"What Do I Say? How Do I Say it?" Twitter as a Knowledge Dissemination Tool for Mental Health Research.

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

Within-herd transmission of Mycoplasma bovis infections after initial detection in dairy cows.

Mycoplasma bovis outbreaks in cattle, including pathogen spread between age groups, are not well understood. Our objective was to estimate within-herd transmission across adult dairy cows, youngstock, and calves. Results from 3 tests (PCR, ELISA, and culture) per cow and 2 tests (PCR and ELISA) per youngstock and calf were used in an age-stratified susceptible-infected-removed/recovered (SIR) model to estimate within-herd transmission parameters, pathways, and potential effects of farm management practices. A cohort of adult cows, youngstock, and calves on 20 Dutch dairy farms with a clinical outbreak of M. bovis in adult cows were sampled, with collection of blood, conjunctival fluid, and milk from cows, and blood and conjunctival fluid from calves and youngstock, 5 times over a time span of 12 wk. Any individual with at least one positive laboratory test was considered M. bovis-positive. Transmission dynamics were modeled using an age-stratified SIR model featuring 3 age strata. Associations with farm management practices were explored using Fisher's exact tests and Poisson regression. Estimated transmission parameters were highly variable among herds and cattle age groups. Notably, transmission from cows to cows, youngstock, or to calves was associated with R-values ranging from 1.0 to 80 secondarily infected cows per herd, 1.2 to 38 secondarily infected youngstock per herd, and 0.1 to 91 secondarily infected calves per herd, respectively. In case of transmission from youngstock to youngstock, calves or to cows, R-values were 0.7 to 96 secondarily infected youngstock per herd, 1.1 to 76 secondarily infected calves per herd, and 0.1 to 107 secondarily infected cows per herd. For transmission from calves to calves, youngstock or to cows, R-values were 0.5 to 60 secondarily infected calves per herd, 1.1 to 41 secondarily infected youngstock per herd, and 0.1 to 47 secondarily infected cows per herd. Among on-farm transmission pathways, cow-to-youngstock, cow-to-calf, and cow-to-cow were identified as most significant contributors, with calf-to-calf and calf-to-youngstock also having noteworthy roles. Youngstock-to-youngstock was also implicated, albeit to a lesser extent. Whereas the primary focus was a clinical outbreak of M. bovis among adult dairy cows, it was evident that transmission extended to calves and youngstock, contributing to overall spread. Factors influencing transmission and specific transmission pathways were associated with internal biosecurity (separate caretakers for various age groups, number of people involved), external biosecurity (contractors, external employees), as well as indirect transmission routes (number of feed and water stations).

An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests.

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging.

INTRODUCTION: Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY: A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS: For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS: When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

Incorporating infectious duration-dependent transmission into Bayesian epidemic models.

Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.

Prevalence and significance of incidental findings on computed tomography pulmonary angiograms: A retrospective cohort study.

OBJECTIVE: The purpose of this study was to analyze the prevalence and significance of incidental findings on computed tomography pulmonary angiography (CTPA) studies and to assess the diagnostic yield of CTPA in identifying an alternate diagnosis to pulmonary embolism (PE) on PE negative exams. METHODS: All patients who had a CTPA exam for PE evaluation between Jan 2016 and Dec 2018 with a negative PE result were included in the study. A total of 2083 patients were identified. We retrospectively queried the electronic medical record and the radiology report and recorded the following: Age, Sex, BMI, Patient location and Incidental findings. The incidental findings were classified into type 1 (Alternate diagnosis other than PE which could explain the patient's symptoms), type 2 (non-emergent findings which needed further work up) and type 3 findings (non-emergent findings which did not need further work up). Logistic regression analysis was performed to determine what factors affected the probability of finding a type 1 incidental (alternate diagnosis) or a type 2 incidental. RESULTS: 74.5% of the patients in our study had at least one incidental finding. Type 1 incidental findings (alternate diagnosis to PE) were found in 864 patients (41.5%). The most common type 1 finding was pneumonia followed by fluid overload. Male sex, increased age and lower BMI were significantly associated with increased odds of a type 1 incidental(p < 0.05). Similarly, all the patient locations had significantly different odds of finding a type-1 incidental, with ICU having the highest odds, followed by inpatient, ED and outpatient locations (p < 0.05). 563 patients (27%) had at least one type 2 incidental findings and the most common type 2 findings were progressive lung malignancy/ metastatic disease and new pulmonary nodule. Increased age was significantly associated with the probability of a type 2 finding (p < 0.05). CONCLUSIONS: CTPA may suggest an alternative diagnosis to pulmonary embolism in approximately 40% of the patients with a negative study. The probability of finding an alternate diagnosis (type 1 incidental) is higher in elderly patients and in patients referred from ICU and inpatient units.

Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.

OBJECTIVES: Despite the robust diagnostic performance of MRI-based radiomic features for differentiating between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) reported on prior studies, the best sequence or a combination of sequences and model performance across various machine learning pipelines remain undefined. Herein, we compare the diagnostic performance of multiple radiomics-based models to differentiate GBM from PCNSL. METHODS: Our retrospective study included 94 patients (34 with PCNSL and 60 with GBM). Model performance was assessed using various MRI sequences across 45 possible model and feature selection combinations for nine different sequence permutations. Predictive performance was assessed using fivefold repeated cross-validation with five repeats. The best and worst performing models were compared to assess differences in performance. RESULTS: The predictive performance, both using individual and a combination of sequences, was fairly robust across multiple top performing models (AUC: 0.961-0.977) but did show considerable variation between the best and worst performing models. The top performing individual sequences had comparable performance to multiparametric models. The best prediction model in our study used a combination of ADC, FLAIR, and T1-CE achieving the highest AUC of 0.977, while the second ranked model used T1-CE and ADC, achieving a cross-validated AUC of 0.975. CONCLUSION: Radiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences. KEY POINTS: • Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably. • ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model. • Embedded feature selection models perform better than models using a priori feature reduction.

CARJ 2021: Year in Review.

The past year has been one of unprecedented challenge for the modern world and especially the medical profession. This review explores some of the most impactful topics published in the CARJ during the COVID-19 pandemic including physician wellbeing and burnout, patient safety, and technological innovations including dual energy CT, quantitative imaging and ultra-high frequency ultrasound. The impact of the COVID-19 pandemic on trainee education is discussed and evidence-based tips for providing value-added care are reviewed. Patient privacy considerations relevant to the development of artificial intelligence applications for medical imaging are explored. These publications in the CARJ demonstrate that although this year has brought adversity, it has also been a harbinger for new and exciting areas of focus in our field.

Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose.

PURPOSE: We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA). METHODS: Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values. RESULTS: Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51-0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001). CONCLUSION: Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure.

Correlation of Values for Indocyanine Green Angiography in the Arterographically Normal Lower Extremity.

INTRODUCTION: Indocyanine green angiography (ICGA) is an assessment tool used to detect the degree of perfusion of tissue. This technology is being utilized in a variety of applications, including in the lower extremity. Principally, ICGA has been reported as a tool to determine pre-, intra-, or post-operative perfusion. The aim of this study is to determine objective values for ICGA in patients with arterographically normal lower extremities. MATERIALS AND METHODS: Prospectively, 20 patients were identified with no history of peripheral vascular disease who were scheduled to undergo distal subtraction angiography of the lower extremity. Based on the angiogram, 18 patients were deemed anatomically normal-a patent posterior tibial, anterior tibial, and peroneal artery with blood flow past the ankle into the foot. These patients then underwent ICGA on the dorsal and plantar aspects of the foot. The time to blush (seconds) and ingress and egress rates (units/second) were calculated. RESULTS: The mean time to blush for the dorsal aspect is 26.7s (standard deviation [SD]=22.16) and the median is 14s (range=4-81s) and for the plantar aspect is 9.33s (SD=18.64), and the median is 3.5s (range=1-68s). The region of interest (ROI) ingress rates for the dorsal foot is: mean 3.29 (SD=5.18) and median is 1.60 (range=0.2-21.7). The ROI egress rates for the dorsal foot is: mean 0.55 (SD=0.43) and median is 0.35 (range=0.3-1.40). The ROI ingress rates for the plantar foot is: mean 10.02 (SD=11.91) and median 2.60 (range=0.30-34.20). The ROI egress rates for the plantar foot is: mean 1.57 (SD=1.65) and median is 1.00 (range=0.10-5.70). CONCLUSIONS: This is the first study attempting to establish objective values for ICGA in the vascularly patent lower extremity.

Effects of Age, Acoustic Challenge, and Verbal Working Memory on Recall of Narrative Speech.

BACKGROUND/STUDY CONTEXT: A common goal during speech comprehension is to remember what we have heard. Encoding speech into long-term memory frequently requires processes such as verbal working memory that may also be involved in processing degraded speech. Here the authors tested whether young and older adult listeners' memory for short stories was worse when the stories were acoustically degraded, or whether the additional contextual support provided by a narrative would protect against these effects. METHODS: The authors tested 30 young adults (aged 18-28 years) and 30 older adults (aged 65-79 years) with good self-reported hearing. Participants heard short stories that were presented as normal (unprocessed) speech or acoustically degraded using a noise vocoding algorithm with 24 or 16 channels. The degraded stories were still fully intelligible. Following each story, participants were asked to repeat the story in as much detail as possible. Recall was scored using a modified idea unit scoring approach, which included separately scoring hierarchical levels of narrative detail. RESULTS: Memory for acoustically degraded stories was significantly worse than for normal stories at some levels of narrative detail. Older adults' memory for the stories was significantly worse overall, but there was no interaction between age and acoustic clarity or level of narrative detail. Verbal working memory (assessed by reading span) significantly correlated with recall accuracy for both young and older adults, whereas hearing ability (better ear pure tone average) did not. CONCLUSION: The present findings are consistent with a framework in which the additional cognitive demands caused by a degraded acoustic signal use resources that would otherwise be available for memory encoding for both young and older adults. Verbal working memory is a likely candidate for supporting both of these processes.

Potential Future Applications for Negative Pressure Wound Therapy and Installation Devices.

Negative-pressure wound therapy (NPWT) applies vacuum pressure to a wound bed sealed by an adhesive dressing to improve wound healing. A cleansing solution, often antibiotics or saline, may be instilled into the wound bed concurrently and removed via suction, thus enhancing the therapeutic effect. The therapeutic effect results from improved blood flow and removal of inflammatory factors. Since 1995, the FDA has approved NPWT for medical use. Since then, this technology has been applied to different types of wounds, including diabetic and decubitus ulcers and postsurgical incisional wounds. There are many applications for NPWT that remain to be explored. In this article, we postulate on novel and future uses for NPWT, including application in targeted drug delivery, stem cell therapy, and the prospect of combination with filtration devices, adaptable smart dressings, and remote monitoring.

To invade or not to invade: two approaches to a prokaryotic predatory life cycle.

Bdellovibrio and like organisms (BALOs) are a group of Gram-negative bacterial predators that are defined as having a periplasmic life cycle, whereby the predator enters into the periplasm of a prey cell. Recently, a predator of Caulobacter crescentus with a novel epibiotic life cycle was identified as a new species - Bdellovibrio exovorus. Therefore, this raises the question as to what determines the type of life cycle of a predator. Six bacterial strains susceptible to predation by B. exovorus JSS were isolated from soil, sewage, and activated sludge. 16S rRNA gene sequence analysis revealed these prey cells to be Acinetobacter johnsonii, Acinetobacter junii, Aeromonas hydrophila, and Delftia acidovorans. The life cycle of B. exovorus was epibiotic on all these prey cells. Environmental samples were enriched with these prey cells; new BALOs were isolated and their life cycle assessed. All new isolates had a periplasmic life cycle. BALOs generally have diverse prey ranges, and thus, not all new prey cells could be used by each new predator. Overall, each prey cell was able to support the growth of predators with either life cycle. Therefore it was confirmed that it is the predator and not the prey that determines the type of life cycle.

Bayesian modeling of dynamic behavioral change during an epidemic.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.

The Relationship Between Rejection of Care Behaviors and Pain and Delirium Severity in Hospital Dementia Care.

Background and Objectives: Rejection of care is common in hospitalized persons living with dementia. However, distinguishing between rejection of care behaviors related to care practices or other causes, such as pain or delirium, is challenging. The purpose of this study is to further understand the relationship between rejection of care and pain and delirium in hospitalized patients with dementia by identifying which rejection of care behaviors are associated with pain and delirium. Research Design and Methods: Care encounters between hospitalized patients with dementia (n = 16) and nursing staff (n = 53) were observed on 88 separate occasions across 35 days. Rejection of care was measured using the 13 behaviors from the Resistiveness to Care Scale. Pain and delirium severity were measured using a variety of scales including the Pain Assessment in Advanced Dementia Scale, Checklist of Nonverbal Pain Indicators, and numeric rating scale for pain severity and the Confusion Assessment Method-Severity short form and Delirium Observation Screening Scale for delirium severity. Linear mixed modeling was used to determine the relationship between rejection of care behaviors and pain and delirium severity for each measure. Results: About 48.9% of the observations included rejection of care, 49.9% included a patient in pain, and 12.5% included a patient with delirium. Cry, push away, scream/yell, and turn away indicated a higher pain severity across pain measures. No rejection of care behaviors were found to indicate delirium severity. Discussion and Implications: Certain rejection of care behaviors may be helpful in identifying pain in hospitalized patients with dementia, suggesting that caregivers should be cognizant of pain when these rejection of care behaviors are present. However, in this sample rejection of care behaviors was not found to be useful for identifying delirium severity in hospital dementia care.

Media reporting on alcohol and other drugs in Australia and the Mindframe guidelines: Baseline data.

INTRODUCTION: The aim of this study was to generate a baseline database of print media reporting on alcohol and other drug (AOD) issues prior to the release of the Mindframe guidelines in March 2019. Specifically, to: (i) describe the content associated with media entries that focus on AOD use in Australian news media; (ii) determine how the media entries compare to several domains associated with recently developed Mindframe guidelines for publicly reporting on AOD; and (iii) identify content factors associated with different scores. METHODS: Media entries between July 2016 and June 2017 were searched for key AOD-related terms using the Australian and New Zealand Newsstream database. Two coding schemes were developed to rate a stratified sample of 50% of the media entries against the Mindframe guidelines. Associations between content and total comparison scores were determined using linear regression models. RESULTS: Detailed coding of the 2007 articles identified as relevant for the current study indicated that a majority (67%) were focused on one of three substances: alcohol, cannabis or methamphetamine. Most of the entries were either law enforcement (22%) or criminal justice related (19%). Entries that focused on methamphetamine scored significantly lower than entries on alcohol when compared to the Mindframe guidelines, similarly entries focused on crime/justice-related topics scored significantly lower than entries focused on positive outcomes. DISCUSSION AND CONCLUSIONS: A disproportionate number of print media entries, particularly those related methamphetamine use, focused on crime or justice-related topics, potentially further contributing to stigma, and emphasising the legal consequences of AOD use.

A Rare Single Case of COVID-19-Induced Acute Myocarditis and Encephalopathy Presenting Simultaneously.

The ongoing coronavirus disease 2019 (COVID-19) pandemic may result in cardiovascular complications such as myocarditis, while encephalitis is a potentially life-threatening COVID-19-associated central nervous system complication. This case illustrates the possibility of developing severe multisystem symptoms from a COVID-19 infection, despite having received the COVID-19 vaccine within the year. Delay in treatment for myocarditis and encephalopathy can lead to permanent and possibly fatal damage. Our patient, a middle-aged female with a complicated medical history, initially came in without characteristic manifestations of myocarditis such as shortness of breath, chest pain, or arrhythmia, but with an altered mental status. Through further laboratory tests, the patient was diagnosed with myocarditis and encephalopathy, which were resolved within weeks through medical management and physical/occupational therapy. This case presentation describes the first reported case of concomitant COVID-19 myocarditis and encephalitis after receiving a booster dose within the year.

Evaluating a state child care assistance program using administrative data.

Government supported child care assistance (CCA) programs support low-income families by subsidizing costs of child care for working parents. In the United States, federal policy outlines several goals designed to guide the development and implementation of state CCA programs, however the intended outcomes are difficult to quantify. Without the collection and analysis of data related to the families and providers involved in the program, states are not able to effectively evaluate their success. This leaves program administrators and community advocates without valuable information needed for decision-making. Administrative data collected by the state CCA programs as part of their implementation offers a low-cost and effective approach to assessing program performance and execution, but there is currently no guidance for distilling the large quantities of information existing in these complex records. In this article, we present a novel evaluation framework for using administrative data to evaluate CCA program success guided by both policy and literature. We illustrate the benefit of our approach using the state of Iowa's CCA data system and offer recommendations for decision-makers seeking to use a data-driven approach to program evaluation.