Significant Predictive Ability Research Articles

Resting-state voxel-wise dynamic effective connectivity predicts risky decision-making in patients with bipolar disorder type I

Patients with Bipolar Disorder type I (BD-I) exhibit maladaptive risky decision-making, which is related to impulsivity, suicide attempts, and aggressive behavior. Currently, there is a lack of effective predictive methods for early intervention in risky behaviors for patients with BD-I. This study aimed to predict risky behavior in patients with BD-I using resting-state functional magnetic resonance imaging (rs-fMRI). We included 48 patients with BD-I and 124 healthy controls (HC) and constructed voxel-wise functional connectivity (FC), dynamic FC (dFC), effective connectivity (EC), and dynamic EC (dEC) for each subject. The Balloon Analogue Risk Task (BART) was employed to measure the risky decision-making of all participants. We applied connectome-based predictive modeling (CPM) with five regression algorithms to predict risky behaviors as well as Barratt Impulsivity Scale (BIS) scores. Results showed that the BD-I had significantly lower risky adjusted pump scores compared to HC. The dEC-based linear regression-CPM model exhibited significant predictive ability for the adjusted pump scores in BD-I, while no significant predictive power was observed in HC. Furthermore, this model successfully predicted non-planning impulsiveness, motor impulsiveness, and BIS total score, but failed for attentional impulsiveness in BD-I. These findings provide a foundation for future work in predicting risky behaviors of psychiatric patients by using voxel-wise dEC underlying resting state.

Uric Acid-HDL Ratio as Predictor of Major Cardiovascular Events in Very High Cardiovascular Risk Patients Treated with High-Intensity Statin Therapy

Introduction: Uric acid levels are positively correlated with cardiovascular disease. High Density Lipoprotein (HDL) cholesterol plays a role in suppressing blood oxidation reactions and protecting vascular endothelial cells. The combination of these two metabolic parameters, the Uric Acid-HDL Ratio (UHR) makes it a very useful predictor marker for metabolic disorders. Statins are known to be the gold standard for raising HDL cholesterol. Some types of statins are also known to reduce serum uric acid levels by involving pleiotropic actions. The aim of this study was to determine baseline UHR as a predictor of MACE after administration of high-intensity statins.Objectives: The Objective of this study was to determine baseline UHR as a predictor of MACE after administration of high-intensity statins.Methods: This study was conducted at the cardiac center of Dr Zainal Abidin Regional General Hospital. This study is an observational study with a prospective cohort design. The target population was patients classified as patients with very high cardiovascular risk and using consecutive sampling techniques.Results: During the study period, a total of 82 patients with ischemic heart disease with very high cardiovascular risk met the inclusion and exclusion criteria. Of these, 49 patients (59.8%) were identified as having major cardiovascular events (MACE) while the remaining 33 patients (40.2%) did not had MACE. The results of this study are that statin therapy may not significantly affect the uric acid-HDL ratio in the context of major cardiovascular events.Conclusions: The uric acid-HDL ratio had no significant predictive ability for major cardiovascular events in patients with very high cardiovascular risk who were treated with high-intensity statin therapy.

ACE and chitotriosidase as possible predictive biomarkers for activity of sarcoidosis in correlation with PET/CT findings

Sarcoidosis is a granulomatous disease which can afflict virtually any tissue in human body, most commonly the mediastinal lymph nodes and lungs. Pathohistological confirmation is the gold standard in establishing a diagnosis; however, determining the activity of the disease requires multiple clinical, radiographic and laboratory procedures. PET/CT scan is considered the gold standard for determining the presence of active granuloma, but has several significant limitations (radioactive material, cost, overall access to device). ACE and chitotriosidase are biomarkers used for diagnosis of sarcoidosis, and could have a place in determining the activity of the disease, when compared with the results of PET/CT scan. We have compared the levels of ACE and chitotriosidase with the levels of SUVmax values in patients with sarcoidosis. SUVmax and chitotriosidase level were significantly correlated both at the baseline and after the follow-up period, regardless of gender, age, duration of disease and radiography stage, while SUVmax and ACE level were not. Chitotriosidase had also shown a significant predictive ability of the decrease of the activity of sarcoidosis represented as the decrease of SUVmax as the effect of therapy in comparison with ACE. In the absence of ideal biomarker for sarcoidosis (high sensitivity, specificity and stability), chitotriosidase can be used in determining the activity of disease, as it had shown a significant correlation to the gold standard- PET/CT scan.

Online Investor Sentiment via Machine Learning

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio.

Investigating soluble CD40 ligand as a prognostic factor among acute coronary syndromes patients: A multi-center prospective case-control study.

Soluble CD40 ligand (sCD40L) is a protein that plays a crucial role in the inflammatory response associated with the development and progression of acute coronary syndrome (ACS). Recent studies have suggested that sCD40L may be a useful prognostic factor for ACS, but the data are conflicting. This study aimed to investigate the potential of sCD40L as a prognostic marker among ACS patients and provide valuable insights for clinical practice. To our knowledge, this is the first study of its type in the Arabic World. A multi-center prospective case-control study was conducted in Damascus, Syria, involving 158 participants with different ACS subtypes (STEMI, NSTEMI, UA) and a control group of healthy individuals. Sociodemographic data, medical history, and sCD40L levels were collected. The predictive ability of sCD40L for ACS, STEMI, NSTEMI, and UA was assessed using receiver operating characteristic (ROC) curves. Statistical analysis was performed using IBM SPSS version 25. The study included 58 STEMI, 33 NSTEMI, 36 UA patients, and 30 healthy individuals. The mean age of participants was 55 years (SD 10.7 years). Analysis of sCD40L levels revealed significantly higher concentrations in ACS patients compared to the control group (P < .001). ROC curve analysis demonstrated that sCD40L had a significant predictive ability for ACS, STEMI, and NSTEMI (P < .05), while its predictive value for UA was not statistically significant. This study provides evidence supporting the potential of sCD40L as a prognostic factor in ACS. The elevated levels of sCD40L observed in these subtypes indicate its potential usefulness in risk stratification and predicting adverse cardiovascular events. Further investigations are warranted to establish standardized sCD40L cutoff values and evaluate its clinical implications in the management of ACS patients.

Artificial neural network based forecasting of diesel engine performance and emissions utilizing waste cooking biodiesel

Ecological and environmental problems resulting from fossil fuels are due to the harmful emissions released into the atmosphere. The rising interest in searching for alternative fuels like biodiesel is growing to solve these problems. Waste cooking oil (WCO) is transformed into methyl ester and combined with biodiesel in percentages of 25, 50, 75, and 100%. Research is done on the impacts of methyl ester blends on engine performance and emissions. Compared to diesel, the methyl ester combination showed 25% lower brake power and 24% loss in thermal efficiency at maximum load and 1500 rpm. However, diesel fuel showed 23% lower specific fuel consumption increase than biodiesel. Compared to diesel, methyl ester exhibits 15% lower air-fuel ratio and 4% volumetric efficiency. Biodiesel lowers CO, HC, and smoke concentrations by 12, 44, and 48%, respectively, compared to diesel. Biodiesel emits 23% higher NOx at 1500 rpm and 100% engine load. To predict the emissions and performance of different percentages of biodiesel at engine speed variation, an artificial neural network (ANN) model is presented. ANN modeling minimizes labor, time, and finances and uses nonlinear data. Predictions were produced about the brake output power, specific fuel consumption, thermal efficiency, air-fuel ratio, volumetric efficiency, and emissions of smoke, CO, HC, and NOx as a function of engine speed and blend ratio. All correlation coefficients (r) over 0.99 and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} values were beyond 0.98 for all variables. There were low values of MSE, MAPE, and MSLE with significant predictive ability. WCO’s biodiesel is a viable diesel engine replacement fuel.

Heart rate to identify non-febrile children with dehydration and acute kidney injury in emergency department: a prospective validation study

We previously developed and retrospectively validated the estimated percentage of heart rate variation (EHRV) as a predictor of the composite outcome of ≥ 5% dehydration and/or acute kidney injury (AKI) in non-febrile children. The current study aimed to prospectively validate EHRV as a predictor for dehydration or AKI in a different cohort of children attending the Pediatric Emergency Department. From July 2022 to August 2023, 256 pediatric patients aged 0–18 years attending the Pediatric Emergency Department were enrolled. EHRV was calculated as follows: [(HR at admission − 50th percentile of HR for age and sex)/HR at admission] × 100. Dehydration was categorized as < 5% or ≥ 5% fluid deficit. AKI was defined according to KDIGO creatinine criteria. Statistical analyses included receiver-operating characteristic (ROC) curves and logistic regression analysis. Among enrolled patients, 52 had ≥ 5% dehydration, 50 had AKI, and 16 had both conditions. EHRV demonstrated significant predictive ability for both ≥ 5% dehydration (AUROC = 0.71; 95% confidence interval (CI), 0.63–0.78; p < 0.001) and AKI (AUROC = 0.78; 95% CI, 0.71–0.84; p < 0.001). An EHRV > 24.5% was associated with an increased odds ratio (OR), adjusted for confounders, of ≥ 5% dehydration (OR = 3.5; 95% CI, 1.6–8.0; p = 0.003) and AKI (OR = 3.4; 95% CI, 1.6–7.3; p = 0.002). The sensitivity and specificity of this cut-off were 34% and 83% for ≥ 5% dehydration and 36% and 84% for AKI, respectively.Conclusions: This study prospectively validates the clinical utility of EHRV in predicting dehydration and AKI in a pediatric emergency care setting. An EHRV > 24.5% could serve as a marker for suspecting dehydration or AKI. Further validation across diverse patient populations and settings is needed. What is Known:• An increased heart rate (HR) is a readily detectable sign of dehydration in children.• In a retrospective validation cohort, an estimated HR variation (EHRV) greater than 24.5% compared to the 50th percentile of HR was predictive of ≥ 5% dehydration and/or acute kidney injury (AKI) in non-febrile patients.What is New:• We prospectively validated the clinical utility of EHRV in predicting dehydration and AKI in a pediatric emergency care setting.• We confirmed that an EHRV greater than 24.5% is associated with increased odds of ≥ 5% dehydration and AKI.

New insights into the identification of biochemical traits linked to rooting percentage in fig (Ficus carica L.) cuttings

BACKGROUND: The fig (Ficus carica L.) tree known for its tasty and nutritious fruits, is typically propagated by cutting. While previous studies have focused on the effects of different treatments and environmental conditions on fig cutting propagation, little attention has been paid to the specific role and association of biochemical properties in leaves, stem bark and fruit on the rooting process. OBJECTIVE: This research explores the complex relationship between 40 biochemical traits and the rooting ability of fig cuttings. To achieve this objective, various machine learning techniques were employed, such as a random forest model, feature importance analysis, linear regression, and principal component analysis (PCA). RESULTS: The random forest model showed significant predictive ability with a classification accuracy of 100%, supported by a high kappa statistic. Feature importance analysis identified a* (a colorimetric parameter in fruit), fruit trans-ferulic acid and leaf total flavonoids as the most influential traits in determining the rooting ability of cuttings. The robustness of these findings is supported by the high R-squared value (0.9002) and low error metrics (MAE 0.7554 and MSE 0.6980) of the linear regression model built on these important traits. In parallel, PCA indicated that a*, leaf total flavonoids and fruit trans-ferulic acid were the dominant traits in samples with lower rooting percentage. CONCLUSIONS: These identified biomarkers can be effectively used by fig breeders and growers to select and introduce fig cultivars with improved rooting ability.

PMFORECAST: leveraging temporal LSTM todeliver in situairqualitypredictions.

The physical and chemical properties of atmospheric aerosol particles are crucial in influencing global climate and ecosystem processes. Given the numerous studies highlighting adverse health effects from exposure to aerosol particulates, particularly Particulate Matter (PM), effective air quality management strategies are under consideration (Annesi-Maesano et al. Eur Respir Soc 29(3):428-431. 2007). Herein, we introduce a predictive model-PMFORECAST-employing a self-adaptive long short-term memory (LSTM) architecture to predict PM values in the real atmosphere. Specifically, we explore adopting a LSTM model to better benefit from temporal dimensions. PMFORECAST is strategically designed with four key phases: preprocessing, temporal attention, prediction horizon, and LSTM layers. By leveraging LSTM's significant predictive ability in time-series data, the inclusion of temporal attention enhances the model's specificity. Temporal dynamics modeling entails generating insights over time, utilizing temporal attention to extract essential characteristics from historical air pollutant concentrations, with the flexibility to adjust the historical data according to the forecasting period. To assess PMFORECAST, we consider measurements collected from the QAMELEO network, a sparse network of air-quality micro-stations deployed in Dijon, France. The self-adaptive capabilities of PMFORECAST allow the model to be dynamically updated, evaluating its performance and continuously tuning hyper-parameters based on the latest data trends. Our empirical evaluation reports that PMFORECAST outperforms the state of the art, achieving notable accuracy in both short-term and long-term predictions. The PMFORECAST deployment at scale can serve as a valuable tool for proactive decision-making and targeted interventions to mitigate the health risks associated with air pollution.

Genomic predictions of genetic variances and correlations among traits for breeding crosses in soybean

Parental selection is perhaps the most critical decision a breeder makes, establishing the foundation of the entire program for years to come. Cross selection based on predicted mean and genetic variance can be further expanded to multiple-trait improvement by predicting the genetic correlation (rG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${r}_{G}$$\\end{document}) between pairs of traits. Our objective was to empirically assess the ability to predict the family mean, genetic variance, superior progeny mean and genetic correlation through genomic prediction in a soybean population. Data made available through the Soybean Nested Association Mapping project included phenotypic data on seven traits (days to maturity, lodging, oil, plant height, protein, seed size, and seed yield) for 39 families. Training population composition followed a leave-one-family-out cross-validation scheme, with the validation family genetic parameters predicted using the remaining families as the training set. The predictive abilities for family mean and superior progeny mean were significant for all traits while predictive ability of genetic variance was significant for four traits. We were able to validate significant predictive abilities of rG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${r}_{G}$$\\end{document} for 18 out of 21 (86%) pairwise trait combinations (P < 0.05). The findings from this study support the use of genome-wide marker effects for predicting rG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${r}_{G}$$\\end{document} in soybean biparental crosses. If successfully implemented in breeding programs, this methodology could help to increase the rate of genetic gain for multiple correlated traits.

Option‐Implied Ambiguity and Equity Return Predictability

ABSTRACTWe propose a model‐guided option‐implied ambiguity measure to capture uncertainty regarding the return distribution of a risky asset underlying a set of options, and investigate its predictive power on the asset return. A representative investor's ambiguous beliefs or prior distributions on the underlying asset returns are extracted from the market prices of options, the expected volatility of which is then defined as the option‐implied ambiguity and is calculated in line with Brenner and Izhakian. Simulated paths of the calibrated models are utilized to compute all pertinent probability characteristics from a forward‐looking perspective. The empirical results with SSE 50 ETF options indicate that the proposed option‐implied ambiguity has strong predictive power for future returns of SSE 50 ETF. Out‐of‐sample tests also verify the significant predictive ability of the option‐implied ambiguity to the equity returns.

Takeover in Europe: Target characteristics and acquisition likelihood

AbstractThis article analyzes characteristics of takeover targets in the European market—relatively less studied compared with US and UK markets—to develop a takeover prediction model. Our sample includes 320 European companies with 140 targets and 180 non‐targets over the period 1994–2007, covering two M&amp;A waves. In this study, we test the discriminating power of many relevant variables including new one that could have a discriminating power in potentially determining (value creation). Our results show that European targets are characterized by a growth‐resource imbalance, are less rich in FCF, have growth opportunities, have a higher level of transaction volume of shares prior to the bid, achieve lower economic performance, and destroy value. Furthermore, we develop several predictive models using targets' financial data from 1 year, 2 years, and 3 years before takeover, along with the 3‐year average. The correct classification power in the original sample is 70% (in‐sample). As for predictive ability, the correct classification power in a control sample is 79.4% (out‐of‐sample). We also noted that predictive models using data from 1 or 2 years before the bid appear to display more significant predictive ability.

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer

BackgroundTNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). MethodsA tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. ResultsThe tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. ConclusionThe TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

Non-invasive Assessment of Liver Damage Degree in Patients with Viral Hepatitis: Is It Feasible and Acceptable?

Background: Hepatitis C and B virus infections significantly contribute to global chronic liver disease mortality. Objectives: This study explores the role of serum markers (AST/ALT ratio, APRI Score, FIB-4 Score, and Forns index) in non-invasively assessing liver damage in patients with chronic hepatitis C and B. Methods: In this single-center, retrospective, observational study, we analyzed data from 327 patients to establish correlations between serological markers and fibrosis grade using Spearman's correlation. Receiver operator characteristic (ROC) analysis evaluated the ability of these markers to predict advanced fibrosis. Results: In hepatitis B and C cohorts, all markers show significant positive correlations with liver fibrosis (P &lt; 0.001). FIB-4 and the Forns index exhibit moderate correlation (Spearman’s rho 0.48), while AST/ALT and APRI score show mild correlation (Spearman’s rho 0.21 and 0.31). In hepatitis C, the Forns index (0.814) and FIB-4 (0.80) outperform other markers. In hepatitis B, Forns (AUC = 0.73), APRI (AUC = 0.68), and FIB-4 (AUC = 0.68) demonstrate significant predictive ability. Conclusions: FIB-4 and the Forns index hold clinical significance as fibrosis biomarkers in the management of chronic viral hepatitis. FIB-4 is a universal marker, while the interpretation of the Forns index requires consideration of the etiology of chronic viral hepatitis.

Information shocks and short-term market overreaction: The role of investor attention

Employing jumps in stock return as a proxy for information shocks, we empirically discover the short-term overaction in the Chinese stock market. Trading strategies short (long) stocks with the largest (smallest) lagged cumulative jump returns earn sizable positive returns. Besides, the information shocks exhibit significant predictive ability for future returns. The market overreaction is robust to firm characteristics, subperiod analysis, and intraday analysis. Investor attention facilitates this short-term market overreaction.

Preeclampsia and Preterm Delivery Risk: The Significance of Obesity in Pregnancy

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Introduction.&amp;lt;/strong&amp;gt; A significant proportion of women globally, consti tuting 38%, are obese, among whom 24.5% reside in Europe. Obe sity elevates the risk of premature birth due to associated maternal conditions, such as preeclampsia. The objectives were to assess: variations in demographic and clinical characteristics among preg nant women across groups; the impact of obesity on the incidence of preeclampsia and preterm delivery; and the influence of obesity on newborn characteristics.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Methods. &amp;lt;/strong&amp;gt;One-year prospective study included 133 pregnant wom en gestational age 11-14 gestational week (GW), divided into two groups: OB (41.35%) and CG (58.65%). The data were analyzed using IBM SPSS version 23.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Results. &amp;lt;/strong&amp;gt;There is statistically significant difference in average body mass index (BMI) (22.01 &amp;amp;plusmn; 1.83 vs. 30.26 &amp;amp;plusmn; 4.52; p &amp;amp;lt; 0.001) and de livery time (37.94 &amp;amp;plusmn; 2.05 vs. 36.87 &amp;amp;plusmn; 2.45; p = 0.003) between CG and OB. Obese pregnant women developed preeclampsia significantly more often than normal weight (61.82% vs. 28.21%; p &amp;amp;lt; 0.001). Body mass index has significant moderate predictive ability to predict preeclampsia (AUC 0.696 (95% CI: [0.601; 0.79]). Higher BMI (OR = 1.19, [1.09; 1.29], p &amp;amp;lt; 0.0001) was associated with higher rates of pre eclampsia. Overweight (OR = 2.41, [1.07; 5.43], p = 0.0335), obesity class I, II and III (OR = 20.36, [4.32; 95.99], p = 0.0001) were associated with higher rates of preeclampsia. A poor negative correlation was found between BMI and GW of pregnancy outcome (p = -0.24; r2 = 0.104; p = 0.006). Higher BMI (&amp;amp;beta; = -0.14, [-0.21; -0.07], p = 0.0002) was associated with lower values of GW of pregnancy outcome.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Conclusions.&amp;lt;/strong&amp;gt; Presence of obesity or overweightness in the first tri mester of pregnancy poses a significant risk factor for preeclampsia and preterm delivery.&amp;lt;/p&amp;gt;

Prediction of severe radiation-induced oral mucositis in locally advanced nasopharyngeal carcinoma using the combined systemic immune-inflammatory index and prognostic nutritional index.

Severe radiation-induced oral mucositis (sRIOM) can seriously affect patients' quality of life and treatment compliance. This study was to investigate the utility of the systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) in predicting sRIOM in patients with locally advanced nasopharyngeal carcinoma (LANPC). 295 patients with LANPC were retrospectively screened. The pre-radiotherapy SII and PNI were calculated based on peripheral blood samples. A receiver operating characteristic (ROC) curve was used to determine the cut-off value. Logistic regression was used for univariate and multivariate analyses. Patients were classified into three groups based on the SII-PNI score: score of 2, high SII (> cut-off value) and low PNI (≤ cut-off value); score of 1, either high SII or low PNI; score of 0, neither high SII nor low PNI. The SII-PNI demonstrated significant predictive ability for sRIOM occurrence, as evidenced by an area under the curve (AUC) of 0.738. The incidence rates of sRIOM with SII-PNI score of 2, 1, and 0 were 73.86%, 44.35%, and 18.07%, respectively. Multivariate analysis confirmed that the SII-PNI score was an independent risk factor for sRIOM. The SII-PNI score is a reliableand convenient indicator for predicting sRIOM in patients with LANPC.

Attention-ResNet-AR-LSTM: An intelligent method for PCCP deformation prediction via structure monitoring based on distributed fiber optics

The prestressed concrete cylinder pipeline (PCCP) is extensively employed in long water diversion projects due to its commendable performance and cost-effectiveness. Nonetheless, the continuous application of load on the prestressed steel wire during the production phase can amplify the creep deformation in both the concrete and steel cylinder of the PCCP. This phenomenon may potentially lead to the development of cracks at the socket end of the PCCP, posing a significant risk to the safe operational integrity of the pipeline. In this study, the strain development prediction model of PCCP concrete and steel cylinder is constructed through the integration of ResNet, AR, and LSTM modules, and the attention mechanism is also applied. Based on the prototype test of PCCP structure monitoring, the strain values of steel cylinder and inner concrete were recorded by pre-set distributed fiber optic sensors on the 1st, 3rd, 5th, 7th, 9th, 11th, 13th, 15th, 17th, and 20th day after the production of PCCP. Then an Attention-ResNet-AR-LSTM model is constructed to predict the strain development of PCCP steel cylinder and concrete based on monitoring data on the 20th day using the data of 1st to 11th days. For steel cylinder and concrete structures, the R2 of the proposed models is above 98%, indicating that the proposed models can capture the strain changes of different structures well. Compared with other deep models and machine learning models, the proposed model also shows a significant predictive ability. This model introduces a novel approach to forecast strain evolution during the manufacturing of PCCP.

The utility of the abdominal series in the emergency setting: a retrospective review

PurposeThe abdominal series (AXR) remains a frequently ordered test in the emergency department (ED), despite existing literature questioning its utility. The aim of this study was to characterize the use of the AXR in the ED by quantifying how often it is ordered and the frequency of subsequent imaging. Additionally, a time estimate in ED associated with the AXR was quantified. We hypothesized that there would be a low clinical utility of the AXR, and long associated time period spent in the ED.MethodsA retrospective audit of AXRs performed in the ED from January to December 2019 was performed. The local picture archiving and communication system (PACS) and electronic medical record were used to collect the variables.ResultsOf 701 AXRs, 438 (62.4%) were reported normal, and 263 (37.6%) were abnormal. A Chi Squared test showed that the two variables (abdominal series result and follow up imaging completion) were significantly related, with p < 0.001. However, the effect size was small (Nagelkerke R square = 0.022). The average time spent in the ED for these patients was 7.27 h, and the average time between the AXR being ordered and interpreted was 1.31 h.ConclusionThe majority of AXRs were reported as normal. Our results showed that AXR had a statistically significant, but low clinically significant predictive ability on subsequent imaging ordering. This supports our hypothesis that the AXR is of low clinical utility with respect to the rate of ordering follow up imaging. The AXR also translated to a quantifiable time interval during the patient’s stay in ED. Minimizing overuse of the AXR may result in a decrease in patient duration in the ED.

Monitoring Minimal Residual Disease in Patients with Multiple Myeloma by Targeted Tracking Serum M-Protein Using Mass Spectrometry (EasyM).

We investigated both the clinical utilities and the prognostic impacts of the clonotypic peptide mass spectrometry (MS)-EasyM, a blood-based minimal residual disease (MRD) monitoring protocol in multiple myeloma. A total of 447 sequential serum samples from 56 patients with multiple myeloma were analyzed using EasyM. Patient-specific M-protein peptides were sequenced from diagnostic samples; sequential samples were quantified by EasyM to monitor the M-protein. The performance of EasyM was compared with serum immunofixation electrophoresis (IFE), bone marrow multiparameter flow cytometry (MFC), and next-generation flow cytometry (NGF) detection. The optimal balance of EasyM sensitivity/specificity versus NGF (10-5 sensitivity) was determined and the prognostic impact of MS-MRD status was investigated. Of the 447 serum samples detected and measured by EasyM, 397, 126, and 92 had time-matching results for comparison with serum IFE, MFC-MRD, and NGF-MRD, respectively. Using a dotp >0.9 as the MS-MRD positive, sensitivity was 99.6% versus IFE and 100.0% versus MFC and NGF. Using an MS negative cutoff informed by ROC analysis (<1.86% of that at diagnosis), EasyM sensitivity remained high versus IFE (88.3%), MFC (85.1%), and NGF (93.2%), whereas specificity increased to 90.4%, 55.8%, and 93.2%, respectively. In the multivariate analysis, older diagnostic age was an independent predictor for progression-free survival [PFS; high risk (HR), 3.15; 1.26-7.86], the best MS-MRD status (MS-MRD negative) was independent predictor for both PFS (HR, 0.25; 0.12-0.52) and overall survival (HR, 0.16; 0.06-0.40). EasyM is a highly sensitive and minimal invasive method of MRD monitoring in multiple myeloma; MS-MRD had significant predictive ability for survival outcomes.