1. Effect of Treatment With Sacubitril/Valsartan in Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial
Douglas L Mann, et al. JAMA Cardiol. 2022 Jan 1;7(1):17-25. doi: 10.1001/jamacardio.2021.4567.
Importance: The use of sacubitril/valsartan is not endorsed by practice guidelines for use in patients with New York Heart Association class IV heart failure with a reduced ejection fraction because of limited clinical experience in this population. Objective: To compare treatment with sacubitril/valsartan treatment with valsartan in patients with advanced heart failure and a reduced ejection fraction and recent New York Heart Association class IV symptoms. Design, setting, and participants: A double-blind randomized clinical trial was conducted; a total of 335 patients with advanced heart failure were included. The trial began on March 2, 2017, and was stopped early on March 23, 2020, owing to COVID-19 risk. Intervention: Patients were randomized to receive sacubitril/valsartan (target dose, 200 mg twice daily) or valsartan (target dose, 160 mg twice daily) in addition to recommended therapy. Main outcomes and measures: The area under the curve (AUC) for the ratio of N-terminal pro-brain natriuretic peptide (NT-proBNP) compared with baseline measured through 24 weeks of therapy. Results: Of the 335 patients included in the analysis, 245 were men (73%); mean (SD) age was 59.4 (13.5) years. Seventy-two eligible patients (18%) were not able to tolerate sacubitril/valsartan, 100 mg/d, during the short run-in period, and 49 patients (29%) discontinued sacubitril/valsartan during the 24 weeks of the trial. The median NT-proBNP AUC for the valsartan treatment arm (n = 168) was 1.19 (IQR, 0.91-1.64), whereas the AUC for the sacubitril/valsartan treatment arm (n = 167) was 1.08 (IQR, 0.75-1.60). The estimated ratio of change in the NT-proBNP AUC was 0.95 (95% CI 0.84-1.08; P = .45). Compared with valsartan, treatment with sacubitril/valsartan did not improve the clinical composite of number of days alive, out of hospital, and free from heart failure events. Aside from a statistically significant increase in non-life-threatening hyperkalemia in the sacubitril/valsartan arm (28 [17%] vs 15 [9%]; P = .04), there were no observed safety concerns. Conclusions and relevance: The findings of this trial showed that, in patients with chronic advanced heart failure with a reduced ejection fraction, there was no statistically significant difference between sacubitril/valsartan and valsartan with respect to reducing NT-proBNP levels. Trial registration: ClinicalTrials.gov Identifier: NCT02816736.
2. First report of the root-knot nematode Meloidogyne incognita on Salvia miltiorrhiza Bunge in Henan Province, China
Yi Wen, Kunyuan Chen, Jiang-Kuan Cui, Tielin Wang, Hongrui Zhang, Fengru Zheng, Wenyang Li, Feng Chen Plant Dis. 2022 Aug 10. doi: 10.1094/PDIS-05-22-0997-PDN. Online ahead of print.
Salvia miltiorrhiza Bunge is an important Chinese herbal medicine, mainly used to treat cardiovascular disease. At present, the planting area of S. miltiorrhiza is near 20,000 hectares in China, mainly in Shandong, Henan, Shanxi, Shaanxi and Sichuan provinces. Root-knot nematode (Meloidogyne spp.) is one of the most devastating pathogens on S. miltiorrhiza.
3. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data
Punit Rathore, Zahra Ghafoori, James C Bezdek, Marimuthu Palaniswami, Christopher Leckie IEEE Trans Cybern. 2019 May;49(5):1629-1641. doi: 10.1109/TCYB.2018.2806886. Epub 2018 Mar 5.
Dunn's internal cluster validity index is used to assess partition quality and subsequently identify a "best" crisp partition of n objects. Computing Dunn's index (DI) for partitions of n p -dimensional feature vector data has quadratic time complexity O(pn2) , so its computation is impractical for very large values of n . This note presents six methods for approximating DI. Four methods are based on Maximin sampling, which identifies a skeleton of the full partition that contains some boundary points in each cluster. Two additional methods are presented that estimate boundary points associated with unsupervised training of one class support vector machines. Numerical examples compare approximations to DI based on all six methods. Four experiments on seven real and synthetic data sets support our assertion that computing approximations to DI with an incremental, neighborhood-based Maximin skeleton is both tractable and reliably accurate.