RESUMEN
Centrosomes play an important role in the microtubule organization of a cell. The sperm's specialized centrosome consists of the canonical barrel-shaped proximal centriole, the funnel-shaped distal centriole, and the pericentriolar material known as striated columns (or segmented columns). Here, we examined the localization of the centriole proteins CEP135 and CP110 in cattle and human spermatozoa. In canonical centrioles, CP110 is a centriole tip protein that controls cilia formation, while CEP135 is a structural protein essential for constructing the centriole. In contrast, we found antibodies recognizing CEP135 and CP110 label near the proximal and distal centrioles at the expected location of the striated columns and capitulum in cattle and humans in an antibody and species-specific way. These findings provide a pathway to understanding the unique functions of spermatozoan centrosome.
RESUMEN
Background Venous thromboembolism (VTE) causes preventable in-hospital morbidity. Pharmacologic prophylaxis reduces VTE in at-risk patients but also increases bleeding. To increase appropriate prescribing, a risk calculator to guide prophylaxis decisions was developed. Despite efforts to promote its use, providers accessed it infrequently. Objective This study aimed to understand provider perspectives on VTE prophylaxis and facilitators and barriers to using the risk calculator. Design This is a qualitative study exploring provider perspectives on VTE prophylaxis and the VTE risk calculator. Participants We interviewed attending physicians and advanced practice providers who used the calculator, and site champions who promoted calculator use. Providers were categorized by real-world usage over a 3-month period: low (<20% of the time), moderate (20-50%), or high (>50%). Approach During semistructured interviews, we asked about experiences with VTE, calculator use, perspectives on its implementation, and experiences with other risk assessment tools. Once thematic saturation was reached, transcripts were analyzed using content analysis to identify themes. Results Fourteen providers participated. Five were high utilizers, three were moderate utilizers, and six were low utilizers. Three site champions participated. Eight major themes were identified as follows: (1) ease of use, (2) perception of VTE risk, (3) harms of thromboprophylaxis, (4) overestimation of calculator use, (5) confidence in own ability, (6) underestimation of risk by calculator, (7) variability of trust in calculator, and (8) validation to withhold prophylaxis from low-risk patients. Conclusions While providers found the calculator is easy to use, routine use may be hindered by distrust of its recommendations. Inaccurate perception of VTE and bleeding risk may prevent calculator use.
RESUMEN
BACKGROUND: Identifying a window of opportunity when patients are motivated to lose weight might improve the effectiveness of weight loss counseling. The onset of chronic disease could create such a window. OBJECTIVE: To determine whether identifying prediabetes was associated with subsequent weight loss. METHODS: Our retrospective cohort study included adults with obesity and a primary care visit between 2015 and 2017. Data were collected and analysed in 2019/2020. We compared patients who developed prediabetes [haemoglobin A1c (HbA1c) ≥5.7 and <6.5] to patients with a normal HbA1c (<5.7). We ran linear regression models to identify the association between identifying prediabetes and percent body mass index (BMI) change at 6 and 12 months. The adjusted model controlled for demographic characteristics at baseline, Charlson comorbidity score, and metformin, antipsychotic, antidepressant and antiobesity medication prescribed in either the first 3 months (for the 6-month outcome) or first 9 months (for 12-month outcome) and clustering within physician. RESULTS: Of 11 290 participants, 43% developed prediabetes. At 6 months, 15% of the prediabetes group lost ≥5% of their BMI compared with 13% of the comparison group. The results were similar at 12 months with 18% of the prediabetes group losing ≥5% of their BMI compared with 17%. The prediabetes group lost a higher percentage of their BMI (ß = -0.7% versus -0.3% at 6 months and ß = -0.5% versus 0.01% at 12 months). CONCLUSIONS: While the percent of BMI change was small, patients with newly identified prediabetes lost more weight than a comparison group.
