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Background: Early diagnosis of acute kidney injury (AKI) after cardiac surgery is based on serum creatinine which is neither a specific nor a sensitive biomarker. In our study, we investigated the role of serum Klotho in early prediction of AKI after cardiac surgery using cardiopulmonary bypass (CPB). Methods: The included patients were classified into three groups according to AKI stages using KDIGO criteria. The measurements of creatinine and Klotho levels in serum were performed before surgery, at the end of CPB, 2 hours after the end of CPB, 24 hours and 48 hours postoperatively. Results: Seventy-eight patients were included in the study. A significant increase of creatinine levels (p

Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.

The purpose of this research was to determine how dose area product, effective dose, absorbed doses to specific organs, and image quality changed according to different automatic exposure control positions in pelvis imaging. The research was carried out in two parts. The study was conducted on an anthropomorphic phantom and 200 patients referred to pelvic imaging. We measured the dose area product, field size, height, and mass. Then we calculated the effective dose and absorbed dose for individual organs accordingly. Lateral ionizing cells were first positioned in line with the iliac crests (head towards position) and subsequently, with the femoral neck (head away position). All the images were independently evaluated by three radiologists using ViewDEX and objective image analysis was performed measuring contrast-to-noise ratio and signal-to-noise ratio. We found no significant differences in the Siemens Luminos unit in any of the inspected parameters. However, there was a significant difference in dose area product (37.3 %), effective dose (35.7 %) and average absorbed dose to selected individual organs (36.7 %) when the head away position of the patient was used and the image quality increased. Based on these results, we can propose that the optimal position of the patient regarding the ionizing cells is the head away position.

This work assesses different approaches for speech and non-speech segmentation of audio data and proposes a new, high-level representation of audio signals based on phoneme recognition features suitable for speech/non-speech discrimination tasks. Unlike previous model-based approaches, where speech and non-speech classes were usually modeled by several models, we develop a representation where just one model per class is used in the segmentation process. For this purpose, four measures based on consonant-vowel pairs obtained from different phoneme speech recognizers are introduced and applied in two different segmentation-classification frameworks. The segmentation systems were evaluated on different broadcast news databases. The evaluation results indicate that the proposed phoneme recognition features are better than the standard mel-frequency cepstral coefficients and posterior probability-based features (entropy and dynamism). The proposed features proved to be more robust and less sensitive to different training and unforeseen conditions. Additional experiments with fusion models based on cepstral and the proposed phoneme recognition features produced the highest scores overall, which indicates that the most suitable method for speech/non-speech segmentation is a combination of low-level acoustic features and high-level recognition features.

Background: Deep sternal wound infection (DSWI) following open heart surgery is associated with excessive morbidity and mortality. Contemporary DSWI risk prediction models aim at identifying high-risk patients with varying complexity and performance characteristics. We aimed to optimize the DSWI risk factor set and to identify additional risk factors for early postoperative detection of patients prone to DSWI., Methods: Single-centre retrospective analysis of patients with isolated multivessel coronary artery disease undergoing myocardial revascularization at Paracelsus Medical University Nuremberg between 2007 and 2022 was performed to identify risk factors for DSWI. Three data sets were created to examine preoperative, intraoperative, and early postoperative parameters, constituting the "Baseline", the "Improved Baseline" and the "Extended" models. The "Extended" data set included risk factors that had not been analysed before. Univariable and stepwise forward multiple logistic regression analyses were performed for each respective set of variables., Results: From 5221 patients, 179 (3.4%) developed DSWI. The "Extended" model performed best, with the area under the curve (AUC) of 0.80, 95%-CI: [0.76, 0.83]. Pleural effusion requiring intervention, postoperative delirium, preoperative hospital stay > 24 h, and the use of fibrin sealant were new independent predictors of DSWI in addition to age, Diabetes Mellitus on insulin, Body Mass Index, peripheral artery disease, mediastinal re-exploration, bilateral internal mammary harvesting, acute kidney injury and blood transfusions., Conclusions: The "Extended" regression model with the short-term postoperative complications significantly improved DSWI risk discrimination after surgical revascularization. Short preoperative stay, prevention of postoperative delirium, protocols reducing the need for evacuation of effusion and restrictive use of fibrin sealant for sternal closure facilitate DSWI reduction., Trial Registration: The registered retrospective study was registered at the study centre and approved by the Institutional Review Board of Paracelsus Medical University Nuremberg (IRB-2019-005)., (© 2024. The Author(s).)

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022., Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance., Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models., Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)., Competing Interests: KS, HG, RG, HJ, RN, BP, FS, JD, DW, SA, AU, GG, ER, NR, DS, YW, NW, LW, JT, GO, TS, DT, LP, EK, JM, MB, NL, JM, JS, JW, JF, BL, IR, JB, RW, IV, VT, MS, MZ, CP, BR, NB, SM, LC, GF, IM, DO, PA, AM, VE, AK, DK, LP, BD, ML, SS, JD, SM, VP, GR, BB, GA, NP, GZ, WB, SH, TH, DS, MG, JA, DM, SA, EA, DL, CP, JB, AR, TZ, AK, JZ, FP, FD, MC, GL, PG, BT, FB, GJ, MM, AF, AS, PM, AA, BH, BL, MM, PP, SV, RB, FD, AG, KG, MG, BK, AM, KN, JN, MR, FR, MS, ES, JZ, JK, BP, KH, YK, MS, BP, MB, MF, RI, TK, TO, JB, SF No competing interests declared, PB, HG, SS, BS Affiliated with IEM, Inc. The author has no financial interests to declare

Background: Remote monitoring was used to assess and manage skin diseases., Objective: To investigate to what extent smartphone photographs along with a self-reported body region (BR) score can be used to evaluate psoriasis severity., Methods: Psoriasis severity was assessed in the clinic using the psoriasis area and severity index and the physician's global assessment. On the same day, the patients took a photograph of a representative lesion from 4 BR (head/neck, upper limbs, trunk, and lower limbs) and completed a questionnaire about BR score. The photographs were rated by 5 dermatologists. Intraclass correlation coefficients with 95% CIs were calculated., Results: Overall, 32 were included, of which 6% had almost clear, 69% had mild, and 25% had moderate psoriasis. Perfect agreement between the self-reported and the doctors' BR score was observed for 59%, and near-perfect agreement (deviation of maximum 1 score) was 92%. The intraclass correlation coefficient between clinical and photographic psoriasis area and severity index was 0.78 (95% CI, 0.55-0.90), and for physician's global assessment, perfect agreement was 53%., Conclusions: The agreement between psoriasis severity assessed clinically and by photographs was good in a study setting. This gives the opportunity to remotely assess psoriasis severity by combining photographs with self-reported BR scores., Competing Interests: Drs JRZ, PD, ADA, IM, and AS were employed by Studies&Me at the time of study execution. Drs ZA, JGH, ARJ, AE, SFT, and CBJ have no conflicts of interest to declare regarding this paper., (© 2023 Published by Elsevier Inc. on behalf of the American Academy of Dermatology, Inc.)

Background: Clinical trials often suffer from recruitment barriers and poor adherence, which increases costs and affects trial outcomes., Objective: To investigate the feasibility of Decentralized Clinical Trial (DCT) design elements to recruit, enroll, and engage patients with type 2 diabetes mellitus (T2DM)., Methods: Patients with T2DM were recruited through a pharmacy and online recruitment using advert on Facebook, to 3 weeks monitoring of glucose and behaviometric parameters. Subjects recruited online could either complete an informed consent conversation in the pharmacy or through live video call managed by the study app.A continuous glucose monitoring (CGM) device to collect glucose data, and a hybrid smartwatch to monitor heart rate, track activity and sleep pattern were delivered by postal service to the participants' home address. The devices were connected to a study specific app on the participant's smartphone also capturing GPS data and questionnaire answers., Results: Twenty-six subjects (3 pharmacy, 23 online) with T2DM were recruited, 85% preferred online informed consent conversation. All participants were able to self-apply the CGM device, use the smartwatch, and download the app. GPS location was captured more than 100 times for each participant, and more than 90% completed all 3 questionnaires. All the participants felt safe with the informed consent process and they felt confident in participating from home. Three participants dropped-out during the study period leaving a retention rate at 87%., Conclusions: Use of DCT design elements to conduct a T2DM study is feasible regarding recruitment, data collection from various electronic devices, and participant engagement.

Objectives: This study aims to improve the early detection of cardiac surgery-associated acute kidney injury using artificial intelligence-based algorithms., Methods: Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modelling. Cardiac surgery-associated acute kidney injury was defined according to the Kidney Disease Improving Global Outcomes criteria. Different machine learning algorithms were trained and validated to detect cardiac surgery-associated acute kidney injury within 12 h after surgery. Demographic characteristics, comorbidities, preoperative cardiac status and intra- and postoperative variables including creatinine and haemoglobin values were retrieved for analysis., Results: From 7507 patients analysed, 1699 patients (22.6%) developed cardiac surgery-associated acute kidney injury. The ultimate detection model, 'Detect-A(K)I', recognizes cardiac surgery-associated acute kidney injury within 12 h with an area under the curve of 88.0%, sensitivity of 78.0%, specificity of 78.9% and accuracy of 82.1%. The optimal parameter set includes serial changes of creatinine and haemoglobin, operative emergency, bleeding-associated variables, cardiac ischaemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease., Conclusions: The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 h after surgery with the best discriminatory characteristics reported so far., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Background: Cardiac surgery in patients with infective endocarditis (IE) is still associated with high mortality and morbidity; an already present inflammation might further be aggravated due to a cardiopulmonary bypass-induced dysregulated immune response. Intraoperative hemoadsorption therapy may attenuate this septic response. Our objective was therefore to assess the efficacy of intraoperative hemoadsorption in active left-sided native- and prosthetic infective endocarditis., Methods: Consecutive high-risk patients with active left-sided infective endocarditis were enrolled between January 2015 and April 2021. Patients with intraoperative hemoadsorption (Cytosorbents, Princeton, NJ, USA) were compared to patients without hemoadsorption (control). Endpoints were the incidence of postoperative sepsis, sepsis-associated death and in-hospital mortality. Predictors for sepsis-associated mortality and in-hospital mortality were analysed by multivariable logistic regression., Results: A total of 202 patients were included, 135 with active left-sided native and 67 with prosthetic valve infective endocarditis. Ninety-nine patients received intraoperative hemoadsorption and 103 patients did not. Ninety-nine propensity-matched pairs were selected for final analyses. Postoperative sepsis and sepsis-related mortality was reduced in the hemoadsorption group (22.2% vs. 39.4%, p = 0.014 and 8.1% vs. 22.2%, p = 0.01, respectively). In-hospital mortality tended to be lower in the hemoadsorption group (14.1% vs. 26.3%, p = 0.052). Key predictors for sepsis-associated mortality and in-hospital mortality were preoperative inotropic support, lactate-levels 24 h after surgery, C-reactive protein levels on postoperative day 1, chest tube output, cumulative inotropes and white blood cell counts on postoperative day 2, and new onset of dialysis. Multivariate regression analysis revealed intraoperative hemoadsorption to be associated with lower sepsis-associated (OR 0.09, 95% CI 0.013-0.62, p = 0.014) as well as in-hospital mortality (OR 0.069, 95% CI 0.006-0.795, p = 0.032)., Conclusions: Intraoperative hemoadsorption holds promise to reduce sepsis and sepsis-associated mortality after cardiac surgery for active left-sided native and prosthetic valve infective endocarditis.

We have recently shown that minor subclinical creatinine dynamic changes enable the excellent detection of acute kidney injury (AKI) within 6-12 h after cardiac surgery. The aim of the present study was to examine a combination of neutrophil gelatinase-associated lipocalin (NGAL), cystatin C (CysC) and creatinine for enhanced AKI detection early after cardiac surgery. Elective patients with normal renal function undergoing cardiac surgery using cardiopulmonary bypass were enrolled. Concentrations of plasma NGAL, serum CysC and serum creatinine were determined after the induction of general anesthesia, at the termination of the cardiopulmonary bypass and 2 h thereafter. Out of 119 enrolled patients, 51 (43%) developed AKI. A model utilizing an NGAL, CysC and creatinine triple biomarker panel including sequential relative changes provides a better prediction of cardiac surgery-associated acute kidney injury than any biomarker alone already 2 h after the termination of the cardiopulmonary bypass. The area under the receiver-operator curve was 0.77, sensitivity 77% and specificity 68%.

Background: Occluding the left atrial appendage (LAA) during cardiac surgery reduces the risk of ischemic stroke; nonetheless, it is currently only softly recommended with "may be considered" by the current guidelines. We aimed to assess thromboembolic risk after LAA amputation in patients with atrial fibrillation (AF) and aortic stenosis undergoing biological aortic valve replacement (AVR) as primary cardiac surgery., Methods: Two cohorts were generated retrospectively: patients with AF undergoing AVR alone or combined with revascularization either with LAA amputation or without. Data were collected from the hospital-specific data system. Follow-up was completed by telephone interview or in person. Thirty-day and follow-up results were compared in patients with vs. without LAA amputation., Results: One hundred and fifty-seven patients were investigated retrospectively, and seventy-four pairs were matched with regard to baseline characteristics. Patients with LAA amputation exhibited a lower incidence of cumulative and late ischemic stroke (6.4% vs. 25%, p = 0.028 and 3.2% vs. 20%, p = 0.008, respectively; hazard ratio 0.30; 95% confidence interval 0.11; 0.84; p = 0.021) during follow-up of 48 months vs. patients without intervention during follow-up of 45 months, p = 0.494. No significant differences were observed in postoperative stroke, 2 (2.7%) vs. 3 (4.1%), p = 1.000, re-exploration for bleeding 3 (4.1%) vs. 6 (8.1), p = 0.494 or late pericardial effusion 2 (2.7%) vs. 3 (4.1%), p = 1.000, in-hospital 2 (2.7%) vs. 4 (5.4%), p = 0.681 and all-cause mortality 15 (23.8%) vs. 9 (15%), p = 0.315 in patients with vs. without LAA amputation, respectively., Conclusions: A combination of leading aortic stenosis and AF in patients undergoing isolated or combined biological AVR represents a subpopulation with excessive thromboembolic risk. Concomitant LAA amputation during cardiac surgery reduces the risk of ischemic stroke without posing an additional periprocedural risk for the patient. Therefore, the minimal invasive approach at the expense of omitting LAA amputation should be discouraged to maximize the clinical benefits of AVR in this setting.

Cardiovascular disease is among the leading causes of mortality in chronic obstructive pulmonary disease (COPD). Nonlinear heart rate variability (NHRV) measures are markers and predictors of cardiovascular disease, particularly arrhythmias. Our aim was to investigate NHRV in patients with COPD and changes after pulmonary rehabilitation. 20-minute ECGs were used to compare NHRV (a) in 45 healthy individuals and 31 patients with COPD and (b) in 16 patients who completed rehabilitation versus 13 age- and sex-matched control patients. We studied detrended fluctuation analysis (DFA1, DFA2), fractal dimension (low, high, average FD) and sample entropy. Compared to healthy individuals, patients with COPD had lower DFA1 (p=.038). During rehabilitation high FD decreased (p=.018) and DFA2 increased (p=.043). Cluster analysis displayed an increase of DFA1 in the rehabilitation cluster with DFA1 values below 1 (p=.032). NHRV reflects altered autonomic regulation in patients with COPD. Reduced DFA1 in patients with COPD implies a stronger pro-arrhythmic substrate and altered parasympathetic modulation.

Objectives: This study aims to improve early detection of cardiac surgery-associated acute kidney injury (CSA-AKI) compared to classical clinical scores., Methods: Data from 7633 patients who underwent cardiac surgery between 2008 and 2018 in our institution were analysed. CSA-AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Cleveland Clinical Score served as the reference with an area under the curve (AUC) 0.65 in our cohort. Based on that, stepwise logistic regression modelling was performed on the training data set including creatinine (Cr), estimated glomerular filtration rate (eGFR) levels and deltas (ΔCr, ΔeGFR) at different time points and clinical parameters as preoperative haemoglobin, intraoperative packed red blood cells (units) and cardiopulmonary bypass time (min) to predict CSA-AKI in the early postoperative course. The AUC was determined on the validation data set for each model respectively., Results: Incidence of CSA-AKI in the early postoperative course was 22.4% (n = 1712). The 30-day mortality was 12.5% in the CSA-AKI group (n = 214) and in the no-CSA-AKI group 0.9% (n = 53) (P < 0.001). Logistic regression models based on Cr and its delta gained an AUC of 0.69; 'Model eGFRCKD-EPI' an AUC of 0.73. Finally, 'Model DynaLab' including dynamic laboratory parameters and clinical parameters as haemoglobin, packed red blood cells and cardiopulmonary bypass time improved AUC to 0.84., Conclusions: Model DynaLab' improves early detection of CSA-AKI within 12 h after surgery. This simple Cr-based framework poses a fundament for further endeavours towards reduction of CSA-AKI incidence and severity., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Despite recent improvements in the composition of the diet, lower mineral bone density and overweight tendencies are incoherently described in patients with phenylketonuria (PKU). The impact of dietary factors and plasma phenylalanine levels on growth, BMI, body composition, and bone mineral density was investigated in our cohort of patients with hyperphenylalaninemia (HPA) with or without dietary treatment. The anthropometric, metabolic, BMI and other nutritional indicators and bone mineral density were compared between the group of 96 treated patients with PKU (58 classic PKU (cPKU) and 38 patients with moderate-mild PKU defined as non-classic PKU (non-cPKU)) and the untreated group of 62 patients with benign HPA. Having compared the treated and untreated groups, there were normal outcomes and no statistically significant differences in BMI, body composition, and bone mineral density. Lower body height standard deviation scores were observed in the treated as compared to the untreated group (P < 0.001), but the difference was not significant when analyzing patients older than 18 years; however, cPKU adults were shorter compared to non-cPKU treated adults (P = 0.012). Interestingly, the whole-body fat was statistically higher in non-cPKU as compared to cPKU patients. In conclusion, the dietary treatment ensured adequate nutrition without significant consequences in BMI, body composition, and bone mineral density. A low protein diet may have delayed the growth in childhood, but the treated patients gained a normal final height. Mild untreated hyperphenylalaninemia characteristic for benign HPA had no negative physiological effect on bone mineral density.

Objective: The early detection of breast cancer is one of the most important predictors in determining the prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) is an important imaging modality for detecting and interpreting the different breast lesions from a time sequence of images and has proved to be a very sensitive modality for breast-cancer diagnosis. However, DCE-MRI exhibits only a moderate specificity, thus leading to a high rate of false positives, resulting in unnecessary biopsies that are stressful and physically painful for the patient and lead to an increase in the cost of treatment. There is a strong medical need for a DCE-MRI computer-aided diagnosis tool that would offer a reliable support to the physician's decision providing a high level of sensitivity and specificity., Methods: In our study we investigated the possibility of increasing differentiation between the malignant and the benign lesions with respect to the spatial variation of the temporal enhancements of three parametric maps, i.e., the initial enhancement (IE) map, the post-initial enhancement (PIE) map and the signal enhancement ratio (SER) map, by introducing additional methods along with the grey-level co-occurrence matrix, i.e., a second-order statistical method already applied for quantifying the spatiotemporal variations. We introduced the grey-level run-length matrix and the grey-level difference matrix, representing two additional, second-order statistical methods, and the circular Gabor as a frequency-domain-based method. Each of the additional methods is for the first time applied to the DCE-MRI data to differentiate between the malignant and the benign breast lesions. We applied the least-square minimum-distance classifier (LSMD), logistic regression and least-squares support vector machine (LS-SVM) classifiers on a total of 115 (78 malignant and 37 benign) breast DCE-MRI cases. The performances were evaluated using ten experiments of a ten-fold cross-validation., Results: Our experimental analysis revealed the PIE map, together with the feature subset in which the discriminating ability of the co-occurrence features was increased by adding the newly introduced features, to be the most significant for differentiation between the malignant and the benign lesions. That diagnostic test - the aforementioned combination of parametric map and the feature subset achieved the sensitivity of 0.9193 which is statistically significantly higher compared to other diagnostic tests after ten-experiments of a ten-fold cross-validation and gave a statistically significantly higher specificity of 0.7819 for the fixed 95% sensitivity after the receiver operating characteristic (ROC) curve analysis. Combining the information from all the three parametric maps significantly increased the area under the ROC curve (AUC) of the aforementioned diagnostic test for the LSMD and logistic regression; however, not for the LS-SVM. The LSMD classifier yielded the highest area under the ROC curve when using the combined information, increasing the AUC from 0.9651 to 0.9755., Conclusion: Introducing new features to those of the grey-level co-occurrence matrix significantly increased the differentiation between the malignant and the benign breast lesions, thus resulting in a high sensitivity and improved specificity., (Copyright © 2013 Elsevier B.V. All rights reserved.)