Paroxysmal atrial fibrillation ablation with a novel temperature-controlled CF-sensing catheter: Q-FFICIENCY clinical and healthcare utilization benefits.

INTRODUCTION: The prospective, nonrandomized, multicenter Q-FFICIENCY study demonstrated the safety and 12-month efficacy of paroxysmal atrial fibrillation (AF) ablation with the novel QDOT MICRO temperature-controlled, contact force-sensing, radiofrequency (RF) catheter. Participants underwent pulmonary vein isolation with very high-power short-duration (vHPSD) mode (90 W, ≤4 s) alone or combined with conventional-power temperature-controlled (CPTC) mode (25-50 W). This study aimed to assess quality-of-life (QOL) and healthcare utilization (HCU) benefits experienced by Q-FFICIENCY study participants. METHODS: Besides evaluating procedural efficiency, QOL and HCU were assessed through 12 months postablation via Atrial Fibrillation Effect on Quality-of-Life Tool (AFEQT) score, antiarrhythmic drug (AAD) use, and incidence of cardioversion and cardiovascular hospitalization. RESULTS: Of 191 participants enrolled, 166 were ablated with the new catheter. Compared to baseline, statistically significant, clinically meaningful improvements in composite and subcategories of AFEQT scores were observed at 3 months and sustained through 12 months (12-month increase, 29.3-44.2 points). Class I/III AAD use decreased from 97.6% (162/166) at baseline to 19.6% (31/158) during Months 6-12, representing a significant 79.9% reduction. The cardioversion rate significantly declined by 93.9% from 31.3% (12 months preablation) to 1.9% (evaluation period). One-year Kaplan-Meier estimates of freedom from all-cause and cardiovascular hospitalization were 80.9% (95% confidence interval [CI], 74.8%-86.9%) and 88.8% (95% CI, 84.0%-93.7%), respectively. CONCLUSIONS: Paroxysmal AF ablation with the novel temperature-controlled RF catheter in vHPSD mode, alone or with CPTC mode, led to clinically meaningful improvement in QOL and significant reduction in AAD use, cardioversion, and cardiovascular hospitalization.

Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms.

In recent decades, the simulation and modeling of water quality parameters have been useful for monitoring and assessment of the quality of water resources. Moreover, the use of multiple modeling techniques, rather than a standalone model, tends to provide more robust and reliable insights. In this present paper, several soft computing techniques were integrated and compared for the modeling of groundwater quality parameters (pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), modified heavy metal index (MHMI), pollution load index (PLI), and synthetic pollution index (SPI)) in Ojoto area, SE Nigeria. Standard methods were employed in the physicochemical analysis of the groundwater resources. It was found that anthropogenic and non-anthropogenic activities influenced the concentrations of the water quality parameters. The PLI, MHMI, and SPI revealed that about 20-25% of the groundwater samples are unsuitable for drinking. Simple linear regression indicated that strong agreements exist between the results of the water quality indices. Principal component and Varimax-rotated factor analyses showed that Pb, Ni, and Zn influenced the judgment of the water quality indices most. Q-mode hierarchical and K-means clustering  algorithms grouped the water samples based on their pH, EC, TDS, TH, MHMI, PLI, and SPI values. Multiple linear regression (MLR) and artificial neural network (ANN) algorithms were used for the simulation and prediction of  the pH, EC, TDS, TH, PLI, MHMI, and SPI. The MLR performed better than the ANN model in predicting EC, TH, and TDS. Nevertheless, the ANN model predicted the pH better than the MLR model. Meanwhile, both MLR and ANN performed equally in the prediction of PLI, MHMI, and SPI.

Prediction modeling of potentially toxic elements' hydrogeopollution using an integrated Q-mode HCs and ANNs machine learning approach in SE Nigeria.

Machine learning techniques have proven to be very useful in environmental and engineering assessments, including water quality studies. This is because they have flexible linear and nonlinear forecasting functions that can efficiently and reliably estimate measurable and continuous variables. The aim of this paper was to classify the water quality and also predict potentially toxic anions (PTAs; e.g., Cl, SO4, HCO3, and NO3) and potentially toxic heavy metals (PTHMs; e.g., Fe, Zn, Ni, Cr, and Pb) in water resources in Ojoto and its surroundings, Nigeria. Q-mode hierarchical clusters (HCs) and artificial neural networks (ANNs) were integrated to achieve the research objectives. Prior to the HCs and ANNs modeling, correlation-, unrotated principal component-, and varimax-rotated factor analyses were performed to flag the level of associations between the input water quality variables. With respect to pH, two water quality cluster groups were identified. However, three and four cluster groups were identified based on the PTAs and PTHMs concentrations, respectively. Four ANN models (two for each group) were used for predicting the PTAs and PTHMs in the waters resources. Using coefficient of determination (R2) and AUC (area under curve) values and direct comparison of parity plots, the performance and accuracy of the ANN models were substantiated. Overall, the results obtained reveal that the proposed ANN models suitably predicted the concentrations of the PTAs and PTHMs. Thus, this paper provides useful information for better monitoring, management, and protection of the water resources. However, more modeling studies are encouraged to validate and/or improve the findings of the current work.

Assessment of 226Ra, 238U, 232Th, 137Cs and 40K activities from the northern coastline of Oman Sea (water and sediments).

Water and sediment samples were collected from northern coast of Oman Sea covering from Goatr to Hormoz canyon seaport. Water and sediment quality assessment for naturally or anthropogenic radionuclides at Oman Sea as a main strategic golf for trade and transit, is an important issue. Correspondence analysis (CA) by R-mode analysis represents that sigma(T)-temperature-conductivity-O2 parameters are well-correlated with 226Ra, 232Th and 40K. Accordingly Q-mode analysis revealed an indicator samples of ST13W for different radionuclides, ST03W for O2, and simultaneously ST34W-ST21W-ST08W-ST04W for sigma(T) parameters. The CA results overlapped with factor and cluster analysis results that explained 85.8% of total variance of water samples. Descriptive analysis of sediments indicates more significant variation than water samples. The 232Th and 226Ra generally showing that ST13D to ST25D sediments that restricted to Pi Bashk coastline are concentrated from 137Cs and 40K. By comparison with reference-values from Iran and other parts of the world, they are acceptable with respect to environmental and radioisotope hazards.

Bud protection: a key trait for species sorting in a forest-savanna mosaic.

Contrasting fire regimes maintain patch mosaics of savanna, thicket and forest biomes in many African subtropical landscapes. Species dominating each biome are thus expected to display distinct fire-related traits, commonly thought to be bark related. Recent Australian savanna research suggests that bud position, not bark protection alone, determines fire resilience via resprouting. We tested first how bud position influences resprouting ability in 17 tree species. We then compared the effect of both bark-related protection and bud position on the distribution of 63 tree species in 253 transects in all three biomes. Tree species with buds positioned deep under bark had a higher proportion of post-fire aboveground shoot resprouting. Species with low bud protection occurred in fire-prone biomes only if they could root-sucker. The effect of bud protection was supported by a good relationship between species bud protection and distribution across a gradient of fire frequency. Bud protection and high bark production are required to survive frequent fires in savanna. Forests are fire refugia hosting species with little or no bud protection and thin bark. Root-suckering species occur in the three biomes, suggesting that fire is not the only factor filtering this functional type.