Evaluation of ENG/CD105 expression, methylation, immuno-response, and cordycepin (CD) regulation as a novel biomarker of breast invasive carcinoma (BRCA).

ENG/CD105 encodes a vascular endothelial glycoprotein and plays a crucial role in modulating angiogenesis. However, the significance of ENG expression, DNA methylation, immuno-response, and cordycepin (CD) regulation as diagnostic, prognostic, and therapeutic markers for breast invasive carcinoma (BRCA) remains unclear. As a result, ENG is decreased in BRCA tissues compared with corresponding healthy tissues. Five isoforms were found, and the utilization for ENG isoform (ENG-002) was the highest, suggesting its potential involvement in important roles in BRCA. ENG DNA was frequently altered in most types of cancer, and overall survival (OS) for mutant ENG was significantly longer than for wild-type cases. High expressions of ENG remarkably correlate with long relapse-free survival (RFS) for breast cancer (BC). Additionally, the ENG methylation level was higher in BRCA tissues compared with matched healthy tissues. The ENG expression and DNA methylation showed a significantly reverse correlation, demonstrating that ENG methylation may be a regulatory mechanism. By constructing diagnostic and prognostic models of ENG methylation for BRCA, we found four CpGs (CpG sites) that ranked with high importance. High methylation for cg14185922 of ENG in BRCA tissues showed shorter OS (high risk), indicating that ENG CpGs' methylation has potential as a diagnostic and prognostic biomarker for BRCA. Moreover, ENG might be a novel target for tumor immune response and immunotherapy in pancancer, including BC. CD, an adenosine analog and anti-cancer agent, increased ENG levels in a dose-dependent manner in animal models. This suggests that CD repressed BC growth and metastasis, at least partially through increasing the expression of the tumor suppressor gene ENG. Thus, our study successfully evaluated ENG/CD105 expression, DNA methylation, immune response, and CD regulation, which act as a novel diagnostic, prognostic, and therapeutic biomarker for BRCA. This research also fills critical knowledge gaps in this ENG/cancer field and highlights ENG's potential importance for the diagnosis, prognosis, and treatment of BRCA.

Comprehensive analysis, diagnosis, prognosis, and cordycepin (CD) regulations for GSDME expressions in pan-cancers.

The Gasdermin E gene (GSDME) plays roles in deafness and cancers. However, the roles and mechanisms in cancers are complex, and the same gene exhibits different mechanisms and actions in different types of cancers. Online databases, such as GEPIA2, cBioPortal, and DNMIVD, were used to comprehensively analyze GSDME profiles, DNA methylations, mutations, diagnosis, and prognosis in patients with tumor tissues and matched healthy tissues. Western blotting and RT-PCR were used to monitor the regulation of GSDME by Cordycepin (CD) in cancer cell lines. We revealed that GSDME expression is significantly upregulated in eight cancers (ACC, DLBC, GBM, HNSC, LGG, PAAD, SKCM, and THYM) and significantly downregulated in seven cancers (COAD, KICH, LAML, OV, READ, UCES, and UCS). The overall survival was longer only in ACC, but shorter in four cancers, including COAD, KIRC, LIHC, and STAD, when GSDME was highly expressed in cancers compared with the corresponding normal tissues. Moreover, the high expression of GSDME was negatively correlated with the poor prognosis of ACC, while the low expression of GSDME was negatively correlated with the poor prognosis of COAD, suggesting that GSDME might serve as a good prognostic factor in these two cancer types. Accordingly, results indicated that the DNA methylations of those 7 CpG sites constitute a potentially effective signature to distinguish different tumors from adjacent healthy tissues. Gene mutations for GSDME were frequently observed in a variety of tumors, with UCES having the highest frequency. Moreover, CD treatment inhibited GSDME expression in different cancer cell lines, while overexpression of GSDME promoted cell migration and invasion. Thus, we have systematically and successfully clarified the GSDME expression profiles, diagnostic values, and prognostic values in pan-cancers. Targeting GSDME with CD implies therapeutic significance and a mechanism for antitumor roles in some types of cancers via increasing the sensitivity of chemotherapy. Altogether, our study may provide a strategy and biomarker for clinical diagnosis, prognostics, and treatment of cancers by targeting GSDME.

Implementing next-generation sequencing for diagnosis and management of hereditary hearing impairment: a comprehensive review.

INTRODUCTION: Sensorineural hearing impairment (SNHI), a common childhood disorder with heterogeneous genetic causes, can lead to delayed language development and psychosocial problems. Next-generation sequencing (NGS) offers high-throughput screening and high-sensitivity detection of genetic etiologies of SNHI, enabling clinicians to make informed medical decisions, provide tailored treatments, and improve prognostic outcomes. AREAS COVERED: This review covers the diverse etiologies of HHI and the utility of different NGS modalities (targeted sequencing and whole exome/genome sequencing), and includes HHI-related studies on newborn screening, genetic counseling, prognostic prediction, and personalized treatment. Challenges such as the trade-off between cost and diagnostic yield, detection of structural variants, and exploration of the non-coding genome are also highlighted. EXPERT OPINION: In the current landscape of NGS-based diagnostics for HHI, there are both challenges (e.g. detection of structural variants and non-coding genome variants) and opportunities (e.g. the emergence of medical artificial intelligence tools). The authors advocate the use of technological advances such as long-read sequencing for structural variant detection, multi-omics analysis for non-coding variant exploration, and medical artificial intelligence for pathogenicity assessment and outcome prediction. By integrating these innovations into clinical practice, precision medicine in the diagnosis and management of HHI can be further improved.

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images.

BACKGROUND: Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts. METHODS: This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint. RESULTS: In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups. CONCLUSION: The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Bearing Dynamics Modeling Based on the Virtual State-Space and Hammerstein-Wiener Model.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein-Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model's input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems.

Tripartite motif-containing 28 (TRIM28) expression and cordycepin inhibition in progression, prognosis, and therapeutics of patients with breast invasive carcinoma.

Breast cancer (BC) is the most common tumor in women worldwide. TRIM28 (RNF96) plays pleiotropic biological functions, such as silencing target genes, facilitating DNA repair, stimulating cellular proliferation and differentiation, and contributing to cancer progression. TRIM28 plays an increasingly crucial role in cancer, but its impact on BC, including breast invasive carcinoma, remains poorly understood. In the current study, analyses of online databases, quantitative real-time quantitative PCR, immunohistochemistry, and western blotting were performed on patients with breast invasive carcinoma (BRCA). Cordycepin (CD) was used to monitor BC progression and TRIM28 expression in vivo. As a result, we observed that TRIM28 is highly expressed in breast invasive carcinoma tissues compared with the corresponding normal tissues and is correlated with metastatic / invasive progression. High expression of TRIM28 might serve as a prognostic marker for long-term survival in triple-negative BC, advanced BC, or breast invasive carcinoma. Although TRIM28 methylation in tumor tissues of breast invasive carcinoma is not significantly changed compared to the matched normal tissues, the expressions and methylation of TRIM28 are significantly reversely correlated. TRIM28 expression was inhibited by CD in the mouse model, indicating its role in preventing BC progression. Thus, TRIM28 might be a potentially valuable molecular target for forecasting the progression / prognosis of patients with breast invasive carcinoma. CD, which represses BC growth/metastasis, may be involved partially through suppressing TRIM28 expression.

University of Ferrara run-to-failure vibration dataset of self-aligning double-row ball bearings.

Data from real systems is an important resource for research in machine diagnostics and prognostics. The demand for data has increased exponentially in recent years due to the growing interest in prognostics and the development of AI technologies for predictive maintenance. When used for fault detection and predictive maintenance, data must be able to provide information about the degradation phenomena that occur in machines. In addition, one goal of prognostics is to predict the remaining useful life (RUL), which requires a large amount of data to apply data-driven techniques or validate physics-based models. Bearings are subject to a wide range of loads and fatigue stresses, and their failure can be catastrophic for the entire machine or plant. The Department of Engineering of the University of Ferrara has carried out an extensive experimental campaign to record the evolution of vibration signals throughout the life of self-aligning double row rolling element bearings. Six accelerated run-to-failure tests were performed, while the acceleration signals were continuously recorded by a uniaxial accelerometer. A radial load was applied to the bearing housing and controlled by a load cell. The shaft speed was kept constant and controlled by an electric motor driven by an inverter. The data set provided contains acceleration signals in the radial direction for the entire duration of the tests and can be used for research or industrial purposes.

Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing Artificial Intelligence Diagnostic Precision.

INTRODUCTION: In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic images of rare or underrepresented GU tissues. We hypothesized that augmenting the training data of computational pathology models with these GAN-generated images, validated through pathologist evaluation and quantitative similarity measures, would significantly enhance model performance in tasks such as tissue classification, segmentation, and disease detection. METHODS: To test this hypothesis, we employed a GAN model to produce synthetic images of eight different GU tissues. The quality of these images was rigorously assessed using a Relative Inception Score (RIS) of 1.27 ± 0.15 and a Fréchet Inception Distance (FID) that stabilized at 120, metrics that reflect the visual and statistical fidelity of the generated images to real histopathological images. Additionally, the synthetic images received an 80% approval rating from board-certified pathologists, further validating their realism and diagnostic utility. We used an alternative Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA) to assess the quality of prostate tissue. This allowed us to make a comparison between original and synthetic data in the context of features, which were further validated by the pathologist's evaluation. Future work will focus on implementing a deep learning model to evaluate the performance of the augmented datasets in tasks such as tissue classification, segmentation, and disease detection. This will provide a more comprehensive understanding of the utility of GAN-generated synthetic images in enhancing computational pathology workflows. RESULTS: This study not only confirms the feasibility of using GANs for data augmentation in medical image analysis but also highlights the critical role of synthetic data in addressing the challenges of dataset scarcity and imbalance. CONCLUSIONS: Future work will focus on refining the generative models to produce even more diverse and complex tissue representations, potentially transforming the landscape of medical diagnostics with AI-driven solutions.

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience.

Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient-provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and "Ever" of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.

Absence of orthopaedia homeobox protein (OTP) expression is associated with disease spread and adverse outcome in pulmonary carcinoid tumour patients.

Pulmonary carcinoid (PC) tumours typically have a good prognosis, although metastases occur, and the disease may progress after a long period of time. Expression of orthopaedia homeobox protein (OTP) has been recognized as a possible independent prognostic marker in PCs. Immunohistochemical (IHC) OTP expression has been associated with better prognosis, but the staining has yet to be implemented in routine clinical diagnostics. In response to this, two new monoclonal OTP antibodies were recently developed.This retrospective study included 164 PC patients operated on at Helsinki University Hospital between 1990 and 2020. Tissue microarray slides, prepared from formalin-fixed and paraffin-embedded primary tumour samples, were stained with OTP IHC using one polyclonal and two novel monoclonal antibodies.Absence of OTP expression was associated with a shorter disease-specific survival (DSS) and disease progression (p < 0.001). Patients without OTP expression had a 5-year DSS of 73-79%, whereas 5-year DSS was 91-94% with OTP expression, depending on the primary antibody. In a univariable Cox regression model, absence of OTP expression was associated with adverse outcome along with atypical histological subtype, metastatic disease, Ki-67 proliferation index > 1%, and larger tumour size. In a multivariable Cox regression model, only absence of OTP expression and lymph node involvement at the time of diagnosis were associated with risk of worse prognosis. All three antibodies showed good concordance with each other.Our findings support the role of OTP as an independent prognostic marker in PCs and applicability of IHC staining in routine clinical use with novel monoclonal antibodies.

Slow feature-based feature fusion methodology for machinery similarity-based prognostics.

Similarity-based prediction methods utilize degradation trend analysis based on degradation indicators (DIs). These methods are gaining prominence in industrial predictive maintenance because they effectively address prognostics for machines with unknown failure mechanisms. However, current studies often neglect the discrepancies in degradation trends when constructing DIs from multi-sensor data and lack automatic normalization of operating regimes during feature fusion. In this study, a feature fusion methodology based on a signal-to-noise ratio metric that leverages slow feature analysis (SFA) is proposed. This customized metric utilizes SFA to quantify degradation trend discrepancies of constructed DIs, while automatically filtering out the effects of multiple operating regimes during feature fusion. The effectiveness and superiority of the proposed method are demonstrated using publicly available aero-engine and rolling bearing datasets.

A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group.

OBJECTIVES: Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation. METHODS: A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed. RESULTS: Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology. CONCLUSIONS: Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

Digital twin-driven prognostics and health management for industrial assets.

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

Advancing a Non-Contact Structural and Prognostic Health Assessment of Large Critical Structures.

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover.

Could MicroRNA be Neurological Prognosis Biomarkers after Cardiac Arrest?

For patients monitored in intensive care units in the aftermath of a cardiac arrest, one of the well-established difficulties of care after resuscitation is the ability to perform the necessary prognostic assessments as accurately and early as possible. Although current guidelines include algorithms to determine prognosis, there are still missing links and uncertainties. Biomarkers obtained from peripheral blood are generally non-invasive and easy to obtain. Although the potential to use microRNA as a prognostic biomarker after cardiac arrest has received less interest recently, its popularity has increased in the last few years. By identifying prognostic biomarkers within 24 h of cardiac arrest, clinicians in intensive care could gain valuable insights to guide patient outcomes and predict both mortality and survival rates.

The Influence of Surgical Complexity and Center Experience on Postoperative Morbidity After Minimally Invasive Surgery in Gynecologic Oncology: Lessons Learned from the ROBOGYN-1004 Trial.

BACKGROUND: This study was a secondary analysis of the ROBOGYN-1004 trial conducted between 2010 and 2015. The study aimed to identify factors that affect postoperative morbidity after either robot-assisted laparoscopy (RL) or conventional laparoscopy (CL) in gynecologic oncology. METHODS: The study used two-level logistic regression analyses to evaluate the prognostic and predictive value of patient, surgery, and center characteristics in predicting severe postoperative morbidity 6 months after surgery. RESULTS: This analysis included 368 patients. Severe morbidity occurred in 49 (28 %) of 176 patients who underwent RL versus 41 (21 %) of 192 patients who underwent CL (p = 0.15). In the multivariate analysis, after adjustment for the treatment group (RL vs CL), the risk of severe morbidity increased significantly for patients who had poorer performance status, with an odds ratio (OR) of 1.62 for the 1-point difference in the WHO performance score (95 % CI 1.06-2.47; p = 0.027) and according to the type of surgery (p < 0.001). A focus on complex surgical acts showed significant more morbidity in the RL group than in the CL group at the less experienced centers (OR, 3.31; 95 % CI 1.0-11; p = 0.05) compared with no impact at the experienced centers (OR, 0.87; 95 % CI 0.38-1.99; p = 0.75). CONCLUSION: The findings suggest that the center's experience may have an impact on the risk of morbidity for patients undergoing complex robot-assisted surgical procedures.

A novel prognostic risk-scoring system based on m5C methylation regulator-mediated patterns for glioma patients.

N5-methylcytosine (m5C) methylation modification plays a crucial role in the epigenetic mechanisms underlying tumorigenesis, aggressiveness, and malignancy in diffuse glioma. Our study aimed to develop a novel prognostic risk-scoring system to assess the impact of m5C modification in glioma patients. Initially, we identified two distinct m5C clusters based on the expression level of m5C regulators in The Cancer Genome Atlas glioblastoma (TCGA-GBM) dataset. Differentially expressed genes (DEGs) between the two m5C cluster groups were determined. Utilizing these m5C regulation-related DEGs, we classified glioma patients into three gene cluster groups: A, B, and C. Subsequently, an m5C scoring system was developed through a univariate Cox regression model, quantifying the m5C modification patterns utilizing six DEGs associated with disease prognosis. The resulting scoring system allowed us to categorize patients into high- or low-risk groups based on their m5C scores. In test (TCGA-GBM) and validation (Chinese Glioma Genome Atlas [CGGA]-1018 and CGGA-301) datasets, glioma patients with a higher m5C score consistently exhibited shorter survival durations, fewer isocitrate dehydrogenase (IDH) mutations, less 1p/19q codeletion and higher World Health Organization (WHO) grades. Additionally, distinct immune cell infiltration characteristics were observed among different m5C cluster groups and risk groups. Our study developed a novel prognostic scoring system based on m5C modification patterns for glioma patients, complementing existing molecular classifications and providing valuable insights into prognosis for glioma patients.

The Expression of the Claudin Family of Proteins in Colorectal Cancer.

Claudins (CLDN1-CLDN24) are a family of tight junction proteins whose dysregulation has been implicated in tumorigeneses of many cancer types. In colorectal cancer (CRC), CLDN1, CLDN2, CLDN4, and CLDN18 have been shown to either be upregulated or aberrantly expressed. In the normal colon, CLDN1 and CLDN3-7 are expressed. Although a few claudins, such as CLDN6 and CLDN7, are expressed in CRC their levels are reduced compared to the normal colon. The present review outlines the expression profiles of claudin proteins in CRC and those that are potential biomarkers for prognostication.

Deep Learning-Based Prognostics and Health Management Model for Pilot-Operated Cryogenic Safety Valves.

This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings.

Comparing the Perspectives of Generative AI, Mental Health Experts, and the General Public on Schizophrenia Recovery: Case Vignette Study.

Background: The current paradigm in mental health care focuses on clinical recovery and symptom remission. This model's efficacy is influenced by therapist trust in patient recovery potential and the depth of the therapeutic relationship. Schizophrenia is a chronic illness with severe symptoms where the possibility of recovery is a matter of debate. As artificial intelligence (AI) becomes integrated into the health care field, it is important to examine its ability to assess recovery potential in major psychiatric disorders such as schizophrenia. Objective: This study aimed to evaluate the ability of large language models (LLMs) in comparison to mental health professionals to assess the prognosis of schizophrenia with and without professional treatment and the long-term positive and negative outcomes. Methods: Vignettes were inputted into LLMs interfaces and assessed 10 times by 4 AI platforms: ChatGPT-3.5, ChatGPT-4, Google Bard, and Claude. A total of 80 evaluations were collected and benchmarked against existing norms to analyze what mental health professionals (general practitioners, psychiatrists, clinical psychologists, and mental health nurses) and the general public think about schizophrenia prognosis with and without professional treatment and the positive and negative long-term outcomes of schizophrenia interventions. Results: For the prognosis of schizophrenia with professional treatment, ChatGPT-3.5 was notably pessimistic, whereas ChatGPT-4, Claude, and Bard aligned with professional views but differed from the general public. All LLMs believed untreated schizophrenia would remain static or worsen without professional treatment. For long-term outcomes, ChatGPT-4 and Claude predicted more negative outcomes than Bard and ChatGPT-3.5. For positive outcomes, ChatGPT-3.5 and Claude were more pessimistic than Bard and ChatGPT-4. Conclusions: The finding that 3 out of the 4 LLMs aligned closely with the predictions of mental health professionals when considering the "with treatment" condition is a demonstration of the potential of this technology in providing professional clinical prognosis. The pessimistic assessment of ChatGPT-3.5 is a disturbing finding since it may reduce the motivation of patients to start or persist with treatment for schizophrenia. Overall, although LLMs hold promise in augmenting health care, their application necessitates rigorous validation and a harmonious blend with human expertise.