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This paper studies comparative static effects in a portfolio selection problem when the investor has mean-variance preferences. Since the security market is complex, there exists the situation where security returns are given by experts’ estimates when they cannot be reflected by historical data. This paper discusses the problem in such a situation. Based on uncertainty theory, the paper first establishes an uncertain mean-variance utility model, in which security returns and background asset returns are uncertain variables and subject to normal uncertainty distributions. Then, the effects of changes in mean and standard deviation of uncertain background asset on capital allocation are discussed. Furthermore, the influence of initial proportion in background asset on portfolio investment decisions is analyzed when investors have quadratic mean-variance utility function. Finally, the economic analysis illustration of investment strategy is presented.

Intervertebral disc degeneration is a natural process during aging and a leading cause of lower back pain. Here, we generate a comprehensive atlas of nucleus pulposus cells using single-cell RNA-seq analysis of human nucleus pulposus tissues (three males and four females, age 41.14 ± 18.01 years). We identify fibrotic late-stage nucleus pulposus cells characterized by upregulation of serglycin expression which facilitate the local inflammatory response by promoting the infiltration of inflammatory cytokines and macrophages. Finally, we discover that daphnetin, a potential serglycin ligand, substantially mitigates the local inflammatory response by downregulating serglycin expression in an in vivo mouse model, thus alleviating intervertebral disc degeneration. Taken together, we identify late-stage nucleus pulposus cells and confirm the potential mechanism by which serglycin regulates intervertebral disc degeneration. Our findings indicate that serglycin is a latent biomarker of intervertebral disc degeneration and may contribute to development of diagnostic and therapeutic strategies. Aging-related intervertebral disc degeneration (IVDD) is a leading cause of lower back pain. Here, the authors perform scRNA-seq analysis of intervertebral disc cells from patients, and identify cell populations and mechanisms associated with IVDD.

The inflammatory response is induced by the overexpression of inflammatory cytokines, mainly interleukin (IL)-1β, and is one of the main causes of intervertebral disc degeneration (IVDD). NLR pyrin domain containing 3 (NLRP3) inflammasome activation is an important source of IL-1β. As an anti-inflammatory neuroendocrine hormone, melatonin plays various roles in different pathophysiological conditions. However, its roles in IVDD are still not well understood and require more examination. First, we demonstrated that melatonin delayed the progression of IVDD and relieved IVDD-related low back pain in a rat needle puncture IVDD model; moreover, NLRP3 inflammasome activation (NLRP3, p20, and IL-1β levels) was significantly upregulated in severely degenerated human discs and a rat IVDD model. Subsequently, an IL-1β/NF-κB-NLRP3 inflammasome activation positive feedback loop was found in nucleus pulposus (NP) cells that were treated with IL-1β. In these cells, expression of NLRP3 and p20 was significantly increased, NF-κB signaling was involved in this regulation, and mitochondrial reactive oxygen species (mtROS) production increased. Furthermore, we found that melatonin disrupted the IL-1β/NF-κB-NLRP3 inflammasome activation positive feedback loop in vitro and in vivo. Melatonin treatment decreased NLRP3, p20, and IL-1β levels by inhibiting NF-κB signaling and downregulating mtROS production. Finally, we showed that melatonin mediated the disruption of the positive feedback loop of IL-1β in vivo. In this study, we showed for the first time that IL-1β promotes its own expression by upregulating NLRP3 inflammasome activation. Furthermore, melatonin disrupts the IL-1β positive feedback loop and may be a potential therapeutic agent for IVDD.

Intervertebral disc degeneration (IVDD) is a main cause of low back pain, and inflammatory factors play key roles in its pathogenesis. Gremlin-1 (Grem1) was reported to induce an inflammatory response in other fields. This study aimed to investigate the mechanisms of Grem1 in the degenerative process of intervertebral discs. Dysregulated genes were determined by analyzing microarray profiles. The expression of Grem1 in 17 human disc samples (male:female = 9:8) and rat models ( n  = 5 each group) was measured by western blotting (WB), real-time quantitative PCR (RT-qPCR), and immunohistochemistry (IHC). The regulatory effects of Grem1 on apoptosis were examined using siRNAs, flow cytometry, immunofluorescence (IF), and WB. The therapeutic effect was evaluated by locally injecting specific Grem1 siRNA into IVDD rats. The expression of Grem1 was significantly increased in human degenerative intervertebral discs; furthermore, the expression of Grem1 positively correlated with the level of intervertebral disc degeneration. Grem1 was significantly overexpressed in tumor necrosis factor (TNF)-α-induced degenerative NP cells. Apoptosis in degenerative NP cells transfected with siRNA targeting Grem1 was significantly lower than that in the control group. Specific Grem1 siRNA markedly repressed the development of IVDD in surgery-induced IVDD rats. These results indicated that the expression of Grem1 was positively correlated with the severity of intervertebral disc degeneration, and Grem1 siRNA could inhibit Grem1-induced apoptosis and extracellular matrix alterations by mediating the TGF-β/Smad signaling pathway. This study may provide a therapeutic strategy for alleviating inflammation-induced apoptosis associated with intervertebral disc degeneration. Disc generation: Protein identified as potential therapeutic target Gene expression profiling reveals an important factor underlying degeneration of the discs that connect and cushion individual vertebrae, a primary cause of lower back pain. This degeneration generally originates in the central portion of the disc known as the nucleus pulposus (NP). Researchers led by Jianru Wang and Zhaomin Zheng of the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, compared NP cells from healthy individuals with those from patients with disc degeneration. They observed strongly increased expression of a protein called Gremlin-1, which is critical to development and also associated with osteoarthritis. Gremlin-1 expression correlated strongly with degeneration and death in cultured NP cells, and the researchers identified key signaling pathways affected by this protein. Targeted inhibition of Gremlin-1 prevented tissue damage in a rat model of disc degeneration, highlighting a potential therapeutic opportunity.

Objective This retrospective study determined the storage time of finished infusion in each hospital ward and assessed whether the storage time of finished infusion was within an acceptable range. Methods The research object was the finished infusion (one bag of infusion with only one drug) that is centrally dosed at the Pharmacy Intravenous Admixture Service (PIVAS) of Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences. We used an automatic scanner to assess the placement time of finished infusion products in various wards of the hospital. We classified the drugs used in various wards, analyzed whether their placement times were reasonable, assessed the reasons for unreasonable placement times, and took intervention measures. Similarly, the storage time of finished infusion was deemed reasonable or unreasonable, the reasons for unreasonable storage times were analyzed, and intervention measures were taken. Results In September 2021, the proportion of infusions stored for an unreasonable time was 12.69%, a decrease of 5.37% compared with August 2021, indicating the effectiveness of intervention measures. Conclusion By using statistical analysis and intervention measures, our PIVAS improved the standardized use of finished infusion products and ensured the safety of medication for patients.

Mental health issues have become a significant global public health challenge. Traditional assessments rely on subjective methods with limited ecological validity. Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. This study aimed to provide a comprehensive review of the current state of passive sensing-based and ML technologies for mental health monitoring. We summarized the technical approaches, revealed the association patterns between behavioral features and mental disorders, and explored potential directions for future advancements. This scoping review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and was prospectively registered on the Open Science Framework. We systematically searched 7 databases (Web of Science, PubMed, IEEE Xplore, Embase, PsycINFO, Scopus, and ACM Digital Library) for studies published between January 2015 and February 2025. We included 42 peer-reviewed studies that used passive sensing from wearables or smartphones with ML to monitor clinically diagnosed mental disorders, such as depression and anxiety. Data were synthesized across technical dimensions (data collection, preprocessing, feature engineering, and ML models) and clinical associations, with behavioral features categorized into 8 domains. The 42 included studies were predominantly cohort designs (23/42, 55%), with a median sample size of 60.5 (IQR 54-99). Most studies focused on depression (23/42, 55%) and anxiety (9/42, 21%) using primarily wrist-worn devices (32/42, 76%) collecting heart rate (28/42, 67%), movement index (25/42, 60%), and step count (17/42, 40%) as key biomarkers. Deep learning models (eg, convolutional neural networks and long short-term memory) showed high accuracy, while traditional ML (eg, random forest) remained prevalent due to better interpretability. We identified critical limitations, including small samples (32/42, 76% with N<100), short monitoring periods (19/42, 45% <7 days), scarce external validation (1/42, 2%), and limited reporting on data anonymization (6/42, 14%). While passive sensing and ML demonstrate promising accuracy (eg, convolutional neural network-long short-term memory achieving 92.16% in anxiety detection), the evidence remains constrained by three key limitations: (1) methodological heterogeneity (32/42, 76% single-device studies; 19/42, 45% with <7-day monitoring), (2) high risk of bias from small samples (median 60.5, IQR 54-99 participants) and scarce external validation (1/42, 2%), and (3) ethical gaps (only 6/42, 14% addressing anonymization). These findings underscore the technology's potential to transform mental health care through objective, continuous monitoring-particularly for depression (heart rate and step count biomarkers) and anxiety (sleep and social interaction patterns). However, clinical translation requires standardized protocols, larger longitudinal studies (≥3 months), and ethical frameworks for data privacy. Future work should prioritize multimodal sensor fusion and explainable artificial intelligence to bridge the gap between technical performance and clinical deployability.

The incidence of Alzheimer’s disease (AD) continues to rise, and predictive models combining artificial intelligence (AI) with wearable devices offer a new approach for its detection and diagnosis. Existing reviews remain focused on traditional biomarkers, making it necessary to supplement the evidence in this field, particularly given the rapid advancements in wearable and AI technologies. The scoping review protocol aims to systematically evaluate AI-based predictive models using wearable devices for AD, with a focus on their measurement outcomes and model development processes. The review will follow the Arksey and O’Malley framework and incorporate PRISMA-ScR guidelines. This study will search multiple databases, including Web of Science, Cochrane Library, and PubMed, covering relevant gray literature. The quality of the included studies will be rigorously assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklists. Two independent reviewers will conduct title and abstract screening, retrieve and assess full-text evidence sources, and extract data. The results will be narratively synthesized and presented in tables and figures. The knowledge gained from this review is expected to provide systematic evidence supporting AI-based predictive models that combine wearable devices for AD, potentially offering insights into model construction details such as data collection and external validation.

In recent years, large language models (LLMs) have experienced rapid development. LLM-based virtual patients have begun to gain attention, offering new opportunities for simulations in medical education. This study aims to systematically analyze the current applications, research trends, and challenges of LLM-based virtual patients in medical education and to explore potential future directions for development. This study adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Five databases (Web of Science Core Collection, PubMed, IEEE Xplore, Embase, and Scopus) were searched from January 1, 2018, to June 24, 2025, to identify studies related to the application of LLM-based virtual patients in medical education. A comprehensive analysis of LLM-based virtual patients from research design to application and evaluation was conducted. A total of 28 studies were included in this scoping review. Analysis revealed that 92.9% (26/28) of the studies were published in the past 2 years, indicating that LLM-based virtual patient research is still in its early stages. The research primarily focuses on medical training and spans a wide range of medical disciplines. When using LLMs, advanced technologies such as social robots, virtual reality, and mixed reality are used to present LLM-based virtual patients. Combining these technologies with various supplementary tools enhances the realism of LLM-based virtual patients and improves user interaction. The evaluation of LLM-based virtual patients mainly emphasizes user experience. However, evaluation methods lack standardization, and only 13% (3/23) of studies used validated tools in assessing LLM-based virtual patients, while only 21.7% (5/23) of studies objectively measured learning outcomes facilitated by LLM-based virtual patients. All included studies expressed a positive attitude toward LLM-based virtual patients; however, they overlook privacy and security considerations in practical applications. LLM-based virtual patients hold significant innovation potential in medical education and are still in the early stages of development. They are primarily applied in medical training and show promise in communication skills training, although they cannot replace real-world interactions. Moreover, the heterogeneity of research designs, the absence of nonverbal cues in interactions, and concerns regarding privacy and security limit their broader implementation. Future research should focus on improving the reliability, realism, safety, and scientific efficacy of LLM-based virtual patients. Open Science Framework Registries 10.17605/OSF.IO/DMC9Q; https://osf.io/DMC9Q/overview.

This study aims to ascertain the median effective time (ET50) and the 95% effective time (ET95) of EMLA cream and tetracaine hydrochloride gel for analgesia during non-coring needle insertion into totally implantable venous access devices (TIVADs). Participants were randomly assigned to either Group E, receiving 2g of EMLA cream, or Group T, receiving 1g of tetracaine hydrochloride gel. Prior to needle insertion, the topical anesthetic was uniformly applied to a 1 cm radius around the puncture site. The initial target application time was set at 60 minutes for Group E and 30 minutes for Group T. For each subsequent participant, the target time was adjusted using a 1:1.1-time gradient, based on the Numeric Rating Scale (NRS) score of the preceding participant. Baseline characteristics, NRS scores, and adverse reactions were documented by a researcher who was not involved in the needle insertion process. The probit analysis method was employed to determine the ET50 and ET95 values. The ET50 and ET95 of patients to achieve painless non-coring needle insertion with EMLA cream were 55.882 minutes (95% Confidence Interval [CI]: 51.369-59.935 minutes) and 63.587 minutes (95% CI: 59.684-92.592 minutes), respectively. In comparison, the ET50 and ET95 for tetracaine hydrochloride gel were 39.092 minutes (95% CI: 36.646-41.678 minutes) and 43.388 minutes (95% CI: 41.111-56.859 minutes), respectively. There was no statistically significant difference between the two groups regarding the incidence of mild and serious adverse reactions. Application of EMLA cream for 64 minutes or tetracaine gel for 44 minutes at the site of TIVADs resulted in a safely conducted, painless non-coring needle puncture for 95% of patients.

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.