Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fß-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

Nurses' rationale for underreporting of patient and visitor perpetrated workplace violence: a systematic review.

BACKGROUND: Patient and visitor perpetrated workplace violence (WPV) is a problem within healthcare and is known to be underreported by nurses and other healthcare workers. However, there are multiple and diverse reasons identified in the literature as to why nurses do not report. This systematic review aimed to investigate nurses' reasons and rationale related to underreporting of violence that occurs in the workplace. METHODS: Following PRISMA guidelines for systematic review reporting, studies conducted between 2011 and early 2022 were identified from MEDLINE, CINAHL, APA PsychInfo, and Psychological and Behavioral Sciences Collection via EBSCOHost. Quantitative studies related to patient and visitor perpetrated violence containing explanations, reasons, or rationale related to underreporting were included. RESULTS: After quality appraisals, 19 studies representing 16 countries were included. The resulting categories identified nursing, management, and organizational factors. The most prominent nursing factors included nurses' fear of consequences after reporting, nurses' perceptions, and their lack of knowledge about the reporting process. Common management factors which contributed to nursing underreporting included lack of visible changes after reporting, non-supportive culture in which to report, and the lack of penalties for perpetrators. Organizational factors included the lack of policies/procedures/training for WPV, as well as a lack of an efficient and user-friendly reporting system. Supportive interventions from management, organizations, and community sources were summarized to provide insight to improve nurse reporting of WPV events. CONCLUSION: Underreporting of WPV is a complex and multi-faceted problem. An investigation into the rationale for underreporting a workplace violent event illustrates nurses, management, and organizations contribute to the problem. Clear and actionable interventions such as educational support for staff and the development of a clear and concise reporting processes are recommended to encourage staff reporting and to help address WPV in healthcare.

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community.

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.

Patient and Family Perpetrated Cyber-Incivility and Cyber-Aggression Within Healthcare: A Cross-Sectional Descriptive Study.

Introduction: Verbal violence may manifest in written form as cyber incivility within patient portal communications. As a form of digital technology, patient portal messages create a physical and emotional distance leading the sender to be disinhibited and disassociated from the recipient nurse. Written patient portal messages may contain uncivil language deemed verbally violent when the content escalates beyond professional standards. When these messages are encountered as part of patient care, they may lead to nurses' psychological distress. Although cyber-incivility has been studied within social media and business, little is known about cyber-incivility within healthcare. Objectives: The purpose of the study was to define cyber-incivility as it manifests within healthcare compared to business, determine sender demographics, and quantify the impact on nurses. Methods: A cross-sectional descriptive study was conducted to analyze portal communications. Nurses forwarded aggressive messages to leadership and quantified their level of distress after receiving the message. Sender demographics were tracked, and content was analyzed using Braun and Clarke's Thematic Analysis. Results: Of the 31 included messages, senders were of varying ages, genders, and marital statuses. Messages rarely related to medications and rarely contained expletives. The most distressing messages were ad hominem, demanding, accusatory, or contained threats. These messages illustrated how healthcare cyber-incivility manifested and deviated from appropriate professional standards to become a form of verbal workplace violence. Message content also identified antecedents; related to unmet expectations, patient accountability, or difficulty navigating healthcare. Conclusions: Written incivil/uncivil/aggressive patient portal messages contained personal attacks and professionally degrading content, which were distressing to nurses. A healthcare-specific framework was created and provided context to understand the difficult and aggressive messages nurses received while providing digital patient care. Awareness of cyber-incivility within healthcare allows for better support of nurses who are exposed to this form of workplace violence and is foundational to future intervention development.

The Subjective Habitability & Acceptability Questionnaire (SHAQ): Development and Validation.

OBJECTIVE: Describe the development and validation of the Subjective Habitability & Acceptability Questionnaire (SHAQ). BACKGROUND: Habitat area size, layout, and design may impact individual and team behavioral health and performance (BHP) outcomes in operational environments. However, there are no standardized measures of these relationships. METHOD: SHAQ is a modular survey consisting of two 6-item scales: BHP Outcomes (Performance of Individual Activities, Performance of Group Activities, Mood, Psychological Stress, Sleep, and Social Interactions) and Habitability Moderators (Privacy, Social Density, Efficiency, Control, Comfort, and Convenience). We collected SHAQ data from NASA's Human Exploration Research Analog (HERA) crews (n = 19) in reference to multiple habitat areas (Sleep/Bedroom, Hygiene/Bathroom, Work/Office/Workshop, and Food Preparation/Kitchen/Galley) in the HERA operational environment, private hotel rooms, and individual home habitats. RESULTS: SHAQ has high construct validity (single factor solutions, mean item factor loading = 0.760, mean % variance = 60.37), internal consistency and reliability (item mean α = 0.880, mean ω=0.894, mean ICC = 0.430), concurrent validity (mean item r with System Usability Scale = 0.42), and discriminant validity (e.g., significantly higher facilitation of group activities in HERA Work/Office/Workshop and Food Preparation/Kitchen/Galley areas vs. Hygiene/Bathroom and Sleep/Bedroom areas; significantly higher ratings of privacy, comfort, and convenience in hotel vs. HERA). CONCLUSION: SHAQ is a reliable, valid, and sensitive measure of BHP impacts of habitat size and layout. APPLICATION: SHAQ can be used to inform evidence-based recommendations and thresholds for habitat area size, layout, and design options to support individual and team BHP in operational environments.

Chemotherapy-Induced Alopecia: Examining Patient Perceptions and Adherence to Home Haircare Recommendations.

BACKGROUND: Chemotherapy-induced alopecia is one of the most distressing side effects experienced by patients with cancer. Although most chemotherapy-induced alopecia is temporary, this side effect can cause significant anxiety and may lead to refusal of curative treatment. OBJECTIVES: The purpose of this study was to examine patient perceptions and measure adherence to haircare recommendations throughout the course of treatment while using scalp cooling therapy in addition to learning which haircare recommendations were the most onerous. METHODS: This was a cross-sectional observational and descriptive study that used repeated-measures survey data. Participants completed electronic surveys during each treatment corresponding with the current treatment phase. FINDINGS: Final survey results revealed that most participants adhered to haircare recommendations with little difficulty and that the recommendations had an insignificant impact on daily lives.

Using fNIRS to Identify Transparency- and Reliability-Sensitive Markers of Trust Across Multiple Timescales in Collaborative Human-Human-Agent Triads.

Intelligent agents are rapidly evolving from assistants into teammates as they perform increasingly complex tasks. Successful human-agent teams leverage the computational power and sensory capabilities of automated agents while keeping the human operator's expectation consistent with the agent's ability. This helps prevent over-reliance on and under-utilization of the agent to optimize its effectiveness. Research at the intersection of human-computer interaction, social psychology, and neuroergonomics has identified trust as a governing factor of human-agent interactions that can be modulated to maintain an appropriate expectation. To achieve this calibration, trust can be monitored continuously and unobtrusively using neurophysiological sensors. While prior studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS), a lightweight neuroimaging technology, in the prediction of social, cognitive, and affective states, few have successfully used it to measure complex social constructs like trust in artificial agents. Even fewer studies have examined the dynamics of hybrid teams of more than 1 human or 1 agent. We address this gap by developing a highly collaborative task that requires knowledge sharing within teams of 2 humans and 1 agent. Using brain data obtained with fNIRS sensors, we aim to identify brain regions sensitive to changes in agent behavior on a long- and short-term scale. We manipulated agent reliability and transparency while measuring trust, mental demand, team processes, and affect. Transparency and reliability levels are found to significantly affect trust in the agent, while transparency explanations do not impact mental demand. Reducing agent communication is shown to disrupt interpersonal trust and team cohesion, suggesting similar dynamics as human-human teams. Contrasts of General Linear Model analyses identify dorsal medial prefrontal cortex activation specific to assessing the agent's transparency explanations and characterize increases in mental demand as signaled by dorsal lateral prefrontal cortex and frontopolar activation. Short scale event-level data is analyzed to show that predicting whether an individual will trust the agent, with data from 15 s before their decision, is feasible with fNIRS data. Discussing our results, we identify targets and directions for future neuroergonomics research as a step toward building an intelligent trust-modulation system to optimize human-agent collaborations in real time.

Nurses with Undiagnosed Hearing Loss: Implications for Practice.

Hearing loss affects 36 million people in the United States of America, including 17% of the adult population. This suggests some nurses will have hearing losses that affect their communication skills and their ability to perform auscultation assessments, potentially compromising patient care and safety. In this article, the authors begin by reviewing the hearing process, describing various types of hearing loss, and discussing noise-induced hearing loss and noise levels in hospitals. Next, they consider the role of hearing in nursing practice, review resources for hearing-impaired nurses, identify the many costs associated with untreated hearing loss, and note nurses' responsibility for maintaining their hearing health. The authors conclude that nurses need to be aware of their risk for hearing loss and have their hearing screened every five years.

Validating TrueAllele® DNA mixture interpretation.

DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all-or-none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two-person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min = 2.32, max = 10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min = 1.00, max = 11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in-depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele(®) DNA mixture interpretation and establish a significant information improvement over human review.