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Background: As a major maternal health concern, the prevalence of human immunodeficiency virus (HIV) among pregnant women was previously investigated in European, African, and Latin American regions other than the North American region. This study analyzed the prevalence of HIV among pregnant women in the North American region including 3 major countries: the USA, Canada, and Mexico., Methods: Relevant studies were screened from 3 online databases: Google Scholar, PubMed, and ScienceDirect using specific search keywords. Ultimately, 10 studies of the North American region were included with a total of 339,831 pregnant women residing in the USA, Canada, and Mexico., Results: The overall pooled prevalence was 0.6% (95% confidence interval [CI]: 0.4-0.8) with a high degree of heterogeneity (I2 = 97%). Pooled prevalence rates of HIV among pregnant women in Canada, Mexico, and the USA were 0.3% (95% CI: 0.1-0.5), 0.5% (95% CI: 0.2-0.8), and 2.3% (95% CI: 0.0-5.7), respectively with high degrees of heterogeneity., Conclusion: The overall prevalence rate of HIV among pregnant women in the USA, Canada, and Mexico was minimal as compared with the countries of Eastern Europe, sub-Saharan Africa, or Latin America. Awareness, adequate testing and healthcare facilities, better socioeconomic, and geopolitical conditions might be crucial to lowering the prevalence of HIV among pregnant women., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics., Competing Interests: Competing interests: E.W.T. is an employee of Sanofi, which manufactures influenza vaccines. J.S. and Columbia University disclose partial ownership of SK Analytics. J.S. discloses consulting for BNI.

This study was conducted to evaluate the in vitro polyphenol content, antioxidant, cytotoxic, antibacterial, anthelmintic properties, and in vivo antinociceptive activity of the ethanol extract of Cissus adnata leaves (EECA) in different experimental models. Polyphenol contents were investigated using spectrophotometric techniques. Antioxidant activity was determined by 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) radical-scavenging, ferric reducing power, and total antioxidant capacity assays. Cytotoxicity was determined by brine shrimp lethality bioassay and disc diffusion method was used for the antibacterial activity. Anthelmintic activity was studied using aquarium worm ( Tubifex tubifex ) whereas antinociceptive activity was evaluated in mice by acetic acid and formalin test. Phytochemical screening of EECA revealed the presence of alkaloids, carbohydrates, flavonoids, phenols, terpenoids, saponins, and tannins. EECA showed strong antioxidant activity with high polyphenol contents. It was observed that EECA possessed significant antibacterial activity with a low toxicity profile. EECA also demonstrated dose-dependent and statistically significant anthelmintic and antinociceptive activities. Our study shows that ethanol extract of C. adnata leaves possess strong antioxidant, antibacterial, anthelmintic and antinociceptive activities with lower toxicity. Further studies are needed to identify bioactive phytomolecules and to understand the mechanism of such actions better., Competing Interests: The authors declare no conflict of interest.

Tight gas sandstones are low porosity media, with a very small permeability (i.e., below 1 mD). Their porosity is below 10%, and it is mainly composed of fine noncemented microcracks, which are present between neighboring quartz grains. While empirical models of permeability are available, their predictions, which do not compare well with macroscopic measurements, are not reliable to assess gas well productivity. The purpose of this work is to compare the permeability measured on centimetric plugs to predictions based on pore structure data. Two macroscopic measurements are performed, namely dry gas permeability and mercury intrusion porosimetry (MIP), together with a series of local measurements including focused ion beam and scanning electron microscopy (FIB-SEM), x-ray computed microtomography (CMT), and standard two-dimensional (2D) SEM. Numerical modeling is performed by combining analyses on two scales, namely the microcrack network scale (given by 2D SEM) and the individual 3D microcrack scale (given by either FIB-SEM or CMT). The network permeability is calculated by means of techniques developed for fracture networks. This permeability is proportional to the microcrack transmissivity, which is determined by solving the Stokes equation in the microcracks measured by FIB-SEM or CMT. Good correlation with experimental permeability values is only found when using transmissivity from 3D CMT data.