Glatiramer acetate treatment persistence - but not adherence - in multiple sclerosis patients is predicted by health-related quality of life and self-efficacy: a prospective web-based patient-centred study (CAIR study)

Background: In patients with relapsing remitting multiple sclerosis (RRMS) the persistence of and adherence to disease modifying drug (DMD) treatment is inadequate. To take individualised measures there is a need to identify patients with a high risk of non-persistence or non-adherence. As patient-related factors have a major influence on persistence and adherence, we investigated whether health-related quality of life (HRQoL) and self-efficacy could predict persistence or adherence. Methods: In a prospective web-based patient-centred study in 203 RRMS patients, starting treatment with glatiramer acatete (GA) 20 mg subcutaneously daily, we measured physical and mental HRQoL (Multiple Sclerosis Quality of Life54 questionnaire), functional and control self-efficacy (Multiple Sclerosis Self-Efficacy Scale), the 12-month persistence rate and, in persistent patients, the percentage of missed doses. HRQoL and self-efficacy were compared between persistent and non-persistent patients, and between adherent and non-adherent patients. Logistic regression analysis was used to assess whether persistence and adherence were explained by HRQoL and self-efficacy. Results: Persistent patients had higher baseline physical (mean 58.1 [standard deviation, SD] 16.9) and mental HRQoL (63.8 [16.8]) than non-persistent patients (49.5 [17.6]; 55.9 [20.4]) (P = 0.001; P = 0.003) with no differences between adherent and non-adherent patients (P = 0.46; P = 0.54). Likewise, in persistent patients function (752 [156]) and control self-efficacy (568 [178]) were higher than in non-persistent patients (689 [173]; 491 [192]) (P = 0.009; P = 0.004), but not in adherent vs. non-adherent patients (P = 0.26; P = 0.82). Logistic regression modelling identified physical HRQoL and control self-efficacy as factors that explained persistence. Based on predicted scores from the model, patients were classified into quartiles and the percentage of non-persistent patients per quartile was calculated: non-persistence in the highest quartile was 23.4 vs. 53.2% in the lowest quartile. Risk differentiation with respect to adherence was not possible. Based on these findings we propose a practical work-up scheme to identify patients with a high risk of nonpersistence and to identify persistence-related factors. Conclusions: Findings suggest that pre-treatment physical HRQoL and control self-efficacy may identify RRMS patients with a high risk of early discontinuation of injectable DMD treatment. Targeting of high-risk patients may enable the efficient use of persistence-promoting measures

Hierarchical Self-Programming in Recurrent Neural Networks

We study self-programming in recurrent neural networks where both neurons (the `processors') and synaptic interactions (`the programme') evolve in time simultaneously, according to specific coupled stochastic equations. The interactions are divided into a hierarchy of $L$ groups with adiabatically separated and monotonically increasing time-scales, representing sub-routines of the system programme of decreasing volatility. We solve this model in equilibrium, assuming ergodicity at every level, and find as our replica-symmetric solution a formalism with a structure similar but not identical to Parisi's $L$-step replica symmetry breaking scheme. Apart from differences in details of the equations (due to the fact that here interactions, rather than spins, are grouped into clusters with different time-scales), in the present model the block sizes $m_i$ of the emerging ultrametric solution are not restricted to the interval $[0,1]$, but are independent control parameters, defined in terms of the noise strengths of the various levels in the hierarchy, which can take any value in [0,\infty\ket. This is shown to lead to extremely rich phase diagrams, with an abundance of first-order transitions especially when the level of stochasticity in the interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.

Zero-field incommensurate spin-Peierls phase with interchain frustration in TiOCl

We report on the magnetic, thermodynamic and optical properties of the quasi-one-dimensional quantum antiferromagnets TiOCl and TiOBr, which have been discussed as spin-Peierls compounds. The observed deviations from canonical spin-Peierls behavior, e.g. the existence of two distinct phase transitions, have been attributed previously to strong orbital fluctuations. This can be ruled out by our optical data of the orbital excitations. We show that the frustration of the interchain interactions in the bilayer structure gives rise to incommensurate order with a subsequent lock-in transition to a commensurate dimerized state. In this way, a single driving force, the spin-Peierls mechanism, induces two separate transitions.Comment: 4 pages, 4 figure

The XY Spin-Glass with Slow Dynamic Couplings

We investigate an XY spin-glass model in which both spins and couplings evolve in time: the spins change rapidly according to Glauber-type rules, whereas the couplings evolve slowly with a dynamics involving spin correlations and Gaussian disorder. For large times the model can be solved using replica theory. In contrast to the XY-model with static disordered couplings, solving the present model requires two levels of replicas, one for the spins and one for the couplings. Relevant order parameters are defined and a phase diagram is obtained upon making the replica-symmetric Ansatz. The system exhibits two different spin-glass phases, with distinct de Almeida-Thouless lines, marking continuous replica-symmetry breaking: one describing freezing of the spins only, and one describing freezing of both spins and couplings.Comment: 7 pages, Latex, 3 eps figure

IZA COVID-19 Crisis Response Monitoring: Netherlands (December 2021)

Hervorming Sociale Regelgevin

A mechanistic understanding of the relationship between skin innervation and chemotherapy-induced neuropathic pain

Neuropathic pain is a frequent complication of chemotherapy-induced peripheral neurotoxicity (CIPN). Chemotherapy-induced peripheral neuropathies may serve as a model to study mechanisms of neuropathic pain, since several other common causes of peripheral neuropathy like painful diabetic neuropathy may be due to both neuropathic and non-neuropathic pain mechanisms like ischemia and inflammation. Experimental studies are ideally suited to study changes in morphology, phenotype and electrophysiologic characteristics of primary afferent neurons that are affected by chemotherapy and to correlate these changes to behaviors reflective of evoked pain, mainly hyperalgesia and allodynia. However, hyperalgesia and allodynia may only represent one aspect of human pain, i.e., the sensory-discriminative component, while patients with CIPN often describe their pain using words like annoying, tiring and dreadful, which are affective-emotional descriptors that cannot be tested in experimental animals. To understand why some patients with CIPN develop neuropathic pain and others not, and which are the components of neuropathic pain that they are experiencing, experimental and clinical pain research should be combined. Emerging evidence suggests that changes in subsets of primary afferent nerve fibers may contribute to specific aspects of neuropathic pain in both preclinical models and in patients with CIPN. In addition, the role of cutaneous neuroimmune interactions is considered. Since obtaining dorsal root ganglia and peripheral nerves in patients is problematic, analyses performed on skin biopsies from preclinical models as well as patients provide an opportunity to study changes in primary afferent nerve fibers and to associate these changes to human pain. In addition, other biomarkers of small fiber damage in CIPN, like corneal confocal microscope and quantitative sensory testing, may be considered

Slowly evolving geometry in recurrent neural networks I: extreme dilution regime

We study extremely diluted spin models of neural networks in which the connectivity evolves in time, although adiabatically slowly compared to the neurons, according to stochastic equations which on average aim to reduce frustration. The (fast) neurons and (slow) connectivity variables equilibrate separately, but at different temperatures. Our model is exactly solvable in equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e. recall of one pattern). These show that, as the connectivity temperature is lowered, the volume of the retrieval phase diverges and the fraction of mis-aligned spins is reduced. Still one always retains a region in the retrieval phase where recall states other than the one corresponding to the `condensed' pattern are locally stable, so the associative memory character of our model is preserved.Comment: 18 pages, 6 figure

Slowly evolving random graphs II: Adaptive geometry in finite-connectivity Hopfield models

We present an analytically solvable random graph model in which the connections between the nodes can evolve in time, adiabatically slowly compared to the dynamics of the nodes. We apply the formalism to finite connectivity attractor neural network (Hopfield) models and we show that due to the minimisation of the frustration effects the retrieval region of the phase diagram can be significantly enlarged. Moreover, the fraction of misaligned spins is reduced by this effect, and is smaller than in the infinite connectivity regime. The main cause of this difference is found to be the non-zero fraction of sites with vanishing local field when the connectivity is finite.Comment: 17 pages, 8 figure

The impact of participation restrictions on everyday life in long-term colorectal cancer survivors in the EnCoRe study:A mixed-method study

Purpose: Knowledge about long-term colorectal cancer (CRC) or treatment related health and functioning problems and on its impact on participation of CRC survivors in domestic life and in society is limited. We aimed to explore the nature and impact of cancer (treatment) related participation restrictions on everyday life of long-term CRC survivors, their current satisfaction with participation, and associations of health and functioning problems with participation satisfaction, using the International Classification of Functioning, Disability and Health (ICF) to comprehensively study participation.Method: Mixed-method study in 2-10 years post-diagnosis stage I-III CRC survivors (n = 151) from the cross-sectional part of the EnCoRe study. Participation restrictions were explored by semi-structured interviews in a subsample reporting participation restrictions (n = 10). Role functioning (SF36-Health Survey), fatigue (Checklist Individual Strength), and peripheral neuropathy symptoms (EORTC QLQ-CIPN20) were assessed in all participants and associations with self-reported participation satisfaction were analyzed by multivariable logistic regression models.Results: 19% of CRC survivors reported dissatisfaction with participation. Participation restrictions were reported for interpersonal relationships, work/employment, and social/civic life. CRC survivors reporting better physical and emotional role functioning were significantly less likely to be dissatisfied with their participation, whereas survivors reporting higher levels of fatigue or more peripheral neuropathy symptoms were more likely to be dissatisfied with participation.Conclusions: Colorectal cancer (treatment) related health and functioning problems negatively impacts the ability of nearly 1 in 5 long-term CRC survivors to participate in everyday life situations and their satisfaction with participation. Follow-up care needs to be able to identify and address these problems.</p