Vitamin K Supplementation Modulates Bone Metabolism and Ultra-Structure of Ovariectomized Mice

Background/Aims: Osteoporosis is a bone metabolic disease that affects mostly post-menopausal women. There has been shown that vitamin K (VK) supplementation during menopause may decrease bone loss as well as risk of bone breaking. Aiming to clarify the beneficial role of VK in bone metabolism during menopause, we investigated mineral metabolism and bone ultrastructure of ovariectomized (OVX) mice. Methods: To determine the effects chronic use of VK in bone structure and mineral metabolism in OVX mice, we used several methods, such as DXA, µCTScan, and SEM as well as biomolecular techniques, such as ELISA and qRT-PCR. In addition, complete analysis of serum hormonal and other molecules associated to bone and lipid metabolism were evaluated overview the effects of VK in menopause murine model. Results: VK treatment significantly affects Pi metabolism independently of OVX, changing Pi plasma, urinary output, balance, and Pi bone mass. Interestingly, VK also increased VLDL in mice independently of castration. In addition, VK increased compact bone mass in OVX mice when we evaluated it by DXA, histomorphometry, µCTScanning. VK increased bone formation markers, osteocalcin, HYP- osteocalcin, and AP whereas it decreased bone resorption markers, such as urinary DPD/creatinine ratio and plasmatic TRAP. Surprisingly, SEM images revealed that VK treatment led to amelioration of microfractures observed in OVX untreated controls. In addition, SHAM operated VK treated mice exhibited higher number of migrating osteoblasts and in situ secretion of AP. OVX led to decreased to in situ secretion of AP that was restored by VK treatment. Moreover, VK treatment increased mRNA expression of bone Calbindin 28KDa independently of OVX. Conclusion: VK treatment in OVX mice exhibited beneficial effects on bone ultrastructure, mostly by altering osteoblastic function and secretion of organic bone matrix. Therefore, VK could be useful to treat osteopenic/osteoporotic patients

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Clinical and Image Outcomes of the Hill-Sachs Injury Approach by the Remplissage Technique on the Anterior Shoulder Instability

Abstract Objective To evaluate the functional outcome of the remplissage technique, the healing of the capsulotenodesis of the infraspinatus tendon in Hill-Sachs lesion, and the degree of fatty infiltration of the infraspinatus muscle and its postoperative strength. Methods Twenty-five patients with recurrent anterior dislocation of the shoulder and Hill-Sachs lesion with a Hardy index > 20% who underwent the remplissage arthroscopic technique were evaluated with a minimum follow-up of 1 year. Patients underwent a clinical evaluation (Carter-Rowe and Walch-Duplay functional scores, measurement of range of motion and strength) and a magnetic resonance imaging (MRI) exam on the operated shoulder. Results Eighty-eight percent and 92% of the patients had good or excellent scores in the functional assessments of the Carter-Rowe andWalch-Duplay scores, respectively. Amean difference of - 1 kg in the strength of the operated limbwas observedwhen compared with the contralateral limb (p < 0.001), as well as amean difference of 10° in external rotation 1 and 2 (p < 0.001), also compared with the contralateral side. All of the patients who underwent an MRI exam presented high-grade filling of the Hill-Sachs lesion by capsulotenodesis, as well as absence of or minimal fatty infiltration in the infraspinatus muscle. Conclusion The remplissage technique had good/excellent functional score results, despite the discrete, albeit statistically significant, loss of strength and of external rotation amplitude. Successful capsulotenodesis healing and filling of the Hill-Sachs defect were demonstrated

Association of PvuII and XbaI polymorphisms on estrogen receptor alpha (ESR1) gene to changes into serum lipid profile of post-menopausal women: Effects of aging, body mass index and breast cancer incidence - Fig 2

<p>A: Linear regression graph comparing serum Total Lipids and age. Each lines slopes are significantly different (p = 0.015). Goodness of fit (r²): XX = 0.097; Xx = 0.007. B: Linear regression graph comparing serum Triglycerides and age. Each lines slopes are significantly different (p<0.05). Goodness of fit (r²): XX = 0.060; Xx = 0.002. C: Linear regression graph comparing serum Total Lipid and BMI. Each lines slopes are not significantly different (p>0.05). Goodness of fit (r²): XX = 0.072; Xx = 0.004. D: Linear regression graph comparing serum triglycerides and BMI. Each lines slopes are significantly different (p<0.05). Goodness of fit (r²): XX = 0.106; Xx = 0.017.</p

Serum lipid profile for each genotype.

<p>Serum lipid profile for each genotype.</p