Teachers recruit mentalizing regions to represent learners' beliefs.

Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers' decisions about what information to communicate. Participants (N = 28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants' examples were best described by a model that selects evidence that maximizes the learner's belief in the correct answer. Consistent with this idea, participants' predictions about how well learners would do closely tracked the performance of an independent sample of learners (N = 140) who were tested on the examples they had provided. In addition, regions that play specialized roles in processing social information, namely the bilateral temporoparietal junction and middle and dorsal medial prefrontal cortex, tracked learners' posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.

Zyxin contributes to coupling between cell junctions and contractile actomyosin networks during apical constriction.

One of the most common cell shape changes driving morphogenesis in diverse animals is the constriction of the apical cell surface. Apical constriction depends on contraction of an actomyosin network in the apical cell cortex, but such actomyosin networks have been shown to undergo continual, conveyor belt-like contractions before the shrinking of an apical surface begins. This finding suggests that apical constriction is not necessarily triggered by the contraction of actomyosin networks, but rather can be triggered by unidentified, temporally-regulated mechanical links between actomyosin and junctions. Here, we used C. elegans gastrulation as a model to seek genes that contribute to such dynamic linkage. We found that α-catenin and ß-catenin initially failed to move centripetally with contracting cortical actomyosin networks, suggesting that linkage is regulated between intact cadherin-catenin complexes and actomyosin. We used proteomic and transcriptomic approaches to identify new players, including the candidate linkers AFD-1/afadin and ZYX-1/zyxin, as contributing to C. elegans gastrulation. We found that ZYX-1/zyxin is among a family of LIM domain proteins that have transcripts that become enriched in multiple cells just before they undergo apical constriction. We developed a semi-automated image analysis tool and used it to find that ZYX-1/zyxin contributes to cell-cell junctions' centripetal movement in concert with contracting actomyosin networks. These results identify several new genes that contribute to C. elegans gastrulation, and they identify zyxin as a key protein important for actomyosin networks to effectively pull cell-cell junctions inward during apical constriction. The transcriptional upregulation of ZYX-1/zyxin in specific cells in C. elegans points to one way that developmental patterning spatiotemporally regulates cell biological mechanisms in vivo. Because zyxin and related proteins contribute to membrane-cytoskeleton linkage in other systems, we anticipate that its roles in regulating apical constriction in this manner may be conserved.

Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study.

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research.

Age and number of lesions predict chalazion recurrence.

PURPOSE: Periocular lesions in pediatric patients usually require general anesthesia for surgical intervention. The US Food and Drug Administration (FDA) warns against multiple exposures to anesthesia in children younger than 3 years due to the increased risk of learning disabilities in this population. This study aimed to evaluate risk factors associated with chalazion recurrence after surgery. METHODS: A retrospective chart review over a five-year period identified 649 patients at our institution undergoing surgical intervention for chalazion. The primary outcomes examined were as follows: (1) return to the operating room for additional surgical intervention and (2) recurrence of chalazion during convalescence from surgery and follow-up. RESULTS: Fewer than one-third of patients suffered a recurrence after surgery. Multivariate logistic regression found younger age (p = 0.01), female sex (p = 0.01), and a greater number of chalazia drained (p < 0.001) were significantly correlated with recurrence of chalazia after surgery. CONCLUSIONS: Patients presenting at a younger age and with a greater number of chalazion were statistically more likely to have a recurrence of chalazion after surgery. Given recurrence is more likely in younger children, reconciling this with the risk-benefit ratio with regard to FDA guidelines on anesthesia in children under three years is a critical consideration for ophthalmologists.

Quantitative CT Analysis of Diffuse Lung Disease.

Quantitative analysis of thin-section CT of the chest has a growing role in the clinical evaluation and management of diffuse lung diseases. This heterogeneous group includes diseases with markedly different prognoses and treatment options. Quantitative tools can assist in both accurate diagnosis and longitudinal management by improving characterization and quantification of disease and increasing the reproducibility of disease severity assessment. Furthermore, a quantitative index of disease severity may serve as a useful tool or surrogate endpoint in evaluating treatment efficacy. The authors explore the role of quantitative imaging tools in the evaluation and management of diffuse lung diseases. Lung parenchymal features can be classified with threshold, histogram, morphologic, and texture-analysis-based methods. Quantitative CT analysis has been applied in obstructive, infiltrative, and restrictive pulmonary diseases including emphysema, cystic fibrosis, asthma, idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, connective tissue-related interstitial lung disease, and combined pulmonary fibrosis and emphysema. Some challenges limiting the development and practical application of current quantitative analysis tools include the quality of training data, lack of standard criteria to validate the accuracy of the results, and lack of real-world assessments of the impact on outcomes. Artifacts such as patient motion or metallic beam hardening, variation in inspiratory effort, differences in image acquisition and reconstruction techniques, or inaccurate preprocessing steps such as segmentation of anatomic structures may lead to inaccurate classification. Despite these challenges, as new techniques emerge, quantitative analysis is developing into a viable tool to supplement the traditional visual assessment of diffuse lung diseases and to provide decision support regarding diagnosis, prognosis, and longitudinal evaluation of disease. ©RSNA, 2019.

A Hierarchical Bayesian Model of Adaptive Teaching.

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

BACKGROUND: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. OBJECTIVE: This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. METHODS: Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. RESULTS: Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. CONCLUSIONS: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Gefitinib, Fluorouracil, Oxaliplatin, and Leucovorin (IFOX) Regimen for Colorectal Cancer.

The complexity of cancer chemotherapy requires pharmacists be familiar with the complicated regimens and highly toxic agents used. This column reviews various issues related to preparation, dispensing, and administration of antineoplastic therapy, and the agents, both commercially available and investigational, used to treat malignant diseases. Questions or suggestions for topics should be addressed to Dominic A. Solimando, Jr, President, Oncology Pharmacy Services, Inc., 4201 Wilson Blvd #110-545, Arlington, VA 22203, e-mail: OncRxSvc@comcast.net; or J. Aubrey Waddell, Professor, University of Tennessee College of Pharmacy; Oncology Pharmacist, Pharmacy Department, Blount Memorial Hospital, 907 E. Lamar Alexander Parkway, Maryville, TN 37804, e-mail: waddfour@charter.net.

Synchronous and opponent thermosensors use flexible cross-inhibition to orchestrate thermal homeostasis.

Body temperature homeostasis is essential and reliant upon the integration of outputs from multiple classes of cooling- and warming-responsive cells. The computations that integrate these outputs are not understood. Here, we discover a set of warming cells (WCs) and show that the outputs of these WCs combine with previously described cooling cells (CCs) in a cross-inhibition computation to drive thermal homeostasis in larval Drosophila WCs and CCs detect temperature changes using overlapping combinations of ionotropic receptors: Ir68a, Ir93a, and Ir25a for WCs and Ir21a, Ir93a, and Ir25a for CCs. WCs mediate avoidance to warming while cross-inhibiting avoidance to cooling, and CCs mediate avoidance to cooling while cross-inhibiting avoidance to warming. Ambient temperature-dependent regulation of the strength of WC- and CC-mediated cross-inhibition keeps larvae near their homeostatic set point. Using neurophysiology, quantitative behavioral analysis, and connectomics, we demonstrate how flexible integration between warming and cooling pathways can orchestrate homeostatic thermoregulation.

Acoustilytix™: A Web-Based Automated Ultrasonic Vocalization Scoring Platform.

Ultrasonic vocalizations (USVs) are known to reflect emotional processing, brain neurochemistry, and brain function. Collecting and processing USV data is manual, time-intensive, and costly, creating a significant bottleneck by limiting researchers' ability to employ fully effective and nuanced experimental designs and serving as a barrier to entry for other researchers. In this report, we provide a snapshot of the current development and testing of Acoustilytix™, a web-based automated USV scoring tool. Acoustilytix implements machine learning methodology in the USV detection and classification process and is recording-environment-agnostic. We summarize the user features identified as desirable by USV researchers and how these were implemented. These include the ability to easily upload USV files, output a list of detected USVs with associated parameters in csv format, and the ability to manually verify or modify an automatically detected call. With no user intervention or tuning, Acoustilytix achieves 93% sensitivity (a measure of how accurately Acoustilytix detects true calls) and 73% precision (a measure of how accurately Acoustilytix avoids false positives) in call detection across four unique recording environments and was superior to the popular DeepSqueak algorithm (sensitivity = 88%; precision = 41%). Future work will include integration and implementation of machine-learning-based call type classification prediction that will recommend a call type to the user for each detected call. Call classification accuracy is currently in the 71-79% accuracy range, which will continue to improve as more USV files are scored by expert scorers, providing more training data for the classification model. We also describe a recently developed feature of Acoustilytix that offers a fast and effective way to train hand-scorers using automated learning principles without the need for an expert hand-scorer to be present and is built upon a foundation of learning science. The key is that trainees are given practice classifying hundreds of calls with immediate corrective feedback based on an expert's USV classification. We showed that this approach is highly effective with inter-rater reliability (i.e., kappa statistics) between trainees and the expert ranging from 0.30-0.75 (average = 0.55) after only 1000-2000 calls of training. We conclude with a brief discussion of future improvements to the Acoustilytix platform.

Hospital Blood Transfusion Patterns During Major Noncardiac Surgery and Surgical Mortality.

We retrospectively examined intraoperative blood transfusion patterns at US veteran's hospitals through description of national patterns of intraoperative blood transfusion by indication for transfusion in the elderly; assessment of temporal trends in the use of intraoperative blood transfusion; and relationship of institutional use of intraoperative blood transfusion to hospital 30-day risk-adjusted postoperative mortality rates.Limited data exist on the pattern of intraoperative blood transfusion by indication for transfusion at the hospital level, and the relationship between intraoperative transfusion rates and institutional surgical outcomes.Using the Department of Veterans Affairs Surgical Quality Improvement Program database, we assigned 424,015 major noncardiac operations among elderly patients (≥65 years) in 117 veteran's hospitals, from 1997 to 2009, into groups based on indication for intraoperative blood transfusion according to literature and clinical guidelines. We then examined institutional variations and temporal trends in surgical blood use based on these indications, and the relationship between these institutional patterns of transfusion and 30-day postoperative mortality.Intraoperative transfusion occurred in 38,056/424,015 operations (9.0%). Among the 64,390 operations with an indication for transfusion, there was wide variation (median: 49.9%, range: 8.7%-76.2%) in hospital transfusion rates, a yearly decline in transfusion rates (average 1.0%/y), and an inverse relationship between hospital intraoperative transfusion rates and hospital 30-day risk-adjusted mortality (adjusted mortality of 9.8 ±â€Š2.8% vs 8.3 ±â€Š2.1% for lowest and highest tertiles of hospital transfusion rates, respectively, P = 0.02). In contrast, for the 225,782 operations with no indication for transfusion, there was little variation in hospital transfusion rates (median 0.7%, range: 0%-3.4%), no meaningful temporal change in transfusion (average 0.0%/y), and similar risk-adjusted 30-day mortality across all tertiles of hospital transfusion rates.Among patients ≥65 years with an indication for intraoperative transfusion, intraoperative transfusion patterns varied widely across hospitals and declined through the 1997 to 2009 study period. Hospitals with higher transfusion rates in these patients have lower risk-adjusted 30-day postoperative mortality rates.