Проблемы увеличения продуктивности АПК в Украине и пути повышения его потенциала

Целью статьи является изучение причин снижения показателей продуктивности в агропромышленном комплексе и путей повышения продуктивности сельскохозяйственных культур

HLA Genes, Islet Autoantibodies and Residual C-Peptide at the Clinical Onset of Type 1 Diabetes Mellitus and the Risk of Retinopathy 15 Years Later

HLA genes, islet autoantibodies and residual C-peptide were studied to determine the independent association of each exposure with diabetic retinopathy (DR), 15 years after the clinical onset of type 1 diabetes in 15-34 year old individuals.The cohort was identified in 1992 and 1993 by the Diabetes Incidence Study in Sweden (DISS), which investigates incident cases of diabetes for patients between 15 and 34 years of age. Blood samples at diagnosis were analyzed to determine HLA genotype, islet autoantibodies and serum C-peptide. In 2009, fundus photographs were obtained from patient records. Study measures were supplemented with data from the Swedish National Diabetes Registry.The prevalence of DR was 60.2% (148/246). Autoantibodies against the 65 kD isoform of glutamate decarboxylase (GADA) at the onset of clinical diabetes increased the risk of DR 15 years later, relative risk 1.12 for each 100 WHO units/ml, [95% CI 1.02 to 1.23]. This equates to risk estimates of 1.27, [95% CI 1.04 to 1.62] and 1.43, [95% CI 1.06 to 1.94] for participants in the highest 25(th) (GADA>233 WHO units/ml) and 5(th) percentile (GADA>319 WHO units/ml) of GADA, respectively. These were adjusted for duration of diabetes, HbA(1c), treated hypertension, sex, age at diagnosis, HLA and C-peptide. Islet cell autoantibodies, insulinoma-antigen 2 autoantibodies, residual C-peptide and the type 1 diabetes associated haplotypes DQ2, DQ8 and DQ6 were not associated with DR.Increased levels of GADA at the onset of type 1 diabetes were associated with DR 15 years later. These results, if confirmed, could provide additional insights into the pathogenesis of the most common microvascular complication of diabetes and lead to better risk stratification for both patient screenings and DR treatment trials

Bipolar disorders

Bipolar disorder is characterized by (hypo)manic episodes and depressive episodes which alternate with euthymic periods. It causes serious disability with poor outcome, increased suicidality risk, and significant societal costs. This chapter describes the findings of the PET/SPECT research efforts and the current ideas on the pathophysiology of bipolar disorder. First, the cerebral blood flow and cerebral metabolism findings in the prefrontal cortex, limbic system, subcortical structures, and other brain regions are discussed, followed by an overview of the corticolimbic theory of mood disorders that explains these observations. Second, the neurotransmitter studies are discussed. The serotonin transporter alterations are described, and the variation in study results is explained, followed by an overview of the results of the various dopamine receptor and transporter molecules studies, taking into account also the relation to psychosis. Third, a concise overview is given of dominant bipolar disorder pathophysiological models, proposing starting points for future molecular imaging studies. Finally, the most important conclusions are summarized, followed by remarks about the observed molecular imaging study designs specific for bipolar disorder.</p

Stimulus chromatic properties affect period doubling in the human cone flicker ERG

INTRODUCTION: Period doubling in the full-field cone flicker electroretinogram (ERG) refers to an alternation in waveform amplitude and/or shape from cycle to cycle, presumably owing to the operation of a nonlinear gain control mechanism. This study examined the influence of stimulus chromatic properties on the characteristics of period doubling in order to better understand the underlying mechanism. METHODS: ERGs were acquired from 5 visually normal subjects in response to sinusoidally modulated flicker presented at frequencies from 25 to 100 Hz. The test stimuli and the pre-test-stimulus adaptation were either long wavelength (R), middle wavelength (G), or an equal combination of long and middle wavelengths (Y), all equated for photopic luminance. Fourier analysis was used to obtain the response amplitude at the stimulus frequency F and at a harmonic frequency of 3F/2, which was used as the index of period doubling. RESULTS: The frequency-response function for 3F/2 typically showed two peaks, occurring at approximately 33.3 and 50 Hz. However, the magnitude of period doubling within these frequency regions was dependent on the chromatic properties of both the test stimulus and the pre-stimulus adaptation. Period doubling was generally smallest when an R test was used, even though the stimuli were luminance-equated and the amplitude of F did not differ between the various conditions. DISCUSSION: The pattern of results indicates that the mechanism that generates period doubling is influenced by chromatic signals from both the test stimulus and the pre-stimulus adaptation, even though the high stimulus frequencies presumably favor the achromatic luminance system

Testing the Predictive Value of Peripheral Gene Expression for Nonremission Following Citalopram Treatment for Major Depression

Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).Fil: Guilloux, Jean Philippe. Universite Paris Sud; Francia. University of Pittsburgh; Estados UnidosFil: Bassi, Sabrina Cecilia. University of Pittsburgh; Estados Unidos. Hospital Italiano. Instituto de Ciencias Básicas y Medicina Experimental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ding, Ying. University of Pittsburgh; Estados UnidosFil: Walsh, Chris. University of Pittsburgh; Estados UnidosFil: Turecki, Gustavo. Douglas Mental Health Institute; CanadáFil: Tseng, George. University of Pittsburgh; Estados UnidosFil: Cyranowski, Jill M.. University of Pittsburgh; Estados UnidosFil: Sibille, Etienne. University of Pittsburgh; Estados Unido