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In addition to their often‐cited role as global service hubs, world cities are also global innovation hubs with their connectivity in knowledge networks crucial for the innovation clusters they harbour. While both aspects of world cities' global connectivity may allow their urban economies to grow, we argue that specialization in one of these two networks may increase economic performance. We provide systematic evidence of this substitution effect by examining the changing positions of 129 world cities in inter‐city innovation and service firm networks (2000–2012). Fixed effects panel analysis of city GDP growth confirm that the two networks are partial substitutes. [ABSTRACT FROM AUTHOR]

Patients who sustain a fracture are at greatest risk of recurrent fracture during the next 2 years. We propose three models to identify subjects most at risk of an imminent fracture, according to fracture site (any fracture, major osteoporotic fracture [MOF] or central). They were constructed using data of the prospective Frisbee cohort, which includes 3560 postmenopausal women aged 60 to 85 years who were followed for at least 5 years. A total of 881 subjects had a first incident validated fragility fracture before December 2018. Among these, we validated 130 imminent fractures occurring within the next 2 years; 79 were MOFs, and 88 were central fractures. Clinical risk factors were re‐evaluated at the time of the index fracture. Fine and Gray proportional hazard models were derived separately for each group of fractures. The following risk factors were significantly associated with the risk of any imminent fracture: total hip bone mineral density (BMD) (p < 0.001), a fall history (p < 0.001), and comorbidities (p = 0.03). Age (p = 0.05 and p = 0.03, respectively) and a central fracture as the index fracture (p = 0.04 and p = 0.005, respectively) were additional predictors of MOFs and central fractures. The three prediction models are presented as nomograms. The calibration curves and the Brier scores based on bootstrap resampling showed calibration scores of 0.089 for MOF, 0.094 for central fractures, and 0.132 for any fractures. The predictive accuracy of the models expressed as area under the receiver operating characteristic (AUROC) curve (AUC) were 0.74 for central fractures, 0.72 for MOFs, and 0.66 for all fractures, respectively. These AUCs compare well with those of FRAX and Garvan to predict the 5‐ or 10‐year fracture probability. In summary, five predictors (BMD, age, comorbidities, falls, and central fracture as the incident fracture) allow the calculation with a reasonable accuracy of the imminent risk of fracture at different sites (MOF, central fracture, and any fracture) after a recent sentinel fracture. © 2021 American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work‐up of neurodegenerative diseases. icobrain dm, a CE‐labeled and FDA‐cleared automatic tool for brain volumetry using clinical MRI scans, has shown great potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts (Struyfs H, et al. (2020)).This study examined the diagnostic value of icobrain dm for AD in routine clinical practice, and compared it to FreeSurfer, one of the standard tools in neuroimaging research. Method: The Belgian Dementia Council (BeDeCo) initiated a retrospective, multi‐center Belgian study (REMEMBER). Through automated volumetric analyses of whole brain, gray matter, cortical gray matter, frontal, parietal, occipital and temporal cortices, hippocampal volumes and lateral ventricles on real‐world clinical brain MRI T1w (n=820) images across the AD continuum, icobrain dm’s (v.4.4.0) ability to differentiate diagnostic groups in the AD continuum vs. HC was compared to FreeSurfer (v.6.0). Using icobrain dm’s automated volumetric output, we also investigated which combined brain volumes contributed most to establish an AD diagnosis and could differentiate between different stages of the disease. Result: The final study population consisted of subjects from eight Belgian memory clinics, with HC (n=89), subjective cognitive decline (SCD, n=89), mild cognitive impairment (MCI, n=350), and ADD (n=250) (Table 1). icobrain dm outperformed FreeSurfer in processing time (15‐30 minutes vs. 9‐32 hours), robustness (0 failures vs. 67 failures due to complete abortion of analysis during topology corrections) and diagnostic performance (AUC, sensitivity and specificity) in univariate differentiation between HC and ADD patients (Figure 1, Table 2). Stepwise backward regression models showed improved diagnostic accuracy for pairwise group differentiations when combining multiple brain volumes computed by icobrain dm, with highest performance obtained for distinguishing HC from ADD patients (AUC=0.914; Specificity 83.0%; Sensitivity 86.3%) (Table 3). Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that icobrain dm’s automated volumetry can improve diagnostic certainty in a real‐world clinical setting with a significantly reduced processing time, robustness and diagnostic performance, as compared to FreeSurfer. [ABSTRACT FROM AUTHOR]

Probabilistic models including clinical risk factors with or without bone mineral density (BMD) have been developed to estimate the 5‐ or 10‐year absolute fracture risk. We investigated the performance of the FRAX and Garvan tools in a well‐characterized population‐based cohort of 3560 postmenopausal, volunteer women, aged 60 to 85 years at baseline, included in the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) cohort, during 5 years of follow‐up. Baseline data were used to calculate the estimated 10‐year risk of hip and major osteoporotic fractures (MOFs) for each participant using FRAX (Belgium). We computed the 5‐year risk according to the Garvan model with BMD. For calibration, the predicted risk of fracture was compared with fracture incidence across a large range of estimated fracture risks. The accuracy of the calculators to predict fractures was assessed using the area under the receiver operating characteristic curves (AUC). The FRAX tool was well calibrated for hip fractures (slope 1.09, p < 0.001; intercept −0.001, p = 0.46), but it consistently underestimated the incidence of major osteoporotic fractures (MOFs) (slope 2.12, p < 0.001; intercept −0.02, p = 0.06). The Garvan tool was well calibrated for "any Garvan" fractures (slope 1.05, p < 0.001; intercept 0.01, p = 0.37) but largely overestimated the observed hip fracture rate (slope 0.32, p < 0.001; intercept 0.006, p = 0.05). The predictive value for hip fractures was better for FRAX (AUC: 0.841, 95% confidence interval [CI] 0.795–0.887) than for Garvan (AUC: 0.769, 95% CI 0.702–0.836, p = 0.01). The Garvan AUC for "any Garvan" fractures was 0.721 (95% CI 0.693–0.749) and FRAX AUC for MOFs was 0.708 (95% CI 0.675–0.741). In conclusion, in our Belgian cohort, FRAX estimated quite well hip fractures but underestimated MOFs, while Garvan overestimated hip fracture risk but showed a good estimation of "any Garvan" fractures. Both models had a good discriminatory value for hip fractures but only a moderate discriminatory ability for MOFs or "any Garvan" fractures. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. [ABSTRACT FROM AUTHOR]

Aim: Dementia with Lewy bodies (DLB) is a common but underdiagnosed type of cognitive impairment and dementia. The current diagnostic criteria for research purposes have a high specificity but lack sensitivity. Moreover, patients who live alone are not always aware that they have core clinical features such as cognitive fluctuations, visual hallucinations, and parasomnia. Anxiety is a common and early manifestation in DLB. Methods: We matched 41 DLB patients with 41 patients with Alzheimer's disease (AD) according to gender, age, and cognitive status and retrospectively analyzed their files for the presence of anxiety, depression, constipation, and the core clinical features of DLB in the documented period before diagnosis. Results: Anxiety, but not depression, occurred significantly more frequently in DLB than in AD (63.4% vs 26.8%). It appears up to 4-5 years before the diagnosis of DLB and is associated with depression and living at home. Anxiety in DLB was often described as intermittent panic attacks without reason or during states of delirium; it was also severe enough to require medical treatment and inpatient or outpatient psychiatric care. It was often mistaken for a psychiatric illness or a manifestation of other common forms of dementia. Anxiety in AD seemed much milder, was often related to the patient's coping with cognitive dysfunction, and was never cited as a specific reason for medical help. The concomitant presence of anxiety with at least one core clinical criterion of DLB enabled us to differentiate it from AD in our study, with a sensitivity of 63.4% but a specificity of 100%. Conclusions: In all patients over 50 who present with cognitive problems and anxiety, DLB should be considered. Patients and informants should be carefully questioned regarding the presence of other typical signs and symptoms of DLB. [ABSTRACT FROM AUTHOR]

The article offers information on direct comparison of the 2005 and 2017 criteria for dementia with Lewy bodies. It mentions diagnosis of probable DLB was to be made when a patient had dementia with at least two of three core features, namely recurrent visual hallucinations, fluctuating cognition, and/or spontaneous parkinsonism.

The article describes the case of a woman with Alzheimer's disease (AD) occurring with Lewy body disease (LBD) who developed Takotsubo cardiomyopathy (TC) following an acute episode of anxiety. Topics covered include the patient's medical history, the findings of the physical neurological and laboratory examinations, and patient outcome after treatment with furosemide and paroxetine after diagnosis and levodopa eighteen months after the diagnosis.