The Russian Federation has among the highest rates of cardiovascular disease (CVD) in the world and a high rate of untreated hypertension remains an important risk factor. Understanding who is at greatest risk is important to inform approaches to primary prevention.
2,353 hypertensive 35–69 year olds were selected from a population-based study, Know Your Heart, conducted in Arkhangelsk and Novosibirsk, Russian Federation, 2015–2018. The associations between untreated hypertension and a range of co-variates related to socio-demographics, health, and health behaviours were examined.
The age-standardised prevalence of untreated hypertension was 51.1% (95% CI 47.8–54.5) in males, 28.8% (25.4–32.5) in females, and 40.0% (37.5–42.5) overall. The factors associated with untreated hypertension relative to treated hypertension were younger ages, self-rated general health as very good-excellent, not being obese, no history of CVD events, no evidence of diabetes or chronic kidney disease, and not seeing a primary care doctor in the past year as well as problem drinking for women and working full time, lower education, and smoking for men.
The study found relatively high prevalence of untreated hypertension, especially, in men. Recent initiatives to strengthen primary care provision and implementation of a general health check programme (dispansarisation) are promising, although further studies should evaluate other, potentially more effective strategies tailored to the particular circumstances of this population.
Differential diagnoses between vegetative and minimally conscious states (VS and MCS, respectively) are frequently incorrect. Hence, further research is necessary to improve the diagnostic accuracy at the bedside. The main neuropathological feature of VS is the diffuse damage of cortical and subcortical connections. Starting with this premise, we used electroencephalography (EEG) recordings to evaluate the cortical reactivity and effective connectivity during transcranial magnetic stimulation (TMS) in chronic VS or MCS patients. Moreover, the TMS-EEG data were compared with the results from standard somatosensory-evoked potentials (SEPs) and event-related potentials (ERPs). Thirteen patients with chronic consciousness disorders were examined at their bedsides. A group of healthy volunteers served as the control group. The amplitudes (reactivity) and scalp distributions (connectivity) of the cortical potentials evoked by TMS (TEPs) of the primary motor cortex were measured. Short-latency median nerve SEPs and auditory ERPs were also recorded. Reproducible TEPs were present in all control subjects in both the ipsilateral and the contralateral hemispheres relative to the site of the TMS. The amplitudes of the ipsilateral and contralateral TEPs were reduced in four of the five MCS patients, and the TEPs were bilaterally absent in one MCS patient. Among the VS patients, five did not manifest ipsilateral or contralateral TEPs, and three of the patients exhibited only ipsilateral TEPs with reduced amplitudes. The SEPs were altered in five VS and two MCS patients but did not correlate with the clinical diagnosis. The ERPs were impaired in all patients and did not correlate with the clinical diagnosis. These TEP results suggest that cortical reactivity and connectivity are severely impaired in all VS patients, whereas in most MCS patients, the TEPs are preserved but with abnormal features. Therefore, TEPs may add valuable information to the current clinical and neurophysiological assessment of chronic consciousness disorders.
In this paper, we present a new method for detecting overlapping communities in net- works with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and parti- tioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.