A cross-species framework for investigating perceptual evidence accumulation.

Cross-species studies are important for a comprehensive understanding of brain functions. However, direct quantitative comparison of behaviors across species presents a significant challenge. To enable such comparisons in perceptual decision-making, we developed a synchronized evidence accumulation task for rodents and humans, by aligning mechanics, stimuli, and training. Rats, mice and humans readily learned the task and exhibited qualitatively similar performance. Quantitative model comparison revealed that all three species employed an evidence accumulation strategy, but differed in speed, accuracy, and key decision parameters. Human performance prioritized accuracy, whereas rodent performance was limited by internal time-pressure. Rats optimized reward rate, while mice appeared to switch between evidence accumulation and other strategies trial-to-trial. Together, these results reveal striking similarities and species-specific priorities in decision-making. Furthermore, the synchronized behavioral framework we present may facilitate future studies involving cross-species comparisons, such as evaluating the face validity of animal models of neuropsychiatric disorders. Highlights: Development of a free response evidence accumulation task for rats and miceSynchronized video game allows direct comparisons with humansRat, mouse and human behavior are well fit by the same decision modelsModel parameters reveal species-specific priorities in accumulation strategy.

Primary pleuropulmonary synovial sarcoma presenting with recurrent spontaneous pneumothorax.

Primary pleuropulmonary synovial sarcoma (PPSS) is a rare mesenchymal neoplasm, accounting for less than 0.5% of all primary lung tumours. Presentation is often vague and may include cough, chest pain or dyspnoea. Due to the tumour's rarity, diagnosis can be challenging, and not much is known about the disease process or optimal treatment course. In this case report, we describe an older female patient who underwent blebectomy for recurrent pneumothorax. No masses or suspected lesions were found on CT imaging besides the bleb. The bleb was then found to be PPSS via RT-PCR cytology. This case aims to increase awareness of malignant tumours clinically presenting as recurrent pneumothorax with no discrete lung mass on CT imaging. We also highlight the importance of cytogenetics to confirm the diagnosis of this rare tumour.

Primary Epithelioid Angiosarcoma of the Colon With Peritoneal Carcinomatosis.

Angiosarcoma is a rare mesenchymal tissue neoplasm, typically involving lymphatic or vascular endothelial cells. The tumor can arise anywhere in the body, though it is most often found as cutaneous lesions in the head and neck region. Due to its rarity, a diagnosis can sometimes be missed, especially when the sarcoma involves an uncommon site like the gastrointestinal tract. In this case, we describe a male patient who was found to have primary epithelioid angiosarcoma of the colon. Initial biopsies with immunohistochemistry staining were weakly positive for anti-cytokeratin (CAM 5.2) and negative for SRY-Box transcription factor 10 (SOX-10) and B-cell-specific activator protein (PAX-5). He was misdiagnosed as having poorly differentiated carcinoma as a result. However, a more in-depth look at the colon specimen after tumor resection revealed CD-31 and factor VIII positivity, which established the diagnosis of epithelioid angiosarcoma of the colon. This case suggests the use of rare histopathology markers as part of the workup for colonic lesions to confirm the diagnosis, especially when tissue biopsy is limited.

Assessing evidence accumulation and rule learning in humans with an online game.

Evidence accumulation, an essential component of perception and decision making, is frequently studied with psychophysical tasks involving noisy or ambiguous stimuli. In these tasks, participants typically receive verbal or written instructions that describe the strategy that should be used to guide decisions. Although convenient and effective, explicit instructions can influence learning and decision making strategies and can limit comparisons with animal models, in which behaviors are reinforced through feedback. Here, we developed an online video game and nonverbal training pipeline, inspired by pulse-based tasks for rodents, as an alternative to traditional psychophysical tasks used to study evidence accumulation. Using this game, we collected behavioral data from hundreds of participants trained with an explicit description of the decision rule or with experiential feedback. Participants trained with feedback alone learned the game rules rapidly and used strategies and displayed biases similar to those who received explicit instructions. Finally, by leveraging data across hundreds of participants, we show that perceptual judgments were well described by an accumulation process in which noise scaled nonlinearly with evidence, consistent with previous animal studies but inconsistent with diffusion models widely used to describe perceptual decisions in humans. These results challenge the conventional description of the accumulation process and suggest that online games provide a valuable platform to examine perceptual decision making and learning in humans. In addition, the feedback-based training pipeline developed for this game may be useful for evaluating perceptual decision making in human populations with difficulty following verbal instructions.NEW & NOTEWORTHY Perceptual uncertainty sets critical constraints on our ability to accumulate evidence and make decisions; however, its sources remain unclear. We developed a video game, and feedback-based training pipeline, to study uncertainty during decision making. Leveraging choices from hundreds of subjects, we demonstrate that human choices are inconsistent with popular diffusion models of human decision making and instead are best fit by models in which perceptual uncertainty scales nonlinearly with the strength of sensory evidence.

Creating Computer Vision Models for Respiratory Status Detection.

This study aims to use computers to detect and recognize ventilation objects (masks and tubes) and their positions on the patient's face. We created two models: the You Only Look Once (YOLO) and the Transfer Learning (TL) models, to perform this computer vision task. The development processes and comparison of performance will be described in this paper. The TL model had a better performance (93%) compared to the YOLO model (93%). Clinical Relevance- Healthcare providers and researchers interested in the field of computer vision applied in medicine, specifically automatic object detection using video streams or real-time video streaming may benefit from findings reported.

Evaluation of functional MRI-based human brain parcellation: a review.

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

Pressure Injury Prediction Model Using Advanced Analytics for At-Risk Hospitalized Patients.

OBJECTIVE: Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI. METHOD: Prediction performances of multiple popular supervised learning were tested. Together with the typical steps of a machine learning project, steps to prevent bias were carefully conducted, in which analysis of correlation covariance, outlier removal, confounding analysis, and cross-validation were used. RESULT: The most accurate model reached an area under receiver operating characteristic curve of 99.7%. Ten-fold cross-validation was used to ensure that the results were generalizable. Random forest and decision tree had the highest prediction accuracy rates of 98%. Similar accuracy rate was obtained on the validation cohort. CONCLUSIONS: We developed a prediction model using advanced analytics to predict PI in at-risk hospitalized patients. This will help address appropriate interventions before the patients develop a PI.

Classification of Respiratory Conditions using Auscultation Sound.

Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance-Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.

Neural circuits and symbolic processing.

The ability to use symbols is a defining feature of human intelligence. However, neuroscience has yet to explain the fundamental neural circuit mechanisms for flexibly representing and manipulating abstract concepts. This article will review the research on neural models for symbolic processing. The review first focuses on the question of how symbols could possibly be represented in neural circuits. The review then addresses how neural symbolic representations could be flexibly combined to meet a wide range of reasoning demands. Finally, the review assesses the research on program synthesis and proposes that the most flexible neural representation of symbolic processing would involve the capacity to rapidly synthesize neural operations analogous to lambda calculus to solve complex cognitive tasks.

Primary monophasic breast synovial sarcoma in a female patient.

Synovial sarcoma (SS) is a rare soft tissue sarcoma usually arising in the deep soft tissues of the limbs, trunk, and head and neck region. Due to its rarity, diagnosis can be difficult to establish, especially when it involves an uncommon location like the breast. In this case report, we describe a young woman who was found to have primary SS of the breast. Initial immunohistochemistry staining was focally positive for cytokeratin and S100 and she was misdiagnosed with atypical spindle cell carcinoma. Due to the unusual presentation, further testing was performed which showed TLE1 and epithelial membrane antigen positivity, establishing the diagnosis of SS of the breast. A FISH was later sent out and was positive for SS18-SSX fusion transcript. This case highlights the importance of considering rare histopathology in breast lesions and using additional staining and cytogenetics to confirm diagnosis.

Middle meningeal artery embolization for chronic subdural hematoma: a systematic review and meta-analysis.

Middle meningeal artery (MMA) embolization has been proposed as a minimally invasive treatment for chronic subdural hematoma (cSDH). The aim of this systematic review and meta-analysis is to compare outcomes after MMA embolization versus conventional management for cSDH. We performed a systematic review of PubMed, Embase, Oxford Journal, Cochrane, and Google Scholar databases from April 1987 to October 2020 in accordance with PRISMA guidelines. Studies reporting outcomes after MMA embolization for ≥3 patients with cSDH were included. A meta-analysis comparing MMA embolization with conventional management was performed. The analysis comprised 20 studies with 1416 patients, including 718 and 698 patients in the MMA embolization and conventional management cohorts, respectively. The pooled recurrence, surgical rescue, and in-hospital complication rates in the MMA embolization cohort were 4.8% (95% CI 3.2% to 6.5%), 4.4% (2.8% to 5.9%), and 1.7% (0.8% to 2.6%), respectively. The pooled recurrence, surgical rescue, and in-hospital complication rates in the conventional management cohort were 21.5% (0.6% to 42.4%), 16.4% (5.9% to 27.0%), and 4.9% (2.8% to 7.1%), respectively. Compared with conservative management, MMA embolization was associated with lower rates of cSDH recurrence (OR=0.15 (95% CI 0.03 to 0.75), p=0.02) and surgical rescue (OR=0.21 (0.07 to 0.58), p=0.003). In-hospital complication rates were comparable between the two cohorts (OR=0.78 (0.34 to 1.76), p=0.55). MMA embolization is a promising minimally invasive therapy that may reduce the need for surgical intervention in appropriately selected patients with cSDH. Additional prospective studies are warranted to determine the long-term durability of MMA embolization, refine eligibility criteria, and establish this endovascular approach as a viable definitive treatment for cSDH.

Are We Ready for Video Recognition and Computer Vision in the Intensive Care Unit? A Survey.

OBJECTIVE: Video recording and video recognition (VR) with computer vision have become widely used in many aspects of modern life. Hospitals have employed VR technology for security purposes, however, despite the growing number of studies showing the feasibility of VR software for physiologic monitoring or detection of patient movement, its use in the intensive care unit (ICU) in real-time is sparse and the perception of this novel technology is unknown. The objective of this study is to understand the attitudes of providers, patients, and patient's families toward using VR in the ICU. DESIGN: A 10-question survey instrument was used and distributed into two groups of participants: clinicians (MDs, advance practice providers, registered nurses), patients and families (adult patients and patients' relatives). Questions were specifically worded and section for free text-comments created to elicit respondents' thoughts and attitudes on potential issues and barriers toward implementation of VR in the ICU. SETTING: The survey was conducted at Mayo Clinic in Minnesota and Florida. RESULTS: A total of 233 clinicians' and 50 patients' surveys were collected. Both cohorts favored VR under specific circumstances (e.g., invasive intervention and diagnostic manipulation). Acceptable reasons for VR usage according to clinicians were anticipated positive impact on patient safety (70%), and diagnostic suggestions and decision support (51%). A minority of providers was concerned that artificial intelligence (AI) would replace their job (14%) or erode professional skills (28%). The potential use of VR in lawsuits (81% clinicians) and privacy breaches (59% patients) were major areas of concern. Further identified barriers were lack of trust for AI, deterioration of the patient-clinician rapport. Patients agreed with VR unless it does not reduce nursing care or record sensitive scenarios. CONCLUSION: The survey provides valuable information on the acceptance of VR cameras in the critical care setting including an overview of real concerns and attitudes toward the use of VR technology in the ICU.

PyRice: a Python package for querying Oryza sativa databases.

SUMMARY: Currently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programing API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system, which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results. AVAILABILITY AND IMPLEMENTATION: https://github.com/SouthGreenPlatform/PyRice. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper.

BACKGROUND: Geotagging is the process of attaching geospatial tags to various media data types. In health care, the goal of geotagging is to gain a better understanding of health-related questions applied to populations. Although there has been a prevalence of geographic information in public health, in order to effectively use and expand geotagging across health care there is a requirement to understand other factors such as the disposition, standardization, data sources, technologies, and limitations. OBJECTIVE: The objective of this document is to serve as a resource for new researchers in the field. This report aims to be comprehensive but easy for beginners to understand and adopt in practice. The optimal geocodes, their sources, and a rationale for use are suggested. Geotagging's issues and limitations are also discussed. METHODS: A comprehensive review of technical instructions and articles was conducted to evaluate guidelines for geotagging, and online resources were curated to support the implementation of geotagging practices. Summary tables were developed to describe the available geotagging resources (free and for fee) that can be leveraged by researchers and quality improvement personnel to effectively perform geospatial analyses primarily targeting US health care. RESULTS: This paper demonstrated steps to develop an initial geotagging and geovisualization project with clear structure and instructions. The geotagging resources were summarized. These resources are essential for geotagging health care projects. The discussion section provides better understanding of geotagging's limitations and suggests suitable way to approach it. CONCLUSIONS: We explain how geotagging can be leveraged in health care and offer the necessary initial resources to obtain geocodes, adjustment data, and health-related measures. The resources outlined in this paper can support an individual and/or organization in initiating a geotagging health care project.

Mifepristone pretreatment followed by misoprostol 200 mcg buccal for the medical management of intrauterine fetal death at 14-28 weeks: A randomized, placebo-controlled, double blind trial.

OBJECTIVE: To evaluate whether fetal and placental expulsion is more likely within 48 h if women receive mifepristone pre-treatment vs placebo pre-treatment followed by misoprostol 200 mcg buccally for treatment of fetal death at 14 weeks 0 days to 28 weeks and 6 days gestation. STUDY DESIGN: We randomized 176 women with a confirmed fetal death between 14 weeks and 0 days to 28 weeks and 6 days to mifepristone 200 mg or placebo; 24 h later all participants received misoprostol 200 mcg buccally every 3 h for up to 16 doses or 48 h. The trial took place in Hanoi, Vietnam and Mexico City in 2015-2018. RESULTS: Complete expulsion of the fetus and placenta within 48 h of misoprostol administration occurred in 74 of 90 women (82.2%, 95% confidence interval (CI), 72.7%-89.5%) in the mifepristone-misoprostol group and in 70 of 86 women (81.4%, 95% CI, 71.6%-89.0%) in the placebo-misoprostol group (Relative Risk (RR) 1.01, 95%CI 0.87-1.16, p = 0.887). The median time from the start of the misoprostol induction to fetal expulsion was shorter among women who received mifepristone-misoprostol compared to women assigned to placebo-misoprostol (7 h vs ±5 vs 12 ± 13 h; p < 0.001). Women in the mifepristone-misoprostol group were more likely to expel the fetus within 24 h of the start of misoprostol administration (96% vs 78%; RR 1.22 (1.09-1.39) p = 0.009). CONCLUSION(S): Mifepristone-misoprostol did not result in a higher rate of complete expulsion of the fetus and the placenta within 48 h of the start of misoprostol administration without any additional surgical intervention or medication (e.g. additional misoprostol doses or oxytocin) than placebo-misoprostol. However, treatment with mifepristone-misoprostol did result in a shorter time to expulsion than placebo misoprostol. IMPLICATIONS: Pretreatment with mifepristone followed by misoprostol bucally resulted in a shorter treatment time for medical management of fetal death than treatment with misoprostol alone. Pre-treatment with mifepristone may be more acceptable to women and providers by both reducing the length of hospital stay and the amount of misoprostol required.

A projection specific logic to sampling visual inputs in mouse superior colliculus.

Using sensory information to trigger different behaviors relies on circuits that pass through brain regions. The rules by which parallel inputs are routed to downstream targets are poorly understood. The superior colliculus mediates a set of innate behaviors, receiving input from >30 retinal ganglion cell types and projecting to behaviorally important targets including the pulvinar and parabigeminal nucleus. Combining transsynaptic circuit tracing with in vivo and ex vivo electrophysiological recordings, we observed a projection-specific logic where each collicular output pathway sampled a distinct set of retinal inputs. Neurons projecting to the pulvinar or the parabigeminal nucleus showed strongly biased sampling from four cell types each, while six others innervated both pathways. The visual response properties of retinal ganglion cells correlated well with those of their disynaptic targets. These findings open the possibility that projection-specific sampling of retinal inputs forms a basis for the selective triggering of behaviors by the superior colliculus.

Sero-epidemiological status and risk factors of toxoplasmosis in pregnant women in Northern Vietnam.

BACKGROUND: In Vietnam, few studies have determined the epidemiological status of toxoplasmosis in pregnant women and no routine prenatal screening is in place. This study was conducted to evaluate the seroprevalence of this zoonotic parasitic infection in pregnant women in Northern Vietnam and to assess the association with awareness, risk factors and congenital toxoplasmosis. METHODS: Approximately 800 pregnant women were included in the study from two hospitals, one in Hanoi and one in Thai Binh province, which is known to have a dense cat population. Serological immunoglobulin G (IgG) and immunoglobulin M (IgM) detection was performed to estimate the seroprevalence of toxoplasmosis and sero-incidence of maternal and congenital toxoplasmosis. In addition, a survey was conducted about awareness, clinical history, presentation of signs and symptoms relating to toxoplasmosis and to detect biologically plausible and socio-demographic risk factors associated with toxoplasmosis. Associations with seroprevalence were assessed using univariable and multivariable analysis. RESULTS: The mean IgG seroprevalence after the full diagnostic process was 4.5% (95% confidence interval(CI): 2.7-7.0) and 5.8% (95% CI: 3.7-8.6) in Hanoi and Thai Binh hospital, respectively, and included one seroconversion diagnosed in Thai Binh hospital. Only 2.0% of the pregnant women in Hanoi hospital and 3.3% in Thai Binh hospital had heard about toxoplasmosis before this study. CONCLUSION: Since the percentage of seronegative, and thus susceptible, pregnant women was high and the awareness was low, we suggest to distribute information about toxoplasmosis and its prevention among women of child bearing age. Furthermore, future studies are recommended to investigate why such a low seroprevalence was seen in pregnant women in Northern Vietnam compared to other countries in South East Asia and globally.

Deep Q-learning for Predicting Asthma Attack with Considering Personalized Environmental Triggers' Risk Scores.

The purpose of our present study was to develop a forecasting method that would help asthmatic individuals to take evasive action when the probability of an attack was at THEIR PERSONAL THRESHOLD levels. The results are encouraging. Risk factor analysis helps improve the agent's performance (by allowing it to consider personalized risk score of asthma attack triggers while making a decision and being able to ignore the non-triggers), increasing transparency of deep reinforcement learning in medicine applications (by using the results of analyzing risk factors and its association to take actions), and increase accuracy over time since the association risk factor indicators are also changing over time with more accuracy rate. It also brings the possibility of including population-based health in personalized health, which could support a more efficient self-management of chronic diseases.

Personalized Prediction of Asthma Severity and Asthma Attack for a Personalized Treatment Regimen.

Control of asthma is critical for disease management and quality of life. Asthma treatment depends on the patient demographic information (e.g., age), and disease severity, which is determined by: (1) how symptoms affect a patient's daily life, (2) measured lung function, and (3) estimated risk of having an asthma attack. In this paper, we will present the Tensorflow Text Classification (TC) method to classify a patient's asthma severity level. We will also propose a Qlearning method to train an agent through trials and errors to improve the prediction accuracy and create a personalized treatment regimen for asthma patients.

High Plasma Concentrations of Zidovudine (AZT) Do Not Parallel Intracellular Concentrations of AZT-Triphosphates in Infants During Prevention of Mother-to-Child HIV-1 Transmission.

OBJECTIVES: Zidovudine (AZT) is mainly used to prevent mother-to-child HIV-1 transmission (PMTCT). Despite serious concerns on AZT-associated toxicity, there is little information on pharmacokinetics of intracellular AZT metabolites in infants. METHODS: We conducted a prospective study in 31 HIV-uninfected infants who received AZT for PMTCT. Blood samples were obtained from 14 infants on postdelivery days (PDD) 1, 7, 14, and 28 and from 17 infants at 0 and 4 hours after dosing on PDD-1. Plasma AZT concentrations (pAZT) and intracellular concentrations of AZT-monophosphate (icAZT-MP), diphosphate (icAZT-DP), and triphosphate (icAZT-TP) were determined. RESULTS: Plasma AZT and icAZT-MP concentrations were 2713 nmol/L and 79 fmol/10 cells in PDD-1, but decreased to 1437 nmol/L and 31 fmol/10 cells by PDD-28 (P = 0.02 and P = 0.07 for all PDDs, respectively), whereas those of icAZT-DP and icAZT-TP remained low throughout the sampling period (P = 0.29 and P = 0.61 for all PDDs, respectively) There were no differences in icAZT-TP between infants of the 2 mg/kg 4 times a day dose and 4 mg/kg twice daily dose (P = 0.25), whereas pAZT and icAZT-MP levels were higher in the latter (P < 0.01 and <0.01, respectively). The pAZT and icAZT-MP significantly increased from 0 to 4 hours after dosing (P < 0.001 and <0.001, respectively), whereas icAZT-DP, icAZT-TP levels were not changed (P = 0.41 and 0.33, respectively). CONCLUSIONS: The level of icAZT-TP did not change with age, time, or a single dose despite the wide range of pAZT concentration. A safer dosage needs to be determined because high pAZT levels do not parallel those of icAZT-TP.