(2S,3S,4R)-4-(3,4-Dimethoxyphenyl)-7-hydroxy-6-methoxy-2,3-dimethyl-3,4-dihydro-1(2H)-naphthalenone1(2H)-Naphthalenone, 4-(3,4-dimethoxyphenyl)-3,4-dihydro-7-hydroxy-6-methoxy-2,3-dimethyl-, [2S-(2α,3α,4β)]-/1(2H)-Naphthalenone, 4-(3,4-dimethoxyphenyl)-3,4-dihydro-7-hydroxy-6-methoxy-2,3-dimethyl-, (2S,3S,4R)-/Arisantetralone C
Methanol; Ethyl Acetate; Chloroform
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Leg length discrepancy is common both in healthy subjects and after total hip arthroplasty (THA). Studies that evaluated leg length following THA have demonstrated a notable inconsistency in restoring leg length. The effects concerning joint load during gait is however not well known. The purpose of this study was to use three-dimensional (3D) gait analysis to evaluate joint load during gait with a simulated leg length discrepancy of 2 and 4 cm. Nine healthy subjects without any history of hip injury participated.
A 3D gait analysis (Vicon, Motion System, Oxford, England) was performed with 6 cameras and 2 force palates using a standard biomechanical gait model. Hip joint moments of force were calculated for all three degrees of motion freedom. ANOVA for repeated measurements was used for statistical calculations.
Abduction peak moment was significantly increased at the short side (P < 0.05) but unaffected on the long side. The adduction moment decreased on the long side between 0 and 4 cm (P < 0.01) but was unaffected on the short side. The internal hip rotation moments were unchanged for both the long and the short side. The external rotation moment was unchanged on the short side and decreased between bare foot and 4 cm on the long side (P < 0.05). Conclusion: A leg length discrepancy of 2 cm or more creates biomechanical changes concerning hip joint load both on the long and the short side and that the effects are larger on the short side. The increased stress may cause problems in the long run.
gait analyses, leg length discrepancy, hip, biomechanics
Hip joint load in relation to leg length discrepancy
Per Wretenberg,1 Anders Hugo,1 and Eva Brostrom2
2008 Aug 11
Leeches can parasitize many vertebrate taxa. In amphibians, leech parasitism often has potential detrimental effects including population decline. Most of studies on the host-parasite interactions involving leeches and amphibians focus on freshwater environments, while they are very scarce for terrestrial amphibians. In this work, we studied the relationship between the leech Batracobdella algira and the European terrestrial salamanders of the genus Hydromantes, identifying environmental features related to the presence of the leeches and their possible effects on the hosts. We performed observation throughout Sardinia (Italy), covering the distribution area of all Hydromantes species endemic to this island. From September 2015 to May 2017, we conducted >150 surveys in 26 underground environments, collecting data on 2629 salamanders and 131 leeches. Water hardness was the only environmental feature correlated with the presence of B. algira, linking this leech to active karstic systems. Leeches were more frequently parasitizing salamanders with large body size. Body Condition Index was not significantly different between parasitized and non-parasitized salamanders. Our study shows the importance of abiotic environmental features for host-parasite interactions, and poses new questions on complex interspecific interactions between this ectoparasite and amphibians.
Parasitism, Cave, Interaction, Leech, Speleomantes, BCI
Batracobdella leeches, environmental features and Hydromantes salamanders
Enrico Lunghi,a,b,c,∗ Gentile Francesco Ficetola,d,e Manuela Mulargia,f Roberto Cogoni,g Michael Veith,a Claudia Corti,b and Raoul Manentid
2018 Jan 9.
Multimorbidity is highly prevalent in the elderly and relates to many adverse outcomes, such as higher mortality, increased disability and functional decline. Many studies tried to reduce the heterogeneity of multimorbidity by identifying multimorbidity clusters or disease combinations, however, the internal structure of multimorbidity clusters and the linking between disease combinations and clusters are still unknown. The aim of this study was to depict which diseases were associated with each other on person-level within the clusters and which ones were responsible for overlapping multimorbidity clusters.
The study analyses insurance claims data of the Gmunder ErsatzKasse from 2006 with 43,632 female and 54,987 male patients who were 65 years and older. The analyses are based on multimorbidity clusters from a previous study and combinations of three diseases (“triads”) identified by observed/expected ratios ≥ 2 and prevalence rates ≥ 1%. In order to visualise a “disease network”, an edgelist was extracted from these triads, which was analysed by network analysis and graphically linked to multimorbidity clusters.
We found 57 relevant triads consisting of 31 chronic conditions with 200 disease associations (“edges”) in females and 51 triads of 29 diseases with 174 edges in males. In the disease network, the cluster of cardiovascular and metabolic disorders comprised 12 of these conditions in females and 14 in males. The cluster of anxiety, depression, somatoform disorders, and pain consisted of 15 conditions in females and 12 in males.
We were able to show which diseases were associated with each other in our data set, to which clusters the diseases were assigned, and which diseases were responsible for overlapping clusters. The disease with the highest number of associations, and the most important mediator between diseases, was chronic low back pain. In females, depression was also associated with many other diseases. We found a multitude of associations between disorders of the metabolic syndrome of which hypertension was the most central disease. The most prominent bridges were between the metabolic syndrome and musculoskeletal disorders. Guideline developers might find our approach useful as a basis for discussing which comorbidity should be addressed.
Multimorbidity, Multimorbidity patterns, Disease combinations, Epidemiology, Chronic diseases, Elderly people, Factor analysis, Network analysis, Observed-expected-ratios, Claims data set
Reducing complexity: a visualisation of multimorbidity by combining disease clusters and triads
Ingmar Schafer,corresponding author Hanna Kaduszkiewicz, Hans-Otto Wagner, Gerhard Schon, Martin Scherer, and Hendrik van den Bussche
2014 Dec 16.