The MyD88+ phenotype is an adverse prognostic factor in epithelial ovarian cancer

The prognosis of epithelial ovarian cancer is poor in part due to the high frequency of chemoresistance. Recent evidence points to the Toll-like receptor-4 (TLR4), and particularly its adaptor protein MyD88, as one potential mediator of this resistance. This study aims to provide further evidence that MyD88 positive cancer cells are clinically significant, stem-like and reproducibly detectable for the purposes of prognostic stratification. Expression of TLR4 and MyD88 was assessed immunohistochemically in 198 paraffin-embedded ovarian tissues and in an embryonal carcinoma model of cancer stemness. In parallel, expression of TLR4 and MyD88 mRNA and regulatory microRNAs (miR-21 and miR-146a) was assessed, as well as in a series of chemosensitive and resistant cancer cells lines. Functional analysis of the pathway was assessed in chemoresistant SKOV-3 ovarian cancer cells. TLR4 and MyD88 expression can be reproducibly assessed via immunohistochemistry using a semi-quantitative scoring system. TLR4 expression was present in all ovarian epithelium (normal and neoplastic), whereas MyD88 was restricted to neoplastic cells, independent of tumour grade and associated with reduced progression-free and overall survival, in an immunohistological specific subset of serous carcinomas, p<0.05. MiR-21 and miR-146a expression was significantly increased in MyD88 negative cancers (p<0.05), indicating their participation in regulation. Significant alterations in MyD88 mRNA expression were observed between chemosensitive and chemoresistant cells and tissue. Knockdown of TLR4 in SKOV-3 ovarian cells recovered chemosensitivity. Knockdown of MyD88 alone did not. MyD88 expression was down-regulated in differentiated embryonal carcinoma (NTera2) cells, supporting the MyD88+ cancer stem cell hypothesis. Our findings demonstrate that expression of MyD88 is associated with significantly reduced patient survival and altered microRNA levels and suggest an intact/functioning TLR4/MyD88 pathway is required for acquisition of the chemoresistant phenotype. Ex vivo manipulation of ovarian cancer stem cell (CSC) differentiation can decrease MyD88 expression, providing a potentially valuable CSC model for ovarian cancer.

Prognostic validity of 3-Minute Nutrition Screening (3-MinNS) in predicting length of hospital stay, readmission, cost of hospitalisation and mortality : a cohort study

Background: It is important to identify patients who are at risk of malnutrition upon hospital admission as malnutrition results in poor outcomes such as longer length of hospital stay, readmission, hospitalisation cost and mortality. The aim of this study was to determine the prognostic validity of 3-Minute Nutrition Screening (3-MinNS) in predicting hospital outcomes in patients admitted to an acute tertiary hospital through a list of diagnosis-related groups (DRG). Methods: In this study, 818 adult patients were screened for risk of malnutrition using 3-MinNS within 24 hours of admission. Mortality data was collected from the National Registry with other hospitalisation outcomes retrieved from electronic hospital records. The results were adjusted for age, gender and ethnicity, and matched for DRG. Results: Patients identified to be at risk of malnutrition (37%) using 3-MinNS had significant positive association with longer length of hospital stay (6.6 ± 7.1 days vs. 4.5 ± 5.5 days, p<0.001), higher hospitalisation cost (S$4540 ± 7190 vs. S$3630 ± 4961, p<0.001) and increased mortality rate at 1 year (27.8% vs. 3.9%), 2 years (33.8% vs. 7.2%) and 3 years (39.1% vs. 10.5%); p<0.001 for all. Conclusions: The 3-MinNS is able to predict clinical outcomes and can be used to screen newly admitted patients for nutrition risk so that appropriate nutrition assessment and early nutritional intervention can be initiated.

A comparative study of prognostic model for bearing failure

An effective prognostics program will provide ample lead time for maintenance engineers to schedule a repair and to acquire replacement components before catastrophic failures occur. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique. For comparative study of the proposed model with the proportional hazard model (PHM), experimental bearing failure data from an accelerated bearing test rig were used. The result shows that the proposed prognostic model based on health state probability estimation can provide a more accurate prediction capability than the commonly used PHM in bearing failure case study.

A review on prognostic techniques for non-stationary and non-linear rotating systems

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Predicting final disposition after stroke using the Orpington Prognostic Score

Background: Prediction of outcome after stroke is important for triage decisions, prognostic estimates for family and for appropriate resource utilization. Prognostication must be timely and simply applied. Several scales have shown good prognostic value. In Calgary, the Orpington Prognostic Score (OPS) has been used to predict outcome as an aid to rehabilitation triage. However, the OPS has not been assessed at one week for predictive capability. Methods: Among patients admitted to a sub-acute stroke unit, OPS from the first week were examined to determine if any correlation existed between final disposition after rehabilitation and first week score. The predictive validity of the OPS at one week was compared to National Institute of Health Stroke Scale (NIHSS) score at 24 hours using logistic regression and receiver operator characteristics analysis. The primary outcome was final disposition after discharge from the stroke unit if the patient went directly home, or died, or from the inpatient rehabilitation unit. Results: The first week OPS was highly predictive of final disposition. However, no major advantage in using the first week OPS was observed when compared to 24h NIHSS score. Both scales were equally predictive of final disposition of stroke patients, post rehabilitation. Conclusion: The first week OPS can be used to predict final outcome. The NIHSS at 24h provides the same prognostic information.

Prognostic value of biochemical liver parameters in neonatal sepsis-associated cholestasis

Aims The aim of the study was to evaluate the significance of total bilirubin, aspartate transaminase (AST), alanine transaminase and gamma-glutamyltransferase (GGT) for predicting outcome in sepsis-associated cholestasis. Methods: A retrospective cohort review of the hospital records was performed in 181 neonates admitted to the Neonatal Care Unit. A comparison was performed between subjects with low and high liver values based on cut-off values from ROC analysis. We defined poor prognosis to be when a subject had prolonged cholestasis of more than 3.5 months, developed severe sepsis, septic shock or had a fatal outcome. Results: The majority of the subjects were male (56%), preterm (56%) and had early onset sepsis (73%). The poor prognosis group had lower initial values of GGT compared with the good prognosis group (P = 0.003). Serum GGT (cut-off value of 85.5 U/L) and AST (cut-off value of 51 U/L) showed significant correlation with the outcome following multivariate analysis. The odds ratio (OR) of low GGT and high AST were OR 4.3 (95% CI:1.6 to11.8) and OR 2.9 (95% CI:1.1 to 8), respectively, for poor prognosis. In subjects with normal AST values, those with low GGT value had relative risk of 2.52 (95% CI:1.4 to 3.5) for poorer prognosis compared with those with normal or high GGT. Conclusion: Serum GGT and AST values can be used to predict the prognosis of patients with sepsis-associated cholestasis

Prognostic value of BRAF mutations in localized cutaneous melanoma

BACKGROUND: BRAF mutations are frequent in melanoma but their prognostic significance remains unclear. OBJECTIVE: We sought to further evaluate the prognostic value of BRAF mutations in localized cutaneous melanoma. METHODS: We undertook an observational retrospective study of 147 patients with localized invasive (stages I and II) cutaneous melanomas to determine the prognostic value of BRAF mutation status. RESULTS: After a median follow-up of 48 months, patients with localized melanomas with BRAF-mutant melanomas exhibited poorer disease-free survival than those with BRAF-wt genotype (hazard ratio 2.2, 95% confidence interval 1.1-4.3) even after adjustment for Breslow thickness, tumor ulceration, location, age, sex, and tumor mitotic rate. LIMITATIONS: The retrospective design and the small number of events are limitations. CONCLUSIONS: Our findings suggest that reappraisal of clinical treatment approaches for patients with localized melanoma harboring tumors with BRAF mutation might be warranted

Cancer stem cells in oesophageal squamous cell carcinoma: Identification, prognostic and treatment perspectives

Cancer stem cells (CSCs) are a vital subpopulation of cells to target for the treatment of cancers. In oesophageal squamous cell carcinoma (ESCC), there are several markers such as CD44, ALDH, Pygo2, MAML1, Twist1, Musashi1, Side population (SP), CD271 and CD90 that have been proposed to identify the cancer stem cells in individual cancer masses. It has also been demonstrated that stem cell markers like ALDH1, HIWI, Oct3/4, ABCG2, SOX2, SALL4, BMI-1, NANOG, CD133 and podoplanin are associated with patient's prognosis, pathological stages, cancer recurrence and therapy resistance. Finding new cancer stem cell targets or designing drugs to manipulate the known molecular targets in CSCs could be useful for improvements in clinical outcomes of the disease. To conclude, data suggest that CSCs in oesophageal squamous cell carcinoma are related to resistance to therapy and poor prognosis of patients with ESCC. Therefore, innovative insights into CSC biology and CSC-targeted therapies will help to achieve more effective management of patients with oesophageal squamous cell carcinoma.

Examination of EXOSC4 as a new prognostic marker and a novel therapeutic avenue in lung adenocarcinoma

Although prevention and early detection of the disease greatly improved over the past few years, lung cancer remains the leading cause of cancer deaths. In order to be able to treat a larger population, we are in urgent need for novel treatments. While it is known that DNA repair genes play a major role in the oncogenic transformation, they also represent a weakness of cancers that constitute a therapeutic opportunity. To identify novel DNA repair genes implicated in Lung cancers, we conducted an in silico investigation to identify genes co-regulated with two DNA repair factors, BRCA2 and hSSB1. This approach allowed for the identification of EXOSC4, a component of the RNA Exosome machinery, as a potential factor involved in the maintenance of genome stability and that is deregulated in lung cancer.

A database ATP simulated waveforms of shunt reactor switching cases with vacuum breakers on motor circuits

This paper presents a database ATP (Alternative Transient Program) simulated waveforms for shunt reactor switching cases with vacuum breakers in motor circuits following interruption of the starting current. The targeted objective is to provide multiple reignition simulated data for diagnostic and prognostic algorithms development, but also to help ATP users with practical study cases and component data compilation for shunt reactor switching. This method can be easily applied with different data for the different dielectric curves of circuit-breakers and networks. This paper presents design details, discusses some of the available cases and the advantages of such simulated data.

Prognosis of bearing failure based on health state estimation

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.

Integrated Diagnosis and Prognosis Model for High Pressure LNG Pump

In condition-based maintenance (CBM), effective diagnostics and prognostics are essential tools for maintenance engineers to identify imminent fault and to predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedules production if necessary. This paper presents a technique for accurate assessment of the remnant life of machines based on historical failure knowledge embedded in the closed loop diagnostic and prognostic system. The technique uses the Support Vector Machine (SVM) classifier for both fault diagnosis and evaluation of health stages of machine degradation. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for multi-class fault diagnosis. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.