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.

Hematologic and inflammatory predictors of outcome in patients with brain arteriovenous malformations

OBJECTIVE

This study investigated the prognostic value of admission blood counts for AVM outcomes and compared admission blood counts for patients with ruptured and unruptured AVMs.

METHODS

A retrospective analysis of patients who underwent surgical treatment for a ruptured cerebral AVM between February 1, 2014, and March 31, 2020, was conducted. The primary outcome was poor neurologic outcome, defined as a modified Rankin Scale score ≥2 in patients with unruptured AVMs or >2 in those with ruptured AVMs.

RESULTS

A total of 235 patients were included; 80 (34%) had ruptured AVMs. At admission, patients with ruptured AVMs had a significantly lower mean (SD) hemoglobin level (12.78 [2.07] g/dL vs 13.71 [1.60] g/dL, p<0.001), hematocrit (38.1% [5.9%] vs 40.7%[4.6%], p<0.001), lymphocyte count (16% [11%] vs 26% [10%], p<0.001), and absolute lymphocyte count (1.41 [0.72]×103/μL vs 1.79 [0.68]×103/μL, p<0.001), and they had a significantly higher mean (SD) white blood cell count (10.4 [3.8] vs 7.6 [2.3]×103/μL, p<0.001), absolute neutrophil count (7.8[3.8]×103/μL vs 5.0[2.5]×103/μL, p<0.001), and neutrophil count (74% [14%] vs 64% [13%], p<0.001). Among patients with unruptured AVMs, white blood cell count ≥6.4×103/μL and absolute neutrophil count ≥3.4×103/μL were associated with a favorable neurologic outcome, whereas hemoglobin level ≥13.4 g/dL was associated with an unfavorable outcome. Among patients with ruptured AVMs, hypertension was associated with a threefold increase in the odds of a poor neurologic outcome.

CONCLUSIONS

This study found that patients with ruptured and unruptured AVMs present with characteristic profiles of hematologic and inflammatory parameters evident in their admission blood work.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study

BACKGROUND

Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability.

OBJECTIVE

The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers.

METHODS

We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods.

RESULTS

Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910).

CONCLUSIONS

RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Rationale and Design of the "DIagnostic and Prognostic Precision Algorithm for behavioral variant Frontotemporal Dementia" (DIPPA-FTD) Study: A Study Aiming to Distinguish Early Stage Sporadic FTD from Late-Onset Primary Psychiatric Disorders

BACKGROUND

The behavioral variant of frontotemporal dementia (bvFTD) is very heterogeneous in pathology, genetics, and disease course. Unlike Alzheimer's disease, reliable biomarkers are lacking and sporadic bvFTD is often misdiagnosed as a primary psychiatric disorder (PPD) due to overlapping clinical features. Current efforts to characterize and improve diagnostics are centered on the minority of genetic cases.

OBJECTIVE

The multi-center study DIPPA-FTD aims to develop diagnostic and prognostic algorithms to help distinguish sporadic bvFTD from late-onset PPD in its earliest stages.

METHODS

The prospective DIPPA-FTD study recruits participants with late-life behavioral changes, suspect for bvFTD or late-onset PPD diagnosis with a negative family history for FTD and/or amyotrophic lateral sclerosis. Subjects are invited to participate after diagnostic screening at participating memory clinics or recruited by referrals from psychiatric departments. At baseline visit, participants undergo neurological and psychiatric examination, questionnaires, neuropsychological tests, and brain imaging. Blood is obtained to investigate biomarkers. Patients are informed about brain donation programs. Follow-up takes place 10-14 months after baseline visit where all examinations are repeated. Results from the DIPPA-FTD study will be integrated in a data-driven approach to develop diagnostic and prognostic models.

CONCLUSIONS

DIPPA-FTD will make an important contribution to early sporadic bvFTD identification. By recruiting subjects with ambiguous or prodromal diagnoses, our research strategy will allow the characterization of early disease stages that are not covered in current sporadic FTD research. Results will hopefully increase the ability to diagnose sporadic bvFTD in the early stage and predict progression rate, which is pivotal for patient stratification and trial design.

Emerging innovations and advancements in the treatment of extremity and truncal soft tissue sarcomas

In this special edition update on soft tissue sarcomas (STS), we cover classifications, emerging technologies, prognostic tools, radiation schemas, and treatment disparities in extremity and truncal STS. We discuss the importance of enhancing local control and reducing complications, including the role of innovative imaging, surgical guidance, and hypofractionated radiation. We review advancements in systemic and immunotherapeutic treatments and introduce disparities seen in this vulnerable population that must be considered to improve overall patient care.

Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems.

Salivary Biomarkers in Periodontitis: A Scoping Review

Periodontal disease is a prevalent and potentially impactful oral health condition, ranging from gingivitis to severe periodontitis. Early detection and precise management are crucial in modern dentistry due to its prevalence and potential systemic health implications. Traditional clinical assessments and radiographic imaging have been the primary diagnostic tools. However, recent advances in oral diagnostics have introduced the concept of non-invasive, easily accessible salivary biomarkers. This review explores the evolving landscape of salivary biomarkers associated with periodontal disease, offering a comprehensive analysis of recent studies. It delves into the key findings, clinical significance, and potential impact of these biomarkers in revolutionizing periodontal disease diagnostics and treatment monitoring. The study emphasizes their diagnostic and prognostic capabilities, including their ability to assess disease severity, correlate with clinical parameters, aid in early detection, and enhance personalized treatment planning. As the field of oral diagnostics continues to advance, understanding the role of salivary biomarkers in periodontal disease management holds the promise of improving precision and effectiveness in oral healthcare. This review underscores the potential for salivary biomarkers to become integral components of routine periodontal care, offering a minimally invasive and patient-centered approach to oral health management.

Machinery Prognostics and High-Dimensional Data Feature Extraction Based on a Transformer Self-Attention Transfer Network

Machinery degradation assessment can offer meaningful prognosis and health management information. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain discriminator to extract invariant features from different machine operating conditions. Case studies were carried out using the FEMTO-ST bearing dataset, and the Monte Carlo method was employed for RUL prediction during the degradation process. When compared to other established techniques such as the RNN-based RUL prediction method, convolutional LSTM network, Bi-directional LSTM network with attention mechanism, and the traditional RUL prediction method based on vibration frequency anomaly detection and survival time ratio, our proposed TSTN method demonstrates superior RUL prediction accuracy with a notable SCORE of 0.4017. These results underscore the significant advantages and potential of the TSTN approach over other state-of-the-art techniques.

Prognostic Factors for Postoperative Chronic Pain after Knee or Hip Replacement in Patients with Knee or Hip Osteoarthritis: An Umbrella Review

Knee and hip osteoarthritis are highly prevalent in the older population. Management of osteoarthritis-related pain includes conservative or surgical treatment. Although knee or hip joint replacement is associated with positive outcomes, up to 30% of patients report postoperative pain in the first two years. This study aimed to synthesize current evidence on prognostic factors for predicting postoperative pain after knee or hip replacement. An umbrella review of systematic reviews was conducted to summarize the magnitude and quality of the evidence for prognostic preoperative factors predictive of postoperative chronic pain (>6 months after surgery) in patients who had received knee or hip replacement. Searches were conducted in MEDLINE, CINAHL, PubMed, PEDro, SCOPUS, Cochrane Library, and Web of Science databases from inception up to 5 August 2022 for reviews published in the English language. A narrative synthesis, a risk of bias assessment, and an evaluation of the evidence confidence were performed. Eighteen reviews (nine on knee surgery, four on hip replacement, and seven on both hip/knee replacement) were included. From 44 potential preoperative prognostic factors, just 20 were judged as having high or moderate confidence for robust findings. Race, opioid use, preoperative function, neuropathic pain symptoms, pain catastrophizing, anxiety, other pain sites, fear of movement, social support, preoperative pain, mental health, coping strategies, central sensitization-associated symptoms, and depression had high/moderate confidence for an association with postoperative chronic pain. Some comorbidities such as heart disease, stroke, lung disease, nervous system disorders, and poor circulation had high/moderate confidence for no association with postoperative chronic pain. This review has identified multiple preoperative factors (i.e., sociodemographic, clinical, psychological, cognitive) associated with postoperative chronic pain after knee or hip replacement. These factors may be used for identifying individuals at a risk of developing postoperative chronic pain. Further research can investigate the impact of using such prognostic data on treatment decisions and patient outcomes.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

CTSL, a prognostic marker of breast cancer, that promotes proliferation, migration, and invasion in cells in triple-negative breast cancer

Introduction

In the world, the incidence of breast cancer has surpassed that of lung cancer, and it has become the first malignant tumor among women. Triple-negative breast cancer (TNBC) shows an extremely heterogeneous malignancy toward high recurrence, metastasis, and mortality, but there is a lack of effective targeted therapy. It is urgent to develop novel molecular targets in the occurrence and therapeutics for TNBC, and novel therapeutic strategies to block the recurrence and metastasis of TNBC.

Methods

In this study, CTSL (cathepsin L) expression in tissues and adjacent tissues of TNBC patients was monitored by immunohistochemistry and western blots. The correlations between CTSL expressions and clinicopathological characteristics in the patient tissues for TNBC were analyzed. Cell proliferation, migration, and invasion assay were also performed when over-expressed or knocked-down CTSL.

Results

We found that the level of CTSL in TNBC is significantly higher than that in the matched adjacent tissues, and associated with differentiated degree, TNM Stage, tumor size, and lymph node metastatic status in TNBC patients. The high level of CTSL was correlated with a short RFS (p<0.001), OS (p<0.001), DMFS (p<0.001), PPS (p= 0.0025) in breast cancer from online databases; while in breast cancer with lymph node-positive, high level of CTSL was correlated with a short DMFS (p<0.001) and RFS (p<0.001). Moreover, in vitro experiments showed that CTSL overexpression promotes the abilities for proliferation, migration, and invasion in MCF-7 and MDA-MB-231 cell lines, while knocking-down CTSL decreases its characteristics in MDA-MB-231 cell lines.

Conclusion

CTSL might involve into the regulation of the proliferation, invasion, and metastasis of TNBC. Thus, CTSL would be a novel, potential therapeutic, and prognostic target of TNBC.

Exosomes: Diagnostic and Therapeutic Implications in Cancer

Exosomes are a subset of extracellular vesicles produced by all cells, and they are present in various body fluids. Exosomes play crucial roles in tumor initiation/progression, immune suppression, immune surveillance, metabolic reprogramming, angiogenesis, and the polarization of macrophages. In this work, we summarize the mechanisms of exosome biogenesis and secretion. Since exosomes may be increased in the cancer cells and body fluids of cancer patients, exosomes and exosomal contents can be used as cancer diagnostic and prognostic markers. Exosomes contain proteins, lipids, and nucleic acids. These exosomal contents can be transferred into recipient cells. Therefore, this work details the roles of exosomes and exosomal contents in intercellular communications. Since exosomes mediate cellular interactions, exosomes can be targeted for developing anticancer therapy. This review summarizes current studies on the effects of exosomal inhibitors on cancer initiation and progression. Since exosomal contents can be transferred, exosomes can be modified to deliver molecular cargo such as anticancer drugs, small interfering RNAs (siRNAs), and micro RNAs (miRNAs). Thus, we also summarize recent advances in developing exosomes as drug delivery platforms. Exosomes display low toxicity, biodegradability, and efficient tissue targeting, which make them reliable delivery vehicles. We discuss the applications and challenges of exosomes as delivery vehicles in tumors, along with the clinical values of exosomes. In this review, we aim to highlight the biogenesis, functions, and diagnostic and therapeutic implications of exosomes in cancer.

GNL3 and PA2G4 as Prognostic Biomarkers in Prostate Cancer

Prostate cancer is a multifocal and heterogeneous disease common in males and remains the fifth leading cause of cancer-related deaths worldwide. The prognosis of prostate cancer is variable and based on the degree of cancer and its stage at the time of diagnosis. Existing biomarkers for the prognosis of prostate cancer are unreliable and lacks specificity and sensitivity in guiding clinical decision. There is need to search for novel biomarkers having prognostic and predictive capabilities in guiding clinical outcomes. Using a bioinformatics approach, we predicted GNL3 and PA2G4 as biomarkers of prognostic significance in prostate cancer. A progressive increase in the expression of GNL3 and PA2G4 was observed during cancer progression having significant association with poor survival in prostate cancer patients. The Receiver Operating Characteristics of both genes showed improved area under the curve against sensitivity versus specificity in the pooled samples from three different GSE datasets. Overall, our analysis predicted GNL3 and PA2G4 as prognostic biomarkers of clinical significance in prostate cancer.

An accurate fully automated panel of plasma biomarkers for Alzheimer's disease

INTRODUCTION

There is a great need for fully automated plasma assays that can measure amyloid beta (Aβ) pathology and predict future Alzheimer's disease (AD) dementia.

METHODS

Two cohorts (n = 920) were examined: Panel A+ (n = 32 cognitively unimpaired [CU], n = 106 mild cognitive impairment [MCI], and n = 89 AD) and BioFINDER-1 (n = 461 CU, n = 232 MCI). Plasma Aβ42/Aβ40, phosphorylated tau (p-tau)181, two p-tau217 variants, ApoE4 protein, neurofilament light, and GFAP were measured using Elecsys prototype immunoassays.

RESULTS

The best biomarker for discriminating Aβ-positive versus Aβ-negative participants was Aβ42/Aβ40 (are under the curve [AUC] 0.83-0.87). Combining Aβ42/Aβ40, p-tau181, and ApoE4 improved the AUCs significantly (0.90 to 0.93; P< 0.01). Adding additional biomarkers had marginal effects (ΔAUC ≤0.01). In BioFINDER, p-tau181, p-tau217, and ApoE4 predicted AD dementia within 6 years in CU (AUC 0.88) and p-tau181, p-tau217, and Aβ42/Aβ40 in MCI (AUC 0.87).

DISCUSSION

The high accuracies for Aβ pathology and future AD dementia using fully automated instruments are promising for implementing plasma biomarkers in clinical trials and clinical routine.

A Scoping Review of Artificial Intelligence Research in Rhinology

BACKGROUND

A considerable volume of possible applications of artificial intelligence (AI) in the field of rhinology exists, and research in the area is rapidly evolving.

OBJECTIVE

This scoping review aims to provide a brief overview of all current literature on AI in the field of rhinology. Further, it aims to highlight gaps in the literature for future rhinology researchers.

METHODS

OVID MEDLINE (1946-2022) and EMBASE (1974-2022) were searched from January 1, 2017 until May 14, 2022 to identify all relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist was used to guide the review.

RESULTS

A total of 2420 results were identified of which 62 met the eligibility criteria. A further 17 articles were included through bibliography searching, for a total of 79 articles on AI in rhinology. Each year resulted in an increase in the number of publications, from 3 articles published in 2017 to 31 articles published in 2021. Articles were produced by authors from 22 countries with a relative majority coming from the USA (19%), China (19%), and South Korea (13%). Articles were placed into 1 of 5 categories: phenotyping/endotyping (n = 12), radiological diagnostics (n = 42), prognostication (n = 10), non-radiological diagnostics (n = 7), surgical assessment/planning (n = 8). Diagnostic or prognostic utility of the AI algorithms were rated as excellent (n = 29), very good (n = 25), good (n = 7), sufficient (n = 1), bad (n = 2), or was not reported/not applicable (n = 15).

CONCLUSIONS

AI is experiencing an increasingly significant role in rhinology research. Articles are showing high rates of diagnostic accuracy and are being published at an almost exponential rate around the world. Utilizing AI in radiological diagnosis was the most published topic of research, however, AI in rhinology is still in its infancy and there are several topics yet to be thoroughly explored.

The Mucin Family of Proteins: Candidates as Potential Biomarkers for Colon Cancer

Mucins (MUC1-MUC24) are a family of glycoproteins involved in cell signaling and barrier protection. They have been implicated in the progression of numerous malignancies including gastric, pancreatic, ovarian, breast, and lung cancer. Mucins have also been extensively studied with respect to colorectal cancer. They have been found to have diverse expression profiles amongst the normal colon, benign hyperplastic polyps, pre-malignant polyps, and colon cancers. Those expressed in the normal colon include MUC2, MUC3, MUC4, MUC11, MUC12, MUC13, MUC15 (at low levels), and MUC21. Whereas MUC5, MUC6, MUC16, and MUC20 are absent from the normal colon and are expressed in colorectal cancers. MUC1, MUC2, MUC4, MUC5AC, and MUC6 are currently the most widely covered in the literature regarding their role in the progression from normal colonic tissue to cancer.

Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model

State-of-health (SOH) is a measure of a battery's capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery's health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery's degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery.

Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data

Laboratory-scale data on a component level are frequently used for prognostics because acquiring them is time and cost efficient. However, they do not reflect actual field conditions. As prognostics is for an in-service system, the developed prognostic methods must be validated using real operational data obtained from an actual system. Because obtaining real operational data is much more expensive than obtaining test-level data, studies employing field data are scarce. In this study, a prognostic method for screws was presented by employing multi-source real operational data obtained from a micro-extrusion system. The analysis of real operational data is more challenging than that of test-level data because the mutual effect of each component in the system is chaotically reflected in the former. This paper presents a degradation feature extraction method for interpreting complex signals for a real extrusion system based on the physical and mechanical properties of the system as well as operational data. The data were analyzed based on general physical properties and the inferred interpretation was verified using the data. The extracted feature exhibits valid degradation behavior and is used to predict the remaining useful life of the screw in a real extrusion system.

"If I Become a Vegetable, Then no": A Thematic Analysis of How Patients and Physicians Refer to Prognosis When Discussing Cardiopulmonary Resuscitation

Background: Documenting decisions about the relevance cardiopulmonary resuscitation (CPR) is a standard practice at hospital admission yet a complex task. Objective: Our aim was to explore how physicians approach and discuss CPR prognosis with older patients recently admitted to a post-acute care unit. Method: We recorded 43 conversations between physicians and patients about the relevancy of CPR that took place at admission at the geriatric rehabilitation service of a Swiss university hospital. Thematic analysis determined (i) who initiated the talk about CPR prognosis, (ii) at what point in the conversation, and (iii) how prognosis was referred to. Results: Prognosis was mentioned in 65% of the conversations. We categorized the content of references to CPR prognosis in five themes: factors determining the prognosis (general health, age, duration of maneuvers); life (association of CPR with life, survival); proximal adverse outcomes (broken ribs, intensive care); long-term adverse outcomes (loss of autonomy, suffering a stroke, pain, generic, uncertainty); and being a burden. Discussion and conclusion: Discussing CPR is important to all patients, including those for whom it is not recommended. Information about CPR prognosis is essential to empower and support patients in expressing their expectations from life-prolonging interventions and attain shared decision-making.

Long Non Coding RNA in Triple Negative Breast Cancer: A Promising Biomarker in Tumorigenesis

Globally, Triple-negative breast cancer (TNBC) is an unsurpassed variant of breast cancer (BC) with a very high fatality rate, and disease burden. Nevertheless, the deficit of diagnostic markers and focused treatment are major hurdles for potent therapeutics. They are also the reason for bad outcomes and causes of a worse prognosis and a high rate of flare up in patients with TNBC diagnosis. Long non-coding RNAs (lncRNA) are a new class of molecules that have recently gained interest in healthcare management due to their potential as biomarkers for human diseases especially cancers. The growing interest in lncRNA in clinical practice has created an unmet need for developing assays to test lncRNA quickly and accurately for early diagnostics. These lncRNA modulate multiple stages of tumor development, including growth, proliferation, invasion, angiogenesis, and metastases, by controlling several genes and changing metabolic networks. Highly invasive phenotype and chemo resistance are prominent characteristics of TNBC subtypes that require accurate diagnostic and prognostic instruments involving lncRNA. This review focusses on the evolving purpose and coalition of lncRNAs in TNBC and accentuates their capable effects in diagnosis and treatment of cancer. Moreover, the extensive literature analysis of our review creates an opportunity in the translational application concerning the TNBC lncRNAs described until now. The depiction of lncRNAs enrolled in TNBC is comprehensive, and sufficient substantiation studies are the need of the hour to authenticate the current outcomes and create imminent upcoming of elemental research setting into clinical practice.

A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM

Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method.

Central nervous system tumefactive demyelinating lesions: Risk factors of relapse and follow-up observations

Objective

To track the clinical outcomes in patients who initially presented with tumefactive demyelinating lesions (TDLs), we summarized the clinical characteristics of various etiologies, and identified possible relapse risk factors for TDLs.

Methods

Between 2001 and 2021, 116 patients initially presented with TDLs in our hospital were retrospectively evaluated. Patients were followed for relapse and clinical outcomes, and grouped according to various etiologies. Demographic information, clinical data, imaging data, and laboratory results of patients were obtained and analyzed. The risk factors of relapse were analyzed by the Log-Rank test and the Cox proportional hazard model in multivariate analysis.

Result

During a median follow-up period of 72 months, 33 patients were diagnosed with multiple sclerosis (MS), 6 patients with Balo, 6 patients with neuromyelitis optica spectrum disorders (NMOSD), 10 patients with myelin oligodendrocyte glycoprotein antibody-associated demyelination (MOGAD), 1 patient with acute disseminated encephalomyelitis (ADEM), and the remaining 60 patients still have no clear etiology. These individuals with an unknown etiology were categorized independently and placed to the other etiology group. In the other etiology group, 13 patients had recurrent demyelinating phases, while 47 patients did not suffer any more clinical events. Approximately 46.6% of TDLs had relapses which were associated with multiple functional system involvement, first-phase Expanded Disability Status Scale score, lesions morphology, number of lesions, and lesions location (P<0.05). And diffuse infiltrative lesions (P=0.003, HR=6.045, 95%CI:1.860-19.652), multiple lesions (P=0.001, HR=3.262, 95%CI:1.654-6.435) and infratentorial involvement (P=0.006, HR=2.289, 95%CI:1.064-3.853) may be independent risk factors for recurrence. Relapse free survival was assessed to be 36 months.

Conclusions

In clinical practice, around 46.6% of TDLs relapsed, with the MS group showing the highest recurrence rate, and lesions location, diffuse infiltrative lesions, and multiple lesions might be independent risk factors for relapse. Nevertheless, despite extensive diagnostic work and long-term follow-up, the etiology of TDLs in some patients was still unclear. And these patients tend to have monophase course and a low rate of relapse.

Applied Machine Learning for IIoT and Smart Production-Methods to Improve Production Quality, Safety and Sustainability

Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain-namely security and safety, asset localization, quality control, maintenance-has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.

Changes in lactate dehydrogenase on admission throughout the COVID-19 pandemic and possible impacts on prognostic capability

Introduction: The enzyme lactate dehydrogenase (LDH) is a good marker of general hyperinflammation correlated with mortality for COVID-19, and is therefore used in prognosis tools. In a current COVID-19 clinical randomized trial (CRT), the blood level of LDH was selected as an inclusion criterion. However, LDH decreased during the pandemic; hence, the impact of this decrease on the prognostic value of LDH for mortality was evaluated. Methods: Data on LDH levels in 843 patients were obtained and analyzed. Relative risk, standard error and receiver operating characteristic curves were calculated for two cutoff values. Results: Relative risk lost validity and the area under the curve narrowed by trimester during the pandemic. Conclusion: The progressive decrease in LDH impacted the capacity to predict mortality in COVID-19. More studies are needed to validate this finding and its implications.

A Trade-Off Analysis between Sensor Quality and Data Intervals for Prognostics Performance

In safety-critical systems such as industrial plants or aircraft, failure occurs inevitably during operation, and it is important to prevent it in order to maintain high availability. To reduce this risk, a lot of efforts are directed from developing sensing technologies to failure prognosis algorithms to enable predictive maintenance. The success of effective and reliable predictive maintenance not only relies on robust prognosis algorithms but also on the selection of sensors or data acquisition strategy. However, there are not many in-depth studies on a trade-off between sensor quality and data storage in the view of prognosis performance. The information about (1) how often data should be measured and (2) how good sensor quality should be for reliable failure prediction can be highly impactful for practitioners. In this paper, the authors evaluate the efficacy of the two factors in terms of remaining useful life (RUL) prediction accuracy and its uncertainty. In addition, since knowing true degradation information is almost impossible in practice, the authors validated the use of the prognosis metric without requiring the true degradation information. A numerical case study is conducted to identify the relationship between sensor quality and data storage. Then, real bearing run-to-failure (RTF) datasets acquired from accelerometer (contact type) and microphone (non-contact type) sensors are evaluated based on the prognosis performance metric and compared in terms of the sensors' cost-effectiveness for predictive maintenance.

A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations

In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

The prognostics and health management disciplines provide an efficient solution to improve a system's durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem's remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.

High serum levels of ferritin may predict poor survival and walking ability for patients with hip fractures: a propensity score matching study

Aim: To identify the relationship between ferritin and outcomes for patients with hip fractures. Patients & methods: All patients with hip fractures presenting between May 2017 and January 2021 were included. Univariate and multivariate analyses were performed to determine the risk factors for 1-year survival. Propensity score matching (PSM) was performed for groups divided by ferritin levels. Results: A total of 165 patients were included of whom 28 died during the first year after surgery. Ferritin levels differed significantly between groups divided by 1-year survival. High ferritin (≥308.5 ng/ml) was related to poor 1-year survival and 6-month and 1-year independent walking rate. Conclusion: High ferritin (≥308.5 ng/ml) may predict poor survival and free-walking abilities after surgery for patients with hip fractures.

Internal Characterization-Based Prognostics for Micro-Direct-Methanol Fuel Cells under Dynamic Operating Conditions

Micro-direct-methanol fuel cells (μDMFCs) use micro-electro mechanical system (MEMS) technology, which offers high energy density, portable use, quick replenishment, and free fuel reforming and purification. However, the μDMFC is limited by a short effective service life due to the membrane electrode’s deterioration in electrochemical reactions. This paper presents a health status assessment and remaining useful life (RUL) prediction approach for μDMFC under dynamic operating conditions. Rather than making external observations, an internal characterization is used to describe the degradation indicator and to overcome intrusive influences in operation. Then, a Markov-process-based usage behavior prediction mechanism is proposed to account for the randomness of real-world operation. The experimental results show that the proposed degradation indicator alleviates the reduction in μDMFC output power degradation behavior caused by the user loading profile. Compared with the predictions of RUL using traditional external observation, the proposed approach achieved superior prognostic performance in both accuracy and precision.

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device’s remaining useful life (RUL). Although data-driven models are commonly used for prognostic applications, they are limited by the necessity of large training datasets and also the optimization algorithms used in such methods run into local minima problems. In order to overcome these drawbacks, we train a Neural Network with Bayesian inference. In this work, we use Neural Networks (NN) as the prediction model and an adaptive Bayesian learning approach to estimate the RUL of electronic devices. The proposed prognostic approach functions in two stages—weight regularization using adaptive Bayesian learning and prognosis using NN. A Bayesian framework (particle filter algorithm) is adopted in the first stage to estimate the network parameters (weights and bias) using the NN prediction model as the state transition function. However, using a higher number of hidden neurons in the NN prediction model leads to particle weight decay in the Bayesian framework. To overcome the weight decay issues, we propose particle roughening as a weight regularization method in the Bayesian framework wherein a small Gaussian jitter is added to the decaying particles. Additionally, weight regularization was also performed by adopting conventional resampling strategies to evaluate the efficiency and robustness of the proposed approach and to reduce optimization problems commonly encountered in NN models. In the second stage, the estimated distributions of network parameters were fed into the NN prediction model to predict the RUL of the device. The lithium-ion battery capacity degradation data (CALCE/NASA) were used to test the proposed method, and RMSE values and execution time were used as metrics to evaluate the performance.

Ex Vivo Test for Measuring Complement Attack on Endothelial Cells: From Research to Bedside

As part of the innate immune system, the complement system plays a key role in defense against pathogens and in host cell homeostasis. This enzymatic cascade is rapidly triggered in the presence of activating surfaces. Physiologically, it is tightly regulated on host cells to avoid uncontrolled activation and self-damage. In cases of abnormal complement dysregulation/overactivation, the endothelium is one of the primary targets. Complement has gained momentum as a research interest in the last decade because its dysregulation has been implicated in the pathophysiology of many human diseases. Thus, it appears to be a promising candidate for therapeutic intervention. However, detecting abnormal complement activation is challenging. In many pathological conditions, complement activation occurs locally in tissues. Standard routine exploration of the plasma concentration of the complement components shows values in the normal range. The available tests to demonstrate such dysregulation with diagnostic, prognostic, and therapeutic implications are limited. There is a real need to develop tools to demonstrate the implications of complement in diseases and to explore the complex interplay between complement activation and regulation on human cells. The analysis of complement deposits on cultured endothelial cells incubated with pathologic human serum holds promise as a reference assay. This ex vivo assay most closely resembles the physiological context. It has been used to explore complement activation from sera of patients with atypical hemolytic uremic syndrome, malignant hypertension, elevated liver enzymes low platelet syndrome, sickle cell disease, pre-eclampsia, and others. In some cases, it is used to adjust the therapeutic regimen with a complement-blocking drug. Nevertheless, an international standard is lacking, and the mechanism by which complement is activated in this assay is not fully understood. Moreover, primary cell culture remains difficult to perform, which probably explains why no standardized or commercialized assay has been proposed. Here, we review the diseases for which endothelial assays have been applied. We also compare this test with others currently available to explore complement overactivation. Finally, we discuss the unanswered questions and challenges to overcome for validating the assays as a tool in routine clinical practice.

Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution

The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.

Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection

INTRODUCTION

In Alzheimer's disease, asymptomatic patients may have amyloid deposition, but predicting their progression rate remains a substantial challenge with implications for clinical trial enrollment. Here, we demonstrate an artificial intelligence approach to use baseline clinical information and images to predict changes in quantitative biomarkers of brain pathology on future images.

METHODS

Patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent positron emission tomography (PET) with the amyloid radiotracer 18F-AV45 (florbetapir) were included. We identified important baseline PET image features using a deep convolutional neural network based on ResNet. These were combined with eight clinical, demographic, and genetic markers using a gradient-boosted decision tree (GBDT) algorithm to predict future quantitative standardized uptake value ratio (SUVR), an established biomarker of brain amyloid deposition. We used this model to better identify individuals with the highest positive change in amyloid deposition on future images and compared this to typical inclusion criteria for clinical trials. We also compared the model's performance to other methods such as multivariate linear regression and GBDT without imaging features.

FINDINGS

Using 2577 PET scans from 1224 unique individuals, we showed that the GBDT with deep image features was significantly more accurate than the other approaches, reaching a root mean squared error of 0.0339 ± 0.0027 for future SUVR prediction. Using this approach, we could identify individuals with the highest 10% SUVR accumulation at rates 2- to 4-fold higher than by random pick or existing inclusion criteria.

DISCUSSION

Predicting quantitative biomarkers on future images using machine learning methods consisting of deep image features combined with clinical data may allow better targeting of treatments or enrollment in clinical trials.

Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer's Disease

Background:

While both cognitive and magnetic resonance imaging (MRI) data has been used to predict progression in Alzheimer’s disease, heterogeneity between patients makes it challenging to predict the rate of cognitive and functional decline for individual subjects.

Objective:

To investigate prognostic power of MRI-based biomarkers of medial temporal lobe atrophy and macroscopic tissue change to predict cognitive decline in individual patients in clinical trials of early Alzheimer’s disease.

Methods:

Data used in this study included 312 patients with mild cognitive impairment from the ADNI dataset with baseline MRI, cerebrospinal fluid amyloid-β, cognitive test scores, and a minimum of two-year follow-up information available. We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects remain stable and which functionally decline over 2 and 3-year follow-up periods.

Results:

Combining both sets of features yields 77%accuracy (81%sensitivity and 75%specificity) to predict cognitive decline at 2 years (74%accuracy at 3 years with 75%sensitivity and 73%specificity). When used to select trial participants, this tool yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25%treatment effect to reduce cognitive decline.

Conclusion:

When used in clinical trials for cohort enrichment, this tool could accelerate development of new treatments by significantly increasing statistical power to detect differences in cognitive decline between arms. In addition, detection of future decline can help clinicians improve patient management strategies that will slow or delay symptom progression.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

Prognostic prospect of soluble programmed cell death ligand-1 in cancer management

Aggressive tissue biopsy is commonly unavoidable in the management of most suspected tumor cases to conclusively verify the presence of cancerous cells through histological assessment. The extracted tissue is also immunostained for detection of antigens (tissue tumor markers) of potential prognostic or therapeutic importance to assist in treatment decision. Although liquid biopsies can be a powerful tool for monitoring treatment response, they are still excluded from standard cancer diagnostics, and their utility is still being debated in the scientific community. With a myriad of soluble tissue tumor markers now being discovered, liquid biopsies could completely change the current paradigms of cancer management. Recently, soluble programmed cell death ligand-1 (sPD-L1), which is found in the peripheral blood, i.e. serum and plasma, has shown potential as a pre-therapeutic predictive marker as well as a prognostic biomarker to monitor treatment efficacy. Thus, this review focuses on the emergence of sPD-L1 and promising technologies for its detection in order to support liquid biopsies for future cancer management.

Increased Pace of Aging in COVID-Related Mortality

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients' pace of aging with BloodAge-a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8-1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001-1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models.

The lipid biology of sepsis

Sepsis, defined as the dysregulated immune response to an infection leading to organ dysfunction, is one of the leading causes of mortality around the globe. Despite the significant progress in delineating the underlying mechanisms of sepsis pathogenesis, there are currently no effective treatments or specific diagnostic biomarkers in the clinical setting. The perturbation of cell signaling mechanisms, inadequate inflammation resolution, and energy imbalance, all of which are altered during sepsis, are also known to lead to defective lipid metabolism. The use of lipids as biomarkers with high specificity and sensitivity may aid in early diagnosis and guide clinical decision making. In addition, identifying the link between specific lipid signatures and their role in sepsis pathology may lead to novel therapeutics. In this review, we discuss the recent evidence on dysregulated lipid metabolism both in experimental and human sepsis focused on bioactive lipids, fatty acids, and cholesterol as well as the enzymes regulating their levels during sepsis. We highlight not only their potential roles in sepsis pathogenesis but also the possibility of using these respective lipid compounds as diagnostic and prognostic biomarkers of sepsis.

The Prognostic Role and Nomogram Establishment of a Novel Prognostic Score Combining with Fibrinogen and Albumin Levels in Patients with WHO Grade II/III Gliomas

Purpose

World Health Organization (WHO) Grades II and III gliomas [also known as low grade gliomas (LGGs)] displayed different malignant behaviors and survival outcomes compared to Grade IV gliomas. This study aimed to identify the prognostic predictive value of a novel cumulative prognostic score [combined with fibrinogen and albumin levels (FA score)], establish and validate a point-based nomogram in LGG patients.

Patients and Methods

A total of 91 patients who underwent total glioma resection at Shengjing Hospital of China Medical University between 2011 and 2013 were enrolled to establish a prognostic nomogram. All patients were histologically diagnosed as grades II/III, and never received radiotherapy or chemotherapy before surgery. Data collection included patient characteristics, clinicopathological factors, and preoperative hematology results. The performance of the nomogram was subsequently validated by the concordance index (c-index), calibration curve, and receiver operating characteristic (ROC) curve.

Results

The FA score was negatively associated with the overall survival (OS) of LGG patients (p < 0.001). The results of multivariate analysis showed that FA score [p = 0.006, HR = 1.92, 95% confidence interval (CI): 1.21–3.05], age (p = 0.002, HR = 3.014, 95% CI:1.52–5.97), and white blood count (p < 0.001, HR = 4.24, 95% CI: 2.08–8.67) were independent prognostic factors for overall survival (OS). The study established a nomogram to predict OS with a c-index of 0.783 (95% CI, 0.72–0.84).

Conclusion

FA score might be a potential prognostic biomarker for LGG patients, and a reliable point-based nomogram will help clinicians to decide on the best treatment plans.

Molecular Biomarkers for Melanoma Screening, Diagnosis and Prognosis: Current State and Future Prospects

Despite significant progress in the development of treatment options, melanoma remains a leading cause of death due to skin cancer. Advances in our understanding of the genetic, transcriptomic, and morphologic spectrum of benign and malignant melanocytic neoplasia have enabled the field to propose biomarkers with potential diagnostic, prognostic, and predictive value. While these proposed biomarkers have the potential to improve clinical decision making at multiple critical intervention points, most remain unvalidated. Clinical validation of even the most commonly assessed biomarkers will require substantial resources, including limited clinical specimens. It is therefore important to consider the properties that constitute a relevant and clinically-useful biomarker-based test prior to engaging in large validation studies. In this review article we adapt an established framework for determining minimally-useful biomarker test characteristics, and apply this framework to a discussion of currently used and proposed biomarkers designed to aid melanoma detection, staging, prognosis, and choice of treatment.

Assessment of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation

Manufacturing processes have become increasingly sophisticated leading to greater usage of robotics. Sustaining successful manufacturing robotic operations requires a strategic maintenance program. Without careful planning, maintenance can be very costly. To reduce maintenance costs, manufacturers are exploring how they can assess the health of their robot workcell operations to enhance their maintenance strategies. Effective health assessment relies upon capturing appropriate data and generating intelligence from the workcell. Multiple data streams relevant to a robot workcell may be available including robot controller data, a supervisory programmable logic controller data, maintenance logs, process and part quality data, and equipment and process fault and failure data. These data streams can be extremely informative, yet the massive volume and complexity of this data can be overwhelming, confusing, and sometimes paralyzing. Researchers at the National Institute of Standards and Technology have developed a test method and companion sensor to assess the health of robot workcells which will yield an additional and unique data stream. The intent is that this data stream can either serve as a surrogate for larger data volumes to reduce the data collection and analysis burden on the manufacturer, or add more intelligence to assessing robot workcell health. This article presents the most recent effort focused on verifying the companion sensor. Results of the verification test process are discussed along with preliminary results of the sensor's performance during verification testing. Lessons learned indicate that the test process can be an effective means of quantifying the sensor's measurement capability particularly after test process anomalies are addressed in future efforts.

Microchip Health Monitoring System Using the FLL Circuit

Here a solution for a Microchip Health Monitoring (MHM) system using MTOL (Multi-Temperature Operational Life) reliability testing assessment data is proposed. The module monitors frequency degradation over time compared to lab tested data. Since trends in performance degradation in recently developed devices have transitioned from multiple failure mechanisms to a single dominant failure mechanism, development of the monitor is greatly simplified. The monitor uses a novel circuit customized to deliver optimum accuracy by combining the concepts of ring oscillator (RO) and phase locked loop (PLL) circuits. The modified circuit proposed is a new form of the frequency locked loop (FLL) circuit. We demonstrate that the collection of frequency degradation data from the ring circuits of each test produces Weibull distributions with steep slopes. This implies that the monitor can predict accurate end-of-life (EOL) predictions at early stages of chip degradations. The design of the microchip health monitoring system projected in this work can have great benefit in all systems using FPGA and ASIC devices.

Reliability Prediction of Acrylonitrile O-Ring for Nuclear Power Applications Based on Shore Hardness Measurements

The degradation of polymeric components is of considerable interest to the nuclear industry and its regulatory bodies. The objective of this work was the development of a methodology to determine the useful life—based on the storage temperature—of acrylonitrile O-rings used as mechanical sealing elements to prevent leakages in nuclear equipment. To this aim, a reliability-based approach that allows prediction of the use-suitability of different storage scenarios (that involve different storage times and temperatures) considering the further required in-service performance, is presented. Thus, experimental measurements of Shore A hardness have been correlated with storage variables (temperature and storage time). The storage (and its associated hardening) was proved to have a direct effect on in-service durability, reducing this by up to 60.40%. Based on this model, the in-service performance was predicted; after the first three years of operation the increase in probability of failure (POF) was practically insignificant. Nevertheless, from this point on, and especially, from 5 years of operation, the POF increased from 10% to 20% at approximately 6 years (for new and stored). From the study, it was verified that for any of the analysis scenarios, the limit established criterion was above that of the storage time premise considered in usual nuclear industry practices. The novelty of this work is that from a non-destructive test, like a Shore A hardness measurement, the useful life and reliability of O-rings can be estimated and be, accordingly, a decision tool that allows for improvement in the management of maintenance of safety-related equipment. Finally, it was proved that the storage strategies of our nuclear power plants are successful, perfectly meeting the expectations of suitability and functionality of the components when they are installed after storage.

Real-Time Prognostics of Engineered Systems under Time Varying External Conditions Based on the COX PHM and VARX Hybrid Approach

In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the challenge mentioned above, a hybrid method for predicting the reliability index and the Remaining Useful Life (RUL) of engineered systems under time-varying external conditions is proposed in this paper. The proposed method is competent in reflecting the influence of time-varying external conditions on the degradation behaviour of engineered systems. Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. During the reliability prediction, the Fourier Grey Model (FGM) is employed with the time series models for long-term forecasting of the external conditions. The results show that the method that is proposed in this paper is competent for the RUL prediction as compared with baseline approaches.

Evaluation and characterization of HSPA5 (GRP78) expression profiles in normal individuals and cancer patients with COVID-19

HSPA5 (BiP, GRP78) has been reported as a potential host-cell receptor for SARS-Cov-2, but its expression profiles on different tissues including tumors, its susceptibility to SARS-Cov-2 virus and severity of its adverse effects on malignant patients are unclear. In the current study, HSPA5 has been found to be expressed ubiquitously in normal tissues and significantly increased in 14 of 31 types of cancer tissues. In lung cancer, mRNA levels of HSPA5 were 253-fold increase than that of ACE2. Meanwhile, in both malignant tumors and matched normal samples across almost all cancer types, mRNA levels of HSPA5 were much higher than those of ACE2. Higher expression of HSPA5 significantly decreased patient overall survival (OS) in 7 types of cancers. Moreover, systematic analyses found that 7.15% of 5,068 COVID-19 cases have malignant cancer coincidental situations, and the rate of severe events of COVID-19 patients with cancers present a higher trend than that for all COVID-19 patients, showing a significant difference (33.33% vs 16.09%, p<0.01). Collectively, these data imply that the tissues with high HSPA5 expression, not low ACE2 expression, are susceptible to be invaded by SARS-CoV-2. Taken together, this study not only indicates the clinical significance of HSPA5 in COVID-19 disease and cancers, but also provides potential clues for further medical treatments and managements of COVID-19 patients.

Implications of neuroendocrine tumor and diabetes mellitus on patient outcomes and care: a matched case-control study

Aim:

We aimed to determine the impact of diabetes mellitus (DM) on survival of patients with neuroendocrine tumors (NETs) and of NETs on glycemic control.

Patients & methods:

Patients with newly diagnosed NETs with/without DM were matched 1:1 by age, sex and diagnosis year (2005–2017), and survival compared (Kaplan–Meier and Cox proportional hazards). Mixed models compared hemoglobin A_1c (HbA_1c) and glucose during the year after cancer diagnosis.

Results:

Three-year overall survival was 72% (95% CI: 60–86%) for DM patients versus 80% (95% CI: 70–92%) for non-DM patients (p = 0.82). Hazard ratio was 1.33 (95% CI: 0.56–3.16; p = 0.51); mean DM HbA_1c, 7.3%.

Conclusion:

DM did not adversely affect survival of patients with NET. NET and its treatment did not affect glycemic control.

The aim of this study was to evaluate the effect of diabetes mellitus (DM) on survival of patients with neuroendocrine tumor (NET) and to determine whether NET affected glycemic control. From an institutional cancer registry, 118 patients with NET were identified and grouped by DM (n = 59) or no DM (n = 59). The two groups were matched by age, sex and year of NET diagnosis. DM did not decrease survival, and NET did not significantly affect glycemic control in patients with DM.

Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

Mortality and glycemic control among patients with diabetes mellitus and uterine or ovarian cancer

Aim:

To evaluate associations between survival and glycemic control in age-matched patients with endometrial or ovarian cancer, with/without diabetes mellitus (DM).

Patients & methods:

Patients with newly diagnosed ovarian or endometrial cancer with and without DM were compared.

Results:

The study included 84 patients with ovarian cancer (28, DM); 96 with endometrial cancer (48 with, 48 without DM). DM patients did not have worse overall or progression-free survival than non-DM patients. Glycemic control was not associated with either cancer.

Conclusion:

There was no association between DM and survival for patients with uterine or ovarian cancer. In addition, there was no association between uterine and ovarian cancer and glycemic control. Additional studies to confirm these observations in larger populations are required.

The aim of this study was to evaluate the effect of diabetes mellitus (DM) on survival of patients with ovarian or uterine cancer and to determine whether ovarian and uterine cancer affected glycemic control. From an institutional cancer registry, patients with ovarian or uterine cancer were identified and grouped by DM or no DM. Groups were matched by age at cancer diagnosis. DM did not decrease survival and ovarian and uterine cancer did not significantly affect glycemic control in patients with DM.

The Prognostic Value of Metabolic Profiling in Older Patients With a Proximal Femoral Fracture

Introduction:

High mortality rates of approximately 20% within 1 year after treatment are observed for patients with proximal femoral fractures. This preliminary study explores the prognostic value of a previously constructed mortality risk score based on a set of 14 metabolites for the survival and functional recovery in patients with proximal femoral fractures.

Materials and Methods:

A prospective observational cohort study was conducted including patients admitted with a proximal femoral fracture. The primary outcome was patient survival, and the recovery of independence in activities of daily living was included as a secondary outcome. The mortality risk score was constructed for each patient and its prognostic value was tested for the whole population.

Results:

Data was available form 136 patients. The mean age of all patients was 82.1 years, with a median follow-up of 6 months. Within this period, 19.0% of all patients died and 51.1% recovered to their prefracture level of independence. The mortality score was significantly associated with mortality (HR, 2.74; 95% CI, 1.61-4.66; P < 0.001), but showed only a fair prediction accuracy (AUC = 0.68) and a borderline significant comparison of the mortality score tertile groups in survival analyses (P = 0.049). No decisive associations were found in any of the analyses for the functional recovery of patients.

Discussion:

These findings support the previously determined prognostic value of the mortality risk score. However, the independent prognostic value when adjusted for potential confounding factors is yet to be assessed. Also, a risk score constructed for this specific patient population might achieve higher accuracies for the prediction of survival and functional recovery.

Conclusions:

A modest prediction accuracy was observed for the mortality risk score in this population. More elaborate studies are needed to validate these findings and develop a tailored model for clinical purposes in this patient population.