Existing neurocomputational and empirical data link deficient neuromodulation of the fronto-parietal and hippocampal-striatal circuitries with aging-related increase in processing noise and declines in various cognitive functions. Specifically, the theory of aging neuronal gain control postulates that aging-related suboptimal neuromodulation may attenuate neuronal gain control, which yields computational consequences on reducing the signal-to-noise-ratio of synaptic signal transmission and hampering information processing within and between cortical networks. Intervention methods such as cognitive training and non-invasive brain stimulation, e.g., transcranial direct current stimulation (tDCS), have been considered as means to buffer cognitive functions or delay cognitive decline in old age. However, to date the reported effect sizes of immediate training gains and maintenance effects of a variety of cognitive trainings are small to moderate at best; moreover, training-related transfer effects to non-trained but closely related (i.e., near-transfer) or other (i.e., far-transfer) cognitive functions are inconsistent or lacking. Similarly, although applying different tDCS protocols to reduce aging-related cognitive impairments by inducing temporary changes in cortical excitability seem somewhat promising, evidence of effects on short- and long-term plasticity is still equivocal. In this article, we will review and critically discuss existing findings of cognitive training- and stimulation-related behavioral and neural plasticity effects in the context of cognitive aging, focusing specifically on working memory and episodic memory functions, which are subserved by the fronto-parietal and hippocampal-striatal networks, respectively. Furthermore, in line with the theory of aging neuronal gain control we will highlight that developing age-specific brain stimulation protocols and the concurrent applications of tDCS during cognitive training may potentially facilitate short- and long-term cognitive and brain plasticity in old age.

Background Growing evidence emphasizes the relevance of sphingolipids for metabolism and immunity of antigen-presenting cells (APC). APCs are key players in balancing tolerogenic and encephalitogenic responses in immunology. In contrast to the well-known prominent effects of sphingosine-1-phosphate (S1P) on lymphocyte trafficking, modulatory effects on APCs have not been fully characterized. Methods Frequencies and activation profiles of dendritic cell (DC) subtypes, monocytes, and T cell subsets in 35 multiple sclerosis (MS) patients were evaluated prior and after undergoing fingolimod treatment for up to 24 months. Impact of fingolimod and S1P on maturation and activation profile, pro-inflammatory cytokine release, and phagocytotic capacity was assessed in vitro and ex vivo. Modulation of DC-dependent programming of naïve CD4+ T cells, as well as CD4+ and CD8+ T cell proliferation, was also investigated in vitro and ex vivo. Results Fingolimod increased peripheral slanDC count—CD1+ DC, and monocyte frequencies remained stable. While CD4+ T cell count decreased, ratio of Treg/Th17 significantly increased in fingolimod-treated patients over time. CD83, CD150, and HLADR were all inhibited, but CD86 was upregulated in DCs after incubation in the presence of fingolimod. Fingolimod but not S1P was associated with reduced release of pro-inflammatory cytokines from DCs and monocytes in vitro and ex vivo. Fingolimod also inhibited phagocytic capacity of slanDCs and monocytes. After fingolimod, slanDCs demonstrated reduced potential to induce interferon–gamma-expressing Th1 or IL-17-expressing Th17 cells and DC-dependent T cell proliferation in vitro and in fingolimod-treated patients. Conclusions We present the first evidence that S1P-directed therapies can act additionally as immunomodulators that decrease the pro-inflammatory capabilities of APCs, which is a crucial element in DC-dependent T cell activation and programming.

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.

Plasmacytoid dendritic cells (pDCs) regulate innate and adaptive immunity. Neurotrophins and their receptors control the function of neuronal tissue. In addition, they have been demonstrated to be part of the immune response but little is known about the effector immune cells involved. We report, for the first time, the expression and immune-regulatory function of the low affinity neurotrophin receptor p75 neurotrophin receptor (p75NTR) by the antigen-presenting pDCs, mediated by toll-like receptor (TLR) 9 activation and differential phosphorylation of interferon regulatory factor 3 and 7. The modulation of p75NTR on pDCs significantly influences disease progression of asthma in an ovalbumin-induced mouse model mediated by the TLR9 signaling pathway. p75NTR activation of pDCs from patients with asthma increased allergen-specific T cell proliferation and cytokine secretion in nerve growth factor concentration-dependent manner. Further, p75NTR activation of pDCs delayed the onset of autoimmune diabetes in RIP-CD80GP mice and aggravated graft-versus-host disease in a xenotransplantation model. Thus, p75NTR signaling on pDCs constitutes a new and critical mechanism connecting neurotrophin signaling and immune response regulation with great therapeutic potential for a variety of immune disorders.

Background: Modern health systems are increasingly faced with the challenge to provide effective, affordable and accessible health care for people with chronic conditions. As evidence on the specific unmet needs and their impact on health outcomes is limited, practical research is needed to tailor chronic care to individual needs of patients with diabetes. Qualitative approaches to describe professional and informal caregiving will support understanding the complexity of chronic care. Results are intended to provide practical recommendations to be used for systematic implementation of sustainable chronic care models. Method: A mixed method study was conducted. A standardised survey (n = 92) of experts in chronic care using mail responses to open-ended questions was conducted to analyse existing chronic care programs focusing on effective, problematic and missing components. An expert workshop (n = 22) of professionals and scientists of a European funded research project MANAGE CARE was used to define a limited number of unmet needs and priorities of elderly patients with type 2 diabetes mellitus and comorbidities. This list was validated and ranked using a multilingual online survey (n = 650). Participants of the online survey included patients, health care professionals and other stakeholders from 56 countries. Results: The survey indicated that current care models need to be improved in terms of financial support, case management and the consideration of social care. The expert workshop identified 150 patient needs which were summarised in 13 needs dimensions. The online survey of these pre-defined dimensions revealed that financial issues, education of both patients and professionals, availability of services as well as health promotion are the most important unmet needs for both patients and professionals. Conclusion: The study uncovered competing demands which are not limited to medical conditions. The findings emphasise that future care models need to focus stronger on individual patient needs and promote their active involvement in co-design and implementation. Future research is needed to develop new chronic care models providing evidence-based and practical implications for the regional care setting.

Background Close, continuous and efficient collaboration between different professions and sectors of care is necessary to provide patient-centered care for individuals with mental disorders. The lack of structured collaboration between in- and outpatient care constitutes a limitation of the German health care system. Since 2012, a new law in Germany (§64b Social code book (SGB) V) has enabled the establishment of cross-sectoral and patient-centered treatment models in psychiatry. Such model projects follow a capitation budget, i.e. a total per patient budget of inpatient and outpatient care in psychiatric clinics. Providers are able to choose the treatment form and adapt the treatment to the needs of the patients. The present study (EVA64) will investigate the effectiveness, costs and efficiency of almost all model projects established in Germany between 2013 and 2016. Methods/design A health insurance data-based controlled cohort study is used. Data from up to 89 statutory health insurance (SHI) funds, i.e. 79% of all SHI funds in Germany (May 2017), on inpatient and outpatient care, pharmaceutical and non-pharmaceutical treatments and sick leave for a period of 7 years will be analyzed. All patients insured by any of the participating SHI funds and treated in one of the model hospitals for any of 16 pre-defined mental disorders will be compared with patients in routine care. Sick leave (primary outcome), utilization of inpatient care (primary outcome), utilization of outpatient care, continuity of contacts in (psychiatric) care, physician and hospital hopping, re-admission rate, comorbidity, mortality, disease progression, and guideline adherence will be analyzed. Cost and effectivity of model and routine care will be estimated using cost-effectiveness analyses. Up to 10 control hospitals for each of the 18 model hospitals will be selected according to a pre-defined algorithm. Discussion The evaluation of complex interventions is an important main task of health services research and constitutes the basis of evidence-guided advancement in health care. The study will yield important new evidence to guide the future provision of routine care for mentally ill patients in Germany and possibly beyond. Trial registration This study was registered in the database “Health Services Research Germany” (trial number: VVfD_EVA64_15_003713).

Background This study aimed to identify anxiety in dental patients visiting a dental clinic using the Dental Anxiety Scale, their level of psychological distress using the Brief Symptom Inventory-18 and therefore identifying a correlation between these groups as well as their gender and age. Methods An adult sample of N = 1549 patients (865 females, 779 males) was examined over the course of three years using the Brief Symptom Inventory-18 to evaluate psychological distress and the Dental Anxiety Scale to determine anxiety before dental treatment. Evaluations were conducted according to age and gender. Results There was no correlation between different age groups of the sample the Dental Anxiety Scale. Anxiety, depression and GSI were more frequent in patients below the age of 46 than above. Women were more susceptible to signs of Anxiety and Somatization and scored higher on the Dental Anxiety Scale and the Global Severity Index than male patients. There was a significant positive correlation between scores of the BSI-18 categories: Somatization, Anxiety and Depression and the DAS for dental patients. Conclusions This study showed that a relationship between dental anxiety and psychological distress exists. It would be an improvement to use a short questionnaire like the Dental Anxiety Scale to evaluate a patient before his first treatment so that more appropriate treatments can be pursued.

Sonderausgabe des 'Dresdner Universitätsjournal' von 2001:Zum Geleit S. 3 Vorwort S. 4 Inhaltsverzeichnis S. 5 Vom Knopf an der Turmspitze der Annenkirche: Die Geometrie des Gotthelf Fischer (1763–1832) S. 6 Frühe Lehrerbildung in Dresden: Lehrer und Eisenbahner – anfänglich stärkste Absolventengruppen S. 8 Junge Wissenschaftler auf neuen Lehrstühlen: Antimathematische Tendenzen – chancenlos unter Gustav Zeuner (1828–1907) S. 10 Von der Feinmechanik zur Mathematik: Die Verbindung von Technik, Kunst und darstellender Geometrie S. 12 Zwischen Mathematik und Physik: Der 2. Mathematische Lehrstuhl unter Aurel Voss (1845–1931) S. 14 Mathematiker als Bibliothekare: Die Katalogisierung – weiterentwickelt von Mathematikern S. 15 Gebündelte Reformbestrebungen: Neuer Aufschwung nach einem schwierigen Jahrzehnt S. 16 Neues vom Kreuzgymnasium: Einführung der Differential- und Integralrechnung in Mathematiklehrpläne S. 18 Mathematiker in der Gesellschaft ISIS: Wachsendes Interesse an mechanischen Rechengeräten S. 20 Rententafeln und Nettotarife: Zur Geschichte des Versicherungstechnischen Seminars S. 22 Dresdner als Ordinarien in Heidelberg: Erfolgreich auf dem Gebiet der kombinatorischen Topologie S. 24 Ein mitreißender Hochschullehrer: Gerhard Kowalewski (1876–1950) – Lehrer von Generationen Studierender S. 26 Frauen leben für die Mathematik: Dresdner Mathematik-Promovendinnen S. 28 Wissenschaftler und Humanist: Erich Trefftz (1888–1937) – „Motor“ der Akademischen Fliegergruppe Dresden S. 30 Mathematik und Politik: Personelle Veränderungen in der Dresdner Mathematik um 1940 S. 32 Impressum / Bildnachweis S. 34