Natural variation in protein kinase D modifies alcohol sensitivity in Caenorhabditis elegans.

Differences in naïve alcohol sensitivity between individuals are a strong predictor of later life alcohol use disorders (AUD). However, the genetic bases for alcohol sensitivity (beyond ethanol metabolism) and pharmacological approaches to modulate alcohol sensitivity remain poorly understood. We used a high-throughput behavioral screen to measure acute behavioral sensitivity to alcohol, a model of intoxication, in a genetically diverse set of over 150 wild strains of the nematode Caenorhabditis elegans . We performed a genome-wide association study to identify loci that underlie natural variation in alcohol sensitivity. We identified five quantitative trait loci (QTL) and further show that variants in the C. elegans ortholog of protein kinase D, dkf-2 , likely underlie the chromosome V QTL. We found that resistance to intoxication was conferred by dkf-2 loss-of-function mutations as well as partly by a PKD inhibitor in a dkf-2 -dependent manner. Protein kinase D might represent a conserved, druggable target to modify alcohol sensitivity with application towards AUD. Significance statement: Here, we identify a novel role for protein kinase D ( dkf-2 ) in C. elegans alcohol sensitivity. PKD, like protein kinase C, might represent a newly discovered druggable target to modify alcohol response. Identifying causal variants in genes of the remaining loci will likewise provide new insights into the genetic basis of variation in naïve alcohol sensitivity in C. elegans and other organisms.

Non-apoptotic role of EGL-1 in exopher production and neuronal health in Caenorhabditis elegans.

While traditionally studied for their pro-apoptotic functions, recent research suggests BH3-only proteins also have non-apoptotic roles. Here, we find that EGL-1, the BH3-only protein in Caenorhabditis elegans, promotes the cell-autonomous production of exophers in adult neurons. Exophers are large, micron-scale vesicles that are ejected from the cell and contain cellular components such as mitochondria. EGL-1 facilitates exopher production potentially through regulation of mitochondrial dynamics. Moreover, an endogenous, low level of EGL-1 expression appears to benefit dendritic health. Our findings provide insights into the mechanistic role of BH3-only protein in mitochondrial dynamics, downstream exopher production, and ultimately neuronal health.

Suppressing APOE4-induced mortality and cellular damage by targeting VHL.

Mortality rate increases with age and can accelerate upon extrinsic or intrinsic damage to individuals. Identifying factors and mechanisms that curb population mortality rate has wide-ranging implications. Here, we show that targeting the VHL-1 (Von Hippel-Lindau) protein suppresses C. elegans mortality caused by distinct factors, including elevated reactive oxygen species, temperature, and APOE4, the genetic variant that confers high risks of neurodegeneration in Alzheimer's diseases and all-cause mortality in humans. These mortality factors are of different physical-chemical nature, yet result in similar cellular dysfunction and damage that are suppressed by deleting VHL-1. Stabilized HIF-1 (hypoxia inducible factor), a transcription factor normally targeted for degradation by VHL-1, recapitulates the protective effects of deleting VHL-1. HIF-1 orchestrates a genetic program that defends against mitochondrial abnormalities, excess oxidative stress, cellular proteostasis dysregulation, and endo-lysosomal rupture, key events that lead to mortality. Genetic Vhl inhibition also alleviates cerebral vascular injury and synaptic lesions in APOE4 mice, supporting an evolutionarily conserved mechanism. Collectively, we identify the VHL-HIF axis as a potent modifier of APOE4 and mortality and propose that targeting VHL-HIF in non-proliferative animal tissues may suppress tissue injuries and mortality by broadly curbing cellular damage.

Clinical Implementation of an Artificial Intelligence Tool in the Detection and Management of Pneumothoraces in Patients With COVID-19.

In this report, we present a series involving critically ill patients with known coronavirus disease (COVID-19) infection where a portable X-ray machine equipped with artificial intelligence (AI) software aided in the urgent radiographic diagnosis of pneumothorax. These cases demonstrate how real-world clinical employment of AI tools capable of analyzing and prioritizing studies in the radiologist's worklist can potentially lead to earlier detection of emergent findings like pneumothorax. The use of AI tools in this manner has the potential to both improve radiology workflow and add significant clinical value in managing critically ill patient populations, such as those with severe COVID-19 infection.

mScarlet and split fluorophore mScarlet resources for plasmid-based CRISPR/Cas9 knock-in in C. elegans.

Fluorescent proteins allow the expression of a gene and the behavior of its protein product to be observed in living animals. The ability to create endogenous fluorescent protein tags via CRISPR genome engineering has revolutionized the authenticity of this expression, and mScarlet is currently our first-choice red fluorescent protein (RFP) for visualizing gene expression in vivo . Here, we have cloned versions of mScarlet and split fluorophore mScarlet previously optimized for C. elegans into the SEC-based system of plasmids for CRISPR/Cas9 knock-in. Ideally, an endogenous tag will be easily visible while not interfering with the normal expression and function of the targeted protein. For low molecular weight proteins that are a fraction of the size of a fluorescent protein tag (e.g. GFP or mCherry) and/or proteins known to be non-functional when tagged in this way, split fluorophore tagging could be an alternative. Here, we used CRISPR/Cas9 knock-in to tag three such proteins with split-fluorophore wrmScarlet: HIS-72, EGL-1, and PTL-1. Although we find that split fluorophore tagging does not disrupt the function of any of these proteins, we were unfortunately unable to observe the expression of most of these tags with epifluorescence, suggesting that split fluorophore tags are often very limited as endogenous reporters. Nevertheless, our plasmid toolkit provides a new resource that enables straightforward knock-in of either mScarlet or split mScarlet in C. elegans.

Evoking natural thermal perceptions using a thin-film thermoelectric device with high cooling power density and speed.

Multimodal sensory feedback from upper-limb prostheses can increase their function and usability. Here we show that intuitive thermal perceptions during cold-object grasping with a prosthesis can be restored in a phantom hand through targeted nerve stimulation via a wearable thin-film thermoelectric device with high cooling power density and speed. We found that specific regions of the residual limb, when thermally stimulated, elicited thermal sensations in the phantom hand that remained stable beyond 48 weeks. We also found stimulation sites that selectively elicited sensations of temperature, touch or both, depending on whether the stimulation was thermal or mechanical. In closed-loop functional tasks involving the identification of cold objects by amputees and by non-amputee participants, and compared with traditional bulk thermoelectric devices, the wearable thin-film device reliably elicited cooling sensations that were up to 8 times faster and up to 3 times greater in intensity while using half the energy and 1/600th the mass of active thermoelectric material. Wearable thin-film thermoelectric devices may allow for the non-invasive restoration of thermal perceptions during touch.

Learning science alongside peers with intellectual and developmental disabilities.

"Lifelong Learning with Friends" provides diversity training to college students by having them learn science alongside adults with intellectual and developmental disabilities (IDDs). Volunteers showed increased interest in IDD-focused research, social interaction, and advocacy.

The Promise of an Evolutionary Perspective of Alcohol Consumption.

The urgent need for medical treatments of alcohol use disorders has motivated the search for novel molecular targets of alcohol response. Most studies exploit the strengths of lab animals without considering how these and other species may have adapted to respond to alcohol in an ecological context. Here, we provide an evolutionary perspective on the molecular and genetic underpinnings of alcohol consumption by reviewing evidence that alcohol metabolic enzymes have undergone adaptive evolution at 2 evolutionary junctures: first, to enable alcohol consumption accompanying the advent of a frugivorous diet in a primate ancestor, and second, to decrease the likelihood of excessive alcohol consumption concurrent with the spread of agriculture and fermentation in East Asia. By similarly considering how diverse vertebrate and invertebrate species have undergone natural selection for alcohol responses, novel conserved molecular targets of alcohol are likely be discovered that may represent promising therapeutic targets.

Transgenerational effects of alcohol on behavioral sensitivity to alcohol in Caenorhabditis elegans.

Alcohol abuse and dependence have a substantial heritable component. Although the genome has been considered the sole vehicle of heritable phenotypes, recent studies suggest that drug or alcohol exposure may induce alterations in gene expression that are transmitted across generations. Still, the transgenerational impact of alcohol use (and abuse) remains largely unexplored in part because multigenerational studies using rodent models present challenges for time, sample size, and genetic heterogeneity. Here, we took advantage of the extremely short generation time, large broods, and clonal form of reproduction of the nematode Caenorhabditis elegans. We developed a model of pre-fertilization parental alcohol exposure to test alterations in behavioral responses to acute alcohol treatment (referred to in short as intoxication) in subsequent F1, F2 and F3 generations. We found that chronic and intermittent alcohol-treatment paradigms resulted in opposite changes to intoxication sensitivity of F3 progeny that were only apparent when controlling for yoked trials. Chronic alcohol-treatment paradigm in the parental generation resulted in alcohol-naïve F3 progeny displaying moderate resistance to intoxication. Intermittent treatment resulted in alcohol-naïve F3 progeny displaying moderate hypersensitivity to intoxication. Further study of these phenomena using this new C. elegans model may yield mechanistic insights into how transgenerational effects may occur in other animals.

Coronary Artery Calcium Scoring: Current Status and Future Directions.

Coronary artery calcium (CAC) scores obtained from CT scans have been shown to be prognostic in assessment of the risk for development of cardiovascular diseases, facilitating the prediction of outcome in asymptomatic individuals. Currently, several methods to calculate the CAC score exist, and each has its own set of advantages and disadvantages. Agatston CAC scoring is the most extensively used method. CAC scoring is currently recommended for use in asymptomatic individuals to predict the risk of developing cardiovascular diseases and the disease-specific mortality. In specific subsets of patients, the CAC score has also been recommended for reclassifying cardiovascular risk and aiding in decision making when planning primary prevention interventions such as statin therapy. The progression of CAC scores on follow-up images has been shown to be linked to risk of myocardial infarction and cardiovascular mortality. While the CAC score is a validated tool used clinically, several challenges, including various pitfalls associated with the acquisition, calculation, and interpretation of the score, prevent more widespread adoption of this metric. Recent research has been focused extensively on strategies to improve existing scoring methods, including measuring calcium attenuation, detecting microcalcifications, and focusing on extracoronary calcifications, and on strategies to improve image acquisition. A better understanding of CAC scoring approaches will help radiologists and other physicians better use and interpret these scores in their workflows. An invited commentary by S. Gupta is available online. Online supplemental material is available for this article. ©RSNA, 2022.

Collaborative Development of a PACS-Integrated Quality Control Dashboard: a Single Institutional Experience.

Regular communication between technologists and radiologists is necessary for maintaining optimal diagnostic image quality throughout a radiology practice. In a large hospital system with multiple sites, this task becomes increasingly difficult without simultaneously causing significant disruptions in the clinical workflow and decreased throughput. Thus, establishing a system for quality control reporting that enables effective communication in a seamless and convenient manner is imperative. In this report, we describe the development of a new integrated system, in collaboration with our PACS vendor, with tools that allow for instant reporting of quality errors and dashboards providing real-time up-to-date quality data across our hospital system, directly accessible from PACS. To date, 8,167 quality reports have been logged in our new system with roughly 355 submissions per month. Early user engagement and consensus feedback among radiologists and technologists have been positive suggesting an overall improvement from prior systems. We hope this report can help inform other radiology enterprises seeking to improve quality control reporting within their clinical practice.

Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients' CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.

Seamless Integration of Artificial Intelligence Into the Clinical Environment: Our Experience With a Novel Pneumothorax Detection Artificial Intelligence Algorithm.

Although interest in artificial intelligence (AI) has exploded in recent years and led to the development of numerous commercial and noncommercial algorithms, the process of implementing such tools into day-to-day clinical practice is rarely described in the burgeoning AI literature. In this report, we describe our experience with the successful integration of an AI-enabled mobile x-ray scanner with an FDA-approved algorithm for detecting pneumothoraces into an end-to-end solution capable of extracting, delivering, and prioritizing positive studies within our thoracic radiology clinical workflow. We also detail several sample cases from our AI algorithm and associated PACS workflow in action to highlight key insights from our experience. We hope this report can help inform other radiology enterprises seeking to evaluate and implement AI-related workflow solutions into daily clinical practice.

The Past, Present, and Future (Liquid Biopsy) of Serum Tumor Markers in Lung Cancer: A Primer for the Radiologist.

ABSTRACT: Lung cancer continues to be a major cause of death throughout the world. The ability to both accurately diagnose lung cancer in its early stages and monitor response to treatment is essential to reducing the morbidity and mortality associated with the disease. Serum tumor markers have been identified as potential biomarkers that may aid in lung cancer diagnosis and surveillance. These markers, when combined with cross-sectional imaging, may result in more robust screening and surveillance protocols. The future role of serum tumor markers in lung cancer includes the advancement of "liquid biopsies," in which peripheral blood samples are analyzed for tumor components without the need for a tissue biopsy.

COVID-19 infection complicated by acute ST-elevation myocardial infarction.

Clinicians should be aware of the potential for cardiovascular involvement in COVID-19 infection. Coronavirus disease-2019 (COVID-19) is a viral illness caused by severe acute respiratory syndrome-coronavirus-2. While it primarily causes a respiratory illness, a number of important cardiovascular implications have been reported. We describe a patient presenting with COVID-19 whose hospital course was complicated by ST elevation myocardial infarction requiring percutaneous coronary intervention. The goal is to help clinicians gain awareness of the possibility of cardiovascular disease in COVID-19 infection, and maintain a high index of suspicion particularly for patients with risk factors or a prior history of cardiovascular disease.

Considerations in imaging interpretations for colitis in critically ill patients during the COVID-19 era.

OBJECTIVE: The study aims to demonstrate risk factors for colitis in intensive care unit patients with and without coronavirus disease 2019 (COVID-19). METHODS: Retrospective review was performed to identify intensive care unit (ICU) patients with the diagnosis of COVID-19 with computed tomography (CT) between March 20 and December 31, 2020. ICU patients without COVID-19 diagnosis with CT between March 20 and May 10, 2020 were also identified. CT image findings of colitis or terminal ileitis as well as supportive treatment including ventilator, vasopressors, or extracorporeal membrane oxygenation (ECMO) were recorded. Statistical analysis was performed to determine if clinical factors differed in patients with and without positive CT finding. RESULTS: Total 61 ICU patients were selected, including 32 (52%) COVID-19-positive patients and 29 (48%) non-COVID-19 patients. CT findings of colitis or terminal ileitis were identified in 27 patients (44%). Seventy-four percent of the patients with positive CT findings (20/27) received supportive therapies prior to CT, while 56% of the patients without abnormal CT findings (19/34) received supportive therapies. Vasopressor treatment was significantly associated with development of colitis and/or terminal ileitis (p = 0.04) and COVID-19 status was not significantly different between these groups (p = 0.07). CONCLUSIONS: In our study, there was significant correlation between prior vasopressor therapy and imaging findings of colitis or terminal ileitis in ICU patients, independent of COVID-19 status. Our observation raises a possibility that the reported COVID-19-related severe gastrointestinal complications and potential poor outcome could have been confounded by underlying severe critically ill status, and warrants a caution in diagnosis of gastrointestinal complication.

Are asymptomatic gastrointestinal findings on imaging more common in COVID-19 infection? Study to determine frequency of abdominal findings of COVID-19 infection in patients with and without abdominal symptoms and in patients with chest-only CT scans.

PURPOSE: To identify incidence of abdominal findings in COVID-19 patients with and without abdominal symptoms on various imaging modalities including chest-only CT scans and to correlate them with clinical, laboratory and chest CT findings. MATERIALS AND METHODS: In this retrospective study, we searched our clinical database between March 1st, 2020 and May 22nd, 2020 to identify patients who had positive real-time reverse transcriptase polymerase chain reaction (RT-PCR) on throat swabs for COVID-19, had availability of clinical, laboratory information and had availability of CT scan of chest or abdominal radiograph, abdominal ultrasound or CT scan within 2 weeks of the diagnosis. Abdominal imaging findings on all imaging modalities were documented. Chest CT severity score (CT-SS) was assessed in all patients. Clinical and laboratory findings were recorded from the electronic medical record. Statistical analysis was performed to determine correlation of abdominal findings with CT-SS, clinical and laboratory findings. RESULTS: Out of 264 patients with positive RT-PCR, 73 patients (38 males and 35 females; 35 African American) with mean age of 62.2 (range 21-94) years were included. The median CTSS was 13.5 (IQR 75-25 18-8). Most common finding in the abdomen on CT scans (n = 72) were in the gastrointestinal system in 13/72 patients (18.1%) with fluid-filled colon without wall thickening or pericolonic stranding (n = 12) being the most common finding. Chest-only CT (n = 49) found bowel findings in 3 patients. CTSS did not differ in terms of age, sex, race or number of comorbidities but was associated with longer duration of hospitalization (p = 0.0.0256), longer intensive care unit stay (p = 0.0263), more frequent serum lactate dehydrogenase elevation (p = 0.0120) and serum C-reactive protein elevation (p = 0.0402). No statistically significant correlation of occurrence of bowel abnormalities with CTSS, clinical or laboratory features. Deep venous thrombosis was seen in 7/72 patients (9.8%) with three patients developing pulmonary embolism CONCLUSION: Abnormal bowel is the most common finding in the abdomen in patients with COVID-19 infection, is often without abdominal symptoms and occurs independent of severity of pulmonary involvement, other clinical and laboratory features.

Non-traumatic Tension Gastrothorax: A Potential Mimicker of Tension Pneumothorax.

Tension gastrothorax is a rare, life-threatening clinical condition caused by intrathoracic herniation of the stomach through a diaphragmatic defect which becomes increasingly distended over time. If not recognized promptly, this can rapidly progress to respiratory distress, mediastinal shift, and hemodynamic compromise. Initial clinical presentation and imaging findings closely mirror those of tension pneumothorax, confounding diagnosis and potentially leading to unnecessary interventions with increased risk of morbidity and mortality. Here, we present a case of an elderly female who presented with a non-traumatic tension gastrothorax and a review of key imaging features and strategies to aid in recognition and accurate diagnosis of this emergent clinical entity.

CT Coronary Angiography Fractional Flow Reserve: New Advances in the Diagnosis and Treatment of Coronary Artery Disease.

Coronary artery disease (CAD) remains the most common cardiovascular disease, accounting for 6% of all Emergency Department visits and 27% of all Emergency Department hospitalizations.1 Invasive coronary angiography with fractional flow reserve (FFR) remains the gold standard to assess for hemodynamically stenosis in CAD patients. However, for low- and intermediate-risk patients, noninvasive modalities have started to gain favor as patients with stable CAD who received optimal medical therapy did as well as patients who underwent percutaneous coronary intervention.2 This led to the incorporation of FFRCT. cCTA provides good spatial resolution for evaluating stenosis. FFR provides additional information regarding whether the stenosis is hemodynamically significant. FFR is the ratio of maximum blood flow in a stenotic artery to the maximum blood flow through that artery without stenosis.3 Computational fluid dynamics involved in FFRCT is based on Navier-Stokes equations, allowing the assessment of pressure and flow across coronary arteries. Limitations do exist with FFRCT which includes false-positive results due to step artifact and left ventricular hypertrophy, as well as manual segmentation and ostial stenosis, which can cause false-negative results. However, there are improvements on the horizon including artificial intelligence-driven computation of FFR and the utilization of virtual stenting for surgical planning. The purpose of this review is to describe the clinical validation, underlying mechanism, and implementation of FFRCT.

Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.

We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction, respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.