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Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks.

Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening. An artificial intelligence (AI) system performs as well as or better than radiologists at detecting breast cancer from mammograms, and using a combination of AI and human inputs could help to improve screening efficiency.

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2–17 and using acute kidney injury—a common and potentially life-threatening condition18—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

We developed a digitally enabled care pathway for acute kidney injury (AKI) management incorporating a mobile detection application, specialist clinical response team and care protocol. Clinical outcome data were collected from adults with AKI on emergency admission before (May 2016 to January 2017) and after (May to September 2017) deployment at the intervention site and another not receiving the intervention. Changes in primary outcome (serum creatinine recovery to ≤120% baseline at hospital discharge) and secondary outcomes (30-day survival, renal replacement therapy, renal or intensive care unit (ICU) admission, worsening AKI stage and length of stay) were measured using interrupted time-series regression. Processes of care data (time to AKI recognition, time to treatment) were extracted from casenotes, and compared over two 9-month periods before and after implementation (January to September 2016 and 2017, respectively) using pre–post analysis. There was no step change in renal recovery or any of the secondary outcomes. Trends for creatinine recovery rates (estimated odds ratio (OR) = 1.04, 95% confidence interval (95% CI): 1.00–1.08, p = 0.038) and renal or ICU admission (OR = 0.95, 95% CI: 0.90–1.00, p = 0.044) improved significantly at the intervention site. However, difference-in-difference analyses between sites for creatinine recovery (estimated OR = 0.95, 95% CI: 0.90–1.00, p = 0.053) and renal or ICU admission (OR = 1.06, 95% CI: 0.98–1.16, p = 0.140) were not significant. Among process measures, time to AKI recognition and treatment of nephrotoxicity improved significantly (p

BACKGROUND Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks.

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Main body Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. Conclusion The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.

Early detection of patient deterioration is key to unlocking the potential for targeted preventative care and improving patient outcomes. This protocol describes a workflow for developing deep learning continuous risk models for early prediction of future acute adverse events from electronic health records (EHR), taking the prediction of the risk of future acute kidney injury (AKI) as an exemplar. The protocol consists of 34 steps grouped into the following stages: formal problem definition, data processing, model architecture selection, risk calibration and uncertainty, and evaluating model generalisability. For the protocol to be applicable to modelling the future risk of a particular condition, the problem formulation should be clinically and physiologically plausible and there needs to be sufficient associated predictive signal in routinely collected EHR data. Prospective validation is key in evaluating whether retrospective models developed by following the proposed protocol are clinically applicable and useful.

Background One reason for the introduction of digital technologies into health care has been to try to improve safety and patient outcomes by providing real-time access to patient data and enhancing communication among health care professionals. However, the adoption of such technologies into clinical pathways has been less examined, and the impacts on users and the broader health system are poorly understood. We sought to address this by studying the impacts of introducing a digitally enabled care pathway for patients with acute kidney injury (AKI) at a tertiary referral hospital in the United Kingdom. A dedicated clinical response team—comprising existing nephrology and patient-at-risk and resuscitation teams—received AKI alerts in real time via Streams, a mobile app. Here, we present a qualitative evaluation of the experiences of users and other health care professionals whose work was affected by the implementation of the care pathway. Objective The aim of this study was to qualitatively evaluate the impact of mobile results viewing and automated alerting as part of a digitally enabled care pathway on the working practices of users and their interprofessional relationships. Methods A total of 19 semistructured interviews were conducted with members of the AKI response team and clinicians with whom they interacted across the hospital. Interviews were analyzed using inductive and deductive thematic analysis. Results The digitally enabled care pathway improved access to patient information and expedited early specialist care. Opportunities were identified for more constructive planning of end-of-life care due to the earlier detection and alerting of deterioration. However, the shift toward early detection also highlighted resource constraints and some clinical uncertainty about the value of intervening at this stage. The real-time availability of information altered communication flows within and between clinical teams and across professional groups. Conclusions Digital technologies allow early detection of adverse events and of patients at risk of deterioration, with the potential to improve outcomes. They may also increase the efficiency of health care professionals’ working practices. However, when planning and implementing digital information innovations in health care, the following factors should also be considered: the provision of clinical training to effectively manage early detection, resources to cope with additional workload, support to manage perceived information overload, and the optimization of algorithms to minimize unnecessary alerts.

Background The development of acute kidney injury (AKI) in hospitalized patients is associated with adverse outcomes and increased health care costs. Simple automated e-alerts indicating its presence do not appear to improve outcomes, perhaps because of a lack of explicitly defined integration with a clinical response. Objective We sought to test this hypothesis by evaluating the impact of a digitally enabled intervention on clinical outcomes and health care costs associated with AKI in hospitalized patients. Methods We developed a care pathway comprising automated AKI detection, mobile clinician notification, in-app triage, and a protocolized specialist clinical response. We evaluated its impact by comparing data from pre- and postimplementation phases (May 2016 to January 2017 and May to September 2017, respectively) at the intervention site and another site not receiving the intervention. Clinical outcomes were analyzed using segmented regression analysis. The primary outcome was recovery of renal function to ≤120% of baseline by hospital discharge. Secondary clinical outcomes were mortality within 30 days of alert, progression of AKI stage, transfer to renal/intensive care units, hospital re-admission within 30 days of discharge, dependence on renal replacement therapy 30 days after discharge, and hospital-wide cardiac arrest rate. Time taken for specialist review of AKI alerts was measured. Impact on health care costs as defined by Patient-Level Information and Costing System data was evaluated using difference-in-differences (DID) analysis. Results The median time to AKI alert review by a specialist was 14.0 min (interquartile range 1.0-60.0 min). There was no impact on the primary outcome (estimated odds ratio [OR] 1.00, 95% CI 0.58-1.71; P=.99). Although the hospital-wide cardiac arrest rate fell significantly at the intervention site (OR 0.55, 95% CI 0.38-0.76; P Conclusions The digitally enabled clinical intervention to detect and treat AKI in hospitalized patients reduced health care costs and possibly reduced cardiac arrest rates. Its impact on other clinical outcomes and identification of the active components of the pathway requires clarification through evaluation across multiple sites.

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents — systems that perceive and act in some environment. In this context, "intelligence" is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document [see page X] gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law and philosophy to computer security, formal methods and, of course, various branches of AI itself. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.

WOS: 000408748500041, Introduction: In parallel with the increasing importance of human resources, researches carried out on human resources and organizational behavior issues have increased. Issues of from which factors are employees' performance and efficient working affected have become the field of interest of many researchers. Employee empowerment and organizational stress are important concepts affecting the working life. Purpose: The purpose of this study is to examine the relationship between employees' empowerment and organizational stress with the perspective of hospital employees. Methods: A questionnaire was applied to 140 employees working in a public hospital in Turkey to accomplish this purpose. Scales used were rated between 1 and 5. Results: As a result of the research, it was determined that participants' employee empowerment score was at a moderate level (2.717 +/- 0.786) and organizational stress score was high (3.420). While employees were affected by "sources of stress regarding the production structure" (3.599 +/- 0.863) at the most, they were affected by "sources of stress regarding the cultural structure" (2.923 +/- 1.026) at the least. As a result of the correlation analysis performed, negative and significant relationships were found between employee empowerment and sources of stress regarding the duty structure, sources of stress regarding the authority structure, sources of stress regarding the production structure, sources of stress regarding the clustering structure, and sources of stress regarding the cultural structure. Conclusion: The fact that employees do not have enough components of employee empowerment increases their stress levels. It is a known fact that the increasing stress level has negative impacts on both employees and the organization. Therefore, administrators should be aware of both concepts and try to create the necessary conditions strengthen their employees.

12th National Spectroscopy Congress -- MAY 18-22, 2011 -- Antalya, TURKEY, WOS: 000303588400007, This study was designed to examine the chemical composition and antioxidant activity of the essential oil and methanol extract of Mentha pulegium. Gas chromatography (GC) and gas chromatography-mass spectroscopy (GC-MS) analysis of the oil resulted in the determination of 22 components representing 98.88% of the oil. The major constituents of the oil were pulegone (71.47%) and menthone (7.67%). Antioxidant activities were determined by three different test systems, namely, DPPH, bcarotene/linoleic acid, and reducing power assay. In the DPPH system, essential oil showed the weakest activity. In the second case, the inhibition capacity (%) of the extract was measured as 63.17% +/- 0.92 (at 2 mg/mL concentration). In the case of reducing power, methanol extract again was the strongest radical reducer when compared with the oil. As expected, the amount of total phenolics was very high in methanol extract (97.20 +/- 0.05 mu g gallic acid equivalents/mg extract). On the other hand, total flavonoid content was found as 20.88 +/- 0.04 mu g quercetin equivalents/mg extract.

Kahya, Asuman ( Aksaray, Yazar ), The Eskişehir magnesite deposits (Süleymaniye, Margı, and Tutluca) are located in the western part of the İzmir–Ankara Suture Zone, northwestern Turkey. These vein and stockwork type magnesite deposits, which occur along major and minor fault systems, are hosted by Alpine-type ultramafic rocks. The purpose of this study was to understand the origin of the hydrothermal waters responsible and the source of carbon dioxide, and to compare these deposits with similar magnesite occurrences in Turkey and elsewhere. Petrographic and XRD analyses indicate that magnesite was the major carbonate mineral formed. Deposits are predominantly micritic and locally microsparitic, but some also contain secondary calcite and dolomite. The δ13C (V-PDB) values of the Süleymaniye magnesites (–2.7‰ to –7.7‰), the Margı magnesites (–7.6‰ to –11.2‰), and the Tutluca magnesites (–8.7‰ to –10.4‰) indicate that sources of carbon may include atmospheric carbon, dissolved inorganic carbon, freshwater carbonate, and mantle derived CO2 . The δ18O (SMOW) compositions of the magnesite range from 27.4‰ to 30.8‰ and show that the oxygen was derived from marine limestone and metamorphic rocks. The Süleymaniye magnesites have heavier carbon isotopic values than the others because of the greater contribution of mantle sourced CO2 , while oxygen isotopic values were similar to those of other altered ultramafic related magnesites in Europe (Former Yugoslavia, Greece). The Margı and the Tutluca magnesite deposits have carbon and oxygen isotopic values similar to those of other ultramafic-related magnesite deposits. Based on isotopic data, we argue that the magnesite deposits in the Eskişehir area formed in a near surface environment at low pressure and temperature. The estimated temperature, using average δ18O values, suggests that magnesite was precipitated from water at 37 °C.

WOS: 000269432000032, PubMed ID: 19563856, Seven Morchella species were analyzed for their antioxidant activities in different test systems namely beta-carotene/linoleic acid, DPPH, reducing power, chelating effect and scavenging effect (%) on the stable ABTS(center dot+), in addition to their heavy metals, total phenolic and flavonoid contents. In beta-carotene/linoleic acid system, the most active mushrooms were M. esculenta var. umbrina and M. angusticeps. In the case of DPPH, methanol extract of M. conica showed high antioxidant activity. The reducing power of the methanol extracts of mushrooms increased with concentration. Chelating capacity of the extracts was also increased with the concentration. On the other hand. in 40 mu g ml(-1) concentration, methanol extract of M. conica, exhibited the highest radical scavenging activity (78.66 +/- 2.07%) when reacted with the ABTS(center dot+) radical. Amounts of seven elements (Cu, Mn, Co, Zn, Fe, Ca, and Mg) and five heavy metals (Ni, Pb, Cd, Cr, and Al) were also determined in all species. M. conica was found to have the highest phenolic content among the samples. Flavonoid content of M. rotunda was also found superior (0.59 +/- 0.01 mu g QEs/mg extract). 2009 Elsevier Ltd. All rights reserved.

WOS: 000255109900015, PubMed ID: 17936619, Essential oils of three different Salvia species [Salvia aucheri var. aucheri (endemic), Salvia aramiensis and Salvia pilifera (endemic)] were screened for their possible antioxidant and antimicrobial properties as well as their chemical compositions. According to the gas chromatography (GC)/EIMS (gas chromatography/electron impact mass spectrum) analysis results; 41 (97.2%), 51 (98.5%) and 83 compounds (98.2%) were identified, respectively. While 1,8-cineole (30.5%), camphor (21.3%) and borneol (8.50%) are the major compounds for S. aucheri var. aucheri oil, beta-pinene (10.3%), was the main constituent for S. aramienesis together with 1,8-cineole (46.0%) and camphor (8.7%). In the case of S. pilifera oil, alpha-thujene (36. 1%) and alpha-pinene (13.8%) determined as the major compounds. Antioxidant activity was employed by two complementary test systems namely 2,2'-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and beta-carotene/linoleic acid systems. Antioxidant activity of S. aramiensis was found to be higher than those of the others for the both systems (12.26 +/- 1.09 and 92.46% +/- 1.64 mu g mg(-1), respectively). Additionally, antioxidant activities of BHT, curcumin, ascorbic acid and alpha-tocopherol were determined in parallel experiments. In the case of antimicrobial activity, similar activity pattern was obtained (both in disc diffusion and MIC tests). Antimicrobial activity of S. aramiensis was followed by S. aucheri var. aucheri and S. pilifera, respectively. In these experiments, the most sensitive microorganism Acinetobacter lwoffii was followed by Candida albicans. (C) 2007 Elsevier Ltd. All rights reserved.

WOS: 000252241700005, The bulk mass of the intrusive rocks found in the surroundings of Agoren/East Anatolia is composed mainly of Kfeldspar, quartz and plagioclase. Moreover the minerals biotite, muscovite and rarely amphiboles are present as minor components and apatite, zircon, chlorite, sericite, tourmaline as well as titanite are found as accessory components. The intrusives can be defined as granites and qaurtzmonzonites by plotting within the QAP diagram. They show a clearly prealuminous character (A/CNK ratio > 1.1) and typical geochemical attributes of S-type granites. The distribution pattern of trace - and rare earth elements in various diagrams indicate that the initial magma was generated by collision processes and accompanyed crust thickening and consequently a partial melting of the underlying Akdag metamorphics and Hinis metaophiolites. (c) 2005 Elsevier GmbH. All rights reserved.