Multimodal Treatment of Patients with Mental Symptom Load: A Pre-Post Comparison.

The Traditional Chinese Medicine (TCM) Hospital in Bad Kötzting, Germany, is treating chronically ill patients, covering a broad range of indications. The aim of this study was to prove the efficacy of a multimodal intervention combining mainstream medicine with TCM treatments on the severity of psychopathological symptoms. Out of 966 patients with chronic psychosomatic disease treated 2017 at the TCM Hospital, we selected 759 patients according to specific criteria and analyzed the outcomes after multimodal intervention. The patients completed a validated questionnaire (International Statistical Classification of Diseases (ICD) Symptom-Rating-(ISR)) at admission, discharge, and follow-up. The most frequent ICD-10 diagnoses were "diseases of the musculoskeletal system and connective tissue" (28.5%), "mental and behavioral disorders" (23.7%), and "diseases of the nervous system" (13.8%). Regarding ISR symptom load, "depressive syndrome" and "anxiety syndrome" were the leading burdens showing remissions of about 40%-60% with moderate (0.588) to strong (1.115) effect sizes (Cohen's d) after treatment. ISR total scores at discharge and follow-up were remarkably lower after intervention (0.64 and 0.75, respectively) compared to 1.02 at admission with moderate to strong effect sizes (0.512-0.815). These findings indicate a clinically relevant relief from mental symptom load after intervention with lasting clinical effects for at least six months.

In silico metabolic network analysis of Arabidopsis leaves.

BACKGROUND: During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants. RESULTS: The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response. CONCLUSIONS: The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.

Novel Approach for High-Throughput Metabolic Screening of Whole Plants by Stable Isotopes.

Here, we demonstrate whole-plant metabolic profiling by stable isotope labeling and combustion isotope-ratio mass spectrometry for precise quantification of assimilation, translocation, and molecular reallocation of (13)CO2 and (15)NH4NO3 The technology was applied to rice (Oryza sativa) plants at different growth stages. For adult plants, (13)CO2 labeling revealed enhanced carbon assimilation of the flag leaf from flowering to late grain-filling stage, linked to efficient translocation into the panicle. Simultaneous (13)CO2 and (15)NH4NO3 labeling with hydroponically grown seedlings was used to quantify the relative distribution of carbon and nitrogen. Two hours after labeling, assimilated carbon was mainly retained in the shoot (69%), whereas 7% entered the root and 24% was respired. Nitrogen, taken up via the root, was largely translocated into the shoot (85%). Salt-stressed seedlings showed decreased uptake and translocation of nitrogen (69%), whereas carbon metabolism was unaffected. Coupled to a gas chromatograph, labeling analysis provided enrichment of proteinogenic amino acids. This revealed significant protein synthesis in the panicle of adult plants, whereas protein biosynthesis in adult leaves was 8-fold lower than that in seedling shoots. Generally, amino acid enrichment was similar among biosynthetic families and allowed us to infer labeling dynamics of their precursors. On this basis, early and strong (13)C enrichment of Embden-Meyerhof-Parnas pathway and pentose phosphate pathway intermediates indicated high activity of these routes. Applied to mode-of-action analysis of herbicides, the approach showed severe disturbance in the synthesis of branched-chain amino acids upon treatment with imazapyr. The established technology displays a breakthrough for quantitative high-throughput plant metabolic phenotyping.

Green pathways: Metabolic network analysis of plant systems.

Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field.