Prognostic Approach Research Articles

Adaptable and Generic Methods for Monitoring and Prognostics of Energy Assets

Monitoring and prognostics of energy assets are crucial for maintaining their reliability and efficiency. Effective monitoring ensures that potential issues are identified early, preventing unexpected failures and optimizing maintenance schedules. However, several challenges complicate this process in real-world scenarios, including poor data quality, low-fidelity and sparse data, the influence of external environmental factors, and diverse operating conditions and asset types. These challenges highlight the need for adaptable and generic solutions that can handle variability and complexity across different energy systems. This Ph.D. project aims to address these challenges by developing scalable, data-driven approaches for monitoring and prognostics. By focusing on creating adaptable and generic frameworks, the research seeks to provide robust solutions for real-world monitoring and prognostic problems for energy assets.

Protease-activated receptor 2 (PAR2) inhibition reduces complement-associated cardiac remodelling in obesity-related heart failure

Abstract Background and Aims Obesity is considered a major risk factor for the development of heart failure (HF) due to lipid accumulation, oxidative stress and inflammation which results in cardiac remodeling. Protease activated receptor 2 (PAR2) has been linked to various cardiovascular and inflammatory diseases. The role of PAR2 has not yet been studied in obesity-related HF. Methods In the experimental mouse study, we used 1-year-old ApoE knockout (ApoE-/-) and ApoE PAR2 double knockout (ApoE-/-PAR2-/-) mice with a C57BL/6J background on a high fat (HF) diet (n=5/ group). Cell culture experiments were performed with human primary cardiac fibroblasts and HepG2 hepatocytes. Patients (n=50) included in the clinical study had a BMI > 25kg/m², symptoms of heart failure (HF), coronary angiography (CA) that excluded coronary artery disease (CAD) and endomyocardial biopsy (EMB) that confirmed the absence of active myocarditis or primary cardiac amyloidosis. Patients were divided into PAR2low (n=22) and PAR2high (n=22) based on the median PAR2 expression measured via qPCR in the EMB of the cohort, and six patients served as healthy controls. Results Evaluation of endomyocardial biopsies (EMB) from overweight/ obese non-ischemic cardiomyopathy (NICM) patients with heart failure (HF) (n=44) revealed that a reduced cardiac PAR2 expression is associated with less cardiac C3 (p≤0.054), C4 (p≤0.041) and C5 gene expression (p≤0.045), as well as plasma levels of complement C3 (1.1g/l± 0.7 vs 2.1g/l±0.2; p≤0.016) and C5b-9 (303.4ng/ml± 215.7 vs 992.7ng/ml± 756.2; p≤0.0002) and as a result, better systolic function i.e. LVEF (46.1%±17.1 vs 36.8%±15.2; p≤0.046) and LV GLS (-14.9%± 5.7 vs -9.5%± 4.6; p≤0.009), less cardiac remodeling and damage. Correspondingly, aged ApoE-/-PAR2-/-mice on a Western diet showed less hepatic and cardiac complement expression (i.e. C1q, C3, C4, C5, C9), cardiac C3 deposition (p≤0.047) and C5b-9 plasma levels (p≤0.049), as well as less cardiac necrosis (p≤0.046) and lower BNP (p≤0.016) than ApoE-/-. PAR2 overexpression in human cardiac fibroblasts (HCFs) independently enhanced and PAR2 antagonism reduced interferon γ (IFNγ)-mediated STAT1-dependent complement C3, C4 and C5 expression. Conclusions Protease-activated receptor 2 regulates complement-mediated cardiac damage and remodeling in HF. The PAR2-IFNγ/STAT1/C3-C4-C5 complement-axis might be a promising prognostic and therapeutic approach to identify and treat high-risk cases of HF in overweight/obese patients.

Multi-level attention graph feature fusion smooth prognostics approach for aircraft engines remaining useful life prediction

In the development of a prognostic and health management system, it is a challenge to mine the degradation characteristics, estimate the RUL reliably and smoothly, and explain the RUL prediction results from the multi-sensor condition monitoring data collected under the non-fixed-length equipment operation cycle and multi-operating conditions. In this paper, a Multi-level Attention Graph feature Fusion Smooth Prognostics Approach (MAGF-SPA) is proposed to achieve accurate, smooth, and interpretable RUL prediction results. In this method, firstly, multivariate time series (MTS) are transformed into graph signals from a purely data-driven perspective. Secondly, the four-level feature extraction strategy, which includes node-level, subgraph-level, cyclical-level, and temporal-level feature extraction, is designed to extract the inherent hidden degradation characteristics of MTS. Finally, a smoothing trick proposed in this paper can optimise the RUL prediction value corresponding to the degradation characteristics in real time. Experimental results show that the proposed method achieves state-of-the-art on the N-CMAPSS dataset.

Transcriptomic Biomarkers Associated with Microbiological Etiology and Disease Severity in Childhood Pneumonia.

Challenges remain in discerning microbiologic etiology and disease severity in childhood pneumonia. Defining host transcriptomic profiles during illness may facilitate improved diagnostic and prognostic approaches. Using whole blood ribonucleic acid sequencing from 222 hospitalized children with radiographic pneumonia and 45 age-matched controls, we identified differentially expressed genes that best identified children according to detected microbial pathogens (viral-only vs. bacterial-only and typical vs. atypical bacterial [+/- viral co-detection]) and an ordinal measure of phenotypic severity (moderate, severe, very severe). Overall, 135 (61%) children had viral-only detections, 15 (7%) had typical bacterial (+/- viral co-detections), and 26 (12%) had atypical bacterial (+/- viral co-detections). Eleven DE genes distinguished between viral-only and bacterial-only detections. Sixteen DE genes distinguished between atypical and typical bacterial detections (+/- viral co-detections). Nineteen DE genes distinguished between levels of pneumonia severity, including four genes also identified in the viral-only vs. bacterial-only model (IGHGP, PI3, CD177, RAP1GAP1) and four genes from the typical vs atypical bacterial model (PRSS23, IFI27, OLFM4, and ABO). We identified transcriptomic biomarkers associated with microbial detections and phenotypic severity in children hospitalized with pneumonia. These DE genes are promising candidates for validation and translation into diagnostic and prognostic tools.

Application of blood flow restriction training in lower limb rehabilitation

With the popularity of national fitness campaigns, sports injuries, especially lower limb injuries such as anterior cruciate ligament tears and ankle sprains, are increasing year by year. There are limitations in traditional treatment such as muscle atrophy. In recent years, the blood flow restriction training method (BFRT) which simulates high-intensity exercise with low intensity and limits arterial and venous blood flow to promote muscle growth and strength enhancement, has drawn attention as a novel approach to rehabilitation. This article reviews the application of BFRT in the rehabilitation of lower limb injuries, discusses its effects on muscle function, pain management and quality of life, and evaluates its safety and prospects for clinical application. BFRT reduces mechanical stress, relieves pain, improves endurance and aerobic capacity, and improves cardiovascular function. However, there are discomfort and potential risks, such as muscle injury and thrombosis, which require individualised adjustments and standardised training. BFRT has shown positive result(reducing pain &oedema and improving function)s in rehabilitation of ACL reconstruction (ACLR) and chronic ankle instability (CAI). In addition, BFRT has been shown to aid in the functional recovery of the lower extremity in patients with Achilles tendon injuries and strokes. Despite the positive effects of BFRT, relevant studies have problems such as small sample sizes and non-uniform parameters. BFRT is considered a safe training modality, but contraindications should be examined before clinical application. Future research endeavours should investigate the impact of BFRT in many rehabilitation contexts and enhance tailored rehabilitation strategies. In order to maximize the therapeutic benefit, a rehabilitation training program should be developed and refined with a minimum effective dose based on the quantification of the in vivo physiological response to BFRT. The identification of the optimal prognostic approach will help to advance the standardisation of BFR treatment methods.

Childhood and adolescent essential thrombocythemia and prefibrotic primary myelofibrosis: insights into diagnosis, outcomes, and treatment from a large Chinese cohort.

The paucity of essential thrombocythemia (ET) and prefibrotic primary myelofibrosis (pre-PMF) in individuals younger than 18 years highlights several unresolved issues in diagnosis, clinical outcomes, and treatment strategies. To address these knowledge gaps, we analyzed a large bidirectional cohort consisting of childhood and adolescent ET (CAA-ET, n = 156) and pre-PMF (CAA-preMF, n = 13), as well as adult ET (n = 349). We introduced immunophenotypic abnormalities as novel clonal markers in CAA-ET and CAA-preMF, establishing a comprehensive method for clonal marker detection that integrated driver and non-driver mutations, positive endogenous erythroid colony formation, immunophenotypic abnormalities, and chromosomal aberrations. Next-generation sequencing revealed distinct mutational profiles between CAA-ET and adult ET, along with different age-related trends in the distribution of driver mutations. Venous thrombosis was more prevalent in CAA-ET, with JAK2 V617F emerging as a potential risk factor (P = 0.018). Immunophenotypic abnormalities were identified as risk factors for disease progression (P = 0.027). Significant differences between expected and actual treatment practices were identified. Compared to CAA-ET, CAA-preMF demonstrated poorer progression-free survival (P < 0.001) and faster disease progression (P = 0.019). This study provides a critical foundation for refining diagnostic, prognostic, and therapeutic approaches for CAA-ET and CAA-preMF.

The emerging role of bacterial extracellular vesicles in human cancers.

Bacterial extracellular vesicles (BEVs) have emerged as pivotal mediators between bacteria and host. In addition to being crucial players in host homeostasis, they have recently been implicated in disease pathologies such as cancer. Hence, the study of BEVs represents an intriguing and rapidly evolving field with substantial translational potential. In this review, we briefly introduce the fundamentals of BEV characteristics, cargo and biogenesis. We emphatically summarize the current relationship between BEVs across various cancer types, illustrating their role in tumorigenesis, treatment responses and patient survival. We further discuss the inherent advantages of BEVs, such as stability, abundance and specific cargo profiles, that make them attractive candidates for non-invasive diagnostic and prognostic approaches. The review also explores the potential of BEVs as a strategy for cancer therapy, considering their ability to deliver therapeutic agents, modulate the tumour microenvironment (TME) and elicit immunomodulatory responses. Understanding the clinical significance of BEVs may lead to the development of better-targeted and personalized treatment strategies. This comprehensive review evaluates the current progress surrounding BEVs and poses questions to encourage further research in this emerging field to harness the benefits of BEVs for their full potential in clinical applications against cancer.

Predicting the development of optic nerve atrophy in children with cerebral palsy

Introduction. Optic nerve atrophy (ONA) holds a leading position in the nosological structure of ophthalmopathology. In children with cerebral palsy (CP), the presence of concomitant pathology such as ONA significantly worsens their health and quality of life. The combination of ONA and CP, as well as the interrelationship of these diseases, represents a relevant subject for research, particulary in studying the clinical and epidemiological features of ONA in children with CP to find optimal clinical solutions and prognostic approaches. Purpose: to investigate the prevalence of optic nerve atrophy in children with cerebral palsy to determine the prognosis for the development of this pathology in the study group. Materials and methods. A retrospective analysis of medical records of 640 patients diagnosed with cerebral palsy who were hospitalized in the clinic from 2015 to 2020 was conducted. The analysis included data from children with early-onset cerebral palsy (up to 3 years old) who were hospitalized for the first time. MS EXCEL software was used to calculate the main indicators of linear regression. Results. The study revealed that optic nerve atrophy in young children develops more frequently in boys (1.5 times more often than in girls), in rural areas, and in those with spastic cerebral palsy (36.3 %). The second most common form was ataxic cerebral palsy (69 children, 21.6 %), followed by infantile hemiplegia (61 children, 19%). Based on the dynamics of the prevalence of optic nerve atrophy in children with cerebral palsy, a mid-term forecast was made using the main indicators of linear regression. The prevalence of optic nerve atrophy in children with cerebral palsy is increasing, with prognostic estimates indicating further growth (14.9% in 2015, 15.4% in 2020, and 16.2% in 2024). Conclusion. The results of the study showed that cerebral palsy in children is often associated with optic nerve atrophy. ONA in young children is more frequently developed in cases of spastic cerebral palsy. As a result of calculating, an increase in the prevalence of optic nerve atrophy in children with cerebral palsy was observed over time, based on the correlation calculated using Spearman’s non-parametric correlation analysis Prognostic estimates indicate a significant increase in the near future.

Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data

In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using opensource datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing data-related and model-related issues and evaluating the limitations of these competitions. The potential solutions to address these challenges are also summarized. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for next-generation ML-PHM development in PHM domain.

A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency

In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models.

Alzheimer's Disease Pathology and Assistive Nanotheranostic Approaches for Its Therapeutic Interventions.

Alzheimer's disease (AD) still prevails and continues to increase indiscriminately throughout the 21st century, and is thus responsible for the depreciating quality of health and associated sectors. AD is a progressive neurodegenerative disorder marked by a significant amassment of beta-amyloid plaques and neurofibrillary tangles near the hippocampus, leading to the consequent loss of cognitive abilities. Conventionally, amyloid and tau hypotheses have been established as the most prominent in providing detailed insight into the disease pathogenesis and revealing the associative biomarkers intricately involved in AD progression. Nanotheranostic deliberates rational thought toward designing efficacious nanosystems and strategic endeavors for AD diagnosis and therapeutic implications. The exceeding advancements in this field enable the scientific community to envisage and conceptualize pharmacokinetic monitoring of the drug, sustained and targeted drug delivery responses, fabrication of anti-amyloid therapeutics, and enhanced accumulation of the targeted drug across the blood-brain barrier (BBB), thus giving an optimistic approach towards personalized and precision medicine. Current methods idealized on the design and bioengineering of an array of nanoparticulate systems offer higher affinity towards neurocapillary endothelial cells and the BBB. They have recently attracted intriguing attention to the early diagnostic and therapeutic measures taken to manage the progression of the disease. In this article, we tend to furnish a comprehensive outlook, the detailed mechanism of conventional AD pathogenesis, and new findings. We also summarize the shortcomings in diagnostic, prognostic, and therapeutic approaches undertaken to alleviate AD, thus providing a unique window towards nanotheranostic advancements without disregarding potential drawbacks, side effects, and safety concerns.

Optimized Prognostics Approach for Early Anomaly-Detection Methodology Used in Small Modular Reactors

Optimized Prognostics Approach for Early Anomaly-Detection Methodology Used in Small Modular Reactors

Can We Improve Mortality Prediction in Patients with Sepsis in the Emergency Department?

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED.

A core space gradient projection-based continual learning framework for remaining useful life prediction of machinery under variable operating conditions

Recently, continual learning has received particular attention in machinery remaining useful life (RUL) prediction, which enables prognostics networks to gradually improve performance without laborious retraining. Existing studies, however, have the following limitations: 1) The impacts of variable operating conditions are not explicitly considered during continual learning. 2) The continual learnability of prognostics network is limited by the lack of knowledge compression and de-redundancy. To overcome the abovementioned limitations, a novel continual learning framework is proposed for RUL prediction of machinery under variable operating conditions. First, a multi-kernel swarm convolution block is devised to automatically capture degradation features during continual learning without increasing computational consumption, which combines convolutional operations at various scales and focuses attention on appropriate scales based on operating condition information. Then, core space gradient projection is proposed for continual learning of prognostics networks, which mitigates forgetting by guiding gradient descent along the orthogonal direction of the previously input subspace. This approach also ensures the network learnability during continual learning by knowledge compression and de-redundancy to identify core space. The proposed framework is verified using accelerated degradation datasets of rolling element bearings with variable operating conditions. Experimental results show that the proposed framework is superior to some existing continual learning-based prognostics approaches for RUL prediction under variable operating conditions.

Deciphering the immune-metabolic nexus in sepsis: a single-cell sequencing analysis of neutrophil heterogeneity and risk stratification.

Metabolic dysregulation following sepsis can significantly compromise patient prognosis by altering immune-inflammatory responses. Despite its clinical relevance, the exact mechanisms of this perturbation are not yet fully understood. Single-cell RNA sequencing (scRNA-seq) was utilized to map the immune cell landscape and its association with metabolic pathways during sepsis. This study employed cell-cell interaction and phenotype profiling from scRNA-seq data, along with pseudotime trajectory analysis, to investigate neutrophil differentiation and heterogeneity. By integrating scRNA-seq with Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, key genes were identified. These genes were used to develop and validate a risk score model and nomogram, with their efficacy confirmed through Receiver Operating Characteristic (ROC) curve analysis. The model's practicality was further reinforced through enrichment and immune characteristic studies based on the risk score and in vivo validation of a critical gene associated with sepsis. The complex immune landscape and neutrophil roles in metabolic disturbances during sepsis were elucidated by our in-depth scRNA-seq analysis. Pronounced neutrophil interactions with diverse cell types were revealed in the analysis of intercellular communication, highlighting pathways that differentiate between proximal and core regions within atherosclerotic plaques. Insight into the evolution of neutrophil subpopulations and their differentiation within the plaque milieu was provided by pseudotime trajectory mappings. Diagnostic markers were identified with the assistance of machine learning, resulting in the discovery of PIM1, HIST1H1C, and IGSF6. The identification of these markers culminated in the development of the risk score model, which demonstrated remarkable precision in sepsis prognosis. The model's capability to categorize patient profiles based on immune characteristics was confirmed, particularly in identifying individuals at high risk with suppressed immune cell activity and inflammatory responses. The role of PIM1 in modulating the immune-inflammatory response during sepsis was further confirmed through experimental validation, suggesting its potential as a therapeutic target. The understanding of sepsis immunopathology is improved by this research, and new avenues are opened for novel prognostic and therapeutic approaches.

Revealing metabolic alterations in brucellosis patients by targeted metabolomics

Brucellosis, a zoonotic disease caused by brucella infection, presents metabolic profile changes in patients that have not been extensively explored. This study utilized an ultra-high performance liquid chromatography tandem mass spectrometry based targeted metabolomic approach to comprehensively investigated metabolic changes in Brucella patients. Serum samples of brucellosis 50 patients and 50 well-matched healthy controls were analyzed for 228 metabolites, revealing significant alterations in 83 metabolites in brucellosis patients. Notably, disruptions were observed in key metabolite pathways, such as amino acid metabolism, urea cycle, tricarboxylic acid cycle (TCA), and fatty acid metabolism. Patients diagnosed with Brucellosis exhibited distinct differences in the levels of aspartate, glutamate, β-alanine, and asparagine when compared to controls. Within the urea cycle, a significant downregulation of arginine was observed, whereas ornithine levels were considerably upregulated. In the TCA cycle, concentrations of 2-oxoglutarate, succinate, and malate were significantly elevated, while citrate levels demonstrated a notable decrease. Due to the interruption of the TCA cycle, glycolysis was accelerated to compensate for the resultant energy deficit in Brucella patients. Concurrently, there was a significant increase in the levels of short and medium-chain fatty acids, while long-chain fatty acids showed a marked decrease. The study systematically revealed significant metabolic alterations in Brucellosis patients and further explored the potential correlation between these changes and clinic symptoms, including fatigue, muscle soreness and prolonged fever. The results enhanced our understanding of Brucellosis, offering valuable insights potentially beneficial in formulating more effective treatment strategies and improving prognostic approaches.

TIM-3/CD68 double-high expression in Glioma: Prognostic characteristics and potential therapeutic approaches

BackgroundImmunotherapy has revolutionized the treatment of various types of tumors, but there has been no breakthrough in the treatment of gliomas. The aim of this study is to discover valuable immunotherapy target in glioma, analyze its expression in glioma and the related microenvironment, explore potential immunotherapy strategies, and propose new possibilities for the treatment of gliomas. MethodsImmunohistochemistry (IHC) and multiplex fluorescence immunohistochemistry (mIHC) were used to analyze the expression of common immune markers and checkpoints in 187 glioma patients from Sun Yat-sen University Caner Center (SYSUCC). Bioinformatics analysis was used to examine the expression of TIM-3 in different macrophages using the Chinese Glioma Genome Atlas (CGGA) single-cell sequencing database. The Kaplan-Meier curve was used to predict the prognostic value of samples with high TIM-3 and CD68 expression. The R package was used to analyze the somatic mutation status and the sensitivity of small molecule inhibitors in TIM-3/CD68 double-high expression samples. ResultsTIM-3 is a relatively highly expressed immune checkpoint in glioma. Unlike other tumors, TIM-3 is mainly expressed on macrophages in the glioma microenvironment. TIM-3/CD68 double-high expression suggests poor survival in glioma and may be a new upgrade marker in both IDH-mutant glioma and IDH-wildtype low-grade glioma (LGG) glioma (P < 0.01). Exploring the combination of TIM-3 inhibitors and p38 MAPK inhibitor may be a potential treatment direction for TIM-3/CD68 double high expression gliomas in the future. ConclusionsThe combination of TIM-3 and CD68 holds significant importance as a potential target for both prognosis and therapeutic intervention in glioma.

Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients

BackgroundThe Sequential Organ Failure Assessment (SOFA) score monitors organ failure and defines sepsis but may not fully capture factors influencing sepsis mortality. Socioeconomic and demographic impacts on sepsis outcomes have been highlighted recently. ObjectiveTo evaluate the prognostic value of SOFA scores against demographic and social health determinants for predicting sepsis mortality in critically ill patients, and to assess if a combined model increases predictive accuracy. MethodsThe study utilized retrospective data from the MIMIC-IV database and prospective external validation from the Penn State Health cohort. A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated. FindingsIn the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3. ConclusionAdding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.

Methylation of a group of microRNA genes as a marker for the diagnosis and prognosis of non-small cell lung cancer

Objectives. Lung cancer, representing a difficult-to-diagnose heterogeneous malignant neoplasm, is characterized by an asymptomatic course up to late stages, a high incidence of adverse outcomes, and a high probability of metastasis. Its most common form is non-small cell lung cancer (NSCLC). Recent studies have demonstrated a significant role of non-coding RNAs—in particular, microRNAs—in the development of NSCLC. MicroRNAs, which function as post-transcriptional regulators of the expression of protein-coding genes, including those associated with oncogenesis, are involved in the processes of cell proliferation, differentiation, and apoptosis. One of the approaches for regulating the expression of microRNAs themselves is to change the methylation of the CpG island adjacent to the microRNA gene or overlapping it. It has been shown that microRNA genes are several times more likely to undergo methylation than protein-coding genes. The aim of the present work is to study changes in the level of methylation of a number of microRNA genes and compile a potential panel of markers for the diagnosis and prognosis of NSCLC.Methods. Samples of NSCLC tumors were collected and clinically characterized at the Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Moscow, Russia. High-molecular-weight DNA was isolated from tissues using a standard method. The level of methylation was analyzed using bisulfite conversion of DNA and quantitative methyl-specific polymerase chain reaction with real-time detection. The significance of differences between the studied groups was assessed by the nonparametric Mann–Whitney U test for independent samples. Differences were considered significant at p &lt; 0.05.Results. The analysis of methylation levels of microRNA genes revealed a significant (p &lt; 0.05) increase in the methylation level of eight microRNA genes: MIR124-1/2/3, MIR125В-1, MIR129-2, MIR137, MIR375, MIR1258, and MIR339 (p &lt; 0.01, false discovery rate ≤ 0.25). On the basis of receiver operating characteristic analysis, a panel of markers is proposed for the diagnosis of NSCLC according to the nature of methylation of the studied microRNA genes in the tumor and in the normal tissue.Conclusions. Our results, which contribute to the understanding of molecular mechanisms involved in NSCLC development, can be used in the development of new diagnostic and prognostic approaches in clinical oncology.

GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoT

Health assessment (HA) & remaining useful life (RUL) estimation, the two essential pillars of the prognostics and health management (PHM) paradigm, help improve industrial equipment reliability while reducing maintenance costs. However, reported works treat HA and RUL estimation as disjoint problems though there exist unexploited similarities among these related issues. Additionally, in practical industrial working scenarios, equipment(s) stay in a normal condition for most of its lifespan, leading to a disproportionate training dataset, hampering the prediction accuracy. To overcome the above problems, we propose a data-driven multi-task learning framework aided by a novel least squares recurrent auxiliary classifier generative adversarial network (LS-R-ACGAN). LS-R-ACGAN employs recurrent neural networks (RNNs) in its generator & discriminator networks for multi-variate fault data generation while overcoming the vanishing gradient problem of ACGANs. Post-data-augmentation, a balanced training dataset, trains a multi-task learning model based on a deep gated RNN (DGRU) for joint HA and RUL estimation. Our simulations use the C-MAPSS dataset for testing the proposed approach’s accuracy. The final results showcase improvements by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 3.54% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 2.38% on the RMSE and Score metric over existing state-of-the-art works suggesting its competitiveness and competence for real-world implementations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study attacks multiple presumptions concerning the maintenance of complex systems including, ample availability of fault samples alongside normal samples and solving only one of the problems, i.e., health assessment (HA) or remaining useful life (RUL) estimations rather than both. Popular artificial intelligence (AI) based solutions rely heavily upon the quantity of training data for desired performance outcomes. However, in real-life industrial scenarios, fault instances are rare, inherently resulting in an imbalanced dataset. Additionally, it is both labor-intensive and costly to repeat multiple experiments for gathering required data as the equipment(s) pass through short-lived fault states. Thus conventional AI-based solutions may end up producing biased predictions. This work proposes to solve the imbalanced training dataset problem along with joint HA and RUL estimations, in one solution pipeline, currently unexplored in majority of the existing works in the literature. LS-R-ACGAN generates ample diverse fault samples for reducing the degree of imbalance between normal and fault classes. Subsequently, after data augmentation, the balanced training dataset helps train a multi-task learning model for joint HA and RUL estimations. The proposed framework has been implemented and tested on a benchmark dataset proving its superiority over multiple existing methods in the literature.