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Stigmatized women's health issues, such as polycystic ovary syndrome (PCOS) and endometriosis, are often marginalized or dismissed in traditional clinical settings. This drives individuals to seek peer support in anonymous online communities such as Reddit. While these digital platforms host critical discussions, they are often designed as static information repositories, failing to account for the complex emotional, temporal, and cultural dynamics that shape users' support needs. There is a disconnect between the lived experiences of users-particularly feelings of clinical dismissal and the need for culturally specific advice-and the design of the sociotechnical systems they rely on. This study aimed to deconstruct support practices in online women's health forums to provide a formative basis for designing more responsive digital health systems. We analyzed the intersections of discussion topics, emotional expression, temporal shifts (specifically the impact of the COVID-19 pandemic), and culturally situated discourse to identify unmet user needs and effective peer-support patterns. We conducted a large-scale, mixed-methods analysis of 4995 posts and 460,317 comments from 5 major women's health subreddits (r/WomensHealth, r/TwoXChromosomes, r/BirthControl, r/Endometriosis, and r/PCOS). Computational methods included Latent Dirichlet Allocation for topic modeling, Valence Aware Dictionary for Sentiment Reasoning for sentiment analysis, and the NRC Emotion Lexicon for granular emotion classification. We segmented the data into pre-, during-, and post-COVID-19 periods to analyze temporal shifts. This quantitative analysis was complemented by a 2-phase qualitative thematic analysis to identify and characterize engagement patterns within 147 validated culturally situated threads. Our analysis revealed that the most prevalent and emotionally negative topic was "Pain & Doctor Visits," which was uniquely characterized by high levels of fear and sadness linked to systemic clinical dismissal. The COVID-19 pandemic triggered a significant topical "turn inward," with discussions shifting away from social or political issues and toward somatic concerns (eg, "PCOS" "Pain & Doctor Visits"). Paradoxically, this period saw a simultaneous rise in both negative emotions (eg, fear and sadness) and expressions of community trust. Critically, our qualitative analysis of culturally situated discourse uncovered a consistent three-stage "playbook" for effective support: (1) Affirmation to establish psychological safety and validate cultural experiences; (2) Information Scaffolding to provide actionable, culturally tailored advice; and (3) Intercultural Bridging to facilitate community-wide learning and empathy. Online health forums operate as essential, resilient sociotechnical infrastructures that actively compensate for failures and gaps in formal health care. The "Affirmation-Scaffolding-Bridging" model identified in our research provides a clear, formative framework for designing future digital health interventions. These findings can guide the development of new platforms that are emotionally aware, culturally responsive, and adaptive to user needs and external crises.

Curiosity plays a fundamental role in human learning, development, and motivation, and emerging evidence suggests that reduced curiosity is linked to poorer mental health outcomes, including depressive symptoms (DS). However, to date, the majority of curiosity research relies on self-report assessments and thus risks biased reporting. Virtual reality (VR), a novel tool increasingly used within mental health research and treatment, might represent a potent tool for offering ecologically valid insights into curiosity-driven behaviors while circumventing issues related to self-report assessments, including demand characteristics and recall bias. The study aimed to enhance the assessment of curiosity by using a novel VR environment and to examine its relevance to DS. Specifically, we tested 2 hypotheses using a novel VR environment: first, that curiosity, as assessed through spontaneous exploratory interactions and behaviors in VR, positively correlates with self-reported curiosity, and second, that VR-based curiosity is inversely associated with DS. This exploratory study used an observational design that included 100 volunteers. All participants completed self-reported assessments of DS and curiosity before engaging in a novel VR scenario. Although progression in the virtual environment required solving cognitive tasks, these were embedded as structural elements rather than framed as the primary objective. Instead, participants' free explorations and interactions with objects formed the basis for the 4 curiosity metrics used in this study. After VR exposure, participants completed a questionnaire assessing cybersickness symptoms. Hypothesis 1 was not supported, as only one curiosity metric, namely object interactions, was positively associated with one aspect of curiosity relating to motivation to seek new knowledge and experiences. Further, diminishing significance after correction for multiple testing warranted caution. Results relating to hypothesis 2 indicated partial support, in that object interaction was significantly associated with DS while controlling for age, sex, and cybersickness levels. Sensitivity analyses showed no associations between object interactions and self-reported anxiety and stress symptoms. VR may be a potent tool for assessing exploratory behaviors in a controlled, yet ecologically valid, environment that avoids issues related to self-report. However, whether such motivations translate to established curiosity constructs warrants further research. This study also provided preliminary insights into how assessing exploratory interactions in VR may be a promising avenue that could enhance the understanding of the etiology and assessment of DS-particularly its early stages.

It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context. The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video data. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning. Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transportation systems.

This study proposes an improved feature-point matching and 3D registration method for augmented reality tourism applications. This method addresses issues such as poor alignment, low stability in complex environments, and low feature-matching accuracy. An improved feature point matching method for tourism images is introduced, which combines the Speeded Up Robust Features (SURF) algorithm with an enhanced Oriented FAST and Rotated BRIEF algorithm. In this method, feature points are initially detected using the SURF algorithm, and their orientation is determined via wavelet response analysis. The Lucas-Kanade optical flow method is employed for feature point tracking. The random sample consensus algorithm is then used to eliminate mistracked points. Furthermore, an augmented reality tourism 3D registration technique based on an improved homography matrix is proposed to overcome the limitations of traditional homography matrices, such as low matching accuracy and registration efficiency. The performance of the proposed method was analyzed through comparative experiments against the SIFT, SURF, and original ORB algorithms under various image transformations, including scale, blur, illumination, and rotation. The correct matching rate and matching time were used as evaluation metrics. Simulation tests were conducted for 3D registration using different 3D models. Registration accuracy and successful registration counts were evaluated under rotational changes. The outcomes indicated that the average correct matching rate of the proposed algorithm is increased by 44.08%, 36.51%, and 16.09% under scale variation than the scale invariant feature transformation algorithm, speeded up robust features algorithm, and unimproved algorithm, respectively. The correct matching rate under fuzzy transformation increased by 33.46%, 19.65%, and 9.35%, respectively. The average registration accuracy of the proposed 3D registration technique was 98.74% under rotational transformation. The outcomes reveal that the study's suggested approach can successfully improve the scene's virtual and real-world fusion effect and offers a fresh approach to the use of augmented reality technology in the travel industry.

Asian American women represent a significant portion of the healthcare workforce, but there has been little research on their experiences of workplace bias, such as microaggressions and discrimination. To describe the experiences of Asian American women healthcare workers (HCWs) on the issues of race, gender, and workplace bias within healthcare organizations. Semi-structured qualitative interviews were conducted via video-conferencing from July 2022 to March 2023. Twenty-five participants who self-identified as Asian American women and worked in the Pacific Northwest were interviewed. Participants held a variety of professional roles, such as nursing assistants (CNAs), medical assistants (MAs), nurses (RNs), advanced practice providers (APPs), and physicians. They were employed in various medical and surgical specialties and worked in both clinics and hospitals in urban and rural settings. The types of workplace bias experienced by Asian American women HCWs, their impact on participants, and how participants coped with these experiences were recorded through one-on-one interviews with study authors. These interviews were transcribed and analyzed using an inductive approach to thematic analysis. Participants reported a variety of interpersonal microaggressions and organizational discrimination with impacts on their wellbeing and career trajectories. Microaggressions included underestimation of a participant's role and competence, stereotypes of homogeneity, assumption of foreignness, and hypersexualization. Participants reported developing a variety of coping strategies to deal with microaggressions and organization-level discrimination. Asian American women HCWs commonly reported facing challenges of racism and sexism and experiencing heightened stress from workplace bias. Healthcare organizations need a greater understanding of these challenges to support women HCWs of color and boost retention of healthcare professionals with diverse backgrounds.

The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines aim to improve the reporting of observational studies, including cohort, case-control, and cross-sectional designs, relevant to assessing associations, risks, and outcomes in real-world clinical settings. They are conceived to optimize the meaningfulness of epidemiological and clinical studies, aligning them with the Aristotelian "mesotes" curve, namely, the principle of achieving balance and avoiding extremes to best reflect the most truthful approximation of reality. This commentary addresses situations where strict adherence to STROBE guidelines may be impossible or inappropriate, potentially distorting results, and shows how statistical tools can mitigate these issues. Specific challenges arise in real-life randomization scenarios, situations lacking placebo arms, and in correcting for multiple comparisons. We discuss these challenges, examine the role of Bonferroni and similar corrections, and propose alternative approaches such as false discovery rate (FDR) and Bayesian hierarchical models. We illustrate these points with examples from the literature and a simulation study evaluating p-value adjustments in multiple hypothesis testing. This work provides a framework for researchers to navigate STROBE guidelines thoughtfully, ensuring that observational studies are both rigorous and relevant.

Emerging Asian economies, such as China, India, Indonesia, South Korea, Malaysia, the Philippines, and Thailand, face the dual challenge of rapid industrialisation and increasing exposure to environmental degradation related issues. The conventional measure of using emissions only captures the cause of environmental degradation, but it fails to account for the broader economic costs of degradation. To address this gap, the study introduces pollution cost as a measure of environmental degradation, making it a more policy-relevant indicator. To further draw nuance insights, this study employs a hybrid methodology that combines a physics-inspired dissipative model with a non-linear Logit model for panel data ranging from 2015 to 2024. This dual approach allows the identification of entropy-driven stress flows, resilience thresholds, and probabilistic regime dynamics. Results show that environmental vulnerability (EV), carbon emissions, financed emissions (FE), and disaster frequency act as stressors that significantly increase the probability of high-cost regimes. In contrast, adaptability and renewable energy penetration reduces probability by 20 to 30%, thereby associated with a reduction in the likelihood. The dissipative estimates identify an indicative resilience threshold ([Formula: see text]= -0.569), beyond which systemic costs escalate sharply. The Logit results further indicate that emissions nearly double the likelihood of high-cost regimes, while renewable adoption counters. These estimates extend beyond the obvious; the interaction effects reveal that disasters weigh in vulnerable economies, whereas financed emissions are less harmful when coupled with energy transitions. Policy takeaways emphasise the critical role of renewable energy, adaptive capacity, and green financial regulation in reducing pollution costs and securing sustainability-oriented growth in Emerging Asia.

Monitoring just-in-time adaptive interventions (JITAIs) is important both during trialing and when the intervention is deployed in a broader healthcare program. While there is increasing interest in using artificial intelligence (AI) algorithms in JITAIs, these algorithms introduce additional complexity that requires additional monitoring. In this paper, we provide guidelines for monitoring online AI decision-making algorithms. Our guidelines include: (1) identifying potential issues, categorizing them by severity (red, yellow, and green), and (2) developing fallback methods (pre-specified procedures that are executed when an issue occurs). To make ideas concrete, we discuss algorithm monitoring systems in two case studies. In both, the monitoring systems detected real-time issues, and fallback methods both safeguarded participants and ensured quality data for post-deployment data analysis to further refine the JITAI. These guidelines and findings give teams the confidence to include online AI decision-making algorithms in JITAIs.

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity in Saudi Arabia. Early identification and management of risk are critical to addressing this burden. Community pharmacies (CPs) offer an accessible setting for proactive screening; however, pharmacy-based CVD initiatives remain underexplored locally despite demonstrated success internationally. Understanding public perspectives is therefore essential to inform service development. To assess the perceptual factors shaping public acceptance of CP-based CVD risk screening services in Saudi Arabia. A convergent parallel mixed-methods design was employed, combining an online questionnaire and semi-structured interviews. Quantitative data were analysed descriptively, while qualitative data were examined using framework analysis guided by the Health Belief Model (HBM), a framework used to explain how beliefs influence health behaviours. A total of 523 survey responses and 9 interviews were analysed. Most participants were young, educated Saudi nationals, and reported visiting CPs more frequently than general practitioners. Over half expressed confidence in pharmacists' competence to conduct CVD risk screening, and 60% felt comfortable discussing CVD-related concerns. HBM constructs highlighted perceived susceptibility, severity, and self-efficacy as key motivators, with accessibility and convenience serving as important cues to action. However, concerns related to privacy, cost, and staffing limitations remained significant deterrents. Public perception of CP-based CVD screening were generally positive, with accessibility, trust in pharmacists, and perceived benefits supporting engagement. However, views regarding suitability were mixed, and concerns related to privacy, cost, and staffing remain important barriers. The Health Belief Model (HBM) provided a useful framework for understanding these motivations and barriers. Addressing these issues is essential for successful implementation.

Adverse events (AEs) detected in tweets and in the FDA Adverse Event Reporting System (FAERS) provide valuable insights into patient experiences with oral hypoglycemic agents including sodium-glucose transporter 2 (SGLT2) and dipeptidyl peptidase-4 (DPP4) inhibitors. This study compared the side effects identified from tweets with those in the FAERS to identify the AEs associated with each drug. We collected AE data through tweet and the FAERS during 2017-2021. Relevant sentences in the tweets were annotated and manually labeled to identify AE terms. The data obtained from both sources were categorized according to the System Organ Class (SOC) of the Medical Dictionary for Regulatory Activities. Renal and urinary disorders were defined as the index comparator with a value of 1.0. The relative frequency of a side effect compared with the index comparator was obtained. Both drugs showed similarities in high-frequency SOCs. The largest difference between the two datasets for DPP4 inhibitors was observed for the Cardiac disorders category. It ranked 12th in FAERS but 1st in tweets data, showing a marked difference in index values (FAERS 0.75, Tweet 10.80). For the SGLT2 inhibitor, the most evident difference was in the Surgical and medical procedures category. In this category, the index from FAERS was 0.26, while that from tweets was 3.49, ranking 12th and 1st, respectively. Despite the differences in the quantity and types of side effects between the two sources, we were able to identify which clinically significant side effects patients were concerned about and worried about. People share their experiences with medicines on social media. In this study, we used Natural Language Processing to read tweets about oral antidiabetic drugs such as sodium‐glucose transporter 2 inhibitors and dipeptidyl peptidase‐4 inhibitors and found side effects that were not listed in official reporting systems. These findings suggest that social media might help identify potential safety issues.

The general practitioners' (GP) workforce is in crisis, with 22% of GPs feeling so stressed by the pressures of general practice they cannot cope. Patient death is the greatest stressor in medical practice and has an impact on the personal stress and well-being of doctors. Providing good holistic care for dying patients in the community keeps patients in their preferred place of care and reduces unnecessary costly interventions and hospital admissions. It is crucial to explore the experiences and learning of GP and general practitioner registrars' (GPR)-who represent the future primary care workforce-in caring for dying patients. We aimed to understand what is known about GPs and GPR experiences of, and learning from, caring for dying patients to outline current knowledge and identify future research options. We included all studies that explored GP and GPR experiences and learning related to adult dying, palliative, terminal care and death. Four electronic databases (MEDLINE, EMBASE, PsycINFO and Cochrane) as well as reference lists and hand-searching key journals were searched from January 2003 until February 2025. Data were extracted and charted by all authors and then a qualitative content approach was used to analyse and interpret the data. The database search yielded 3378 publications; 17 studies have been included in the scoping review. This includes over 4412 participants, mostly GPs/GPRs. GPs/GPRs gain knowledge, skills and confidence when they have exposure and hands-on experience with dying patients. Uncertainty, intolerance of ambiguity, fear of initiating conversations around dying and perceived lack of knowledge were barriers for caring for dying patients. Facilitators such as safe learning environments with ongoing support from supervisors and protected time to discuss, debrief and reflect were valuable. Timely understanding of the current structural, practice level factors such as learning and emotional issues and challenges is required to upskill and support doctors, which can lead to improved emotional well-being and workforce retention-all of which will directly benefit dying patients and relatives at this significant part of their lives.

In Bangladesh, as well as throughout the world, children's screen time has significantly increased. Children spend a lot of time on the internet and digital screens for entertainment, education, and communication, which has increased their daily screen time. However, the potential detrimental impacts of excessive screen time on children's mental, physical, and social health have drawn attention. This study aimed to explore the effect of high exposure to screens on the health and mental well-being of school-going children and adolescents in Dhaka, Bangladesh. This cross-sectional descriptive study was conducted from July 2022 to June 2024. A total of 420 school-going children and adolescents aged 6 to 14 years were enrolled from 3 English-language and 3 Bangla-language schools in Dhaka using a stratified random sampling technique. Anthropometric measurements, a semistructured questionnaire, and the Pittsburgh Sleep Quality Index, the Development and Well-Being Assessment scale, and the Strengths and Difficulties Questionnaire, all of which were validated in Bangla, were used to gather data. We considered students who were exposed to screens for less than 2 hours a day as the low-exposure group and those who were exposed for more than 2 hours a day as the high-exposure group. A total of 83.3% (350/420) of the students were in the high-exposure group, and their average screen time per day was 4.6 (SD 2.3) hours. Eye problems were reported by 35.7% (150/420) of the students, and a significant difference was found between the low- and high-exposure groups. In total, 96% (144/150) of the students with eye problems were from the high-exposure group, whereas 4% (6/150) were from the low-exposure group. Headaches were reported by 80% (336/420) of the students, and they were common in the high-exposure group (279/336, 83%). Moreover, students from the high-exposure group had a short duration and poor quality of sleep (mean 7.3, SD 1.4 hours), which was statistically significant. Furthermore, obesity was more predominant in the high-exposure group (P<.001). Our study revealed that, overall, 31% (130/420) of the students had at least one mental health problem and 9.8% (41/420) had more than one mental health problem using the Development and Well-Being Assessment scale, and mental health problems were greater in the high-exposure group than the low-exposure group. Although behavioral problems such as conduct issues (119/420, 28.3%) and peer difficulties (121/420, 28.8%) were observed among the participants, no statistically significant difference was found between the 2 groups. A collaborative and coordinated multistage approach is essential to create effective and acceptable guidelines and policies for the optimum and positive use of digital screens for the children of Bangladesh. Further prospective studies on a larger scale can be conducted to determine the impacts of screen time on aspects of health.

The growing penetration of electric vehicles (EVs) and renewable energy sources has introduced significant nonlinear and stochastic disturbances in modern power systems, posing major challenges to conventional load frequency control (LFC) strategies. To address these issues, a Long Short-Term Memory-based Proportional Integral (LSTM-PI) controller is proposed to enable adaptive frequency regulation in a two-area EV-integrated system. The LSTM network dynamically tunes the proportional and integral gains in response to temporal variations in frequency and tie-line states. The learning module is trained using an Integral of Time weighted Absolute Error (ITAE) objective. Practical non-linearities such as governor deadband, generation-rate constraints, valve saturation, and EV state-of-charge limits are incorporated into both the training dataset and the online control evaluation. A constrained Model Predictive Control (CMPC-PI) is developed as a modern benchmark to compare the proposed technique under identical sampling rates and actuator limits. The proposed method shows better performance under dynamic conditions. Statistical performance analysis has been carried out through 30 runs of Monte Carlo analysis with a mean of 95% confidence intervals. Stability analysis under time-varying gains is supported by a bounded-gain Lyapunov Framework and a delay-margin analysis that addresses sensing and actuation delays. The result analysis confirms that the LSTM-PI controller provides a scalable intelligent solution for modern EV-integrated smart grid frequency regulation. The proposed intelligent adaptive controller provides a scalable, low-complexity, and stability-assured solution for robust load-frequency regulation in nonlinear, EV-integrated smart power grids.

Iron(III) and copper(II) ions are ubiquitous in the environment, posing potential risks to the environment and human health, which urgently demands rapid detection methods. In this study, a ratiometric fluorescent probe was developed by integrating biomass-based carbon dots with rhodamine B (RhB) for the rapid and accurate detection of Fe&#xb3;&#x207a; and Cu&#xb2;&#x207a;. Under normal conditions, the strong coordination ability of Fe&#xb3;&#x207a; and Cu&#xb2;&#x207a; enables them to rapidly occupy the active sites on the surface of carbon dots (CDs), resulting in rapid fluorescence changes. RhB provides a stable reference signal and achieves self-calibrated dual-emission output, thereby reducing matrix and environmental interferences. To address the coexistence of Fe&#xb3;&#x207a; and Cu&#xb2;&#x207a; in practical detection, ethylenediaminetetraacetic acid (EDTA) was introduced as a selective masking agent, which preferentially forms a stable complex with Fe&#xb3;&#x207a;. This reduces the signal interference of Fe&#xb3;&#x207a; in low-concentration systems and realizes highly selective detection of Cu&#xb2;&#x207a;. In the detection of Cu&#xb2;&#x207a; in actual water samples, the probe exhibits high sensitivity (4.91 &#xb5;M) and favorable accuracy with recoveries ranging from 97.3% to 99.5%, demonstrating strong reliability in complex samples. This study not only provides a green and sensitive method for Cu&#xb2;&#x207a; detection, but also presents an effective strategy for resolving specific detection issues in complex matrices using classical masking agents.

With the rise of large-scale genomic studies, large gene lists targeting important diseases are increasingly common. While evaluating each study individually gives valuable insights on specific samples and study designs, the wealth of available evidence in the literature calls for robust and efficient meta-analytic methods. Crucially, the diverse assumptions and experimental protocols underlying different studies require a flexible but rigorous method for aggregation. To address these issues, we propose BiGER, a Bayesian rank aggregation method for the inference of latent global rankings. Unlike existing methods in the field, BiGER accommodates mixed gene lists with top-ranked and top-unranked genes as well as bottom-tied and missing genes, by design. Using a Bayesian hierarchical framework combined with variational inference, BiGER efficiently aggregates large-scale gene lists, consistently achieving state-of-the-art accuracy, while providing valuable insights into source-specific reliability for researchers. Through both simulated and real datasets, we show that BiGER is a useful tool for reliable meta-analysis in genomic studies.

The era of Industry 5.0 emphasizes human centrality, sustainability, and resilience, posing new requirements for the direction of talent cultivation in modern higher education. Engineering education is also adapting and leading the development of Industry 5.0. This study proposes an innovative framework integrating human factors into the modern operations management education system in higher education. Operations management involves the management of the entire process, including supply chain, inventory, quality control, etc. With the evolution from Industry 4.0 to 5.0, operations management is centered on humans, emphasizing human-machine collaboration and employee empowerment. Therefore, the content of teaching needs to be adjusted to help students learn how to use human factors principles to optimize work processes, reduce employee fatigue, and mitigate risk. This paper proposes the application of human factors in operations management by creating a virtual, interconnected production environment that simulates real assembly lines, detects human factors issues, and reduces deviations through numerical analysis. This protocol enables the identification and evaluation of ergonomic risks at the design stage, providing targeted improvement measures. This protocol helps students understand how to reduce the incidence of accidents, improve work efficiency, and reduce the additional costs associated with corrective interventions during the teaching of operations management. Through digital ergonomics, students can comprehend and become proficient in ergonomic risk evaluation methods. Therefore, this paper analyzes how to better integrate human factors into operations management, attaining the integration of engineering technology and human factors, and proposing new ideas and directions for education and practice in the field of operations management. In future applications, combining human factors with human digital twin (HDT) and Artificial intelligence-generated content (AIGC) will empower smart manufacturing and enable human-centric smart manufacturing in engineering practice.

The interaction between immune cytokines and sleep can be bidirectional, and the circadian rhythm balance has a crucial role to the immune and inflammatory regulation. Insomnia is a prevalent sleep disorder, in which occur modifications in immune expression, including the cytokines pattern. Some somnogenic cytokines, such as interleukin (IL)-1&#x3b2;, IL-6 and tumor necrosis factor-&#x3b1; (TNF-&#x3b1;) have been classically recognized for their direct association with sleep behavior and circadian alignment. More recent evidence has increasingly implicated the role of IL-17A in sleep disturbances and neuroinflammation. However, its specific relationship with insomnia disorder remains underexplored. This cytokine is considered as a relevant immune component in the pathways involved in inflammatory and autoimmune issues. Previous and rare studies on the behavior of IL-17A in insomnia related with comorbid disease suggested that increased IL-17A serum levels may be linked to poor sleep quality. We suggest that further experimental and clinical studies examining the role of IL-17A should shed more light in its relationship with insomnia; and on the immunological and neuroinflammatory associations with this sleep disorder.

During the late 1950s, in the USA, sex research started to develop itself as an epistemic community comprising individual researchers, organizations, journals, symposia, and conferences, based on a scientific paradigm aimed at multidisciplinarity and the internationalization of research in the ill-defined field of sexuality. This article traces the history of sex research from its origins in Germany (late nineteenth century) and focuses on its revival in the USA in 1922 within the Committee for Research in Problems of Sex. It focuses mainly on its development as an autonomous field which took place at the end of the 1950s with the foundation of the first organizations entirely dedicated to scientific research on sexuality and the creation of specialized scientific journals. The "sex research" community developed some time before the mid-1990s, when new social demands emerged, particularly in response to the HIV epidemic, the development of struggles for the emancipation of LGBT communities, sexual rights concerns, and sexual medicine supported by the pharmaceutical industry. Sex research then branched off in different directions, with critical sexuality studies more oriented toward qualitative approaches and committed to struggles for emancipation, the development of sexual medicine more focused on health issues, sexual dysfunction, therapy, and sexual health toward education, counseling, and prevention. After having exercised a certain hegemony in the field of sexuality research for more than decades, "sex research," which still has its own autonomous organizations and journals of a high scientific level, now represents only one of the facets of scientific research in sexuality, which continues to exert a significant influence on research developments in these fields.