Search Results (27)

Background: In the emergency setting, many diagnostic pathways incorporate change in high-sensitivity cardiac troponin (hs-cTn) concentrations (i.e., the delta) to classify patients as low-risk (rule-out) or high-risk (rule-in) for possible myocardial infarction (MI). However, the impact of analytical variation on the delta for correct classification is unknown, especially at concentrations below and around the 99th percentile. Our objective was to assess the impact of delta variation for correct risk classification across the European Society of Cardiology (ESC 0/1 h and 0/2 h), the High-STEACS, and the common change criteria (3C) pathways. Methods: A yearlong accuracy study for hs-cTnT was performed where laboratories across Canada tested three patient-based samples (level 1 target value = 6 ng/L, level 2 target value = 9 ng/L, level 3 target value = 12 ng/L) monthly across 41 different analyzers. The assigned low-delta between levels 1 and 2 was 3 ng/L (i.e., 9 − 6 = 3 ng/L) and the assigned high-delta between levels 1 and 3 was 6 ng/L (i.e., 12 − 6 = 6 ng/L). The low- and high-deltas for each analyzer were determined monthly from the measured values, with the difference calculated from the assigned deltas. The obtained deltas were then assessed via the different pathways on correct classification (i.e., percent correct with 95% confidence intervals, CI) and using non-parametric analyses. Results: The median (interquartile range) difference between the measured versus assigned low-delta (n = 436) and high-delta (n = 439) was −1 ng/L (−1 to 0). The correct classification differed among the pathways. The ESC 0/1 h pathway yielded the lowest percentage of correct classification at 35.3% (95% CI: 30.8 to 40.0) for the low-delta and 90.0% (95% CI: 86.8 to 92.6) for the high-delta. The 3C and ESC 0/2 h pathways yielded higher and equivalent estimates on correct classification: 95.2% (95% CI: 92.7 to 97.0) for the low-delta and 98.2% (95% CI: 96.4 to 99.2) for the high-delta. The High-STEACS pathway yielded 99.5% (95% CI: 98.4 to 99.9) of correct classifications for the high-delta but only 36.2% (95% CI: 31.7 to 40.9) for the low-delta. Conclusions: Analytical variation will impact risk classification for MI when using hs-cTn deltas alone per the pathways. The 3C and ESC 0/2 h pathways have <5% misclassification when using deltas for hs-cTnT in this dataset. Additional studies with different hs-cTnI assays at concentrations below and near the 99th percentile are warranted to confirm these findings. Full article

Fault detection, classification, and precise location identification in power transmission lines are critical issues for energy transmission and power systems. Accurate fault diagnosis is essential for system stability and safety as it enables rapid problem resolution and minimizes interruptions in electrical energy supply. The characteristic parameters of mixed-conductor power transmission lines connected to the grid were calculated using the relevant line data. Based on these parameters, a dataset was created with computer-derived values. This dataset included variations in arc resistance and the short circuit power of the corresponding bus, facilitating the performance testing of various machine learning algorithms. It was observed that the correct determination of the faulty phase was of high importance in the correct determination of the fault position. For this reason, a gradual structure was preferred. It was achieved with a 100 percent success rate in fault detection with the ensemble bagged algorithm. It was obtained with the neural network algorithm with a 99.97 percent success rate in faulty phase detection. The most successful location results were obtained with the interaction linear algorithm with 0.0066 MAE for phase-to-phase faults and the stepwise linear algorithm with 0.0308 MAE for phase ground faults. Using the proposed algorithm, fault locations were identified with a maximum error of 26 m for phase-to-ground faults and 110 m for phase-to-phase faults on a transmission line with a mixed conductor of approximately 178 km. Additionally, we compared the training and testing results of several machine learning algorithms metrics including the accuracy, total error, mean absolute error, root mean square, and root mean square error to provide informed recommendations based on their performance. The findings aim to guide users in selecting the most effective machine learning models for predicting failures in transmission lines. Full article

As a fast detection method, Fourier transform infrared attenuated total reflection (ATR-FTIR) spectroscopy is seldom used for monitoring soluble sugars in crops. This study aimed to demonstrate the feasibility of leveraging ATR-FTIR coupled with chemometrics to quantify and sort the contents of soluble sugar in tomatoes. Firstly, 192 tomato samples were scanned using ATR-FTIR; subsequently, a quantitative model was developed using PLSR with selected wavelength variables as inputs. Finally, a classification model was estimated through probabilistic neural network (PNN) to determine the samples. The results indicated that ATR-FTIR had successfully captured the spectra from the cellular layers of tomatoes, resulting in a robust PLSR model created by 468 selected variables with a R² value of 0.86, a RMSEP of 0.71%, a ratio of performance to relative percent deviation (RPD) of 1.87, and a ratio of prediction to interquartile range (RPIQ) of 2.1. Meanwhile, the PNN model demonstrated a high rate correct (RC) of 92.17% in identifying whether the samples with a higher soluble sugar content than the limit of detection (LOD at 2.1%). Overall, ATR-FTIR coupled with chemometrics has proven effective for non-destructive determination of soluble sugars in tomatoes, offering new insights into internal monitoring techniques for crop quality assurance. Full article

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the “Serrana” and “Preta de Montesinho” breeds certified as “Cabrito Transmontano” and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the “Serrana” breed proved to be more tender than that of the “Preta de Montesinho”. However, the meat from the “Preta de Montesinho” breed showed higher intramuscular fat content and was lighter than that from the “Serrana” breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard–Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R² > 0.99. Full article

Background: Forty percent of women will experience prolapse in their lifetime. Vaginal pessaries are considered the first line of treatment in selected patients. Major complications of vaginal pessaries rarely occur. Methods: PubMed and Embase were searched from 1961 to 2022 for major complications of vaginal pessaries using Medical Subject Headings (MeSH) and free-text terms. The keywords were pessary or pessaries and: vaginal discharge, incontinence, entrapment, urinary infections, fistula, complications, and vaginal infection. The exclusion criteria were other languages than English, pregnancy, complications without a prior history of pessary placement, pessaries unregistered for clinical practice (herbal pessaries), or male patients. The extracted data included symptoms, findings upon examination, infection, type of complication, extragenital symptoms, and treatment. Results: We identified 1874 abstracts and full text articles; 54 were assessed for eligibility and 49 met the inclusion criteria. These 49 studies included data from 66 patients with pessary complications amenable to surgical correction. Clavien–Dindo classification was used to grade the complications. Most patients presented with vaginal symptoms such as bleeding, discharge, or ulceration. The most frequent complications were pessary incarceration and fistulas. Surgical treatment included removal of the pessary under local or general anesthesia, fistula repair, hysterectomy and vaginal repair, and the management of bleeding. Conclusions: Pessaries are a reasonable and durable treatment for pelvic organ prolapse. Complications are rare. Routine follow-ups are necessary. The ideal patient candidate must be able to remove and reintroduce their pessary on a regular basis; if not, this must be performed by a healthcare worker at regular intervals. Full article

Mindset theory assumes that students’ beliefs about their intelligence—whether these are fixed or can grow—affects students’ academic performance. Based on this assumption, mindset theorists have developed growth mindset interventions to teach students that their intelligence or another attribute can be developed, with the goal of improving academic outcomes. Though many papers have reported benefits from growth mindset interventions, others have reported no effects or even detrimental effects. Recently, proponents of mindset theory have called for a “heterogeneity revolution” to understand when growth mindset interventions are effective and when—and for whom—they are not. We sought to examine the whole picture of heterogeneity of treatment effects, including benefits, lack of impacts, and potential detriments of growth mindset interventions on academic performance. We used a recently proposed approach that considers persons as effect sizes; this approach can reveal individual-level heterogeneity often lost in aggregate data analyses. Across three papers, we find that this approach reveals substantial individual-level heterogeneity unobservable at the group level, with many students and teachers exhibiting mindset and performance outcomes that run counter to the authors’ claims. Understanding and reporting heterogeneity, including benefits, null effects, and detriments, will lead to better guidance for educators and policymakers considering the role of growth mindset interventions in schools. Full article

There is no consensus on the best equation to estimate glomerular filtration rate (eGFR) in obese patients (OP). Objective: to evaluate the performance of the current equations and the new Argentinian Equation (“AE”) to estimate GFR in OP. Two validation samples were used: internal (IVS, using 10-fold cross-validation) and temporary (TVS). OP whose GFR was measured (mGFR) with clearance of iothalamate between 2007/2017 (IVS, n = 189) and 2018/2019 (TVS, n = 26) were included. To evaluate the performance of the equations we used: bias (difference between eGFR and mGFR), P30 (percentage of estimates within ±30% of mGFR), Pearson’s correlation (r) and percentage of correct classification (%CC) according to the stages of CKD. The median age was 50 years. Sixty percent had grade I obesity (G1-Ob), 25.1% G2-Ob and 14.9% G3-Ob, with a wide range in mGFR (5.6–173.1 mL/min/1.73 m²). In the IVS, AE obtained a higher P30 (85.2%), r (0.86) and %CC (74.4%), with lower bias (−0.4 mL/min/1.73 m²). In the TVS, AE obtained a higher P30 (88.5%), r (0.89) and %CC (84.6%). The performance of all equations was reduced in G3-Ob, but AE was the only one that obtained a P30 > 80% in all degrees. AE obtained better overall performance to estimate GFR in OP and could be useful in this population. Conclusions from this study may not be generalizable to all populations of obese patients since they were derived from a study in a single center with a very specific ethnic mixed population. Full article

The coronavirus disease pandemic (COVID-19) is a contemporary disease. It first appeared in 2019 and has sparked a lot of attention in the public media and recent studies due to its rapid spread around the world in recent years and the fact that it has infected millions of individuals. Many people have died in such a short time. In recent years, several studies in artificial intelligence and machine learning have been published to aid clinicians in diagnosing and detecting viruses before they spread throughout the body, recovery monitoring, disease prediction, surveillance, tracking, and a variety of other applications. This paper aims to use chest X-ray images to diagnose and detect COVID-19 disease. The dataset used in this work is the COVID-19 RADIOGRAPHY DATABASE, which was released in 2020 and consisted of four classes. The work is conducted on two classes of interest: the normal class, which indicates that the person is not infected with the coronavirus, and the infected class, which suggests that the person is infected with the coronavirus. The COVID-19 classification indicates that the person has been infected with the coronavirus. Because of the large number of unbalanced images in both classes (more than 10,000 in the normal class and less than 4000 in the COVID-19 class), as well as the difficulties in obtaining or gathering more medical images, we took advantage of the generative network in this project to produce fresh samples that appear real to balance the quantity of photographs in each class. This paper used a conditional generative adversarial network (cGAN) to solve the problem. In the Data Preparation Section of the paper, the architecture of the employed cGAN will be explored in detail. As a classification model, we employed the VGG16. The Materials and Methods Section contains detailed information on the planning and hyperparameters. We put our improved model to the test on a test set of 20% of the total data. We achieved 99.76 percent correctness for both the GAN and the VGG16 models with a variety of preprocessing processes and hyperparameter settings. Full article

Organic food consumption has become a significant trend in consumer behaviour, determined by various motives, among which the price does not play a major role, thus reflecting the Lancaster approach to the microeconomic consumer theory. Additionally, artificial neural networks (ANNs) have proven to have significant potential in providing accurate and efficient models for predicting consumer behaviour. Considering these two trends, this study aims to deploy the Lancaster approach in the emerging area of artificial intelligence. The paper aims to develop the ANN-based predictive model to investigate the relationship between organic food consumption, demographic characteristics, and health awareness attitudes. Survey research has been conducted on a sample of Croatian inhabitants, and ANN models have been used to assess the importance of various determinants for organic food consumption. A Three-layer Multilayer Perceptron Neural Networks (MLPNN) structure has been constructed and validated to select the optimal number of neurons and transfer functions. One layer is used as the first input, while the other two are hidden layers (the first covers the radially symmetrical input, sigmoid function; the second covers the output, softmax function). Three versions of the testing, training, and holdout data structures were used to develop ANNs. The highest accuracy was achieved with a 7-2-1 partition. The best ANN model was determined as the model that was showing the smallest percent of incorrect predictions in the holdout stage, the second-lowest cross-entropy error, the correct classification rate, and the area under the ROC curve. The research results show that the availability of healthy food shops and consumer awareness of these shops strongly impacts organic food consumption. Using the ANN methodology, this analysis confirmed the validity of the Lancaster approach, stating that the characteristics or attributes of goods are defined by the consumer and not by the product itself. Full article

Introduction: Acute Otitis Media (AOM) is the most commonly-occurring bacterial complication in childhood. After making certain corrections to the patients’ dietary habits, which we found to be excessively high in animal-based and industrially-processed foods, we observed a significant reduction in recurrent colds and their bacterial complications. We promote an original way of treating these diseases, since until now the conventional treatment is based on pharmacological and surgical treatment. From our point of view, the mucosa that covers the entire ENT area is in a pro-inflammatory and hyper-reactive state, as a consequence of the alterations produced by an inadequate diet. For us there is no difference in the nutritional treatment of the different mucous membranes that cover the ENT area. The purpose of the study was to assess the effects of the Traditional Mediterranean Diet (TMD) on patients diagnosed with Recurring Acute Otitis Media (RAOM). Methods: prospective pre-postest comparison study with 48 girls and 42 boys aged 1–5 years, each of whom had been and included on the 1-year programme “Learning to eat the Mediterranean Way”, designed to encourage the adoption of the TMD. We studied clinical and therapeutic variables and various anthropometric parameters. Results: all the symptomatic indicators studied (number and intensity of episodes of otitis and emergency admissions) showed a positive and statistically significant evolution in RAOM. By the end of the study, none of the patients met the criteria for classification as RAOM, and 60% percent of patients did not present any further episodes of AOM. In line with the above, the use of anti-microbial drugs and symptomatic treatments reduced considerably; the use of antibiotics dropped from 4.30 occasions/patient/year, to 0.66 (p < 0.001), and the used of symptomatic treatments dropped from 7.63 to 2.75 (p < 0.001). The level of family satisfaction was very high. Conclusions: the adoption of the Traditional Mediterranean Diet has been demonstrated to significantly reduce occurrence of acute otitis media and may contribute to the treatment of patients diagnosed with recurrent acute otitis media. Full article

Skin lesion detection and analysis are very important because skin cancer must be found in its early stages and treated immediately. Once installed in the body, skin cancer can easily spread to other body parts. Early detection would represent a very important aspect since, by ensuring correct treatment, it could be curable. Thus, by taking all these issues into consideration, there is a need for highly accurate computer-aided systems to assist medical staff in the early detection of malignant skin lesions. In this paper, we propose a skin lesion classification system based on deep learning techniques and collective intelligence, which involves multiple convolutional neural networks, trained on the HAM10000 dataset, which is able to predict seven skin lesions including melanoma. The convolutional neural networks experimentally chosen, considering their performances, to implement the collective intelligence-based system for this purpose are: AlexNet, GoogLeNet, GoogLeNet-Places365, MobileNet-V2, Xception, ResNet-50, ResNet-101, InceptionResNet-V2 and DenseNet201. We then analyzed the performances of each of the above-mentioned convolutional neural networks to obtain a weight matrix whose elements are weights associated with neural networks and classes of lesions. Based on this matrix, a new decision matrix was used to build the multi-network ensemble system (Collective Intelligence-based System), combining each of individual neural network decision into a decision fusion module (Collective Decision Block). This module would then have the responsibility to take a final and more accurate decision related to the prediction based on the associated weights of each network output. The validation accuracy of the proposed system is about 3 percent better than that of the best performing individual network. Full article

Lung cancer is among the most hazardous types of cancer in humans. The correct diagnosis of pathogenic lung disease is critical for medication. Traditionally, determining the pathological form of lung cancer involves an expensive and time-consuming process investigation. Lung cancer is a leading cause of mortality worldwide, with lung tissue nodules being the most prevalent way for doctors to identify it. The proposed model is based on robust deep-learning-based lung cancer detection and recognition. This study uses a deep neural network as an extraction of features approach in a computer-aided diagnosing (CAD) system to assist in detecting lung illnesses at high definition. The proposed model is categorized into three phases: first, data augmentation is performed, classification is then performed using the pretrained CNN model, and lastly, localization is completed. The amount of obtained data in medical image assessment is occasionally inadequate to train the learning network. We train the classifier using a technique known as transfer learning (TL) to solve the issue introduced into the process. The proposed methodology offers a non-invasive diagnostic tool for use in the clinical assessment that is effective. The proposed model has a lower number of parameters that are much smaller compared to the state-of-the-art models. We also examined the desired dataset’s robustness depending on its size. The standard performance metrics are used to assess the effectiveness of the proposed architecture. In this dataset, all TL techniques perform well, and VGG 16, VGG 19, and Xception for 20 epoch structure are compared. Preprocessing functions as a wonderful bridge to build a dependable model and eventually helps to forecast future scenarios by including the interface at a faster phase for any model. At the 20th epoch, the accuracy of VGG 16, VGG 19, and Xception is 98.83 percent, 98.05 percent, and 97.4 percent. Full article

Abrupt environmental changes are faced by Leishmania parasites during transmission from a poikilothermic insect vector to a warm-blooded host. Adaptation to harsh environmental conditions, such as nutrient deprivation, hypoxia, oxidative stress and heat shock needs to be accomplished by rapid reconfiguration of gene expression and remodeling of protein interaction networks. Chaperones play a central role in the maintenance of cellular homeostasis, and they are responsible for crucial tasks such as correct folding of nascent proteins, protein translocation across different subcellular compartments, avoiding protein aggregates and elimination of damaged proteins. Nearly one percent of the gene content in the Leishmania genome corresponds to members of the HSP40 family, a group of proteins that assist HSP70s in a variety of cellular functions. Despite their expected relevance in the parasite biology and infectivity, little is known about their functions or partnership with the different Leishmania HSP70s. Here, we summarize the structural features of the 72 HSP40 proteins encoded in the Leishmania infantum genome and their classification into four categories. A review of proteomic data, together with orthology analyses, allow us to postulate cellular locations and possible functional roles for some of them. A detailed study of the members of this family would provide valuable information and opportunities for drug discovery and improvement of current treatments against leishmaniasis. Full article

Many forestry models require the input of stand size class information, a variable with multiple definitions across forest inventories. This work describes a crosswalk between stand age class and stand size class for several forest type groups in Minnesota to facilitate consistency and availability of the latter information. Refinements to the crosswalk include ratio adjustments that redistribute the number of plots (or hectares) from one estimated size class to another, based on known crosswalk error rates. The results showed that 61.9% of all plots were correctly classified, and 95.5% were within one size class. Correct classifications for individual forest type/size class combinations ranged from 16.7–79.2%. Applying the crosswalk to a validation dataset produced percent errors from −46.4% to 49.4%, but with the ratio adjustment, errors dropped to −20.1% to 14.4%. These results suggest that the crosswalk and ratio adjustments provide a coarse, yet reasonable, substitute to using more complex stand size classification methodologies, particularly when forecasting future stand conditions. Full article

Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of ‘3N’ biosignals—nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result. Full article