Predicted Value Research Articles

Medical diagnostic accuracy measures: an innovative approach based on the area under predictive values curves

ABSTRACT Positive and negative estimates are commonly used by clinicians to evaluate the likelihood of a disease stage being present based on test results. The predicted values are dependent on the prevalence of the underlying illness. However, for certain diseases or clinical conditions, the prevalence is unknown or different from one region to another or from one population to another, leading to an erroneous diagnosis. This article introduces innovative post-test diagnostic precision measures for continuous tests or biomarkers based on the combined areas under the predictive value curves for all possible prevalence values. The proposed measures do not vary as a function of the prevalence of the disease. They can be used to compare different diagnostic tests and/or biomarkers’ abilities for rule-in, rule-out, and overall accuracy based on the combined areas under the predictive value curves. The relationship of the proposed measures to other diagnostic accuracy measures is discussed. We illustrate the proposed measures numerically and use a real data example on breast cancer.

Jowar and Wheat Yield Prediction using a Wavelet based Fusion of Landsat and Sentinel Data with Meteorological Parameters

Objectives: The objective of this study is to improve the accuracy of crop yield prediction models, specifically focusing on wheat and jowar crops in Maharashtra during the Rabi season, by integrating Landsat and Sentinel satellite data with meteorological parameters. Methods: The study utilizes Landsat 8 and Sentinel satellite datasets covering Maharashtra State. Atmospheric correction is applied to extract surface properties, followed by wavelet-based fusion to combine the images. Normalized Difference Vegetation Index (NDVI) is calculated and combined with meteorological parameters using ensemble learning techniques, including Random Forest and Ada-Boost algorithms. Comparative analysis is conducted against existing models, considering parameters such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Findings: Significant findings reveal that the proposed methodology outperforms existing models, achieving lower MAE, MSE, and RMSE values for wheat and jowar yield predictions. Additionally, our research highlights the superiority of wheat production over jowar in the Rabi season, based on comprehensive analysis of crop yield predictions. Novelty: This study introduces a novel approach that integrates multiple data sources and employs ensemble learning techniques to enhance crop yield prediction accuracy. By combining Landsat and Sentinel satellite data with meteorological parameters, our methodology provides a more comprehensive understanding of crop growth dynamics, leading to more reliable predictions compared to existing methods. Keywords: Satellite imagery, Machine learning, Normalized Difference Vegetation Index, Fusion, Ensemble learning

A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm.

PIN diodes, due to their simple structure and variable resistance characteristics under high-frequency high-power excitation, are often used in radar front-end as limiters to filter high power microwaves (HPM) to prevent its power from entering the internal circuit and causing damage. This paper carries out theoretical derivation and research on the HPM effects of PIN diodes, and then uses an optimized neural network algorithm to replace traditional physical modeling to calculate and predict two types of HPM limiting indicators of PIN diode limiters. We proposes a neural network model for each of the following two prediction scenarios: in the scenario of time-junction temperature curves under different HPM irradiation, the weighted mean squared error (MSE) between the predicted values from the test dataset and the simulated values is below 0.004. While in predicting PIN limiter's power limitation threshold, insertion loss, and maximum isolation under different HPM irradiation, the MSE of the test set prediction values and simulation values are all less than 0.03. The method proposed in this research, which applies an optimized neural network algorithm to replace traditional physical modeling algorithms for studying the high-power microwave effects of PIN diode limiters, significantly improves the computational and simulation speed, reduces the calculation cost, and provides a new method for studying the high-power microwave effects of PIN diode limiters.

Physical activity and lung function association in a healthy community-dwelling European population

BackgroundThe association of physical activity (PA) and lung function (LF) varies from no measurable effect to delayed LF decline. We assessed the association between accelerometery-assessed PA and LF in a sample of apparently healthy, community-dwelling subjects.MethodsWe included two cross-sectional studies using data from the PneumoLaus study (2014–17 and 2018–21), conducted in Lausanne, Switzerland. PA was assessed by accelerometry and categorised as inactivity, light, moderate or vigorous. Forced expiratory volume in 1 second (FEV1), forced volume capacity (FVC) and maximal mid-expiratory flow (MMEF) were measured by spirometry and expressed in percentage of predicted value (PV).ResultsOverall, 1′910 (54.7% women, 62.0 ± 9.7 years) and 1′174 (53.4% women, 65.8 ± 9.5 years) participants were included in the first and the second surveys, respectively. In both surveys, moderate and vigorous PA showed a weak but significant correlation with FEV1 in percentage (PV) (R = 0.106 and 0.132 for the first and 0.111 and 0.125 for the second surveys, p < 0.001). Similar correlations with FVC (p < 0.001) were found. Associations held irrespective of smoking status and remained after multivariable adjustment. Fewer associations were detected between LF and light PA or between MMEF and PA.ConclusionModerate and vigorous intensity PA are associated with increased LF regardless of smoking status in apparently healthy community-dwelling European population. These associations are statistically but not clinically significant due to the small correlation coefficients (R < 0.30), corresponding to a weak association.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

Efficient Quantification of Soluble and Insoluble Oxalates in Clay Mineral Mixtures

ABSTRACT Quantification of oxalate salts in soil clay minerals is necessary to study oxalate biogeochemistry, but existing analytical techniques are expensive and time-consuming. We aim to develop an efficient attenuated total reflectance mid-infrared (MIR) spectroscopic technique to quantify oxalate salts in a clay mineral matrix. We calibrated MIR models for analysis of oxalate anion concentrations in standard solutions (0–0.01 M) by using a partial least-squares regression algorithm. MIR models were also developed for analysis of sodium oxalate (NaOx) and calcium oxalate (CaOx) content in clay mineral mixtures with composition like soils of a semi-arid region and with contrasting concentrations (0–1.0 g g−1 for both oxalate salts) to test sensitivity of analyses. Validation plots (true vs predicted values) showed excellent model fit (R2 ≥ 0.96) and accuracy (normalized root mean squared error of prediction ≤ 0.06) for CaOx, NaOx and oxalic acid components. Once predictive models are stored in analytical software, MIR spectroscopic analyses of samples are much more efficient than chemical techniques. Our MIR spectral-based models are suitable for direct quantification of oxalate salts in clay mineral mixtures for samples like those used for model calibration.

A multi-model evaluation of probabilistic streamflow predictions via residual error modelling

Probabilistic streamflow predictions are valuable tools for predictive uncertainty estimation, hydrologic risk management, and support for decision-making in water resources. Usually, predictive uncertainty quantification is developed and assessed using only a single hydrological model, making it difficult to generalize to other model configurations. To tackle this issue, we assess changes in the model performance ranking of diverse streamflow models by applying a residual error model post-processing approach to multiple basins and multiple models. This assessment employed 141 basins from the Great Lakes watershed covering the USA and Canada, and 13 diverse streamflow models, which are evaluated using deterministic and probabilistic performance metrics. As the first study to implement probabilistic methods to diverse streamflow models applied to a multitude of basins, the analysis here examines the dependence of probabilistic streamflow estimation quality on model quality. Our findings show that streamflow model choice influences the robustness of probabilistic predictions. It was found that moving from deterministic to probabilistic predictions using a post-processing approach does not change the streamflow model performance ranking for the best and worst deterministic models, but models of intermediate rank in deterministic evaluation do not have consistent ranking when evaluated in probabilistic mode. Post-processing residual errors of long short-term memory (LSTM) network models are consistently the best-performing model in terms of deterministic and probabilistic metrics. This study highlights the significance of combining deterministic streamflow model predictions with residual error models for improving the quality and increasing the value of hydrological predictions, quantifying uncertainty, and facilitating decision-making in operational water management. It also clarifies the degree to which probabilistic predictions depend upon good model performance and can compensate for poor model performance.

Predicting the Value of Agricultural GDP in Iraq for the Period 2019—2030 by Applying the Markov Transition Matrix

This research aims to predict the values of agricultural GDP in Iraq for the period 2019–2030 using the Markov Transition Matrix, whereby the fifth state was chosen due to the convergence of the predicted values with the most recent real values in the original time series. In addition, the predictive error or predictive accuracy of the selected state was better compared to other states. The reason for choosing this methodology in forecasting is that it is based on probabilities derived from old historical data, but this methodology does not need old historical data for the purpose of extracting predictive values, even if the time series includes long-time series data. The predicted values follow the same path as the original time series and are not affected by the general trend of the original data, which gives us an indication that there is a problem of stagnation of agricultural GDP. Therefore, a recommendation that can be given to economic policymakers, in particular the agricultural ones, is the existent need to address the problems that the agricultural sector has always suffered from in order to break this stagnation.

Effectiveness of Implementing Modified Early Warning System and Rapid Response Team for General Ward Inpatients.

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GWnon-MEWS and 71,023 GWMEWS. The numbers of inpatients who underwent an unplanned ICU admission in GWnon-MEWS and GWMEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GWMEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

Prognosis and chemotherapy drug sensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature

Disulfidptosis, a new type of regulated cell death associated with the actin cytoskeleton, provides a new therapeutic tool for cancers. The direct relationship between disulfidptosis-related lncRNAs(DRLs) in liver hepatocellular carcinoma(HCC) remains unclear. We acquired transcriptomic data, corresponding clinical data, and tumor mutation data of HCC from the TCGA database. First of all, DRLs were determined through correlation analysis. Then, a prognostic model containing six DRLs was created by adopting univariate Cox regression, LASSO algorithm and multivariate Cox regression analysis. Based on the model, 424 HCC patients were divided into high- and low-risk groups. Next, we structured ROC curves and PCA through combining the model and clinical data. Enrichment analysis and immune infiltration analysis were adopted to further explore the relationship between the model and prognosis. In addition, we explored the relationship between the model and tumor mutation burden (TMB). There were significant differences between high- and low- risk groups, and patients in the high-risk group showed poor prognosis. Enrichment analysis suggested that metabolic progress was obviously different between the two groups. According to the analysis of immune infiltration, there were several differences in immune cells, function, and checkpoints. Patients with high-risk and high TMB demonstrated the least favorable prognosis. The two risk groups both manifested visiblly in chemotherapy drug sensitivity. To sum up, we set up a DRL-based signature and that may provide a predictable value for the prognosis and use of chemotherapy drugs for HCC patients.

Exploring the Feasibility of Applying Deep Learning for the Early Prediction of Arthritis

Introduction: Arthritis is one of the most common chronic diseases. Early detection of arthritis and its progression can facilitate early intervention measures, lowering disease severity in patients. As electronic health records (EHR) become more accessible, this study assesses whether general health information and arthritis-related questionnaires can be used in arthritis diagnosis, without the involvement of costly imaging methods. Therefore, we created deep learning (DL) and machine learning (ML) models to explore the feasibility of combining EHR and modern computational tools to diagnose arthritis. Methods: A total of 782 arthritis patients and 4014 control patients were identified from the Osteoarthritis Initiative (OAI) – a ten-year-long observational study that included patient EHR in five time points. Six hundred variables were filtered by random forest classifier followed by manual filtering. Data were split properly to training, testing and validation set, and the training set was balanced. Sequential, nonsequential DL models, and five independent DL models for each time points were used. The accuracy, positive prevalence value (PPV), negative prevalence value (NPV), and area under curve (AUC), were assessed and compared with four classical ML models. SHAP (SHapley Additive exPlanations) summary analysis was also conducted. Results: Sequential and non-sequential deep learning models showed accuracies of ~ 0.97, and the four classical machine learning approaches showed accuracies of above 0.9. High positive and negative predicted values (&gt; 0.90) for all of the models suggested the potential clinical applicability of the model, while the SHAP analysis demonstrated its interpretability. Discussion: We tested various models and showed the ability to use machine learning methods for early diagnosis of arthritis with EHR. The models can be used as a screening tool to select susceptible patients for confirmatory tests such as X-ray and MRI. Identification of early disease states could facilitate protective measures that slow disease progression.

Parameters optimization for laser-cladding CoCrFeNiMn high-entropy alloy layer based on GRNN and NSGA-II

In order to improve the bonding strength of FeCoCrNiMn cladding layer, laser power, scanning speed and powder feeding rate were selected as the optimization variables; the aspect ratio, dilution rate and heat affected zone depth of the cladding layer were selected as the optimization indicators; a three-factor three-level full-factor experiment was designed; a nonlinear model of cladding parameters and cladding performance was established by generalized regression neural network algorithm (GRNN), while the error of prediction value and experimental results was less than 10%. A set of optimal Pareto frontiers were obtained by the Non-Dominance Ordering Genetic Algorithm (NSGA-II), and the optimal individuals were obtained based on the equal weights, and the optimal cladding parameters were as follows: laser power 2.33 kW, scanning speed 2.90 m/min, and powder feeding rate 12.46 g/min. The cladding layer prepared under the optimal parameters has a dense cross-section with no defects such as cracks and porosity. The joint optimization using the GRNN neural network and the NSGA-II genetic algorithm can obtain excellent results, and the relative error between the experimental and predicted values can be controlled within 10%. The application of optimized laser process parameters can significantly improve defects such as poor adhesion, cracks and porosity on the surface of the cladding layer, and also improve the surface hardness and wear resistance. The experimental results show that the wear rate of the cladding layer prepared by the optimized parameters is 3.21×10−5 mm3/N·m, which is 1.5 times higher than that of the cladding layer prepared by P=2.1 kW, V=3 m/min and C=9 g/min. The wear mechanism mainly includes abrasive wear, plastic deformation and oxidative wear.

Pap smear in Early Detection of Cervical Precancerous Lesions in Erbil

Background and objectives: Cervical cancer remains common. This may be because Iraq has no cervical cancer screening or human papilloma virus vaccination program. The goal of the study is to figure out how well the Pap smear works as a way to find cervical precancerous lesions also identify the prevalence of cervical cancer among women and to identify the potential risk factors for cervical cancer. Methods: A prospective cross-sectional study conducted using closed end questionnaire tool for the purpose of data collection which started from 1st of October 2021 until 1st of September 2022. Convenient type of sampling was used in collecting data which include 470 women attending to cervical screening unit in Maternity Teaching Hospital, Erbil, Kurdistan Region /Iraq. The questionnaire composed of two main parts including the socio-demographic data of the patients such as age, gender, occupation, education level, religion, income status, age at the marriage, duration of marriage…etc. The result of pap smear and cone biopsy performed during colposcopy procedure to match the accuracy of pap smear. Results: The findings revealed that 36% of the women aged between 40-49 years, the majority (80%) of them were married at the age of 15-24 years, 30% of their marriage lasted from 4-14 years, and most (72.6%) of them were housewives. The result of sensitivity, accuracy, positive and negative predicted value was equal to 45.6%, 97.7%, 80%, 89.9% respectively. The kappa test was 0.52 which indicate agreement between screening and gold standard test is a diagnosis of cervical cancer is still colposcopy-guided biopsy, which is then staged on the basis of the clinical examination and the outcome of imaging procedures, most obtained results were statistically significant. Conclusion: Pap smear had a lower sensitivity, but a higher specificity in the current study. The most common complaint of the women was post-coital bleeding. Severe acute cervicitis was the most common finding for the Pap smear.

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.

Causal relationship recognition algorithm based on external semantic and contextual structural features

Identifying the causal relationship of events plays an important role in determining the development of known events and evaluating the possible outcomes of different decisions. At present, neural network models are widely used to identify the relationships between events. Based on obtaining events, researchers distinguish the relationships between events by mining their semantics. However, due to the complexity of events and the dynamic changes in relationships between events, models often cannot fully meet the needs of accurately identifying causal relationships by only learning simple event descriptions in sentences; Moreover, focusing too much on the events themselves often leads to neglecting the structural features of statements and neglecting the impact of specific structural patterns on the relationships between events. In this article, we propose an event masking algorithm that combines external semantics to address the aforementioned issues. In this algorithm, external semantics are first introduced into the statement to enrich the information behind the event, allowing the model to mine the deep connections between events through a wider range of background knowledge; Then, the event masking module is used to enhance the model's extraction of sentence structured features, mining specific contextual representations that are unrelated to the event. The results show that compared to existing neural network algorithms, the algorithm proposed in this paper improves the F1 value of predictions on publicly available datasets by more than 4%.

Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD.

The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort. Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 eGFR values during ≥1-year follow-up. Mayo HtTKV class stability, kidney growth rates, and eGFR decline rates were calculated. The observed eGFR decline was compared with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear eGFR decline. Kaplan-Meier survival analysis and Cox regression models were used to assess time to kidney failure using Mayo HtTKV class as a predictor variable. We analyzed 618 patients with a mean age of 47±11 years and mean eGFR of 64±25 ml/min per 1.73 m 2 at baseline. Most patients (82%) remained in their baseline Mayo HtTKV class. During a mean follow-up of 5.1±2.2 years, the mean total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per year, respectively. Kidney growth and eGFR decline showed considerable overlap between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D but significantly slower for class 1E. This was also observed in patients aged younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear eGFR decline provided more accurate slope predictions. Ninety-seven patients (16%) developed kidney failure during follow-up. The classification predicted the development of kidney failure, although the sensitivity and positive predictive values were limited. The Mayo Imaging Classification demonstrated acceptable stability and generally predicted kidney failure and eGFR decline rate. However, there was marked interindividual variability in the rate of disease progression within each class.

Artificial intelligence and acute kidney injury

Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in arelevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of athreatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.

Outcome of real-time telescreening for retinopathy of prematurity using videoconferencing in a community setting in Eastern India.

To evaluate the feasibility and outcome of a real-time retinopathy of prematurity (ROP) telescreening strategy using videoconferencing in a community setting in India. In a prospective study, trained allied ophthalmic personnel obtained the fundus images in the presence of the parents and local childcare providers. Analysis of images and parental counseling were done in real time by an ROP specialist located at a tertiary center using videoconferencing software. A subset of babies was also examined using bedside indirect ophthalmoscopy by an ROP care-trained ophthalmologist. The data were analyzed using descriptive statistics, sensitivity, specificity, positive and negative predictive values, and correlation coefficient. Over 9 months, we examined 576 babies (1152 eyes) in six rural districts of India. The parents accepted the model as they recognized that a remotely located specialist was evaluating all images in real time. The strategy saved the travel time for ROP specialists by 477 h (47.7 working days) and for parents (47,406 h or 1975.25 days), along with the associated travel cost. In a subgroup analysis (100 babies, 200 eyes), the technology had a high sensitivity (97.2%) and negative predictivity value (92.7%). It showed substantial agreement (k = 0.708) with the bedside indirect ophthalmoscopy by ROP specialists with respect to the detection of treatment warranting ROP. Also, the strategy helped train the participants. Real-time ROP telescreening using videoconferencing is sensitive enough to detect treatment warranting ROPs and saves skilled workforce and time. The real-time audiovisual connection allows optimal supervision of imaging, provides excellent training opportunities, and connects ophthalmologists directly with the parents.

Diagnostic value of liver stiffness measurement combined with risk scores for esophagogastric variceal bleeding in patients with hepatitis B cirrhosis

PurposeTo assess the predictive value of liver stiffness measurement (LSM) and three bleeding risk scoring systems for esophagogastric varices bleeding (EGVB) in patients with hepatitis B cirrhosis during hospitalization. MethodsIn this study, 210 patients who had hepatitis B cirrhosis were selected as the subjects. They were categorized into two groups based on whether EGVB occurred during hospitalization: a bleeding group (70 cases) and a non-bleeding group (140 cases). Logistic regression was used to analyze the factors related to the occurrence of EGVB, and the diagnostic performance was evaluated using a receiver operating characteristic (ROC) curve. ResultsSignificant differences were observed between the two groups in systolic blood pressure, platelet count, albumin, urea nitrogen, LSM, pre-endoscopic Rockall score (PRS), Glasgow-Blatchford score (GBS), and AIMS65 score (P < 0.05). The correlation analysis showed that LSM had significant positive relationship with PRS, GBS and AIMS65 score. Logistic regression analysis revealed that LSM and GBS score were independent risk factors for EGVB occurrence during hospitalization. ROC curve analysis showed that the combined prediction model of LSM and GBS score had the best prediction performance for EGVB occurrence, with an ROC curve area of 0.811, which was significantly better than the three risk scoring systems (P < 0.05), but similar to the predicted value of LSM (P = 0.335). ConclusionsThe combination of LSM and GBS score can significantly improve the predictive efficacy of EGVB occurrence in patients with hepatitis B cirrhosis during hospitalization, which has important clinical significance for patients' prognosis.

Improving the Methodology for Determining the Biomass/Coal Co-Combustion Ratio: Predictive Modeling of the 14C Activity of Pure Biomass

Sampling and 14C detection of biomass are now essential steps to ensure the accuracy of the 14C method, but they require additional time and economic investment. When there are multiple types of biomass fuels, it is not possible to guarantee the uniformity of sampling. The 14C activity of biomass fuels exhibits variability, and this value significantly impacts the precision of the 14C method. Therefore, this study aims to investigate the influencing factors of 14C activity in biomass fuels. It also provides predicted values of 14C activity for different types of biomass fuels for each year from 2020 to 2030. Additionally, this study discusses the potential blending ratio measurement errors that may arise due to the uncertainties of the predicted values. The reduction in the 14C activity of biomass fuels can occur due to the utilization of fossil fuels, human activities, and the photosynthesis mode of C3 plants. This study presents a prediction method for determining the reduction factor. The other component of the prediction methodology involves determining the original 14C activity of biomass fuels. The 14C activity of the annual biomass is equal to the 14CO2 activity (the 14C activity of CO2) of the surrounding environment, and it experiences a decline of 0.355 pMC/year. The 14C activity has ranges of five types of perennial biomass fuels, including wood chips and branches, bark, leaves, wasted furniture, and abandoned building wood, for the time period between 2020 and 2030, are 97.34~102.84, 96.35~106.27, 96.35~102.64, 111.00~118.60, and 111.32~129.47 pMC, respectively. Based on these, this study introduces a new formula for calculating blending ratios, which enhances the current methodology. The calculation errors of blending ratios caused by the uncertainties of the predicted values are generally negligible, with the exception of wasted furniture and construction wood. The annual decrease in the blending ratio calculation error, caused by the uncertainty associated with the predicted value, can be observed. This study aims to reduce the implementation time and economic cost of the 14C method while ensuring the accuracy of biomass blending ratio detection.