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The Center for Disease Control and Prevention estimates that 75% of reported cases of traumatic brain injury (TBI) are mild, where chronic pain and depression are 2 of the most common symptoms. In this study, we used a murine model of repeated mild TBI to characterize the associated pain hypersensitivity and affective-like behavior and to what extent microglial reactivity contributes to these behavioral phenotypes. Male and female C57BL/6J mice underwent sham or repeated mild traumatic brain injury (rmTBI) and were tested for up to 9 weeks postinjury, where an anti-inflammatory/neuroprotective drug (minocycline) was introduced at 5 weeks postinjury in the drinking water. Repeated mild traumatic brain injury mice developed cold nociceptive hypersensitivity and negative affective states, as well as increased locomotor activity and risk-taking behavior. Minocycline reversed negative affect and pain hypersensitivities in male but not female mice. Repeated mild traumatic brain injury also produced an increase in microglial and brain-derived neurotropic factor mRNA transcripts in limbic structures known to be involved in nociception and affect, but many of these changes were sex dependent. Finally, we show that the antiepileptic drug, gabapentin, produced negative reinforcement in male rmTBI mice that was prevented by minocycline treatment, whereas rmTBI female mice showed a place aversion to gabapentin. Collectively, pain hypersensitivity, increased tonic-aversive pain components, and negative affective states were evident in both male and female rmTBI mice, but suppression of microglial reactivity was only sufficient to reverse behavioral changes in male mice. Neuroinflammation in limbic structures seems to be a contributing factor in behavioral changes resulting from rmTBI.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidneys active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

The limitation of conventional visual acuity assessment, which primarily focuses on individual eye performance (monocular visual acuity tests). This study addresses this limitation by emphasizing the importance of binocular vision, where both eyes work together. Binocular vision provides numerous advantages, such as improved depth perception, a wider field of view, reduced visual fatigue, and binocular summation. The article introduces a new mathematical model based on the physics of constructive interference in light waves and the anatomy of visual nerves. This model yields an equation that quantifies how weakness in one eye affects the visual acuity of the other. By analyzing the correlation between the rates of visual weakness in each eye and the corresponding distances, utilizing the Snellen 6-meter test, valuable insights are gained into the complex interplay between eye deficiencies and their collective impact on overall visual acuity. The equations derived from this study can have significant implications for eye care professionals, clinicians, and patients, contributing to a more comprehensive understanding of visual impairments and enriching the field of optics and vision science.

Abstract Background The Nightmare Distress Questionnaire (NDQ), known and commonly used for its adequate psychometric properties, is the most widely used instrument for the measurement of nightmare distress. This study aimed to assess the psychometric properties of a newly translated Arabic version of the Nightmare Distress Questionnaire (NDQ-AV). Methods A total of 546 Lebanese adolescents was recruited for this study and completed the NDQ-AV, the Insomnia Severity Index (ISI), and the eight-item Depression, Anxiety and Stress Scale (DASS-8). Results The results of the exploratory factor analysis (EFA) and the confirmatory factor analysis (CFA) supported both a one-factor model and a two-factor model of the NDQ-AV, with the latter consisting of seven items within both factors. The first factor was referred to as the “general nightmare distress and coping” while the second was named “nightmare impact and perception”. The reliability of the scale was excellent (α = 0.930 and ω = 0.915). Moreover, measurement invariance was shown across gender, demonstrating that this measure performs consistently for both men and women. Additionally, the NDQ-AV scores exhibited excellent reliability alongside factorial and concurrent validity. Conclusion In conclusion, these results support the psychometric validity of the Arabic version of the NDQ. The availability of the NDQ-AV is expected to facilitate the understanding of nightmare distress within the Lebanese context.

Abstract Associations between brain and obesity are bidirectional: changes in brain structure and function underpin over-eating, while chronic adiposity leads to brain atrophy. Investigating brain-obesity interactions across the lifespan can help better understand these relationships. This study explores the interaction between obesity and cortical morphometry in children, young adults, adults, and older adults. We also investigate the genetic, neurochemical, and cognitive correlates of the brain-obesity associations. Our findings reveal a pattern of lower cortical thickness in fronto-temporal brain regions associated with obesity across all age cohorts and varying age-dependent patterns in the remaining brain regions. In adults and older adults, obesity correlates with neurochemical changes and expression of inflammatory and mitochondrial genes. In children and older adults, adiposity is associated with modifications in brain regions involved in emotional and attentional processes. Thus, obesity might originate from cognitive changes during early adolescence, leading to neurodegeneration in later life through mitochondrial and inflammatory mechanisms.

Abstract Background Individuals experiencing homelessness face unique physical and mental health challenges, increased morbidity, and premature mortality. COVID -19 creates a significant heightened risk for those living in congregate sheltering spaces. In March 2020, the COVID-19 Community Response Team formed at Women’s College Hospital, to support Toronto shelters and congregate living sites to manage and prevent outbreaks of SARS-CoV-2 using a collaborative model of onsite mobile testing and infection prevention. From this, the Women’s College COVID-19 vaccine program emerged, where 14 shelters were identified to co-design and support the administration of vaccine clinics within each shelter. This research seeks to evaluate the impact of this partnership model and its future potential in community-centered integrated care through three areas of inquiry: (1) vaccine program evaluation and lessons learned; (2) perceptions on hospital/community partnership; (3) opportunities to advance hospital-community partnerships. Methods Constructivist grounded theory was used to explore perceptions and experiences of this partnership from the voices of shelter administrators. Semi-structured interviews were conducted with administrators from 10 shelters using maximum variation purposive sampling. A constructivist-interpretive paradigm was used to determine coding and formation of themes: initial, focused, and theoretical. Results Data analysis revealed five main categories, 16 subcategories, and one core category. The core category “access to healthcare is a human right; understand our communities” emphasizes access to healthcare is a consistent barrier for the homeless population. The main categories revealed during a time of confusion, the hospital was seen as credible and trustworthy. However, the primary focus of many shelters lies in housing, and attention is often not placed on health resourcing, solidifying partnerships, accountability, and governance structures therein. Health advocacy, information sharing tables, formalized partnerships and educating health professionals were identified by shelter administrators as avenues to advance intersectoral relationship building. Conclusion Hospital-community programs can alleviate some of the ongoing health concerns faced by shelters – during a time of COVID-19 or not. In preparation for future pandemics, access to care and cohesion within the health system requires the continuous engagement in relationship-building between hospitals and communities to support co-creation of innovative models of care, to promote health for all.

BackgroundDiabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss. ObjectiveWe evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center. MethodsWe prospectively recruited adult patients with diabetes at the Centre hospitalier de l’Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings. ResultsThis study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes. ConclusionsOur study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.

Summary: Background: Research in healthy young adults shows that characteristic patterns of brain activity define individual “brain-fingerprints” that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson’s disease (PD). Methods: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. Findings: The arrhythmic spectral components of cortical activity in patients with Parkinson’s disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson’s brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson’s symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson’s brain-fingerprint aligns with that of neurotransmitter systems affected by the disease’s pathophysiology. Interpretation: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson’s disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson’s disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. Funding: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer’s Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Relevance. Many patients suffer from anxiety when planning a surgical procedure, which leads them to either postpone it or go through it with all those negative feelings that may affect the course of the surgical work or even its outcomes. Modern medicine aims to find non-pharmaceutical ways, such as music, to put these emotions under control so that the patient feels a sense of calm and tranquility throughout the surgical operation and comes out with less negative feelings and good memories, which prevents the formation of any psychological trauma. Our investigation aims to study the effect of three types of music, on the psychological state of the patient during surgery, by evaluating the data of systolic and diastolic pressure, pulse, and oxygen level in the blood. Materials and Methods . 36 patients who visited the Medical Center of the RUDN University on a daily basis for dental implants and wisdom tooth extractions were randomly selected to undergo the experiment. They were divided into four groups, the first was the control group which was not exposed to music, the second was exposed to classical music, the third was exposed to Buddhism music, and the fourth was exposed to music generated by Artificial Intelligence. Pressure, pulse, and oxygen level were recorded in three phases and changes assessed using Student’s t-test and Mann-Whitney U analyses. Results and Discussion. The final results obtained did not show any significant changes in the values of pressure, pulse, and blood oxygenation during the period of exposure to music when compared with control group. Conclusion. Exposing to music didn’t show any positive effect on stress levels during dental implantation and extraction.

Abstract There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney’s active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

This paper used the flexible and efficient least squares residual power series method (LSRPSM) to solve the time-fractional derivative Cahn-Hilliard and Gardener equations. The LSRPSM combines the residual power series method (RPSM) and the least squares method. These calculations were found based on Caputo's sense and the fractional Wronskian concept. Approximate solutions are found more accurately, faster, and with fewer expansion terms than the classical (RPS) method.

Background This paper presents an optimized clustering approach applied to image segmentation. Accurate image segmentation impacts many fields like medical, machine vision, object detection. Applications involve tumor detection, face detection and recognition, and video surveillance. Methods The developed approach is based on obtaining an optimum number of clusters and regions of an image. We combined Region-based and contour-based approaches. Initial rough regions are obtained using edge detection. We have used Gabor wavelets for texture classification and spatial resolutions. Color frequencies are also used to determine the number of clusters of the Fuzzy c-means (FCM) algorithm which gave an optimum number of clusters or regions. Results We have compared our approach with other similar wavelet and clustering techniques. Our algorithm gave better values for segmentation metrics like SNR, PSNR, and MCC. Conclusions Optimizing the number of clusters or regions has a significant effect on the performance of the image segmentation techniques. This will result in better detection and localization of the segmentation-based application.

Key Clinical Message Inferior vena cava atresia is a rare condition with highly variable anatomy due to the complexity of caval embryology. When endovascular venovenous reconstruction is considered for severe persistent sequelae, multimodality imaging with CT and invasive venography is used to determine the appropriateness of intervention and for procedural planning.

Introduction:. The orthopaedic surgery match has experienced a consistent increase in both the number of applicants and applications submitted per applicant. Preference signaling was implemented during the 2022 to 2023 application cycle in part to curtail the rising application burden on both applicants and residency programs. Our aim was to explore the impact of the preference signaling system on applicant and residency program leader attitudes, behaviors, and outcomes. Methods:. We distributed surveys to American Orthopaedic Association/Council of Orthopaedic Residency Directors member program leaders (program directors, assistant program directors, and program coordinators) and applicants registered for the Electronic Standardized Letter of Recommendation after Universal Interview Offer Day 2022 (Fall Survey) and Match Day 2023 (Spring Survey). The surveys contained multiple-choice and numeric response questions on attitudes, behaviors, and outcomes that were analyzed and reported as percentages and medians, respectively. Open-text responses were reviewed for dominant themes. Results:. One hundred program leaders and 378 applicants (47%) completed the Fall Survey, and 146 program leaders and 290 applicants (36%) completed the Spring Survey. A majority of applicants (71%) and program leadership (91%) support the continued use of signaling. Applicants reported a 16% reduction in the number of programs to which they applied. Program directors largely used signaling as a tool for screening applications (75%), with few programs using signaling in the ranking process (20%). Applicants reported that 81% of their interviews were from programs they signaled. Slightly more than half of programs (53%) reported filling their last slot at a higher rank order position than the average of the previous 5 years. Qualitative analysis suggests a need for more transparency in the use of signals, consideration of application and/or interview caps, and reconsideration of the other components of the application. Conclusion:. Preference signaling in the orthopaedic surgery match was met with positive feedback and led to a reduction in the number of applications. Future research will examine the continued impact of preference signaling and assess alterations for optimizing the match process.

Background This paper presents an optimized clustering approach applied to image segmentation. Accurate image segmentation impacts many fields like medical, machine vision, object detection. Applications involve tumor detection, face detection and recognition, and video surveillance. Methods The developed approach is based on obtaining an optimum number of clusters and regions of an image. We combined Region-based and contour-based approaches. Initial rough regions are obtained using edge detection. We have used Gabor wavelets for texture classification and spatial resolutions. Color frequencies are also used to determine the number of clusters of the Fuzzy c-means (FCM) algorithm which gave an optimum number of clusters or regions. Results We have compared our approach with other similar wavelet and clustering techniques. Our algorithm gave better values for segmentation metrics like SNR, PSNR, and MCC. Conclusions Optimizing the number of clusters or regions has a significant effect on the performance of the image segmentation techniques. This will result in better detection and localization of the segmentation-based application.

Tickling is commonly perceived as juvenile play associated with laughter. However, its potential connection to adult sexual behavior has largely remained unexplored. Our online survey, primarily distributed among individuals interested in tickle fetishism, explored tickling and its association with sexual behavior. Ticklishness types, tools, preferred body parts, and partner preferences, were examined. Results revealed diverse patterns of ticklishness changes over time and distinct body-part preferences for different types of tickling. Childhood experiences and exposure to tickling content in television were found to shape individuals’ affinity for tickle fetishism. A quarter of respondents reported experiencing orgasms exclusively from tickling, while around 88% expressed sexual satisfaction through tickling alone, indicating its sufficiency as a sexual stimulus among fetishists. Tickling desire decreased after orgasm, indicating an association between tickling and sexual activity. Moreover, ticklishness degree predicted preferences for being tickled rather than tickling others. Exploratory factor analysis identified three factors underlying tickling and sexual experiences: enjoyment and frequency of tickling during sexual activity; preference for intense sexual experiences; age of becoming sexually active. In conclusion, this study provides unique insights into tickling and its connections to sexual context, enhancing our understanding of diverse human sexual behavior and tickle fetishism as a distinct preference.

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature’s focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems.