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The COVID-19 pandemic produced widespread disruption to schooling, impacting 90% of the world's students and moving entire school systems to remote and online learning. In the state of New South Wales, Australia, most students engaged in learning from home for at least eight weeks, with subsequent individual and intermittent school closures. However, while numerous claims have circulated in the popular media and in think tank reports, internationally, about the negative impacts on learning, there is limited empirical evidence of decreased student achievement. Drawing on data from more than 4800 Year 3 and 4 students from 113 NSW government schools, this paper compares student achievement during 2019 and 2020 in a sample of matched schools to examine the effects of the system-wide disruption. Somewhat surprisingly, our analysis found no significant differences between 2019 and 2020 in student achievement growth as measured by progressive achievement tests in mathematics or reading. A more nuanced picture emerges when the sample is examined by dis/advantage (ICSEA) and Year level. The Year 3 cohort in the least advantaged schools (ICSEA < 950) achieved 2 months less growth in mathematics, while the Year 3 students in mid-ICSEA schools (950-1050) achieved 2 months' additional growth. No significant differences were identified for Indigenous students or students located in regional locations. These results provide an important counter-narrative to widespread speculation about alarming levels of 'learning loss' for all students. While the lower achievement growth in mathematics for Year 3 students in lower ICSEA schools must be addressed as a matter of urgency to avoid further inequities, most students are, academically, where they are expected to be. Our findings are a testament to the dedicated work of teachers during the 2020 pandemic to ensure that learning for most students was not compromised, despite unusually trying circumstances. [Author abstract]

This follow-up study aims to determine the physical parameters which govern the differential radiosensitization capacity of two tumor cell lines and one immortalized normal cell line to 1.9 nm gold nanoparticles. In addition to comparing the uptake potential, localization, and cytotoxicity of 1.9 nm gold nanoparticles, the current study also draws on comparisons between nanoparticle size and total nanoparticle uptake based on previously published data. We quantified gold nanoparticle uptake using atomic emission spectroscopy and imaged intracellular localization by transmission electron microscopy. Cell growth delay and clonogenic assays were used to determine cytotoxicity and radiosensitization potential, respectively. Mechanistic data were obtained by Western blot, flow cytometry, and assays for reactive oxygen species. Gold nanoparticle uptake was preferentially observed in tumor cells, resulting in an increased expression of cleaved caspase proteins and an accumulation of cells in sub G(1) phase. Despite this, gold nanoparticle cytotoxicity remained low, with immortalized normal cells exhibiting an LD(50) concentration approximately 14 times higher than tumor cells. The surviving fraction for gold nanoparticle-treated cells at 3 Gy compared with that of untreated control cells indicated a strong dependence on cell type in respect to radiosensitization potential. Gold nanoparticles were most avidly endocytosed and localized within cytoplasmic vesicles during the first 6 hours of exposure. The lack of significant cytotoxicity in the absence of radiation, and the generation of gold nanoparticle-induced reactive oxygen species provide a potential mechanism for previously reported radiosensitization at megavoltage energies.

Gold nanoparticle radiosensitization represents a novel technique in enhancement of ionising radiation dose and its effect on biological systems. Variation between theoretical predictions and experimental measurement is significant enough that the mechanism leading to an increase in cell killing and DNA damage is still not clear. We present the first experimental results that take into account both the measured biodistribution of gold nanoparticles at the cellular level and the range of the product electrons responsible for energy deposition. Combining synchrotron-generated monoenergetic X-rays, intracellular gold particle imaging and DNA damage assays, has enabled a DNA damage model to be generated that includes the production of intermediate electrons. We can therefore show for the first time good agreement between the prediction of biological outcomes from both the Local Effect Model and a DNA damage model with experimentally observed cell killing and DNA damage induction via the combination of X-rays and GNPs. However, the requirement of two distinct models as indicated by this mechanistic study, one for short-term DNA damage and another for cell survival, indicates that, at least for nanoparticle enhancement, it is not safe to equate the lethal lesions invoked in the local effect model with DNA damage events.

Reintroduction practitioners must often make critical decisions about reintroduction protocols despite having little understanding of the reintroduction biology of the focal species. To enhance the available knowledge on the reintroduction biology of the warru, or black-footed rock-wallaby Petrogale lateralis MacDonnell Ranges race, we conducted a trial reintroduction of 16 captive individuals into a fenced predator and competitor exclosure on the An̲angu Pitjantjatjara Yankunytjatjara Lands in South Australia. We conducted seven trapping sessions and used radio-tracking and camera traps to monitor survival, reproduction and recruitment to the population over 36 months. Blood samples were collected pre-release and during two trapping sessions post-release to assess nutritional health. The survival rate of founders was 63%, with all losses occurring within 10 weeks of release. Post-release blood biochemistry indicated that surviving warru adapted to their new environment and food sources. Female warru conceived within 6 months of release; 28 births were recorded during the study period and 52% of births successfully recruited to the population. Our results suggest that captive-bred warru are capable of establishing and persisting in the absence of introduced predators. However, the high mortality rate immediately post-release, with only a modest recruitment rate, suggests that future releases into areas where predators and competitors are present should use a trial approach to determine the viability of reintroduction. We recommend that future releases of warru into unfenced areas include an intensive monitoring period in the first 3 months post-release followed by a comprehensive long-term monitoring schedule to facilitate effective adaptive management.

Background Communities of color have been disproportionately impacted by COVID-19. We explored barriers and facilitators to COVID-19 vaccine uptake among African American, Latinx, and African immigrant communities in Washington, DC. Methods A total of 76 individuals participated in qualitative interviews and focus groups, and 208 individuals from communities of color participated in an online crowdsourcing contest. Results Findings documented a lack of sufficient, accurate information about COVID-19 vaccines and questions about the science. African American and African immigrant participants spoke about the deeply rooted historical underpinnings to their community’s vaccine hesitancy, citing the prior and ongoing mistreatment of people of color by the medical community. Latinx and African immigrant participants highlighted how limited accessibility played an important role in the slow uptake of COVID-19 vaccines in their communities. Connectedness and solidarity were found to be key assets that can be drawn upon through community-driven responses to address social-structural challenges to COVID-19 related vaccine uptake. Conclusions The historic and ongoing socio-economic context and realities of communities of color must be understood and respected to inform community-based health communication messaging to support vaccine equity for COVID-19 and other infectious diseases.

To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify on a large scale. To develop and compare 2 natural language processing methods, a rules-based approach and a machine learning (ML) approach, for identifying bleeding events in clinical notes. This diagnostic study used deidentified notes from the Medical Information Mart for Intensive Care, which spans 2001 to 2012. A training set of 990 notes and a test set of 660 notes were randomly selected. Physicians classified each note as present or absent for a clinically relevant bleeding event during the hospitalization. A bleeding dictionary was developed for the rules-based approach; bleeding mentions were then aggregated to arrive at a classification for each note. Three ML models (support vector machine, extra trees, and convolutional neural network) were developed and trained using the 990-note training set. Another instance of each ML model was also trained on a sample of 450 notes, with equal numbers of bleeding-present and bleeding-absent notes. The notes were represented using term frequency-inverse document frequency vectors and global vectors for word representation. The main outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for each model. Following training, the models were tested on the test set and sensitivities were compared using a McNemar test. The 990-note training set represented 769 patients (296 [38.5%] female; mean [SD] age, 67.42 [14.7] years). The 660-note test set represented 527 patients (211 [40.0%] female; mean [SD] age, 67.86 [14.7] years). Bleeding was present in 146 notes (22.1%). The extra trees down-sampled model and rules-based approaches were similarly sensitive (93.8% vs 91.1%; difference, 2.7%; 95% CI, -3.8% to 7.9%; P = .44). The positive predictive value for the extra trees model, however, was 48.6%. The rules-based model had the best performance overall, with 84.6% specificity, 62.7% positive predictive value, and 97.1% negative predictive value. Bleeding is a common complication in health care, and these results demonstrate an automated and scalable detection method. The rules-based natural language processing approach, compared with ML, had the best performance in identifying bleeding, with high sensitivity and negative predictive value.

People with learning disabilities are affected by significantly more health problems than the general population and are much more likely to have significant health risks.Yet evidence suggests they are not receiving the same level of health education and health promotion opportunities as other members of society.