Rodent Mediated Seed Dispersal of Joshua tree (Yucca brevifolia)

The Joshua tree is a charismatic and popular symbol of the Mojave Desert. Despite its popularity, we know little about the ecology of this species. The seed dispersal of Joshua tree, in particular, has not been thoroughly studied to this point. Here I examine the possible mechanisms acting to disperse Joshua tree seeds and their resulting fate. I hypothesized that Joshua tree seeds are cached by scatter-hoarding rodents and that other dispersal syndromes are unlikely. The majority of Joshua tree fruits monitored were taken directly from Joshua tree canopy by white-tailed antelope ground squirrels, and seeds and fruits on the soil surface were quickly removed by animals. Rodents given seeds labeled with scandium - 46 cached them between 0.1 cm and 4.1 cm deep. Seedling emergence was most common for seeds planted between 1 cm and 3 cm in the field, and at 1 cm in a growth chamber. Seeds placed on the soil surface were unlikely to germinate. Anemochory is unlikely because the wind speeds required to move Joshua tree seeds and fruits across the soil surface were high (mean 43.6 km/h and 31.9 km/h respectively), and rodents are likely to remove seeds before abiotic burial. These data show that the most common fate of Joshua tree seeds is hoarding by rodents. Caches made by rodents are an effective means of dispersal for Joshua tree

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Weight status and associated comorbidities in children and adults with Down syndrome, autism spectrum disorder and intellectual and developmental disabilities

This is the peer reviewed version of the following article: Ptomey, L. T., Walpitage, D. L., Mohseni, M., Dreyer Gillette, M. L., Davis, A. M., Forseth, B., Dean, E. E., and Waitman, L. R. (2020) Weight status and associated comorbidities in children and adults with Down syndrome, autism spectrum disorder and intellectual and developmental disabilities. Journal of Intellectual Disability Research, 64: 725– 737. https://doi.org/10.1111/jir.12767, which has been published in final form at https://doi.org/10.1111/jir.12767. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.Background Little is known about body weight status and the association between body weight and common comorbidities in children and adults with Down syndrome (DS), autism spectrum disorder (ASD) and other intellectual and developmental disabilities (IDDs). Methods Data were extracted from the University of Kansas Medical Center's Healthcare Enterprise Repository for Ontological Narration clinical integrated data repository. Measures included demographics (sex, age and race), disability diagnosis, comorbid health conditions, height, weight and body mass index percentiles (BMI%ile; <18 years of age) or BMI (≥18 years of age). Results Four hundred and sixty-eight individuals with DS (122 children and 346 adults), 1659 individuals with ASD (1073 children and 585 adults) and 604 individuals with other IDDs (152 children and 452 adults) were identified. A total of 47.0% (DS), 41.9% (ASD) and 33.5% (IDD) of children had overweight/obese (OW/OB), respectively. Children with DS were more likely to have OW/OB compared with children with IDD or ASD [odds ratio (OR) = 1.91, 95% confidence interval (CI): (1.49, 2.46); OR = 1.43, 95% CI: (1.19, 1.72)], respectively. A total of 81.1% (DS), 62.1% (ASD), and 62.4% (IDD) of adults were OW/OB, respectively. Adults with DS were more likely to have OW/OB compared with those with IDD [OR = 2.56, 95% CI: (2.16, 3.02)]. No significant differences were observed by race. In children with ASD, higher OW/OB was associated with significantly higher (compared with non-OW/OB) occurrence of sleep apnoea [OR = 2.94, 95% CI: (2.22, 3.89)], hypothyroidism [OR = 3.14, 95% CI: (2.17, 4.25)] and hypertension [OR = 4.11, 95% CI: (3.05, 5.54)]. In adults with DS, OW/OB was significantly associated with higher risk of sleep apnoea and type 2 diabetes [OR = 2.93, 95% CI: (2.10, 4.09); OR = 1.76, 95% CI: (1.11, 2.79) respectively]. Similarly, in adults with ASD and IDD, OW/OB was significantly associated with higher risk of sleep apnoea [OR = 3.39, 95% CI: (2.37, 4.85) and OR = 6.69, 95% CI: (4.43, 10.10)], type 2 diabetes [OR = 2.25, 95 % CI: (1.68, 3.01) and OR = 5.49, 95% CI: (3.96, 7.61)] and hypertension [OR = 3.55, 95% CI: (2.76, 4.57) and 3.97, 95% CI: (3.17, 4.97)]. Conclusion Findings suggest higher rates of OW/OB in individuals with DS compared with ASD and IDD. Given the increased risk of comorbidities associated with the increased risk of OW/OB, identification of effective interventions for this special population of individuals is critical

Computer-based insulin infusion protocol improves glycemia control over manual protocol.

OBJECTIVE: Hyperglycemia worsens clinical outcomes in critically ill patients. Precise glycemia control using intravenous insulin improves outcomes. To determine if we could improve glycemia control over a previous paper-based, manual protocol, authors implemented, in a surgical intensive care unit (SICU), an intravenous insulin protocol integrated into a care provider order entry (CPOE) system. DESIGN: Retrospective before-after study of consecutive adult patients admitted to a SICU during pre (manual protocol, 32 days) and post (computer-based protocol, 49 days) periods. MEASUREMENTS: Percentage of glucose readings in ideal range of 70-109 mg/dl, and minutes spent in ideal range of control during the first 5 days of SICU stay. RESULTS: The computer-based protocol reduced time from first glucose measurement to initiation of insulin protocol, improved the percentage of all SICU glucose readings in the ideal range, and improved control in patients on IV insulin for \u3e or =24 hours. Hypoglycemia (/dl) was rare in both groups. CONCLUSION: The CPOE-based intravenous insulin protocol improved glycemia control in SICU patients compared to a previous manual protocol, and reduced time to insulin therapy initiation. Integrating a computer-based insulin protocol into a CPOE system achieved efficient, safe, and effective glycemia control in SICU patients

Environment Scan of Generative AI Infrastructure for Clinical and Translational Science

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies

Liraglutide, a once-daily human GLP-1 analogue, added to a sulphonylurea over 26 weeks produces greater improvements in glycaemic and weight control compared with adding rosiglitazone or placebo in subjects with Type 2 diabetes (LEAD-1 SU)

Aim: To compare the effects of combining liraglutide (0.6, 1.2 or 1.8 mg/day) or rosiglitazone 4 mg/day (all n ≥ 228) or placebo (n = 114) with glimepiride (2-4 mg/day) on glycaemic control, body weight and safety in Type 2 diabetes. Methods: In total, 1041 adults (mean ± sd), age 56 ± 10 years, weight 82 ± 17 kg and glycated haemoglobin (HbA1c) 8.4 ± 1.0% at 116 sites in 21 countries were stratified based on previous oral glucose-lowering mono : combination therapies (30 : 70%) to participate in a five-arm, 26-week, double-dummy, randomized study. Results: Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (-1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (-0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. Liraglutide 0.6 mg was less effective (-0.6%, baseline 8.4%). Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l). Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [-2.5 to -2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (-0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (-1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). Changes in body weight with liraglutide 1.8 mg (-0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (-0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg). Main adverse events for all treatments were minor hypoglycaemia (< 10%), nausea (< 11%), vomiting (< 5%) and diarrhoea (< 8%). Conclusions: Liraglutide added to glimepiride was well tolerated and provided improved glycaemic control and favourable weight profile. Diabet. Med. 26, 268-278 (2009)

The effectiveness of computerized clinical guidelines in the process of care: a systematic review

<p>Abstract</p> <p>Background</p> <p>Clinical practice guidelines have been developed aiming to improve the quality of care. The implementation of the computerized clinical guidelines (CCG) has been supported by the development of computerized clinical decision support systems.</p> <p>This systematic review assesses the impact of CCG on the process of care compared with non-computerized clinical guidelines.</p> <p>Methods</p> <p>Specific features of CCG were studied through an extensive search of scientific literature, querying electronic databases: Pubmed/Medline, Embase and Cochrane Controlled Trials Register. A multivariable logistic regression was carried out to evaluate the association of CCG's features with positive effect on the process of care.</p> <p>Results</p> <p>Forty-five articles were selected. The logistic model showed that Automatic provision of recommendation in electronic version as part of clinician workflow (Odds Ratio [OR]= 17.5; 95% confidence interval [CI]: 1.6-193.7) and Publication Year (OR = 6.7; 95%CI: 1.3-34.3) were statistically significant predictors.</p> <p>Conclusions</p> <p>From the research that has been carried out, we can conclude that after implementation of CCG significant improvements in process of care are shown. Our findings also suggest clinicians, managers and other health care decision makers which features of CCG might improve the structure of computerized system.</p

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions

Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes

BACKGROUND: Data are lacking on the long-term effect on cardiovascular events of adding sitagliptin, a dipeptidyl peptidase 4 inhibitor, to usual care in patients with type 2 diabetes and cardiovascular disease. METHODS: In this randomized, double-blind study, we assigned 14,671 patients to add either sitagliptin or placebo to their existing therapy. Open-label use of antihyperglycemic therapy was encouraged as required, aimed at reaching individually appropriate glycemic targets in all patients. To determine whether sitagliptin was noninferior to placebo, we used a relative risk of 1.3 as the marginal upper boundary. The primary cardiovascular outcome was a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. RESULTS: During a median follow-up of 3.0 years, there was a small difference in glycated hemoglobin levels (least-squares mean difference for sitagliptin vs. placebo, -0.29 percentage points; 95% confidence interval [CI], -0.32 to -0.27). Overall, the primary outcome occurred in 839 patients in the sitagliptin group (11.4%; 4.06 per 100 person-years) and 851 patients in the placebo group (11.6%; 4.17 per 100 person-years). Sitagliptin was noninferior to placebo for the primary composite cardiovascular outcome (hazard ratio, 0.98; 95% CI, 0.88 to 1.09; P<0.001). Rates of hospitalization for heart failure did not differ between the two groups (hazard ratio, 1.00; 95% CI, 0.83 to 1.20; P = 0.98). There were no significant between-group differences in rates of acute pancreatitis (P = 0.07) or pancreatic cancer (P = 0.32). CONCLUSIONS: Among patients with type 2 diabetes and established cardiovascular disease, adding sitagliptin to usual care did not appear to increase the risk of major adverse cardiovascular events, hospitalization for heart failure, or other adverse events