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Epidemiological data reveal that there is a need for prevention measures specifically targeted at children with low SES. In the German federal state Mecklenburg-Western Pomerania preschools in socially deprived regions can apply for additional funds to support children with developmental risks. Mandatory criteria for obtaining these funds involve an annual assessment of all children using the "Dortmunder Developmental Screening for Preschools (DESK 3-6 R)." This instrument can detect and monitor developmental risks in the domains fine motor skills, gross motor skills, language, cognition, and social development. In this study, we examine the domain "Attention and concentration," which is included for the 5 to 6-year-old age group, using data from two consecutive survey waves (sw). Research questions: (1) Does the prevalence rate ratio (PRR) improve over time? (2) Is the rate of improvements (developmental risk at sw1, no developmental risk at sw2) higher than the rate of deteriorations (no developmental risk at sw1, developmental risk at sw2)? Prospective cohort analysis (n = 940). The prevalence rate of a developmental risk in this DESK domain decreases over time (PRR = 0.78; p = 0.019). The ratio of the rate of improvements is 8.47 times higher than the rate of deteriorations. The results provide evidence of the effectiveness of targeted intervention measures in preschools focusing on skills that improve attention and concentration. This is significant considering the small-time interval and the categorization method of DESK scores. Nevertheless, over the same time period, the DESK results of some children deteriorated. Therefore, preschools also have to be aware that it is natural for some children to show modest declines in their skills over time.

Abstract Background Schizophrenia is the collective term for an exclusively clinically diagnosed, heterogeneous group of mental disorders with still obscure biological roots. Based on the assumption that valuable information about relevant genetic and environmental disease mechanisms can be obtained by association studies on patient cohorts of ≥ 1000 patients, if performed on detailed clinical datasets and quantifiable biological readouts, we generated a new schizophrenia data base, the GRAS (Göttingen Research Association for Schizophrenia) data collection. GRAS is the necessary ground to study genetic causes of the schizophrenic phenotype in a 'phenotype-based genetic association study' (PGAS). This approach is different from and complementary to the genome-wide association studies (GWAS) on schizophrenia. Methods For this purpose, 1085 patients were recruited between 2005 and 2010 by an invariable team of traveling investigators in a cross-sectional field study that comprised 23 German psychiatric hospitals. Additionally, chart records and discharge letters of all patients were collected. Results The corresponding dataset extracted and presented in form of an overview here, comprises biographic information, disease history, medication including side effects, and results of comprehensive cross-sectional psychopathological, neuropsychological, and neurological examinations. With >3000 data points per schizophrenic subject, this data base of living patients, who are also accessible for follow-up studies, provides a wide-ranging and standardized phenotype characterization of as yet unprecedented detail. Conclusions The GRAS data base will serve as prerequisite for PGAS, a novel approach to better understanding 'the schizophrenias' through exploring the contribution of genetic variation to the schizophrenic phenotypes.

Catalytic activity of microbial communities maintains the services and functions of soils. Microbial communities require energy and carbon for microbial growth, which they obtain by transforming organic matter (OM), oxidizing a fraction of it and transferring the electrons to various terminal acceptors. Quantifying the relations between matter and energy fluxes is possible when key parameters such as reaction enthalpy (∆rH), energy use efficiency (related to enthalpy) (EUE), carbon use efficiency (CUE), calorespirometric ratio (CR), carbon dioxide evolution rate (CER), and the apparent specific growth rate (μapp) are known. However, the determination of these parameters suffers from unsatisfying accuracy at the technical (sample size, instrument sensitivity), experimental (sample aeration) and data processing levels thus affecting the precise quantification of relationships between carbon and energy fluxes. To address these questions under controlled conditions, we analyzed microbial turnover processes in a model soil amended using a readily metabolizable substrate (glucose) and three commercial isothermal microcalorimeters (MC-Cal/100P, TAM Air and TAM III) with different sample sizes meaning varying volume-related thermal detection limits (LODv) (0.05−1mW L−1). We conducted aeration experiments (aerated and un-aerated calorimetric ampoules) to investigate the influence of oxygen limitation and thermal perturbation on the measurement signal. We monitored the CER by measuring the additional heat caused by CO2 absorption using a NaOH solution acting as a CO2 trap. The range of errors associated with the calorimetrically derived μapp, EUE, and CR was determined and compared with the requirements for quantifying CUE and the degree of anaerobicity (ηA). Calorimetrically derived μapp and EUE were independent of the instrument used. However, instruments with a low LODv yielded the most accurate results. Opening and closing the ampoules for oxygen and CO2 exchange did not significantly affect metabolic heats. However, regular opening during calorimetrically derived CER measurements caused significant measuring errors due to strong thermal perturbation of the measurement signal. Comparisons between experimentally determined CR, CUE,ηA, and modeling indicate that the evaluation of CR should be performed with caution.

An improvement in the reliability and comparability of tissue characterization results is crucial for enabling further progress in cancer detection and the assessment of therapeutic effects. This can only be achieved by integrating quantitative methods into well-established qualitative characterization routines. This case study presents a hybrid metrological approach for tissue characterisation including vibrational Fourier Transform InfraRed (FTIR) spectroscopy and traceable reference-free X-Ray Fluorescence analysis (XRF). Through the combination of spatially resolved qualitative molecular information with quantitative elemental concentrations an all-encompassing sample characterisation can be provided. The study was performed on tissue sections of syngeneic murine pancreatic ductal adenocarcinoma KPC (KrasG12D/+; Trp53R172H/+; Pdx-1-Cre) tumours ex-vivo. Sections from healthy pancreatic tissues, sham-exposed tumours and tumours subjected to low dose radiotherapy treatment (2 Gray and 6 Gray) were analysed using both methods. Additional sample integrity studies using Near Edge X-ray Absorption Fine Structure (NEXAFS) spectroscopy at the carbon and nitrogen K-edges were performed to assess the effect of sample aging and XRF investigations on the samples. Results showed an increase in the concentrations of elemental biomarkers, including S, K and amide I structures in malignant pancreatic tissue compared to healthy pancreatic tissue. The exposure of tumours to 6 Gy radiation decreases the levels of these elements towards a phenotype seen in the healthy pancreas. A protocol for hybrid investigations is presented, with emphasis on the sample preparation, minimizing the impact of consecutive applied methods on their measurands, and ensuring the compatibility and reliability of achieved results. The study demonstrates the cancer recognition capabilities, and the sensitivity for low dosage radiotherapy treatment monitoring for each method individually and assesses the potential of combining molecular fingerprinting with non-destructive quantitative elemental information for tissue sample characterization.