RESUMEN
One of the most important areas of nanotechnology is the use of nanoparticles (NPs) for a variety of environmental and biological applications, with silver nanoparticles (Ag-NPs) gaining a lot attention due to their distinct properties. The current study deals with the synthesis of Ag-NPs from Dicliptera bupleuroides Nees leaf extract and to determine their antioxidant, antimicrobial potential and effects on wheat seed germination and growth. UV-Visible spectrum revealed a prominent absorption peak at 442 nm, elucidating the conformation of the Ag-NPs synthesis. Scanning electron microscopy (SEM) showed distinctive triangular, pyramidal, and irregular shape. X-ray diffraction (XRD) demonstrated their crystalline nature, with average crystallite size of the Ag-NPs measured at 20.52 nm. Fourier-transform infrared spectroscopy (FT-IR) further confirmed the presence of functional groups such as Phenols (O-H stretch), transition metal carbonyls N-H, ≡C-H, C ≡ N, C ≡ C, C-Cl, C-Br and O-H bonds on the surface Ag-NPs. The antibacterial activity of the Ag-NPs was most pronounced against Bacillus subtilis, with a zone of inhibition (ZOI) measuring 11 mm ± 0.57 at a concentration of 1000 µg/mL (45% inhibition). Likewise, Ag-NPs exhibited highest antioxidant potential (73.2%) at 100 µg/mL compared with standard (ascorbic acid) which showed (76%) at the same concentration. Furthermore, the effect of D. bupleuroides mediated Ag-NPs on wheat seeds growth and germination was recorded maximum at high concentrations (200-300 ppm). In conclusion, D. bupleuroides mediated Ag-NPs showed safe, cost effective and environmentally friendly synthesis which can be used as an antibacterial and antioxidant agent as well as for enhancing the growth and seed germination of crop seeds globally. RESEARCH HIGHLIGHTS: Nanotechnology is the study of nanoparticles for biological and environmental applications. Ag-NPs among other NPs have received broad attention because of their unique properties. D. bupleuroides Ag-NPs: 45% antibacterial, 73.2% antioxidant, enhance wheat germination. D. bupleuroides-mediated Ag-NPs are both cost-effective and environmentally beneficial.
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BACKGROUND: In nonobstructive hypertrophic cardiomyopathy (nHCM), there are no approved medical therapies. Impaired myocardial energetics is a potential cause of symptoms and exercise limitation. Ninerafaxstat, a novel cardiac mitotrope, enhances cardiac energetics. OBJECTIVES: This study sought to evaluate the safety and efficacy of ninerafaxstat in nHCM. METHODS: Patients with hypertrophic cardiomyopathy and left ventricular outflow tract gradient <30 mm Hg, ejection fraction ≥50%, and peak oxygen consumption <80% predicted were randomized to ninerafaxstat 200 mg twice daily or placebo (1:1) for 12 weeks. The primary endpoint was safety and tolerability, with efficacy outcomes also assessed as secondary endpoints. RESULTS: A total of 67 patients with nHCM were enrolled at 12 centers (57 ± 11.8 years of age; 55% women). Serious adverse events occurred in 11.8% (n = 4 of 34) in the ninerafaxstat group and 6.1% (n = 2 of 33) of patients in the placebo group. From baseline to 12 weeks, ninerafaxstat was associated with significantly better VE/Vco2 (ventilatory efficiency) slope compared with placebo with a least-squares (LS) mean difference between the groups of -2.1 (95% CI: -3.6 to -0.6; P = 0.006), with no significant difference in peak VO2 (P = 0.90). The Kansas City Cardiomyopathy Questionnaire Clinical Summary Score was directionally, though not significantly, improved with ninerafaxstat vs placebo (LS mean 3.2; 95% CI: -2.9 to 9.2; P = 0.30); however, it was statistically significant when analyzed post hoc in the 35 patients with baseline Kansas City Cardiomyopathy Questionnaire Clinical Summary Score ≤80 (LS mean 9.4; 95% CI: 0.3-18.5; P = 0.04). CONCLUSIONS: In symptomatic nHCM, novel drug therapy targeting myocardial energetics was safe and well tolerated and associated with better exercise performance and health status among those most symptomatically limited. The findings support assessing ninerafaxstat in a phase 3 study.
Asunto(s)
Cardiomiopatía Hipertrófica , Humanos , Cardiomiopatía Hipertrófica/tratamiento farmacológico , Femenino , Masculino , Persona de Mediana Edad , Método Doble Ciego , Resultado del Tratamiento , Anciano , Consumo de Oxígeno/efectos de los fármacosRESUMEN
Background: Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives: The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores. Results: Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]). Conclusions: An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.