Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application to PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analysis of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a ß-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images, and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC data set including nine patients and two disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the data set and had a tendency to underestimate CHC counts for regions of interest (ROIs) containing relatively large amounts of cells (>50,000) when using the conventional enumeration method. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the ß-VAE embeddings achieved an F1 score of 0.80, matching the average performance of human annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

Optimizing inpatient bed management in a rural community-based hospital: a quality improvement initiative.

BACKGROUND: Appropriate use of available inpatient beds is an ongoing challenge for US hospitals. Historical capacity goals of 80% to 85% may no longer serve the intended purpose of maximizing the resources of space, staff, and equipment. Numerous variables affect the input, throughput, and output of a hospital. Some of these variables include patient demand, regulatory requirements, coordination of patient flow between various systems, coordination of processes such as bed management and patient transfers, and the diversity of departments (both inpatient and outpatient) in an organization. METHODS: Mayo Clinic Health System in the Southwest Minnesota region of the US, a community-based hospital system primarily serving patients in rural southwestern Minnesota and part of Iowa, consists of 2 postacute care and 3 critical access hospitals. Our inpatient bed usage rates had exceeded 85%, and patient transfers from the region to other hospitals in the state (including Mayo Clinic in Rochester, Minnesota) had increased. To address these quality gaps, we used a blend of Agile project management methodology, rapid Plan-Do-Study-Act cycles, and a proactive approach to patient placement in the medical-surgical units as a quality improvement initiative. RESULTS: During 2 trial periods of the initiative, the main hub hospital (Mayo Clinic Health System hospital in Mankato) and other hospitals in the region increased inpatient bed usage while reducing total out-of-region transfers. CONCLUSION: Our novel approach to proactively managing bed capacity in the hospital allowed the region's only tertiary medical center to increase capacity for more complex and acute cases by optimizing the use of historically underused partner hospital beds.

Is the Lifetime Malignancy Risk in United States Military Personnel Sustaining Combat-related Trauma Increased Because of Radiation Exposure From Diagnostic Imaging?

BACKGROUND: Patients with complex polytrauma in the military and civilian settings are often exposed to substantial diagnostic medical radiation because of serial imaging studies for injury diagnosis and subsequent management. This cumulative radiation exposure may increase the risk of subsequent malignancy. This is particularly true for combat-injured servicemembers who receive care at a variety of facilities worldwide. Currently, there is no coordinated effort to track the amount of radiation exposure each servicemember receives, nor a surveillance program to follow such patients in the long term. It is important to assess whether military servicemembers are exposed to excessive diagnostic radiation to mitigate or prevent such occurrences and monitor for carcinogenesis, when necessary. The cumulative amount of radiation exposure for combat-wounded and noncombat-wounded servicemembers has not been described, and it remains unknown whether diagnostic radiation exposure meets thresholds for an increased risk of carcinogenesis. QUESTIONS/PURPOSES: We performed this study to (1) quantify the amount of exposure for combat-wounded servicemembers based on medical imaging in the first year after injury and compare those exposures with noncombat-related trauma, and (2) determine whether the cumulative dose of radiation correlates to the Injury Severity Score (ISS) across the combat-wounded and noncombat-wounded population combined. METHODS: We performed a retrospective study of servicemembers who sustained combat or noncombat trauma and were treated at Walter Reed National Military Medical Center from 2005 to 2018. We evaluated patients using the Department of Defense Trauma Registry. After consolidating redundant records, the dataset included 3812 unique servicemember encounters. Three percent (104 of 3812) were excluded because of missing radiation exposure data in the electronic medical record. The final cohort included 3708 servicemembers who had combat or noncombat injury trauma, with a mean age at the time of injury of 26 ± 6 years and a mean ISS of 18 ± 12. The most common combat trauma mechanisms of injury were blast (in 65% [2415 of 3708 patients]), followed by high-velocity gunshot wounds (in 22% [815 of 3708 patients]). We calculated the cumulative diagnostic radiation dose exposure at 1 year post-traumatic injury in patients with combat-related trauma and those with noncombat trauma. We did this by multiplying the number of imaging studies by the standardized effective radiation dose for each imaging study type. We then performed analysis of variance for four data subsets (battle combat trauma, nonbattle civilian trauma, high ISS, and high radiation exposure [> 50 mSv]) independently. To evaluate whether the total number of imaging studies, radiation exposure, and ISS values differed between battle-wounded and nonbattle-wounded patients, we performed a pairwise t-test. RESULTS: The mean radiation exposure for combat-related injuries was 35 ± 26 mSv while the mean radiation exposure for noncombat-related injuries was 22 ± 33 mSv in the first year after injury. In the first year after trauma, 44% of patients (1626 of 3708) were exposed to high levels of radiation that were greater than 20 mSv, and 23% (840 of 3708) were exposed to very high levels of radiation that were greater than 50 mSv. Servicemembers with combat trauma-related injuries had eight more imaging studies than those who sustained noncombat injuries. Servicemembers with combat trauma injuries (35 ± 26 mSv) were exposed to more radiation (approximately 4 mSv) than patients treated for noncombat injuries (22 ± 33 mSv) (p = 0.01). We found that servicemembers with combat injuries had a higher ISS than servicemembers with noncombat trauma (p < 0.001). We found a positive correlation between radiation exposure and ISS for servicemembers. The positive relationship between radiation exposure and ISS held for combat trauma (r 2 = 0.24; p < 0.001), noncombat trauma (r 2 = 0.20; p < 0.001), servicemembers with a high ISS (r 2 = 0.10; p < 0.001), and servicemembers exposed to high doses of radiation (r 2 = 0.09; p < 0.001). CONCLUSION: These data should be used during clinical decision-making and patient counseling at military treatment facilities and might provide guidance to the Defense Health Agency. These recommendations will help determine whether the benefits of further imaging outweigh the risk of carcinogenesis. If not, we need to develop interdisciplinary clinical practice guidelines to reduce or minimize radiation exposure. It is important for treating physicians to seriously weigh the risk and benefits of every imaging study ordered because each test does not come without a cumulative risk. LEVEL OF EVIDENCE: Level III, therapeutic study.

Survival of anterior cruciate ligament reconstructions in active-duty military populations.

PURPOSE: Anterior cruciate ligament tears and anterior cruciate ligament reconstruction (ACLR) are common in young athletes. The modifiable and non-modifiable factors contributing to ACLR failure and reoperation are incompletely understood. The purpose of this study was to determine ACLR failure rates in a physically high-demand population and identify the patient-specific risk factors, including prolonged time between diagnosis and surgical correction, that portend failure. METHODS: A consecutive series of military service members with ACLR with and without concomitant procedures (meniscus [M] and/or cartilage [C]) done at military facilities between 2008 and 2011 was completed via the Military Health System Data Repository. This was a consecutive series of patients without a history of knee surgery for two years prior to the primary ACLR. Kaplan-Meier survival curves were estimated and evaluated with Wilcoxon test. Cox proportional hazard models calculated hazard ratios (HR) with 95% confidence intervals (95% CI) to identify demographic and surgical factors that influenced ACLR failure. RESULTS: Of the 2735 primary ACLRs included in the study, 484/2,735 (18%) experienced ACLR failure within four years, including (261/2,735) (10%) undergoing revision ACLR and (224/2,735) (8%) due to medical separation. The factors that increased failure include Army Service (HR 2.19, 95% CI 1.67, 2.87), > 180 days from injury to ACLR (HR 1.550, 95% CI 1.157, 2.076), tobacco use (HR 1.429 95% CI 1.174, 1.738), and younger patient age (HR 1.024, 95% CI 1.004, 1.044). CONCLUSION: The overall clinical failure rate of service members with ACLR is 17.7% with minimum four-year follow-up, where more patients are likely to fail due to revision surgery than medical separation. The cumulative probability of survival at 4 years was 78.5%. Smoking cessation and treating ACLR patients promptly are modifiable risk factors impacting either graft failure or medical separation. LEVEL OF EVIDENCE: Level III.

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum.

Mutations in the transcription factor Forkhead box p1 (FOXP1) are causative for neurodevelopmental disorders such as autism. However, the function of FOXP1 within the brain remains largely uncharacterized. Here, we identify the gene expression program regulated by FoxP1 in both human neural cells and patient-relevant heterozygous Foxp1 mouse brains. We demonstrate a role for FoxP1 in the transcriptional regulation of autism-related pathways as well as genes involved in neuronal activity. We show that Foxp1 regulates the excitability of striatal medium spiny neurons and that reduction of Foxp1 correlates with defects in ultrasonic vocalizations. Finally, we demonstrate that FoxP1 has an evolutionarily conserved role in regulating pathways involved in striatal neuron identity through gene expression studies in human neural progenitors with altered FOXP1 levels. These data support an integral role for FoxP1 in regulating signaling pathways vulnerable in autism and the specific regulation of striatal pathways important for vocal communication.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool.

BACKGROUND: Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry. METHODS: We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989-2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models' clinical utility. We characterized the magnitude and direction of model features. RESULTS: The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV). CONCLUSIONS: We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

FOXP1 negatively regulates intrinsic excitability in D2 striatal projection neurons by promoting inwardly rectifying and leak potassium currents.

Heterozygous loss-of-function mutations in the transcription factor FOXP1 are strongly associated with autism. Dopamine receptor 2 expressing (D2) striatal projection neurons (SPNs) in heterozygous Foxp1 (Foxp1+/-) mice have higher intrinsic excitability. To understand the mechanisms underlying this alteration, we examined SPNs with cell-type specific homozygous Foxp1 deletion to study cell-autonomous regulation by Foxp1. As in Foxp1+/- mice, D2 SPNs had increased intrinsic excitability with homozygous Foxp1 deletion. This effect involved postnatal mechanisms. The hyperexcitability was mainly due to down-regulation of two classes of potassium currents: inwardly rectifying (KIR) and leak (KLeak). Single-cell RNA sequencing data from D2 SPNs with Foxp1 deletion indicated the down-regulation of transcripts of candidate ion channels that may underlie these currents: Kcnj2 and Kcnj4 for KIR and Kcnk2 for KLeak. This Foxp1-dependent regulation was neuron-type specific since these same currents and transcripts were either unchanged, or very little changed, in D1 SPNs with cell-specific Foxp1 deletion. Our data are consistent with a model where FOXP1 negatively regulates the excitability of D2 SPNs through KIR and KLeak by transcriptionally activating their corresponding transcripts. This, in turn, provides a novel example of how a transcription factor may regulate multiple genes to impact neuronal electrophysiological function that depends on the integration of multiple current types - and do this in a cell-specific fashion. Our findings provide initial clues to altered neuronal function and possible therapeutic strategies not only for FOXP1-associated autism but also for other autism forms associated with transcription factor dysfunction.

A systematic review of engagement in care and health care utilization among older adults living with HIV and non-communicable diseases.

It is critical to understand health care engagement and utilization among older persons living with HIV (OPWH) who may have greater burden for non-communicable diseases. Following the PRISMA guidelines, a systematic review using 5 electronic databases was conducted to appraise and synthesize the current literature on the relationship of non-communicable diseases on engagement in care and health care utilization among OPWH. Original studies published in English between 2009 and 2019 were included, yielding 16 relevant articles. Overall, having co-morbid non-communicable diseases was associated with a decreased likelihood of initiating and adhering to ART. Being on ART and viral suppression were associated with better engagement in non-communicable disease care. Findings also suggest that an increasing number of co-morbidities is associated with higher health care utilization and financial burden. This review underscores the need for preventing and managing co-morbidities to enhance engagement in HIV care and that health care practitioners need to ensure that OPWH are engaged in care for both HIV and their co-morbid conditions by providing coordinated and integrated care.

A Machine-Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery.

PURPOSE: To develop a machine-learning algorithm and clinician-friendly tool predicting the likelihood of prolonged opioid use (>90 days) following hip arthroscopy. METHODS: The Military Data Repository was queried for all adult patients undergoing arthroscopic hip surgery between 2012 and 2017. Demographic, health history, and prescription records were extracted for all included patients. Opioid use was divided into preoperative use (30-365 days before surgery), perioperative use (30 days before surgery through 14 days after surgery), postoperative use (14-90 days after surgery), and prolonged postoperative use (90-365 days after surgery). Six machine-learning algorithms (Naïve Bayes, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Elastic Net Regularization, and artificial neural network) were developed. Area under the receiver operating curve and Brier scores were calculated for each model. Decision curve analysis was applied to assess clinical utility. Local-Interpretable Model-Agnostic Explanations were used to demonstrate factor weights within the selected model. RESULTS: A total of 6,760 patients were included, of whom 2,762 (40.9%) filled at least 1 opioid prescription >90 days after surgery. The artificial neural network model showed superior discrimination and calibration with area under the receiver operating curve = 0.71 (95% confidence interval 0.68-0.74) and Brier score = 0.21 (95% confidence interval 0.20-0.22). Postsurgical opioid use, age, and preoperative opioid use had the most influence on model outcome. Lesser factors included the presence of a psychological comorbidity and strong history of a substance use disorder. CONCLUSIONS: The artificial neural network model shows sufficient validity and discrimination for use in clinical practice. The 5 identified factors (age, preoperative opioid use, postoperative opioid use, presence of a mental health comorbidity, and presence of a preoperative substance use disorder) accurately predict the likelihood of prolonged opioid use following hip arthroscopy. LEVEL OF EVIDENCE: III, retrospective comparative prognostic trial.

Synthesis, Biological Evaluation and Docking Studies of Ring-Opened Analogues of Ipomoeassin F.

The plant-derived macrocyclic resin glycoside ipomoeassin F (Ipom-F) binds to Sec61α and significantly disrupts multiple aspects of Sec61-mediated protein biogenesis at the endoplasmic reticulum, ultimately leading to cell death. However, extensive assessment of Ipom-F as a molecular tool and a therapeutic lead is hampered by its limited production scale, largely caused by intramolecular assembly of the macrocyclic ring. Here, using in vitro and/or in cellula biological assays to explore the first series of ring-opened analogues for the ipomoeassins, and indeed all resin glycosides, we provide clear evidence that macrocyclic integrity is not required for the cytotoxic inhibition of Sec61-dependent protein translocation by Ipom-F. Furthermore, our modeling suggests that open-chain analogues of Ipom-F can interact with multiple sites on the Sec61α subunit, most likely located at a previously identified binding site for mycolactone and/or the so-called lateral gate. Subsequent in silico-aided design led to the discovery of the stereochemically simplified analogue 3 as a potent, alternative lead compound that could be synthesized much more efficiently than Ipom-F and will accelerate future ipomoeassin research in chemical biology and drug discovery. Our work may also inspire further exploration of ring-opened analogues of other resin glycosides.