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Occipital nerve regional stimulation (ONS) is reported to improve pain in several studies. We examined long-term pain and functional outcomes of ONS in an open-label prospective study.
Patients with medically refractory and disabling craniofacial pain were prospectively selected for ONS. Primary outcome was a change in mean daily pain intensity on the numeric pain rating scale (NPRS) at 6 months. Secondary outcomes included changes in NPRS, Headache Impact Test-6 (HIT-6), Migraine Disability Assessment (MIDAS), Pain Disability Index (PDI), Center for Epidemiologic Studies Depression Scale – Revised (CESD-R), and Short Form-36 version 2 (SF36) at last follow-up.
Thirteen patients (mean age 49.7 ± 8.4) diagnosed with occipital neuralgia (6), hemicrania continua (2), persistent idiopathic facial pain (2), post-traumatic facial pain (1), cluster headache (1), and chronic migraine (1) were enrolled. Mean NPRS improved by 2.1 ± 2.1 at 6 months and 2.1 ± 1.9 at last follow-up (23.5 ± 18.1 months). HIT-6 decreased by 8.7 ± 8.8, MIDAS decreased by 61.3 ± 71.6, and PDI decreased by 17.9 ± 18. SF36 physical functioning, bodily pain, and social functioning improved by 16.4 ± 19.6, 18.0 ± 31.6, and 26.1 ± 37.3, respectively. Moderate to severe headache days (defined as ≥50% of baseline mean NPRS) were reduced by 8.9 ± 10.2 days per month with ONS.
ONS reduced the long-term NPRS and moderate–severe monthly headache days by 30% and improved functional outcomes and quality of life. A prospective registry for ONS would be helpful in accumulating a larger cohort with longer follow-up in order to improve the use of ONS.
In this prospective cohort of 1,012 Swiss hospital employees, 3 different assays were used to screen serum for SARS-CoV-2 antibodies. Seropositivity was 1%; the positive predictive values of the lateral-flow immunoassay were 64% (IgG) and 13% (IgM). History of fever and myalgia most effectively differentiated seropositive and seronegative participants.
A major limitation in nanoindentation analysis techniques is the inability to accurately quantify pile-up/sink-in around indentations. In this work, the contact area during indentation is determined simultaneously using both contact mechanical models and direct in situ observation in the scanning electron microscope. The pile-up around indentations in materials with low H/E ratios (nanocrystalline nickel and ultrafine-grained aluminum) and the sink-in around a material with a high H/E ratio (fused silica) were quantified and compared to existing indentation analyses. The in situ projected contact area measured by Scanning Electron Microscopy using a cube-corner tip differs significantly from the classical models for materials with low H/E modulus ratio. Using a Berkovich tip, the in situ contact area is in good agreement with the contact model suggested by Loubet et al. for materials with low H/E ratio and in good agreement with the Oliver and Pharr model for materials with high H/E ratio.
Depressive symptoms are highly prevalent in nursing home (NH) residents. The relationship between depressive symptoms and everyday competence in terms of basic (BaCo) and expanded everyday competence (ExCo; see Baltes et al., 2001) in the NH setting is, however, not clear. Applying Lewinsohn's depression model, we examined how residents’ BaCo and ExCo relate to their depressive symptoms. Furthermore, we investigated the mediating role of perceived control.
Cross-sectional data from 196 residents (Mage = 83.7 years, SD = 9.4 years) of two German NHs were analyzed. Study variables were assessed by the Geriatric Depression Scale-Residential (GDS-12R), maximal gait speed (BaCo), proxy ratings of residents’ in-home activity participation, and self-initiated social contact done by staff (ExCo). Structural equation modeling (SEM) was used and a simulation study was included to determine power and potential estimation bias.
At the descriptive level, one quarter of the residents showed symptoms of depression according to the GDS-12R cut-off criterion. Residents’ BaCo and ExCo were independently and equally strongly associated with their depressive symptoms in the SEM analysis. These findings were affected neither by cognitive impairment, sex, nor age. Perceived control mediated between BaCo but not ExCo and depressive symptoms.
Future research needs to follow the connection between residents’ everyday competence and their depressive symptoms longitudinally to better understand the underlying mechanisms.
A quantum Monte Carlo method is simply a Monte Carlo method applied to a quantum problem. What distinguishes a quantum Monte Carlo method from a classical one is the initial effort necessary to represent the quantum problem in a form that is suitable for Monte Carlo simulation. It is in making this transformation that the quantum nature of the problem asserts itself not only through such obvious issues as the noncommutivity of the physical variables and the need to symmetrize or antisymmetrize the wave function, but also through less obvious issues such as the sign problem. Almost always, the transformation replaces the quantum degrees of freedom by classical ones, and it is to these classical degrees of freedom that the Monte Carlo method is actually applied. Succeeding chapters present and explain many of the quantum Monte Carlo methods being successfully used on a variety of quantum problems. In Chapters 1 and 2 we focus on discussing what the Monte Carlo method is and why it is useful.
The Monte Carlo method
The Monte Carlo method is not a specific technique but a general strategy for solving problems too complex to solve analytically or too intensive numerically to solve deterministically. Often a specific strategy incorporates several different Monte Carlo techniques. In what is likely the first journal article to use the phrase “Monte Carlo,” Metropolis and Ulam (1949) discuss this strategy. To paraphrase them,
The Monte Carlo method is an iterative stochastic procedure, consistent with a defining relation for some function, which allows an estimate of the function without completely determining it.
This is quite different from the colloquialism, “a method that uses random numbers.” Let us examine the definition piece by piece. A key point will emerge.
Ulam and Metropolis were presenting the motivation and a general description of a statistical approach to the study of differential and integro-differential equations. These equations were their “defining relation for some function.” The “function” was the solution of these equations. This function is of course unknown a priori. Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) a few years later would propose a statistical approach to the study of equilibrium statistical mechanics. The defining relation there was a thermodynamic average of a physical quantity over the Boltzmann distribution. The function was the physical quantity, and the unknown its average.