Search Results (6)

Bacterial diseases of the gastrointestinal (GI) tract continue to be a major worldwide cause of human morbidity and mortality. Among various enteric pathogens, Shigella spp. are some of the most common and deadly bacterial pathogens. They are responsible for ~125 million worldwide cases of shigellosis, and ~14,000 deaths annually, the majority in children under the age of 5 and occurring in developing countries. Preventing and treating shigellosis with conventional drugs (e.g., vaccines and antibiotics) has proven to be very difficult. Here, we assessed the safety and tolerability of ShigActive™, a lytic bacteriophage preparation targeting Shigella spp., in a randomized, placebo-controlled, double-blind Phase 1 clinical trial. Ten participants randomized 4:1 received ShigActive™ or placebo co-administered with sodium bicarbonate orally three times daily for 7 days. Solicited and unsolicited adverse events (AEs) were observed for 29 days. Fifty percent of the subjects receiving ShigActive™ reported mild GI-related symptoms, while one participant experienced moderate fatigue. No serious or medically attended AEs occurred through day 90. Additionally, no significant differences in GI-associated inflammatory mediators or fecal microbiome changes were observed between placebo- and ShigActive™-treated subjects, or from a participants’ baseline value. The results of this first-in-human (FIH) randomized, controlled Phase 1 trial of ShigActive™ demonstrate that it is safe and well tolerated when orally administered with no significant differences compared to placebo controls. Full article

Red needle cast (RNC), mainly caused by Phytophthora pluvialis, is a very damaging disease of the widely grown species radiata pine within New Zealand. Using a combination of satellite imagery and weather data, a novel methodology was developed to pre-visually predict the incidence of RNC on radiata pine within the Gisborne region of New Zealand over a five-year period from 2019 to 2023. Sentinel-2 satellite imagery was used to classify areas within the region as being disease-free or showing RNC expression from the difference in the red/green index (R/G_diff) during a disease-free time of the year and the time of maximum disease expression in the upper canopy (early spring–September). Within these two classes, 1976 plots were extracted, and a classification model was used to predict disease incidence from mean monthly weather data for key variables during the 11 months prior to disease expression. The variables in the final random forest model included solar radiation, relative humidity, rainfall, and the maximum air temperature recorded during mid–late summer, which provided a pre-visual prediction of the disease 7–8 months before its peak expression. Using a hold-out test dataset, the final random forest model had an accuracy of 89% and an F1 score of 0.89. This approach can be used to mitigate the impact of RNC by focusing on early surveillance and treatment measures. Full article

This study demonstrates a framework for using high-resolution satellite imagery to automatically map and monitor outbreaks of red needle cast (Phytophthora pluvialis) in planted pine forests. This methodology was tested on five WorldView satellite scenes collected over two sites in the Gisborne Region of New Zealand’s North Island. All scenes were acquired in September: four scenes were acquired yearly (2018–2020 and 2022) for Wharerata, while one more was obtained in 2019 for Tauwhareparae. Training areas were selected for each scene using manual delineation combined with pixel-level thresholding rules based on band reflectance values and vegetation indices (selected empirically) to produce ‘pure’ training pixels for the different classes. A leave-one-scene-out, pixel-based random forest classification approach was then used to classify all images into (i) healthy pine forest, (ii) unhealthy pine forest or (iii) background. The overall accuracy of the models on the internal validation dataset ranged between 92.1% and 93.6%. Overall accuracies calculated for the left-out scenes ranged between 76.3% and 91.1% (mean overall accuracy of 83.8%), while user’s and producer’s accuracies across the three classes were 60.2–99.0% (71.4–91.8% for unhealthy pine forest) and 54.4–100% (71.9–97.2% for unhealthy pine forest), respectively. This work demonstrates the possibility of using a random forest classifier trained on a set of satellite scenes for the classification of healthy and unhealthy pine forest in new and completely independent scenes. This paves the way for a scalable and largely autonomous forest health monitoring system based on annual acquisitions of high-resolution satellite imagery at the time of peak disease expression, while greatly reducing the need for manual interpretation and delineation. Full article

Accurate estimates of pasture biomass in grazing lands are currently a time-consuming and resource-intensive task. The process generally includes physically cutting, bagging, labelling, drying, and weighing grass samples using multiple “quadrats” placed on the ground. Quadrats vary in size but are typically in the order of 0.25 m² (i.e., 0.5 m × 0.5 m) up to 1.0 m². Measurements from a number of harvested quadrats are then averaged to get a site estimate. This study investigated the use of photographs and ‘machine learning’ to reduce the time factor and difficulty in taking pasture biomass measurements to potentially make the estimations more accessible through the use of mobile phone cameras. A dataset was created from a pre-existing archive of quadrat photos and corresponding hand-cut pasture biomass measurements taken from a diverse range of field monitoring sites. Sites were clustered and one was held back per model for testing. The models were based on DenseNet121. Individual quadrat errors were large but more promising results were achieved when estimating the site mean pasture biomass. Another two smaller additional datasets were created post-training which were used to further assess the ensemble; they provided similar absolute errors to the original dataset, but significantly larger relative errors. The first was made from harvested quadrats, and the second was made using a pasture height meter in conjunction with a mobile phone camera. The models performed well across a variety of situations and locations but underperformed when assessed on some sites with very different vegetation. More data and refinement of the approach outlined in the paper will reduce the number of models needed and help to correct errors. These models provide a promising start, but further investigation, refinement, and data are needed before becoming a usable application. Full article

The administration of broad-spectrum antibiotics is often associated with antibiotic-associated diarrhea (AAD), and impacts gastrointestinal tract homeostasis, as evidenced by the following: (a) an overall reduction in both the numbers and diversity of the gut microbiota, and (b) decreased short-chain fatty acid (SCFA) production. Evidence in humans that probiotics may enhance the recovery of microbiota populations after antibiotic treatment is equivocal, and few studies have addressed if probiotics improve the recovery of microbial metabolic function. Our aim was to determine if Bifidobacterium animalis subsp. lactis BB-12 (BB-12)-containing yogurt could protect against antibiotic-induced fecal SCFA and microbiota composition disruptions. We conducted a randomized, allocation-concealed, controlled trial of amoxicillin/clavulanate administration (days 1–7), in conjunction with either BB-12-containing or control yogurt (days 1–14). We measured the fecal levels of SCFAs and bacterial composition at baseline and days 7, 14, 21, and 30. Forty-two participants were randomly assigned to the BB-12 group, and 20 participants to the control group. Antibiotic treatment suppressed the fecal acetate levels in both the control and probiotic groups. Following the cessation of antibiotics, the fecal acetate levels in the probiotic group increased over the remainder of the study and returned to the baseline levels on day 30 (−1.6% baseline), whereas, in the control group, the acetate levels remained suppressed. Further, antibiotic treatment reduced the Shannon diversity of the gut microbiota, for all the study participants at day 7. The magnitude of this change was larger and more sustained in the control group compared to the probiotic group, which is consistent with the hypothesis that BB-12 enhanced microbiota recovery. There were no significant baseline clinical differences between the two groups. Concurrent administration of amoxicillin/clavulanate and BB-12 yogurt, to healthy subjects, was associated with a significantly smaller decrease in the fecal SCFA levels and a more stable taxonomic profile of the microbiota over time than the control group. Full article

Burns affect Australia’s First Nations children more than other Australian children, they also experience longer lengths of stay in tertiary burns units and face barriers in accessing burn aftercare treatment. Data sets from two studies were combined whereby 19 families, 11 First Nations Health Worker (FNHW) and 56 multidisciplinary burn team members from across Australia described the actual or perceived role of FNHW in multidisciplinary burn care. Data highlighted similarities between the actual role of FNHW as described by families and as described by FNHW such as enabling cultural safety and advocacy. In contrast, a disconnect between the actual experience of First Nations families and health workers and that as perceived by multidisciplinary burn team members was evident. More work is needed to understand the impact of this disconnect and how to address it. Full article