains of trauma-informed care, self-management support, social service linkages, and quality improvement. Opportunities for future advancement in integration of general health services were identified in the major domains of screening and referral, evidence-based treatments, care teams, and sustainability. The clinics also described potential benefits of the framework to further advance and implement GHI best practices. CONCLUSIONS: The clinics could use the framework as a practice assessment of integration efforts with minimal guidance and identify several evidence-based integration interventions. Some GHI interventions were seen as strengths and as opportunities for further advancement. Longitudinal evaluation among a larger number of and more geographically diverse behavioral health clinics seeking to advance their GHI practices will improve the GHI framework's generalizability and potential for dissemination.

Integrating Primary Care Into Assertive Community Treatment.

OBJECTIVE: This program evaluation examined integration of primary care nurse practitioners into assertive community treatment (ACT). METHODS: From January to June 2019, primary care nurse practitioners in a postgraduate fellowship program were assigned to five ACT teams (N=305 participants). Focus groups explored staff members' and participants' experiences. Screening rates for hemoglobin A1c and cholesterol for ACT participants were compared over time. RESULTS: Staff and participants in ACT described improved engagement in primary care, citing benefit from colocation and consultation. Field visits were not found to be an efficient use of the primary care nurse practitioners' time to serve most ACT participants. A significant increase in screening was observed after 6 months for the ACT teams with integrated primary care. CONCLUSIONS: An integrated primary care nurse practitioner readily available for participant engagement and consultation with the ACT team, using a cardiometabolic registry to guide care, may offer a sustainable model of integration.

Characterization of imine reductases in reductive amination for the exploration of structure-activity relationships.

Imine reductases (IREDs) have shown great potential as catalysts for the asymmetric synthesis of industrially relevant chiral amines, but a limited understanding of sequence activity relationships makes rational engineering challenging. Here, we describe the characterization of 80 putative and 15 previously described IREDs across 10 different transformations and confirm that reductive amination catalysis is not limited to any particular subgroup or sequence motif. Furthermore, we have identified another dehydrogenase subgroup with chemoselectivity for imine reduction. Enantioselectivities were determined for the reduction of the model substrate 2-phenylpiperideine, and the effect of changing the reaction conditions was also studied for the reductive aminations of 1-indanone, acetophenone, and 4-methoxyphenylacetone. We have performed sequence-structure analysis to help explain clusters in activity across a phylogenetic tree and to inform rational engineering, which, in one case, has conferred a change in chemoselectivity that had not been previously observed.

The impact of body composition parameters on ipilimumab toxicity and survival in patients with metastatic melanoma.

BACKGROUND: Body composition is an important predictor of drug toxicity and outcome. Ipilimumab (Ipi), a monoclonal antibody used to treat metastatic melanoma, has specific toxicities. No validated biomarkers that predict Ipi toxicity and efficacy exist. Also, the impact of Ipi on body composition has not been established. METHODS: Patients with metastatic melanoma treated with Ipi between 2009 and 2015 were included. Body composition was assessed by computed tomography at baseline and after four cycles of Ipi. Sarcopenia and low muscle attenuation (MA) were defined using published cut-points. All adverse events (AEs) and immune-related AEs (irAEs) were recorded (Common Terminology Criteria For Adverse Event V.4.0). RESULTS: Eighty-four patients were included in this study (62% male, median age 54 years). At baseline, 24% were sarcopenic and 33% had low MA. On multivariate analysis, sarcopenia and low MA were significantly associated with high-grade AEs (OR=5.34, 95% CI: 1.15-24.88, P=0.033; OR=5.23, 95% CI: 1.41-19.30, P=0.013, respectively), and low MA was associated with high-grade irAEs (OR=3.57, 95% CI: 1.09-11.77, P=0.036). Longitudinal analysis (n=59) revealed significant reductions in skeletal muscle area (SMA), total body fat-free mass, fat mass (all P<0.001) and MA (P=0.030). Mean reduction in SMA was 3.3%/100 days (95% CI: -4.48 to -1.79%, P<0.001). A loss of SMA ⩾7.5%/100 days (highest quartile) was a significant predictor of overall survival in multivariable Cox regression analysis (HR: 2.1, 95% CI: 1.02-4.56, P=0.046). CONCLUSIONS: Patients with sarcopenia and low MA are more likely to experience severe treatment-related toxicity to Ipi. Loss of muscle during treatment was predictive of worse survival. Treatments to increase muscle mass and influence outcome warrant further investigation.

Receptor Activity-modifying Protein-directed G Protein Signaling Specificity for the Calcitonin Gene-related Peptide Family of Receptors.

The calcitonin gene-related peptide (CGRP) family of G protein-coupled receptors (GPCRs) is formed through the association of the calcitonin receptor-like receptor (CLR) and one of three receptor activity-modifying proteins (RAMPs). Binding of one of the three peptide ligands, CGRP, adrenomedullin (AM), and intermedin/adrenomedullin 2 (AM2), is well known to result in a Gαs-mediated increase in cAMP. Here we used modified yeast strains that couple receptor activation to cell growth, via chimeric yeast/Gα subunits, and HEK-293 cells to characterize the effect of different RAMP and ligand combinations on this pathway. We not only demonstrate functional couplings to both Gαs and Gαq but also identify a Gαi component to CLR signaling in both yeast and HEK-293 cells, which is absent in HEK-293S cells. We show that the CGRP family of receptors displays both ligand- and RAMP-dependent signaling bias among the Gαs, Gαi, and Gαq/11 pathways. The results are discussed in the context of RAMP interactions probed through molecular modeling and molecular dynamics simulations of the RAMP-GPCR-G protein complexes. This study further highlights the importance of RAMPs to CLR pharmacology and to bias in general, as well as identifying the importance of choosing an appropriate model system for the study of GPCR pharmacology.

"Real world" atypical antipsychotic prescribing practices in public child and adolescent inpatient settings.

This article examines the factors that influence antipsychotic use among youth treated in public inpatient facilities. By combining data from 11 focus groups, a survey of 43 researchers and clinicians, and a chart review of 100 closed patient charts, we investigated the interplay between physicians' and staff members' perceptions of problems related to antipsychotic prescribing, their beliefs concerning optimal approaches, their actual recorded prescribing behaviors, and the discrepancies between their beliefs and their recorded practices. We discovered that antipsychotics are prescribed broadly to treat a variety of conditions, including nonpsychotic disorders among children in public inpatient facilities. Despite overall expert consensus regarding "best practices," physicians described systemic obstacles that prevent the application of these practices, and our data confirmed that best practices are not always followed. Future research should be done with this patient population and should investigate the factors that influence antipsychotic use among inpatient youth.