Prediction Of Curves Research Articles

Heart rate characteristics predict risk of mortality in preterm infants in low and high target oxygen saturation ranges.

The Neonatal Oxygenation Prospective Meta-analysis found that in infants <28 weeks gestational age, targeting an oxygen saturation (S pO2 ) range of 85-89% versus 91-95% resulted in lower rates of retinopathy of prematurity but increased mortality. We aimed to evaluate the accuracy of the heart rate characteristics index (HRCi) in assessing the dynamic risk of mortality among infants managed with low and high target S pO2 ranges. We linked the SUPPORT and HRCi datasets from one centre in which the randomised controlled trials overlapped. We examined the maximum daily HRCi (MaxHRCi24) to predict mortality among patients randomised to the lower and higher target S pO2 groups by generating predictiveness curves and calculating model performance metrics, including area under the receiver operating characteristics curve (AUROC) at prediction windows from 1-60 days. Cox proportional hazards models tested whether MaxHRCi24 was an independent predictor of mortality. We also conducted a moderation analysis. There were 84 infants in the merged dataset. MaxHRCi24 predicted mortality in infants randomised to the lower target S pO2 (AUROC of 0.79-0.89 depending upon the prediction window) and higher target S pO2 (AUROC 0.82-0.91). MaxHRCi24 was an important additional predictor of mortality in multivariable modelling. In moderation analysis, in a model that also included demographic predictor variables, the individual terms and the interaction term between MaxHRCi24 and target S pO2 range all predicted mortality. Associations between HRCi and mortality, at low and high S pO2 target ranges, suggest that future research may find HRCi metrics helpful to individually optimise target oxygen saturation ranges for hospitalised preterm infants.

Sales Forecasting Based on Transformer-LSTM Model

As a matter of fact, sales forecasting becomes a hot topic in recent years. The LSTM model is a classic model in the field of sales forecasting. Recently, Transformer has gained a lot of popularity, and many researchers are applying it to time series forecasting. To be specific, this study proposes a novel Transformer-LSTM framework for the one-step sales prediction task. This study retained the classical Transformer structure, using it as the model's encoder, and then combined attention mechanisms with the LSTM model in the decoder section. This hybrid approach allows the model to leverage the advantages of Transformer in handling long sequences while benefiting from LSTM's unique strengths in processing time series data. Ultimately, the model's performance was evaluated using prediction curve comparisons and RMSE as validation metrics, achieving significant results. In conclusion, these results present a novel model for sales forecasting and time series analysis, contributing to improved prediction accuracy in these domains.

Development of an opportunistic diagnostic prediction algorithm for osteoporosis and fragility fracture risk estimates from forearm radiographs (The OFFER1 Study).

Osteoporosis and associated fractures are an increasingly prevalent concern with an ageing population. This study reports testing of IBEX Bone Health (IBEX BH) software, applied following acquisition of forearm radiographs. IBEX Bone Health analyses the radiograph to measure areal bone mineral density (aBMD) at the examination site. A non-randomized cross-sectional study design was performed involving 261 (254 after exclusions) participants (112/142m/f; mean age 70.8years (SD+/-9.0); 53 with osteoporosis). They underwent posterior-anterior distal forearm radiographs; dual X-ray absorptiometry (DXA) of the wrists, hips, and lumbar spine; and questionnaires exploring clinical risk factors. IBEX Bone Health automatically identifies regions of interest (ROI) at the ultra-distal (UD) and distal third (TD) regions of the radius. Analysis investigated area under the receiver operating characteristics curve performance of IBEX BH for prediction of (i) osteoporosis (based on clinical reporting of the hip and spine DXA) and (ii) treatment recommendations by Fracture Risk Assessment Tool (FRAX) inclusive of neck of femur (NoF) areal bone mineral density (aBMD) results following National Osteoporosis Guideline Group (NOGG) guidelines. Area under the receiver operating characteristics curve for osteoporosis prediction at the UD and TD ROIs were 0.86 (99% confidence interval (CI) [0.80, 0.91]) and 0.81 (99% CI [0.75, 0.88]), respectively. Area under the receiver operating characteristics curve for treatment recommendation using FRAX inclusive of NoF aBMD at the UD and TD ROIs were 0.95 (99% CI [0.91, 1.00]) and 0.97 (99% CI [0.93,1.00]), respectively. With a matched sensitivity to FRAX (without NoF aBMD) 0.93 (99% CI [0.78, 0.99]), IBEX BH predicted at the UD and TD ROIs recommended treatment outcomes by NOGG guidelines using FRAX (with NoF aBMD) with specificity 0.89 (99% CI 0.83, 0.94]) and 0.93 (99% CI [0.87, 0.97]), respectively. This is compared with 0.60 (99% CI [0.51, 0.69]) for FRAX (without NoF aBMD). Results demonstrate the potential clinical utility of IBEX BH as an opportunistic screening tool.

Simplified S1 Vertebral Bone Quality Score in the Assessment of Patients with Vertebral Fragility Fractures

ObjectiveTo evaluate the effectiveness of the S1 vertebral bone quality (VBQ) score in assessing bone quality among patients with vertebral fragility fractures (VFF). Additionally, whether the combination of S1 VBQ and Hounsfield unit (HU) values improves the predictive accuracy of VFF. MethodsUsing lumbar non-contrast CT and T1-weighted MRI, we measured L1 HU values, S1 VBQ, and L1-L4 VBQ. To assess their predictive performance for VFF, we constructed receiver operating characteristic (ROC) curves. We also compared the diagnostic efficacy of HU values with that of S1 VBQ and L1-L4 VBQ values for the joint diagnosis of VFF. The Delong test was used to compare the value of individual or combined predictions of VFF. ResultsIn comparison to the non-fracture group, all patients exhibited markedly elevated S1 VBQ and L1-L4 VBQ and notably reduced HU values (p<0.001). Multivariate analysis revealed that elevated S1 VBQ, increased L1-L4 VBQ, and decreased HU values independently correlated with VFF development. The areas under the curve (AUC) for VFF prediction were 0.806 for S1 VBQ, 0.799 for L1-L4 VBQ, and 0.820 for HU values. According to the Delong test, the combination of HU values with S1 VBQ/L1-L4 VBQ significantly improved the diagnostic accuracy. ConclusionsThe simplified S1 VBQ is a valuable tool for predicting the occurrence of VFF and can be used as an alternative to the L1-L4 VBQ. In addition, the combination of S1 VBQ and HU values can significantly improve the predictive value of VFF.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data

BackgroundAcute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. ObjectiveWe aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). MethodsThe initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. ResultsTen critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better. ConclusionSepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Predictive Model for Prognosis of Sudden Sensorineural Hearing Loss by Nomogram.

Objective: To explore the prognostic factors of patients with sudden sensorineural hearing loss (SSNHL), analyze the possible influencing factors, and construct a nomogram for personalized evaluation of their prognosis. Methods: A retrospective study was conducted on 269 patients with SSNHL. The prognostic factors were analyzed by univariate analysis and multivariate logistic regression analysis. The nomogram was constructed based on the results of multivariate logistic regression analysis, and the model was verified by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Among the 269 patients hospitalized, 136 cases were improved (44 cases were cured, 28 cases were markedly effective, 64 cases were effective) and 133 cases were ineffective. After univariate analysis, it was found that age, duration from onset to treatment, audiometric configuration, serum albumin (ALB), and neutrophil-to-lymphocyte ratio (NLR) were associated with hearing outcomes. Duration from onset to treatment and audiometric configuration were independent risk factors when the treatment outcome was divided into ineffective, effective, significant improvement, and complete recovery groups or divided into improvement and ineffective groups after multivariate logistic regression analysis. The factors according to univariate analysis and multivariate logistic regression analysis results were included in the nomogram to construct the prediction models. The area under the ROC curve of model discrimination was 0.752 [95% confidence interval (CI): 0.695-0.808] when the treatment outcome was divided into 2 groups. The calibration curve showed the consistency of the results, and the DCA prediction curve showed good clinical efficacy. The C-index was 0.756 (95% CI: 0.710-0.802) when the treatment outcome was divided into 4 groups. Conclusion: Age, duration from onset to treatment, audiometric configuration, ALB, and NLR are influencing factors for SSNHL. Duration from onset to treatment and audiometric configuration are independent risk factors for SSNHL. The nomogram presents the prognosis of patients with SSNHL in an intuitive, visual, and readable graph, providing clinicians with a personalized assessment.

Hub Genes Involved in the Progression of Nonalcoholic Fatty Liver Disease to Hepatocellular Carcinoma.

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. With an increasing number of patients, NAFLD has been identified as a risk factor for Hepatocellular Carcinoma (HCC). The precise pathophysiology of NAFLD-related HCC has not been completely understood recently. We analyzed the hub genes related to NAFLD and HCC to predict the risk of NAFLD progressing to HCC. Two datasets of NAFLD were used to identify differentially expressed genes. Lasso-Cox regression analysis was performed to determine a gene model to predict the risk of the progression from NAFLD to HCC. Three validation datasets were analyzed to evaluate the performance of the gene model, including normal and NAFLD with fibrosis, NAFLD with fibrosis and NAFLD-related HCC, and normal and NASH-related HCC. Seven genes, including COL1A1, TIPM1, VCAN, FOS, CD79A, CXCL9, and VWF, were identified as the hub genes, and then a gene model was constructed. By calculating, the area under the receiver operating characteristic curves (AUCs) for risk prediction were 0.97, 0.886, and 0.751 in the three validation datasets, respectively. Gene set enrichment analysis indicated that the MAPK, TGFβ, p53, PPAR, insulin signaling pathways, and fatty acid metabolism were significantly upregulated in the high-risk group. GTPase activity and intrinsic apoptotic signaling pathway had significant upregulation in the low-risk group. The seven hub genes may predict the risk of NAFLD developing into HCC by mediating the potential molecular mechanism, which could be used as biomarkers for predicting the progression, diagnosis, and treatment of NAFLD.

Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

A Predictive Model for Recurrence of Atrial Fibrillation Based on P-Wave Duration in Patients with Early Persistent Atrial Fibrillation Who Underwent Radiofrequency Catheter Ablation

Purpose: To construct a predictive model for the recurrence of atrial fibrillation (AF) based on P-wave duration (PWD) in patients with early persistent AF (PeAF) who underwent radiofrequency catheter ablation (RFCA), with the aim of helping clinicians accurately adjust clinical strategies. Methods: Data from patients with early PeAF, who were admitted to the Department of Cardiology at the authors' hospital were collected. Based on predefined inclusion and exclusion criteria, only those who successfully underwent RFCA for the first time were included in the analysis. Pre- and postoperative clinical, echocardiographic, and electrocardiographic data were collected and recorded. Multivariate logistic regression was used to construct a predictive model for AF recurrence based on PWD. The predictive efficacy of each continuous variable and the predictive model were compared using the area under the receiver operating characteristic (ROC) curve. The corresponding nomogram for the predictive model was constructed. Interaction tests were performed to evaluate the predictive efficacy of the model for AF recurrence. Results: A total of 237 patients were enrolled and divided into two groups: recurrence (n = 59); and sinus rhythm (n = 178). PWD was greater and left atrial appendage emptying velocity (LAAV) was lower in the recurrence group; these differences were statistically significant (p &lt; 0.001). The ROC curve for univariate prediction of AF recurrence revealed that the area under the ROC curve (AUC) for PWD and LAAV were 0.7912 and 0.7713, respectively, which were greater than those of other continuous variables. Compared with PWD alone, the multivariate predictive model containing PWD, left ventricular ejection fraction, and LAAV demonstrated no statistically significant difference in AUC (p = 0.0553) but improved the prediction efficiency of correctly reclassifying recurrence rates, net reclassification improvement 14.13% (95% confidence interval: 0.19–28.07%; p = 0.0469). The interaction effect did not significantly alter the effectiveness of the predictive models. Conclusions: The multivariate model based on PWD measured after RFCA demonstrated better predictive efficacy than the univariate model in patients with early PeAF. These results may contribute to evidence supporting the formulation of personalised treatments for patients with AF.

A Comparative Study of Lubrication Performance for Bs 080M46 Medium Carbon Steel Using Ring Compression Test and Finite Element Simulation

This study investigates the impact of lubrication on friction factors during the hot ring compression test of BS 080M46 medium carbon steel. Hot forging processes are crucial in industries due to the strength and durability of forged products, but friction-related issues can arise. Four lubrication conditions are focused: dry, oil to black graphite, water to black graphite, and water to colorless graphite. The ring compression test procedure, including sample dimensions and lubrication application, is explained. By employing predictive calibration curves generated through FEM which monitored height and internal diameter changes during compression. The study successfully aligns FEM simulation results with experimental data, thereby enhancing the accuracy of friction factor estimations and visualizing material behavior under various lubrication conditions. Results indicate that lubrication significantly affects friction factors, with oil to black graphite performing the best, yielding a friction factor of 0.15. A comparison between theoretical and experimental friction factors shows varying agreement levels, with water-to-black graphite, and water-to-colorless graphite respectively demonstrating excellent alignment with 0.990% and 0.971%. This study has practical implications for selecting lubricants in industrial applications, potentially enhancing manufacturing processes and product quality.

Probability model for gas–liquid annular flow in dynamic equilibrium among the processes of atomization, deposition, breakup, and coalescence

In this paper, an improved probability model is introduced to provide a more comprehensive prediction of annular flow. Unlike previous work, which did not consider atomization and deposition, as well as breakup and coalescence, simultaneously, the improved model integrates all four processes into its framework. The mechanisms of these processes are described in detail by the present model. When annular flow is fully developed, the four processes reach dynamic equilibrium. A numerical program is compiled based on the influence of the four processes on the droplets. Five important parameters, including the droplet-diameter probability density distribution, characteristic droplet diameters, entrainment ratio, liquid film thickness, and interfacial shear-stress coefficient, are calculated when the annular flow is in dynamic equilibrium. The validity and accuracy of the improved model are assessed by comparison with 377 cases from 12 experiments. For most cases used to validate the predicted droplet-diameter probability density distribution, the prediction curves closely match the experimental data. The mean absolute percentage errors (MAPEs) for the two experiments used to validate the predicted characteristic droplet diameters are 20.74% and 24.04%, respectively. Additionally, the mean MAPEs of the entrainment ratio, liquid film thickness, and interfacial shear-stress coefficient are found to be 34.11%, 20.60%, and 27.28%, respectively. This demonstrates the effectiveness and reliability of the improved probability model in predicting annular flow.

Harnessing Machine Learning for Accurate Cardiovascular Disease Prediction

Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision-making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. performance of the proposed ML algorithm was estimated using numerous conditions to recognize the most suitable machine learning algorithm in the class of models. RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.

CX3CL1/Fractalkine as a biomarker for early pregnancy prediction of preterm premature rupture of membranes.

The objective of our study was to evaluate serum CX3CL1/Fractalkine, a monocyte/macrophage chemoattractant expressed in cytotrophoblasts and decidual cells, as a predictive biomarker for the occurrence of preterm premature rupture of membranes (PPROM). A case-control study of 438 pregnancies including 82 PPROM cases and 64 preterm labor with intact membranes cases with blood samples collected at first trimester, second trimester and delivery was conducted. The predictive ability of CX3CL1 and maternal risk factors for the occurrence of PPROM was assessed by receiver operating characteristic curve analysis. A second, independent cohort was prospectively constituted to confirm the case-control study results. First trimester CX3CL1 was significantly increased in PPROM cases when compared to matched controls. Multivariate regression analysis highlighted a significant difference for CX3CL1 measured during the first trimester (p<0.001). Alone, CX3CL1 predicts PPROM with a 90 % sensitivity and a specificity around 40 %. The area under the receiver operating characteristic curve for PPROM prediction were 0.64 (95% confidence interval: 0.57-0.71) for first trimester CX3CL1, and 0.61 (95% confidence interval: 0.54-0.68) for maternal risk factors (body mass index<18.5 kg/m2, nulliparity, tobacco use and the absence of high school diploma). The combination of CX3CL1 and maternal risk factors significantly improved the area under the curve: 0.72 (95% confidence interval: 0.66-0.79) (p<0.001). The results were confirmed on a second independent cohort. CX3CL1 is a promising blood biomarker in the early (first trimester) prediction of PPROM.

Integration of advanced technologies to improve the efficiency of gas condensate field development

In the context of constantly growing global energy demand and rapid changes in the energy sector, the study and implementation of advanced technologies in the development of gas condensate fields is of critical importance. The purpose of this study is to investigate methods to increase the efficiency of production and sustainable use of energy resources by optimising the development of gas condensate fields using advanced technologies. The methods used include analytical method, classification, functional method, statistical method, synthesis. Within the framework of this study, the technological aspects of using wells of the Altyguyi gas condensate field were investigated and extensive laboratory and field analyses were conducted aimed at the correct implementation of the double injection method for simultaneous extraction of gas from one reservoir and oil from another, with an emphasis on their contribution to the development of wells through this approach. The paper also considers aspects of operation and technology, including hydrodynamic and thermohydrodynamic studies, when analysing well designs considering compatibility intervals and mining and geological drilling conditions, based on predictive curves of reservoir pressure and rock fracture pressure. As a result of the analyses, studies and calculations, the implementation of the method of intensification of gas condensate field production using the oil and gas approach of dual injection in one well was substantiated. This approach is focused on reducing capital investments and accelerating the development process. The practical significance of this research lies in the development and implementation of innovative technologies to optimise the production processes of gas condensate fields, which contributes to improving the efficiency of hydrocarbon production and promotes the sustainable use of energy resources

Assessing Long-Term Excavation Damage in Switzerland’s Bedretto Adit: A Field Investigation in the Northern Section

The Bedretto adit, in the Alps of Switzerland, was excavated in 1972–1982 as an auxiliary adit for the main Furka Base Tunnel. The adit is 5218 m long, being excavated mostly in the Rotondo Granite with drill and blast methods to create a horseshoe cross-section. Much of the tunnel has remained unlined and such long observation periods are seldom available since tunnels typically are lined shortly after construction and mines are often closed or backfilled. Previous research in 2004 characterized the depth of failure (DoF) around the adit and found on average a depth of 1.1 ± 0.03 m. A site investigation was carried out in 2017, aimed to measure the DoF through 3D light detection and ranging scanned sections again. The new 3D scans covered a longer section of the tunnel with each scan, up to 6 m in length, compared to the previous laser scans with a single circumferential pass. The results were compared with the data from 2004 and with empirical and numerical prediction curves for Excavation Damage Zone depths. The findings indicate that between 2004 and 2017, the measured DoF generally extended, on average, by 0.1 m, implying that failure has progressed with time. Furthermore, the analysis showed that the volume of failure from 2004 extended up to 1.9 m3/m, and this volume increased to 3.2 m3/m in 2017 on average. The VoF was used as a means to overcome the challenge of comparing past to present scan results, since the exact location of the 2004 measurements were not known. Significant variations in the measured DoF at sections of the adit near faults and below a glacier indicate that these external factors can significantly influence the rock mass behaviour. When comparing the DoF to empirical excavation damage zone prediction intervals, the level of damage at the Bedretto adit is comparable with the outer excavation damage zone. This is a region of isolated micro-cracking, and it conceptually suggests that the continued spalling in the Bedretto adit is the results of crack propagation from initial excavation induced damage that leads to interaction even after 41 years since excavation works ceased.

Identification of a necroptosis-related gene signature for making clinical predictions of the survival of patients with lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major pathological subtype of malignant lung cancer with a poor prognosis. Necroptosis is a caspase-independent programmed cell death mode that plays a pivotal role in cancer oncogenesis and metastasis. Here, we explore the prognostic values of different necroptosis-related genes (NRGs) in LUAD. mRNA expression data and related clinical information for LUAD samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. NRGs were identified using the GeneCards database. Least absolute shrinkage and selection operator Cox regression and multivariate Cox analysis were used to construct a prognostic risk model. Time-dependent receiver-operating characteristic curves and a nomogram were constructed to validate the predictive values of the prognostic signatures. A necroptosis-related protein-protein interaction network was visualised using the STRING database and Cytoscape software. Functional analyses, including Gene Ontology, Kyoto Encyclopaedia of Genes and Genomes pathway enrichment, gene set enrichment, and gene set variation analyses, were conducted to explore the underlying molecular mechanisms. Finally, the mRNA expression of the prognostic signatures in LUAD cell lines was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. A prognostic model was established for eight NRGs (CALM1, DDX17, FPR1, OGT, PGLYRP1, PRDX1, TUFM, and CPSF3) based on TCGA-cohort data and validated with the GSE68465 cohort. Patients with low-risk scores had better survival outcomes than those with high-risk scores (p = 0.00013). The nomogram was used to predict the prognosis of patients with LUAD. The prediction curves for 1-, 3-, and 5-year OS showed good predictive performance and the accuracy of the nomograms increased over time. RT-qPCR results demonstrated that these eight genes, especially CALM1, PRDX1, and PGLYRP1, were differentially expressed in LUAD cells. We constructed a reliable eight-NRG signature that provides new insights for guiding clinical practice in the prognosis and treatment of LUAD.

Comparison of P-POSSUM Score and NELA Score as Prime Predictors of Mortality in Emergency Laparotomies at a Tertiary Care Centre: A Retrospective Cohort Study

Introduction: Emergency laparotomies carry significant morbidity and mortality. Preoperative risk assessment is the key part for all emergency laparotomies. Therefore, the use of Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (POSSUM) and National Emergency Laparotomy Audit (NELA) scores helps stratify the risk and highlight the importance of high-risk patients, aiding in the provision of appropriate treatment plans and care. Aim: To determine and compare the Portsmouth-POSSUM (P-POSSUM) score and NELA score in predicting mortality at 30 days and 60 days in patients undergoing emergency laparotomies. Materials and Methods: This retrospective cohort study included all patients who underwent emergency laparotomy at a tertiary care centre from January 2021 to March 2023. A total of 116 patients were included, and demographic variables, laboratory values, and operative details were recorded. Mortality on postoperative day 30 and 60 was observed. All data were recorded in an MS excel spreadsheet, and statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 19.0. An independent sample t-test was used. Results: Among the total study sample of 116 patients, the 30- day mortality rate was 8.6% (10 out of 116), and the 60-day mortality rate was 13.8% (16 out of 116). The area under the ROC curve (AUC) for mortality prediction was 0.533 (0.361- 0.726) for the P-Possum score and 0.873 (0.799-0.948) for the NELA score, which was significantly higher. Conclusion: The NELA Score demonstrated greater accuracy in predicting early and late mortality compared to the P-POSSUM score in this study. Therefore, the authors highly recommend the use of the NELA score for risk assessment in all emergency laparotomies.

Clinical outcomes of rhabdomyolysis & validation of McMahon Score for risk prediction.

Rhabdomyolysis in tropics has a unique aetiology and clinical profile. The objective of this study was to determine the aetiology and clinical outcomes of rhabdomyolysis and validate the McMahon risk prediction score in affected individuals from south India. A retrospective study of affected individuals with rhabdomyolysis admitted to a tertiary care hospital in south India, between January 2015 and June 2020, was undertaken. In-patients who were ≥15 yr in age and had creatinine phosphokinase ≥5000 U/l were included in the study. Cardiac, stroke, chronic muscular diseases and chronic kidney disease on maintenance haemodialysis were excluded. The incidence of acute kidney injury (AKI) in this group was calculated. Other clinical outcomes determined were 28-day mortality, proportion of individuals who required renal replacement therapy (RRT), intensive care unit (ICU) admission, vasopressors, mechanical ventilation (MV), number of days on mechanical ventilator and length of stay in ICU and hospital. Validation of McMahon risk prediction score for the requirement of RRT and mortality was performed. Major aetiologies identified in the 75 study participants included were infections, trauma and seizures. Twenty eight-day mortality was 24 per cent (n=18). AKI incidence was 68 per cent, out of which 43.1 per cent had RRT. AKI in all survivors became dialysis independent. Vasopressors, MV and ICU requirement were 30.7, 32 and 77.3 per cent, respectively. Receiver operator characteristic curve for RRT and mortality risk prediction based on the McMahon Score showed a sensitivity of 71.4 per cent and specificity of 77.8 per cent for a cut-off ≥7.8. Rhabdomyolysis in tropics is associated with significant organ dysfunction and mortality. Although the incidence of AKI and RRT is high, the overall renal outcome is good among survivors. The wide confidence intervals for the area under curve for McMahon Score limit its predictability for RRT and mortality.

Blood glucose and lactate levels as early predictive markers in patients presenting with cardiogenic shock: A retrospective cohort study.

Lactate and glucose are widely used biochemical parameters in current predictive risk scores for cardiogenic shock. Data regarding the relationship between lactate and glucose levels in cardiogenic shock are limited. Thus, we aimed to analyze glucose and lactate as early markers for in-hospital mortality in cardiogenic shock. In this retrospective cohort study, 312 patients presenting with cardiogenic shock to a tertiary-care hospital between 2016 and 2018 were included. Apparent cardiogenic shock was defined as hypoperfusion with hemodynamic compromise and biochemical marker increase due to diminished tissue perfusion, corresponding to SCAI shock stages. In-hospital mortality was assessed as the primary endpoint. The median age of the study population was 71 (60-79) years and the etiology of cardiogenic shock was acute myocardial infarction in 45.8%. Overall in-hospital mortality was 67.6%. In the receiver operating curve analysis, the area under the receiver-operating curve (AUC) for prediction of in-hospital mortality was higher for lactate (AUC: 0.757) than for glucose (AUC: 0.652). Both values were significantly associated with outcome (groups created with best cutoff values obtained from the Youden index). Correlation analysis showed a significant non-linear association of both values. In a multivariable stepwise Cox regression analysis, lactate remained an independent predictor for in-hospital mortality, whilst glucose, despite being implicated in energy metabolism, was not independently predictive for mortality. Together, these data suggest that lactate at admission is superior for mortality prediction in patients with apparent cardiogenic shock. Glucose was not independently predictive for mortality.

A prediction model based on digital breast pathology image information.

The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance. In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model. The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis. The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.