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This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Nigde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.

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Background: Cardiovascular disease is the leading cause of death in the world and is associated with significant morbidity. Atherosclerosis is the main cause of cardiovascular disease (CVD), including myocardial infarction (MI), heart failure, and stroke. The mechanism of atherosclerosis has not been well investigated in different aspects, such as the relationship between oxidative stress and endothelial function. This project aims to investigate whether an oxidative enzyme vascular peroxidase 1 (VPO1) and activating transcription factor 4 (ATF4) can be used as biomarkers in highlighting the pathogenesis of the disease and in evaluating the prognosis of the relationship with endoplasmic reticulum and oxidative stress. This paper used artificial neural network analysis to predict cardiovascular disease risk based on new generation biochemical markers that combine vascular inflammation, oxidative and endoplasmic reticulum stress. Methods: For this purpose, 80 patients were evaluated according to the coronary angiography results. hs-CRP, lipid parameters and demographic characteristics, VPO1, ATF4 and Glutathione peroxidase 1(GPx1) levels were measured. Results: We found an increase in VPO1 and hs-CRP levels in single-vessel disease as compared to controls. On the contrary, ATF4 and GPx1 levels were decreased in the same group, which was not significant. Our results showed a significant positive correlation between ATF4 and lipid parameters. A statistically significant positive correlation was also observed for VPO1 and ATF4 (r=0.367, P<0.05), and a negative correlation was found for ATF4 and GPx1 (r=-0.467, P<0.01). A significant negative relationship was noted for GPx1 and hs-CRP in two/three-vessel disease (r=-0.366, P<0.05). Artificial neural network analysis stated that body mass index (BMI) and smoking history information give us an important clue as compared to age, gender and alcohol consumption parameters when predicting the number of blocked vessels. Conclusions: VPO1 and ATF4 might be potential biomarkers associated with coronary artery disease, especially in the follow-up and monitoring of treatment protocols, in addition to traditional risk factors.

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The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City that participated in this study. Data regarding demographics, geriatric syndromes, comorbidities, physical performance, and body composition were obtained. The sample was divided by sex, and the multidimensional analysis included age, gait speed over height, grip strength over body mass index, one-legged stance, lean appendicular mass percentage, and fat percentage. Using the SOM neural network, seven profile types for older men and women were identified. This analysis provided maps depicting a set of clusters qualitatively characterizing groups of older adults that share similar profiles of body composition and physical performance. The SOM neural network proved to be a useful tool for analyzing multidimensional health care data and facilitating its interpretability. It provided a visual representation of the non-linear relationship between physical performance and body composition variables, as well as the identification of seven characteristic profiles in this cohort.

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Employees' engagement (EE) and well-being (WB) are considered two interesting issues by many scientific researchers and practitioners within organizations. Most research confirms a positive correlation between EE and WB. EE is an essential premise for specific dimensions of employees' WB. At the same time, satisfied and physically and mentally healthy employees increase EE, both EE and WB thus being fundamental to individual and organizational performance. This paper aims to evaluate the relationships between EE and WB and between the dimensions of these two complex constructs. These relationships were assessed based on data obtained from a sample of 269 employees in Romania, using as a method a mix of analyses based on structural equation modeling (SEM) and artificial neural network analysis (ANN). The results highlighted a positive two-way relationship between EE and WB. Among the dimensions of EE, motivation and work environment are those that ensure a more pronounced perception of WB by the employee. Emotional WB, occupational WB, and social WB are the dimensions of WB with a significant influence on the general level of EE.

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INTRODUCTION: There is still a lack of availability of high-quality multiparametric magnetic resonance imaging (mpMRI) interpreted by experienced uro-radiologists to rule out clinically significant PC (csPC). Consequently, we developed a new imaging method based on computed tomographic ultrasound (US) supported by artificial neural network analysis (ANNA). METHODS: Two hundred and two consecutive patients with visible mpMRI lesions were scanned and recorded by robotic CT-US during mpMRI-TRUS biopsy. Only significant index lesions (ISUP ≥2) verified by whole-mount pathology were retrospectively analyzed. Their visibility was reevaluated by 2 blinded investigators by grayscale US and ANNA. RESULTS: In the cohort, csPC was detected in 105 cases (52%) by mpMRI-TRUS biopsy. Whole-mount histology was available in 44 cases (36%). In this subgroup, mean PSA level was 8.6 ng/mL, mean prostate volume was 33 cm3, and mean tumor volume was 0.5 cm3. Median PI-RADS and ISUP of index lesions were 4 and 3, respectively. Index lesions were visible in grayscale US and ANNA in 25 cases (57%) and 30 cases (68%), respectively. Combining CT-US-ANNA, we detected index lesions in 35 patients (80%). CONCLUSIONS: The first results of multiparametric CT-US-ANNA imaging showed promising detection rates in patients with csPC. US imaging with ANNA has the potential to complement PC diagnosis.

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This study aims to identify the most accurate prediction model for the possibility of victory from the annual average data of 25 seasons (1993-2017) of the Ladies Professional Golf Association (LPGA), and to determine the importance of the predicting factors. The four prediction models considered in this study were a decision tree, discriminant analysis, logistic regression, and artificial neural network analysis. The mean difference in the classification accuracy of these models was analyzed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA) and the one-way analysis of variance (ANOVA). When the prediction was based on technical variables, the most important predicting variables for determining victory were greens in regulation (GIR) and putting average (PA) in all four prediction models. When the prediction was based on the output of the technical variables, the most important predicting variable for determining victory was birdies in all four prediction models. When the prediction was based on the season outcome, the most important predicting variables for determining victory were the top 10 finish% (T10) and official money. A significant mean difference in classification accuracy was observed while performing the one-way ANOVA, and the least significant difference post-hoc test showed that artificial neural network analysis exhibited higher accuracy than the other models, especially, for larger data sizes. From the results of this study, it can be inferred that the player who wants to win the LPGA should aim to increase GIR, reduce PA, and improve driving distance and accuracy through training to increase the birdies chance at each hole, which can lead to lower average strokes and increased possibility of being within T10.

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Infliximab (IFX) is an effective medication for ulcerative colitis (UC) patients. However, one-third of UC patients show primary non-response (PNR) to IFX. Our study analyzed three Gene Expression Omnibus (GEO) datasets and used the RobustRankAggreg (RRA) algorithm to assist in identifying differentially expressed genes (DEGs) between IFX responders and non-responders. Then, an artificial intelligence (AI) technology, artificial neural network (ANN) analysis, was applied to validate the predictive value of the selected genes. The results showed that the combination of CDX2, CHP2, HSD11B2, RANK, NOX4, and VDR is a good predictor of patients' response to IFX therapy. The range of repeated overall area under the receiver-operating characteristic curve (AUC) was 0.850 ± 0.103. Moreover, we used an independent GEO dataset to further verify the value of the six DEGs in predicting PNR to IFX, which has a range of overall AUC of 0.759 ± 0.065. Since protein detection did not require fresh tissue and can avoid multiple biopsies, our study tried to discover whether the key information, analyzed by RNA levels, is suitable for protein detection. Therefore, immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with IFX and a receiver-operating characteristic (ROC) analysis were used to further explore the clinical application value of the six DEGs at the protein level. The IHC staining of colon tissues from UC patients confirmed that VDR and RANK are significantly associated with IFX efficacy. Total IHC scores lower than 5 for VDR and lower than 7 for RANK had an AUC of 0.828 (95% CI: 0.665-0.991, p = 0.013) in predicting PNR to IFX. Collectively, we identified a predictive RNA model for PNR to IFX and explored an immune-related protein model based on the RNA model, including VDR and RANK, as a predictor of IFX non-response, and determined the cutoff value. The result showed a connection between the RNA and protein model, and both two models were available. However, the composite signature of VDR and RANK is more conducive to clinical application, which could be used to guide the preselection of patients who might benefit from pharmacological treatment in the future.

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We consider the current and future role of transrectal ultrasound imaging in the diagnosis of prostate cancer, with a particular focus on the pre-biopsy localization and targeting role that multiparametric MRI (mpMRI) has come to occupy for some men in recent years. We draw a distinction between transrectal ultrasound (TRUS) used only as a means of distributing zonal biopsies with its employment as a means for identifying and targeting sonographically abnormal lesions. The role of AI in lesion identification and targeting will be reviewed. Comparisons of cost and availability, frequency of contraindications and diagnostic accuracy between these two imaging modalities will be drawn.

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Objectives: Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder presenting with: unusually abrupt onset of obsessive compulsive disorder (OCD) or severe eating restrictions, with at least two concomitant cognitive, behavioral, or affective symptoms such as anxiety, obsessive-compulsive behavior, and irritability/depression. This study describes the clinical and laboratory variables of 39 children (13 female and 26 male) with a mean age at recruitment of 8.6 years (standard deviation 3.1). Methods: Using a mathematical approach based on Artificial Neural Networks, the putative associations between PANS working criteria, as defined at the NIH in July 2010 (Swedo et al. 2012), were explored by the Auto Contractive Map (Auto-CM) system, a mapping method able to compute the multidimensional association of strength of each variable with all other variables in predefined dataset. Results: The PANS symptoms were strictly linked to one another on the semantic connectivity map, shaping a central "diamond" encompassing anxiety, irritability/oppositional defiant disorder symptoms, obsessive-compulsive symptoms, behavioral regression, sensory motor abnormalities, school performance deterioration, sleep disturbances, and emotional lability/depression. The semantic connectivity map also showed the aggregation between PANS symptoms and laboratory and clinical variables. In particular, the emotional lability/depression resulted as a highly connected hub linked to autoimmune disease in pregnancy, allergic and atopic disorders, and low Natural Killer percentage. Also anxiety symptoms were shown to be strongly related with recurrent infectious disease remarking the possible role of infections as a risk factor for PANS. Conclusion: Our data mining approach shows a very specific constellation of symptoms having strong links to laboratory and clinical variables consistent with PANS feature.

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Drug-induced liver injury (DILI) can cause hepatic failure and result in drug withdrawal from the market. It has host-related and compound-dependent mechanisms. Preclinical prediction of DILI risk is very challenging and safety assessments based on animals inadequately forecast human DILI risk. In contrast, human-derived in vitro cell culture-based models could improve DILI risk prediction accuracy. Here, we developed and validated an innovative method to assess DILI risk associated with various compounds. Fifty-four marketed and withdrawn drugs classified as DILI risks of "most concern", "less concern", and "no concern" were tested using a combination of four assays addressing mitochondrial injury, intrahepatic lipid accumulation, inhibition of bile canalicular network formation, and bile acid accumulation. Using the inhibitory potencies of the drugs evaluated in these in vitro tests, an algorithm with the highest available DILI risk prediction power was built by artificial neural network (ANN) analysis. It had an overall forecasting accuracy of 73%. We excluded the intrahepatic lipid accumulation assay to avoid overfitting. The accuracy of the algorithm in terms of predicting DILI risks was 62% when it was constructed by ANN but only 49% when it was built by the point-added scoring method. The final algorithm based on three assays made no DILI risk prediction errors such as "most concern " instead of "no concern" and vice-versa. Our mechanistic approach may accurately predict DILI risks associated with numerous candidate drugs.

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Objective To investigate the application of artificial neural network analysis on computerized transrectal ultrasound (ANNAcTRUS) in early detection of prostate cancer.Methods Sixty men with or without prior biopsies, either due to elevated PSA or abnormal digital rectal findings, were included in this study from January 2014 to July 2015.Patient's mean age was (65.6 ± 8.9) years (51-89 years).Their PSA level was (9.8 ± 4.9)μg/L.The patients received the ANNAcTRUS targeted 6-core biopsy.Each patient received six targeted biopsies of suspicious regions, which was identified by ANNAcTRUS online system.Histopathologic examination was further carried out to confirm the result of the targeted biopsies.Results According to the results of ANNAcTRUS,52 of 60 patients received biopsy in ANNAcTRUS group.ANNAcTRUS targeted 6-core biopsy was able to detect prostate cancer in 24 (46.2％) of 52 patients.The distribution of Gleason Score was as follows : 3 + 3 (n =9), 3 + 4 (n =8), 4 + 3 (n =4), 4 +4 (n =2) and 5 +4 (n =1).For patients without prior negative biopsy,ANNAcTRUS targeted 6-core biopsy was able to detect prostate cancer in 17 (51.5％) of 33 patients.Conclusions ANNAcTRUS targeted 6-core biopsy illustrates a higher detection rate of prostate cancer.Furthermore, ANNAcTRUS targeted 6-core biopsy tends to detect low-grade prostate cancer.

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This paper focuses on whether the on-going dramatic decrease in alcohol consumption in Italy, especially of wine, during 1961-2008, was associated with which parallel sociodemographic and economic changes and with alcohol control policies. The study, using both time series (TS) and artificial neural network (ANN)-based analyses documents that its selected sociodemographic and economic factors, and particularly urbanization, had a definite connection with wine consumption decrease, spirits decrease, and the increase in beer consumption over time. On the other hand, control policies showed no effect on the decline in alcohol consumption, since no alcohol control policy existed in Italy between 1960 and 1987. A few policies introduced since 1988 (BAC and sale restrictions during mass events) may have contributed to reducing or to maintaining the on-going reduction. Study limitations are noted and future needed research is suggested.

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This AMPHORA study's aim was to investigate selected factors potentially affecting changes in consumption of alcoholic beverages in 12 European countries during the 1960s-2008 (an average increase in beer, decreases in wine and spirits, total alcohol drinking decrease). Both time series and artificial neural networks-based analyses were used. Results indicated that selected socio-demographic and economic factors showed an overall major impact on consumption changes; particularly urbanization, increased income, and older mothers' age at their childbirths were significantly associated with consumption increase or decrease, depending on the country. Alcoholic beverage control policies showed an overall minor impact on consumption changes: among them, permissive availability measures were significantly associated with consumption increases, while drinking and driving limits and availability restrictions were correlated with consumption decreases, and alcohol taxation and prices of the alcoholic beverages were not significantly correlated with consumption. Population ageing, older mother's age at childbirths, increased income and increases in female employment, as well as drink driving limitations were associated with the decrease of transport mortality. Study's limitations are noted.

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Hungary has always belonged to the group of nations characterized by high alcoholic beverage consumption and it is still one of the leading liver cirrhosis mortality countries in Europe and in the world. This research studies changes in selected contextual factors and control policy measures that are most strongly correlated with changes in alcohol consumption and selected related harms in Hungary between 1960 and 2008. The method to analyze the association between different variables was similar to that of the European AMPHORA project. The analysis, which has been done, highlights the central role of urbanization and population ageing in Hungary in affecting the increase of consumption of alcoholic beverages, especially beer. Alcohol control policy measures show little explanatory power to interpret consumption changes; having had no effect in curbing alcohol consumption and no, or little impact on alcohol consumption-related deaths. Study's limitations are noted.

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