The chronic fatigue syndrome – an update

Background –  In this article, current scientific knowledge on the chronic fatigue syndrome (CFS) is reviewed. The US case definition of CFS (the CDC-definition) is most widespread in research and clinical practice. Estimates of prevalence vary from 0.2% to above 2%. The female–male ratio is approximately 3:1.
Clinical Features –  Severe fatigue is the dominating complaint; it is worsened from exertions and not substantially relieved by rest. In addition, the patients might have a varying combination of accompanying symptoms. Clinical evaluation should be based upon standardized guidelines, including an assessment of functional impairments.
Pathophysiology –  The pathophysiology should be interpreted within a biopsychosocial framework. Present knowledge suggests that certain genetic polymorphisms and personality traits might be regarded as predisposing factors, some infections and severe psychosocial stress constitute precipitating factors, whereas disturbances of immunity, skeletal muscle, cognitive abilities, endocrine control and cardiovascular homeostasis are possible perpetuating factors.
Treatment –  Cognitive behavioural therapy and graded exercise therapy are of proven value in randomized controlled trials. Several pharmaceutical measures have been explored and found to have no beneficial effect. Most patients might expect long-term improvement, but full recovery is rare; however, the prognosis is better among adolescents.     

Does bronchial hyperresponsiveness in childhood predict active asthma in adolescence?

Rationale: Bronchial hyperresponsiveness (BHR) is an important, but not specific, asthma characteristic. Objectives: We aimed to assess the predictive value of BHR tested by methacholine and exercise challenge at age 10 years for active asthma 6 years later. Methods: From a Norwegian birth cohort, 530 children underwent methacholine challenge and exercise-induced bronchoconstriction (EIB) test (n = 478) at 10 years and structured interview and clinical examination at age 16 years. The methacholine dose causing 20% reduction in FEV(1) (PD(20)) and the reduction in FEV(1) (%) after a standardized treadmill test were used for BHR assessment. Active asthma was defined with at least two criteria positive: doctor's diagnosis of asthma, symptoms of asthma, and/or treatment for asthma in the last year. Measurements and Main Results: PD(20) and EIB at 10 years of age increased the risk of asthma (β = 0.94 [95% confidence interval (CI), 0.92-0.96] per μmol methacholine and β = 1.10 [95% CI, 1.06-1.15] per %, respectively). Separately the tests explained 10 and 7%, respectively, and together 14% of the variation in active asthma 6 years later. The predicted probability for active asthma at the age of 16 years increased with decreasing PD(20) and increasing EIB. The area under the curve (receiver operating characteristic curves) was larger for PD(20) (0.69; 95% CI, 0.62-0.75) than for EIB (0.60; 95% CI, 0.53-0.67). Conclusions: BHR at 10 years was a significant but modest predictor of active asthma 6 years later, with methacholine challenge being superior to exercise test.     

On instabilities of deep learning in image reconstruction and the potential costs of AI

Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.     

Human skeletal muscle fiber type percentage and area after reduced muscle use: A systematic review and meta-analysis

The main objective of this systematic review was to examine the effect of reduced muscle activity on the relative number of type 1 muscle fibers (%) in the human vastus lateralis muscle. Other objectives were changes in type 2A and 2X percentages and muscle fiber cross-sectional area. We conducted systematic literature searches in eight databases and included studies assessing type 1 fiber percentage visualized by ATPase or immunohistochemical staining before and after a period (≥14 days) of reduced muscle activity. The reduced muscle activity models were detraining, leg unloading, and bed rest. Forty-two studies comprising 451 participants were included. Effect sizes were calculated as the mean difference between baseline and follow-up and Generic Inverse Variance tests with random-effects models were used for the weighted summary effect size. Overall, the mean type 1 muscle fiber percentage was significantly reduced after interventions (−1.94%-points, 95% CI [−3.37, −0.51], P = .008), with no significant differences between intervention models (P = .86). Meta-regression showed no effect of study duration on type 1 fiber percentage (P = .98). Conversely, the overall type 2X fiber percentage increased after reduced muscle activity (P < .001). The CSA of the muscle fiber types decreased after the study period (all P-values < 0.001) with greater reductions in type 2 than type 1 fibers (P < .001). The result of this meta-analysis display that the type 1 muscle fiber percentage decrease as a result of reduced muscle activity, although the effect size is relatively small.     

Molecular characterization of Corynebacterium diphtheriae isolates, Russia, 1957-1987

In the 1990s, the Newly Independent and Baltic States of the former Soviet Union experienced the largest diphtheria outbreak since the 1960s; it was caused by Corynebacterium diphtheriae strains of a unique clonal group. To address its origin, we studied 47 clinical isolates from Russia and demonstrated that this clonal group was an integral part of the endemic reservoir that existed in Russia at least 5 years before the epidemic began.