Prognostic Method Research Articles

Patched Homolog Gene (PTCHI) as a Reliable Marker for Predicting Imatinib Response in Chronic Phase Chronic Myeloid Leukemia Patients in Correlation with Early Response to First Line of Treatment Imatinib

Abstract Background Chronic myeloid leukaemia BCR-ABL1 + (CML) is a clonal disorder of a pluripotent stem cell. The disease accounts for around 15% of leukaemias and may occur at any age. The diagnosis of CML is rarely difficult and is assisted by the characteristic presence of the Philadelphia (Ph) chromosome. This results from the t (9;22) (q34; q11) translocation between chromosomes 9 and 22, as a result of which part of the oncogene ABL1 is moved to the BCR gene on chromosome 22. and part of chromosome 22 moves to chromosome 9. The abnormal chromosome 22 is the Ph chromosome. Aim of the Work To measure the level of PTCH1 in newly diagnosed CP-CML and to find correlation between the level of PTCH1in those patients and response to first line of treatment; imatinib and other prognostic factors Patients and Methods In our study there are 50 patients newly diagnosed cml recruited from hematology out patients clinic in ain shams university within duration august 2021 –april 2022.We have measured level of PTCH1 protein in newly diagnosed CP-CML and its follow up after 6months Results the studies show that 25% of patients discontinue imatinib due to lack of efficacy. A few years ago, two second-generation tyrosine kinase inhibitors (TKIs), Dasatinib and Nilotinib, were approved for first-line treatment, increasing the options available for patients diagnosed with CP- CML.Currently, the best way to adjust treatment to fit the severity of the disease is by assessing the response at different milestones. One milestone is the BCR-ABL1/ABL1 transcript level at 3 months; a level >10% 3 months after starting treatment has been related to a worse subsequent imatinib response. However, whether a change in therapy is warranted based on this single determination is controversial. Conclusion Patched homolog 1 gene (PTCH1) is marker for predicting first line of treatment imatinib response in chronic myeloid leukemia patients in chronic phase in correlation with response to imatinib. It is detected by PCR and proved to be a reliable marker to detect early imatinib response in chronic myeloid leukemia patients in chronic phase. In our study, we measured level of (PTCH1) in chronic myeloid leukemia patients in chronic phase using ELISA as a cost benefit in comparison to PCR in newly diagnosed cml patients on first line of treatment imatinib then follow up after 6 months. We found that it is a reliable marker, sensitive and specific to detect imatinib response.

The Concept of “The Third Place” in the Context of Modernising the Library Space

The aim of the article is to study the concept of “the third place” in extrapolation to the library space, and to identify peculiarities of its implementation in public libraries of Ukraine. Research methods. An analytical-synthetic, typological, systemic-structural and dialectical methods are applied, due to which the features of modernising the space of Ukrainian public libraries in the context of “the third place” paradigm are determined, modern forms and peculiarities of cultural and leisure activities corresponding to “the third place” concept are revealed, as well as a prognostic and analytical method is used, the purpose of which is to outline prospective directions of modernisation tendencies in the space of public libraries of Ukraine, taking into account the current socio-cultural situation. Scientific novelty. The process of updating the library space is studied in the context of the leading tendencies of the first decades of the 21st century. The concept of “the third place” in extrapolation to the library space is researched. The information and communication requests of modern society are analysed as a factor of transforming the traditional library space. The peculiarities of implementing the concept of “the third place” in public libraries of Ukraine are identified, as well as the trends of cultural and leisure activities of domestic libraries at the beginning of the third decade of the 21st century are clarified on the example of Odesa Regional Universal Scientific Library named after M. S. Hrushevskyi, and the Central District Library named after I. Franko (Kyiv). The prospects for the further development of leisure practices are outlined according to the positioning of the modern library, including as a place for users’ spare time. Main conclusions. One of the concepts that is actively used in the process of improving and modernising the activities of cultural institutions in general and libraries in particular is the concept of “the third place”, according to which much attention is paid to the visitors’ requests and needs. In this way, the library is positioned not only as an institution where the visitor gets the information he needs, but also as a unique meeting place where social statuses are equalised. The concept of innovative library space in the context of “the third place” paradigm includes both technical and technological, and software tools of library activity, which are primarily aimed at meeting the socio-communicative, cultural-leisure and informational needs of its visitors. Innovative libraries of the new type presented in Ukraine at the beginning of the 2020s, operating according to the concept of “the third place”, can be roughly divided into several formats: co-working library, library-cafe, library-club, library-museum, bibliohub, etc. Their main characteristics are locality, accessibility and openness for all segments of the population, comfortable atmosphere that promotes relaxed interaction, providing opportunities for cultural and leisure activities, and a variety of entertainment. In conditions of decrease in the demand for traditional library services caused by automation and digitisation of library services, Ukrainian libraries actively introduce various cultural and leisure practices into their activity, thereby expand their own functions.

Quantitative Biomarkers of Diabetic Retinopathy Using Ultra-Widefield Fluorescein Angiography.

Diabetic retinopathy (DR) is a leading cause of blindness. Retinal imaging is an important tool to monitor the progression of DR. While seven-standard retinal fields are the traditional method for evaluating DR, ultra-widefield (UWF) imaging allows for improved visualization of peripheral areas of nonperfusion (NP) and neovascularization (NV), which could be used as biomarkers to monitor and predict progression of DR. A retrospective, cross-sectional study was conducted on 651 eyes from 363 patients diagnosed with type 1 or type 2 diabetes who received UWF-FA over 10 years. Fluorescein Angiography (FA) images were segmented, and surface areas of NP and NV were analyzed using multivariate regression to determine if biomarkers of DR and DR severity are associated with increasing areas of NP and NV. Each additional year with a diagnosis of DR was associated with a 10.75 mm2 increase in the total NP (95% CI, 1.94-19.56; P = 0.02) and 7.87 mm2 increase in NP far-periphery (95% CI, 1.62-14.13; P = 0.01). A one-unit change in severity as defined by the Early Treatment of Diabetic Retinopathy Study (ETDRS) classification was associated with a 25.75 mm2 increase in total NP (95% CI, 11.16-40.33; P = 0.001), a 13.15 mm2 increase in mid-periphery NP (95% CI, 6.93-19.38; P < 0.0001), and a 12.29 mm2 increase in far-periphery NP (95% CI, 3.62-20.97; P = 0.01). Biomarkers identified through UWF imaging such as total and regional areas of NP can be used to monitor and predict the progression of DR. This may provide a quantitative method for prognostication in patients with DR.

Theoretical and Practical Elements of the Educational Component “Management of Information, Library and Archival Studies” in Forming Future Managers’ Professional Competences

The aim of the article is to study theoretical and practical elements of the educational component “Management of Information, Library and Archival Studies” in grounding general and special competences of applicants for the Master’s degree in speciality 029 “Information, Library and Archival Studies.” Research methods: the article uses both general scientific and special research methods. The method of analysis and synthesis contributes to studying the theoretical foundation of the theme; systemic, structural and functional ones allow to determine the structure of the educational component and the peculiarities of its teaching. The prognostic method makes it possible to outline the ways of improving organisational foundations of the theoretical and practical training of the Master’s degree students. The scientific novelty consists in substantiating the interrelationship of the theoretical and practical elements of the educational component “Management of Information, Library and Archival Studies” in forming general and special competences of the Mmaster’s degree sudents as future managers of the sphere. Main conclusions. The theoretical and practical elements of the educational component presented in this article contribute to the formation of general and special competencies of future sphere managers, particularly, the ability to motivate people and move towards a common goal, promote the formation of an effective sphere management system; organise the work and manage information and analytical structural departments at enterprises, organisations and institutions. The programme results of mastering the educational component include the ability to form strategies for system organisation, modernisation, to increase the efficiency of management in the sphere, to use applied socio-communicative technologies in order to organise effective communication at the professional, scientific and social levels. Thus, the educational component is necessary in grounding the competencies of future professional managers. Nowadays, the work is underway to improve the content and technology of teaching the educational component, which will contribute to higher quality training of students for the Master’s degree in speciality 029 “Information, Library and Archival Studies.”

R2-CHA2DS2-VASc Score for Cardiovascular Event Prediction After Bioprosthetic Valve Replacement - Subanalysis From the BPV-AF Registry.

There are few studies evaluating the prognostic prediction method in atrial fibrillation (AF) patients after bioprosthetic valve (BPV) replacement. The R2-CHA2DS2-VASc score is increasingly used for the prediction of cardiovascular (CV) events in patients with AF, device implantation, and acute coronary syndrome. We aimed to evaluate the predictive value of the R2-CHA2DS2-VASc score for future CV events in AF patients after BPV replacement. The BPV-AF, an observational, multicenter, prospective registry, enrolled AF patients who underwent BPV replacement. The primary outcome measure was a composite of stroke, systemic embolism, CV events including heart failure requiring hospitalization, and cardiac death. A total of 766 patients was included in the analysis. The mean R2-CHA2DS2-VASc score was 5.7±1.8. Low (scores 0-1), moderate (scores 2-4), and high (scores 5-11) R2-CHA2DS2-VASc score groups consisted of 12 (1.6%), 178 (23.2%), and 576 (75.2%) patients, respectively. The median follow-up period was 491 (interquartile range 393-561) days. Kaplan-Meier analysis showed a higher incidence of the composite CV events in the high R2-CHA2DS2-VASc score group (log rank test; P<0.001). Multivariate Cox proportional hazards regression analysis revealed that the R2-CHA2DS2-VASc score as a continuous variable was an independent predictor of composite CV outcomes (hazard ratio 1.36; 95% confidence interval 1.18-1.55; P<0.001). The R2-CHA2DS2-VASc score is useful for CV risk stratification in AF patients after BPV replacement.

AI-Mediated Methods For Cancer Treatment

The integration of Artificial Intelligence (AI) into cancer treatment represents a major advancement in diagnostic, prognostic, and therapeutic methods. This article explores how AI enhances drug response predictions, optimizes treatment regimens, and improves diagnostic imaging precision, leading to early and accurate tumor detection. AI accelerates drug discovery by identifying novel compounds, optimizing dosages, and repurposing existing drugs, thereby enhancing treatment development. In clinical trials, AI improves trial design, patient selection, and monitoring, increasing efficiency and success rates. Its application in medical imaging, including tumor localization and organ segmentation, provides detailed insights for precise diagnostic and treatment planning. Despite challenges such as data privacy and model transparency, AI's continuous learning and integration into personalized medicine highlight its transformative potential in cancer care. This convergence of AI and medical science promises to revolutionize oncology, improve patient outcomes, and tackle complex challenges.

Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network.

Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

A federated data fusion-based prognostic model for applications with multi-stream incomplete signals

Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not large enough to train a reliable prognostic model. To address this challenge, this article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model using their multi-stream, high-dimensional, and incomplete data while keeping each user’s data local and confidential. The prognostic model first employs multivariate functional principal component analysis to fuse the multi-stream degradation signals. Then, the fused features coupled with the times-to-failure are utilized to build a (log)-location-scale regression model for failure prediction. To estimate parameters using distributed datasets and keep the data privacy of all participants, we propose a new federated algorithm for feature extraction. Numerical studies indicate that the performance of the proposed model is the same as that of classic non-federated prognostic models and is better than that of the models constructed by each user itself.

Prognostic prediction of ovarian cancer based on hierarchical sampling & fine-grained recognition convolution neural network

Ovarian cancer ranks among the deadliest gynecological malignancies, with high-grade serous adenocarcinoma (HGSA) constituting 75 % of ovarian cancer cases and accounting for 80 %–90 % of ovarian cancer-related fatalities. Accurate prognosis prediction for ovarian HGSA is of critical clinical significance. However, existing prognostic analysis methods exhibit suboptimal performance in identifying prognostically relevant pathological markers, making it challenging, even for experienced pathologists, to forecast the prognosis of ovarian HGSA patients accurately. Deep learning holds promise in enhancing prognostic prediction accuracy. However, a significant challenge in this field arises from the impracticality of directly inputting histopathology whole-slide images with millions to billions of pixels into existing deep learning networks for training and inference. To address this issue, we propose a prognostic analysis network for ovarian cancer based on hierarchical sampling and fine-grained recognition. This network comprises a two-stage hierarchical sampling sub-network, a fine-grained image recognition sub-network, and a prognostic analysis sub-network. We assess the system's performance using a pathological dataset of 450 cases of ovarian HGSA diagnosed and treated at the Pathology Department of the West China Second University Hospital of Sichuan University. Results indicate that: (1) The proposed prognostic analysis network based on two-stage hierarchical sampling sub-network can effectively analyze the histopathology whole-slide image; (2) the use of fine-grained image recognition and the introduction of clinical information can improve the performance of HGSA prognosis analysis method, with an improvement range of 4.77–10.66%; and (3) the proposed method can be used for the model analysis of pathological datasets of HGSA. Moreover, this method can be used to explore effective characteristics from the multi-modal dataset through automatic learning with prediction recall, accuracy, and precision rates of 80.0%, 81.1%, and 81.8%, respectively, underscoring its clinical potential. This study reveals the reliability and effectiveness of the proposed prognosis evaluation method of HGCA. Conclusions can help clinicians precisely evaluate the recurrence risk of patients, take the initiative to master the diagnosis and treatment, and increase the long-term survival rate of patients.

Improving production and maintenance planning with meta-learning-based failure prediction

Digital manufacturing systems allow for dynamic production and maintenance planning adaptation based on internal and external evolving changes. In this context, data-driven predictive techniques can improve maintenance planning. However, dynamic production environments require the development of novel methods capable of efficiently managing the extensive data generated by manufacturing systems and processes while providing valuable insights for decision-making. Thus, this research proposes an integrated production and maintenance planning (IPMP) approach for digital manufacturing systems featuring a dynamic simulation-based optimization and a meta-learning-based failure prediction, which selects the best prognostic method to predict machine failures in real-time. The proposed model was applied to the Minifab case, in the semiconductor industry, providing a reduction of 20.9 % in lead time and 25.9 % in tardy jobs compared to setups with static dispatching rules and single-prognostic-method failure prediction. Such findings support the pertinence of the proposed novel approach in the area of production planning and control, contributing to the improvement of business performance, reliability, and competitiveness.

Maintenance & failure data analysis of an offshore wind farm

Offshore Wind (OW) continues to grow globally at a rapid pace, with growth estimates of 630GW by 2050. To facilitate this rapid growth, costs must continue to be reduced. Reducing operations and maintenance (O&M) costs, which are estimated at 30% of the lifetime costs of wind farms, offers opportunity. This could be achieved by moving current maintenance strategies to a prescriptive strategy. Prescriptive strategies use the turbine monitoring data to determine component remaining useful lifetimes or predict failure windows and then provide an optimised maintenance plan. The first stage of a framework, that can be applied to operational assets, for improving maintenance schedules with failure predictions is presented. Analysis of the SCADA system and the maintenance logs, at an operational offshore wind farm (OWF), with the purpose of identifying turbine failure rates, availabilities and losses and costs from maintenance and failures has been performed. The analysis has revealed two types of maintenance actions, one is cost of maintenance driven and the other cost of downtime driven. It is proposed that, given different characteristics, they should be approached differently in the context of failure predictions. It is also revealed that electrical components are critical to the failure rate and energy losses due to maintenance at the OWF. Electrical components represent approximately 28% of all failures and nearly 40% of revenue loss due downtimes from failure for the period analysed. The power converters drive most electrical failures and are of key commercial interest to the farm. As a result, the power converters should be the target for future prognostic model development. The analysis also shows that with perfect prediction and maintenance scheduling, this OWF could generate an extra 0.26% revenue and a generic 1GW OWF could generate an extra 0.6% extra energy and approximately £1.4m in revenue. This analysis did not reveal the benefit of taking fewer maintenance actions, which should be assessed in future work. Producing a combined prognostic maintenance scheduling method will generate extra wind farm revenues, reduce the number of maintenance actions taken and facilitate the work of maintenance teams.

#328 Non-invasive assessment of urinary exfoliated proximal tubule cells using multispectral autofluorescence imaging features for early detection of CKD

Abstract Background and Aims Early detection of CKD is important to allow for timely intervention which may reduce adverse clinical outcomes and cost of renal replacement therapies. Histopathological diagnosis of CKD remains the gold standard, but routine kidney biopsy is costly and associated with risks such as pain and bleeding. There is a need to develop accurate non-invasive methods for early diagnosis and prognostication of CKD. Kidney cell exfoliation into urine is an active process, which may generate significant information regarding an individual's kidney status. In particular, proximal tubule cells (PTCs) make up majority of the kidney mass and represents the hallmark of CKD in reflecting the degree of tubulointerstitial fibrosis and atrophy. Multispectral assessment of native cell autofluorescence has been specifically shown to be sensitive to metabolic changes and oxidative stress, which are key factors involved in kidney function decline. Hence, we evaluated whether multispectral autofluorescence signals in urinary exfoliated PTCs reflect kidney function as determined by eGFR. Method Individuals aged 18 or above were included and informed consent was obtained from all participants involved in this study. Spot urine samples were collected and stored at -80°C. On assessment, exfoliated PTCs were extracted from thawed urine using a validated specific immunomagnetic separation method based on anti-CD13 and anti-SGLT2 antibodies. Multispectral autofluorescence imaging was performed using a custom-made autofluorescence microscopy system (standard Olympus iX83™ fluorescence microscope and multispectral excitation lamp from Quantitative™, AU). Images were captured by a Nüvü™ EMCCD camera HNü 1024 cooled to -65°C in our laboratory to reduce sensor-induced noise. For the 34 image channels taken per sample, image acquisition times of up to 5 seconds per channel, with multiple averaging (3 times) was applied to optimize image quality. Image analysis was conducted through a workflow encompassing data preprocessing, data normalization and outlier handling, followed by basic model training and evaluation. An entropy-based image feature selection methodology was initially completed by hand, with results subsequently compared to that generated from the AutoGluon machine-learning framework [1]. The models developed from this process were assessed using ROC (Receiver Operating Characteristic) curves and AUC (Area Under the Curve) values. Two discriminative studies were performed – an overall study between exfoliated PTCs of individuals eGFR≥60 and eGFR &amp;lt; 60 ml/min/1.73 m2 and a subgroup study between that of individuals eGFR≥90 and eGFR 60-90 ml/min/1.73 m2. Results 40 individuals were included in our study. 20 individuals had eGFR≥60 and 20 had eGFR &amp;lt; 60 ml/min/1.73 m2. Multispectral assessment of cell autofluorescence features differentiated between exfoliated PTCs of these 2 groups with AUC value of 0.97 (95% CI 0.94-1.00) (Fig. 1), model accuracy being 82%. Subgroup analysis included 20 individuals of which 10 had eGFR≥90 and 10 had eGFR 60-90 ml/min/1.73 m2. Multispectral assessment of cell autofluorescence features differentiated between exfoliated PTCs of these 2 groups with AUC value of 0.99 (95% CI 0.99-1.00) (Fig. 2), model accuracy being 85%. Conclusion An excellent degree of differentiation between urinary PTCs derived from those with normal or high eGFR and mild degrees of CKD is now reliably demonstrated, complementing conclusions from our group's original small pilot study which evaluated between urinary PTCs from CKD and non-CKD cohorts [2]. Further studies are required to determine whether our novel method can be used to non-invasively predict individuals at risk of progressive kidney disease and to monitor whether intervention is effective.

Prognostic score-based methods for estimating center effects based on survival probability: Application to post-kidney transplant survival.

In evaluating the performance of different facilities or centers on survival outcomes, the standardized mortality ratio (SMR), which compares the observed to expected mortality has been widely used, particularly in the evaluation of kidney transplant centers. Despite its utility, the SMR may exaggerate center effects in settings where survival probability is relatively high. An example is one-year graft survival among U.S. kidney transplant recipients. We propose a novel approach to estimate center effects in terms of differences in survival probability (ie, each center versus a reference population). An essential component of the method is a prognostic score weighting technique, which permits accurately evaluating centers without necessarily specifying a correct survival model. Advantages of our approach over existing facility-profiling methods include a metric based on survival probability (greater clinical relevance than ratios of counts/rates); direct standardization (valid to compare between centers, unlike indirect standardization based methods, such as the SMR); and less reliance on correct model specification (since the assumed model is used to generate risk classes as opposed to fitted-value based 'expected' counts). We establish the asymptotic properties of the proposed weighted estimator and evaluate its finite-sample performance under a diverse set of simulation settings. The method is then applied to evaluate U.S. kidney transplant centers with respect to graft survival probability.

СОВРЕМЕННЫЙ ПОДХОД К ОЦЕНКЕ ПЕРИНАТАЛЬНОГО РИСКА, ОСНОВАННЫЙ НА ИСПОЛЬЗОВАНИИ ПРОГНОСТИЧЕСКИХ КРИТЕРИЕВ АНТЕНАТАЛЬНЫХ, ИНТРАНАТАЛЬНЫХ И РАННИХ НЕОНАТАЛЬНЫХ ПОТЕРЬ

The problem of perinatal losses is still actual in obstetrics today. At the same time there are cases of non-estimation or extra-estimation of perinatal risk in obstetrical practice. The usage of famous prognostic systems gives the opportunity to select pregnant women into groups of high risk and provide the necessary organizational and tactic measures. But to define the character of possible complications, to establish the degree of their severity and time of appearance is really impossible. Objective of the study: to develop a new approach to estimation of perinatal risk based on the determination of prognostic criteria for antepartum and intrapartum fetal death, early neonatal death of a newborn in order to increase the efficacy of predicting of unfavourable perinatal outcomes. Materials and research methods. The retrospective case-control study in patients with antepartum (n=134) and intrapartum (n=41) fetal death, early neonatal death of the newborn (n=61) and favourable perinatal outcome (n=50) was conducted. There was used the Bayes algorhythm with Wald's consecutive analysis in the modification of Genkin-Gubler to determine prognostic criteria. The comparative estimation of the efficacy of the proposed prognostic method and traditional method of determining perinatal risk was performed retrospectively in patients with perinatal losses (n=102) and favourable perinatal outcome (n=100). ROC analysis was performed. Results. There were identified 42 risk factors divided into three groups: universal for all types of perinatal losses, common for two of them and specific for each of them (antepartum and intrapartum fetal death, early neonatal death of a newborn). There was determined the prognostic influence of factors in their presence and absence. It was found that universal and common risk factors had different prognostic influence for each type of perinatal losses. There was presented the method for differential predicting of perinatal losses. The sensitivity of the new and traditional prognostic methods was 95.1% and 69.6%, specificity was 80% and 53%, and the accuracy of predicting of unfavourable outcome was 87.6% and 61.4% respectively. Conclusion. The investigation permitted to propose the new approach to estimation of perinatal risk, based on differential predicting of perinatal losses being is more effective than traditional method in predicting adverse perinatal outcomes.

Construction Price Forecasting Models in the Construction Industry: A Comparative Analysis

Construction prices rose rapidly during 2020 and 2021, making it almost impossible for contractors to adhere to agreed contract prices. For this reason, there was a request from contractors to adjust the contract price after signing a contract for work. During the implementation of the construction contracts, they were unable to comply with the fixed contract price. Forecasting the development of price indices could solve this problem by creating a reserve that would limit the adjustment of the contract price and the contractors’ withdrawal from the contracts. The forecast could be enshrined in the contractual conditions before the start of construction, which would eliminate the risk of changing the agreed contract price for the investor and the possible occurrence of additional work. Data from statistical offices were used to create the price index forecast. In this article, four methods were used in the search for a more accurate forecast: regression analysis, exponential smoothing, the naïve method, and the Autoregressive Integrated Moving Average (ARIMA) model. From these methods, the most appropriate method was selected by multi-criteria decision-making, which was subsequently verified with actual published price index data. The main goals of this study are to determine the most suitable prognostic method for forecasting the development of the prices of construction materials and work and then comparing the forecasted data with the actual published data of statistical offices in the countries of Central Europe.

Is a Combination of Six Clinical Tests Useful as a Measure to Predict Short-Term Prognosis in Terminal Cancer Patients? A Prospective Observational Study in a Japanese Palliative Care Unit.

To address the need for short-term prognostic methods using objective measures, we developed a method to predict a 2- or 3-week prognosis using only six clinical tests (known as the WPCBAL score). However, the method has not yet been directly compared with globally accepted prognostic methods. This study aimed to clarify the usefulness of the WPCBAL score by comparing it with other prediction methods. A prospective observational study was conducted with patients admitted to the palliative care unit of a Municipal Hospital in Japan between November 2017 and May 2021. The primary endpoint was each prediction method's accuracy-the WPCBAL score, Glasgow Prognostic Score (GPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP), Delirium-Palliative Prognostic Score (D-PaP), and Prognosis in Palliative Care Study predictor models (PiPS-A, PiPS-B)-in predicting a prognosis at 2 or 3 weeks. The secondary endpoints were sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, and each prediction method's feasibility rate. In total, 181 patients were included in this study. For the 3-week prognosis, the PaP had the highest accuracy (0.746), followed by the D-PaP (0.735), WPCBAL (0.696), PPI (0.652), and GPS (0.575). For the 2-week prognosis, the PiPS-B had the highest accuracy (0.702), followed by the WPCBAL (0.696) and PiPS-A (0.641). The WPCBAL score's accuracy in predicting a 2- or 3-week prognosis was comparable to that of commonly used prognostic methods, thus suggesting its usefulness as a short-term prognostic method.

Conditional survival of patients with primary bone lymphoma of the spine: how survival changes after initial diagnosis.

The current survival prediction methodologies for primary bone lymphoma (PBL) of the spine are deficient. This study represents the inaugural utilization of conditional survival (CS) to assess the outcome of this disease. Moreover, our objective was to devise a CS-based nomogram for predicting overall survival (OS) in real-time for spinal PBL. Patients with PBL of the spine diagnosed between January 2000 and December 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The OS was determined through the Kaplan-Meier method. The CS characteristic of patients with spinal PBL was delineated, with the CS being estimated utilizing the formula: CS(α|β) = OS(α+β)/OS(β). CS(α|β) denotes the probability of additional α-year survivorship, assuming the patient has already survived β years after the time of observation. Three methods including univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) regression were used to identify predictors for CS-based nomogram construction. Kaplan-Meier analysis was executed to determine the OS rate for these patients, revealing a survival rate of 68% and subsequently 63% at the 3-year and 5-year mark respectively. We then investigated the CS patterning exhibited by these patients and discovered the survival of PBL in the spine progressively improved with time. Meanwhile, through three different prognostic factor selection methods, we identified the best predicter subset including age, tumor histology, tumor stage, chemotherapy and marital status, for survival prediction model construction. Finally, we successfully established and validated a novel CS-based nomogram model for real-time and dynamic survival estimation. Moreover, we further designed a risk stratification system to facilitate the identification of high-risk patients. This is the first study to analyze the CS pattern of PBL of the spine. And we have also developed a CS-based nomogram that provide dynamic prognostic data in real-time, thereby aiding in the formulation of personalized treatment strategies in clinical practice.

Gas turbine prognostics via Temporal Fusion Transformer

Abstract Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

Single-cell RNA-seq reveals T cell exhaustion and immune response landscape in osteosarcoma.

T cell exhaustion in the tumor microenvironment has been demonstrated as a substantial contributor to tumor immunosuppression and progression. However, the correlation between T cell exhaustion and osteosarcoma (OS) remains unclear. In our present study, single-cell RNA-seq data for OS from the GEO database was analysed to identify CD8+ T cells and discern CD8+ T cell subsets objectively. Subgroup differentiation trajectory was then used to pinpoint genes altered in response to T cell exhaustion. Subsequently, six machine learning algorithms were applied to develop a prognostic model linked with T cell exhaustion. This model was subsequently validated in the TARGETs and Meta cohorts. Finally, we examined disparities in immune cell infiltration, immune checkpoints, immune-related pathways, and the efficacy of immunotherapy between high and low TEX score groups. The findings unveiled differential exhaustion in CD8+ T cells within the OS microenvironment. Three genes related to T cell exhaustion (RAD23A, SAC3D1, PSIP1) were identified and employed to formulate a T cell exhaustion model. This model exhibited robust predictive capabilities for OS prognosis, with patients in the low TEX score group demonstrating a more favorable prognosis, increased immune cell infiltration, and heightened responsiveness to treatment compared to those in the high TEX score group. In summary, our research elucidates the role of T cell exhaustion in the immunotherapy and progression of OS, the prognostic model constructed based on T cell exhaustion-related genes holds promise as a potential method for prognostication in the management and treatment of OS patients.