Mortality burden attributable to exceptional PM2.5 air pollution events in Australian cities: A health impact assessment.

Background: People living in Australian cities face increased mortality risks from exposure to extreme air pollution events due to bushfires and dust storms. However, the burden of mortality attributable to exceptional PM2.5 levels has not been well characterised. We assessed the burden of mortality due to PM2.5 pollution events in Australian capital cities between 2001 and 2020. Methods: For this health impact assessment, we obtained data on daily counts of deaths for all non-accidental causes and ages from the Australian National Vital Statistics Register. Daily concentrations of PM2.5 were estimated at a 5 km grid cell, using a Random Forest statistical model of data from air pollution monitoring sites combined with a range of satellite and land use-related data. We calculated the exceptional PM2.5 levels for each extreme pollution exposure day using the deviation from a seasonal and trend loess decomposition model. The burden of mortality was examined using a relative risk concentration-response function suggested in the literature. Findings: Over the 20-year study period, we estimated 1454 (95 % CI 987, 1920) deaths in the major Australian cities attributable to exceptional PM2.5 exposure levels. The mortality burden due to PM2.5 exposure on extreme pollution days was considerable. Variations were observed across Australia. Despite relatively low daily PM2.5 levels compared to global averages, all Australian cities have extreme pollution exposure days, with PM2.5 concentrations exceeding the World Health Organisation Air Quality Guideline standard for 24-h exposure. Our analysis results indicate that nearly one-third of deaths from extreme air pollution exposure can be prevented with a 5 % reduction in PM2.5 levels on days with exceptional pollution. Interpretation: Exposure to exceptional PM2.5 events was associated with an increased mortality burden in Australia's cities. Policies and coordinated action are needed to manage the health risks of extreme air pollution events due to bushfires and dust storms under climate change.

Esophageal squamous cell carcinoma at the site of tracheo-esophageal fistula repair in a patient with cystic fibrosis.

With increasing survival in patients with cystic fibrosis (CF), complications such as gastrointestinal (GI) malignancies are becoming more apparent, especially in transplanted patients. In patients with CF, these malignancies are most commonly found in the small bowel, colon, biliary tract and pancreas. We describe a patient with esophageal squamous cell cancer at the site of trachea-esophageal fistula repair in the setting of long-standing CF. Many factors such as low expression of CF transmembrane conductance regulator gene, inflammation and resulting metaplasia, bacterial dysbiosis, dysregulation of Wnt/ß-catenin signalling, immune cell infiltration, disruption of intestinal stem cell homeostasis and intestinal barrier integrity have all been implicated in the causation of GI malignancy in patients with CF. Based on shared decision-making in high-risk transplanted individuals, esophagogastroduodenoscopy can be considered alongside colon cancer screening which is currently recommended starting at age 30-35 years.

A novel methodology for fast reservoir simulation of single-phase gas reservoirs using machine learning.

Reservoir simulation is needed for forecasting hydrocarbon production, determining pressure and saturation, well planning, and field development, among other things. The primary objective is to estimate reservoir performance over a period of time and use that data to enhance hydrocarbon recovery under existing operating conditions. In commercial reservoir simulators, a large number of grid blocks are employed to capture the comprehensive information about a reservoir model, such as porosity and permeability, when the reservoir becomes heterogeneous and complicated. This large number of grid blocks is associated with a large number of mass balance equations that need to be solved simultaneously thereby increasing the amount of computational time it takes to solve them. During reservoir simulation, while moving from one-time level to the next requires a large number of iterations if the properties of reservoir fluids are pressure-sensitive. These further increases the computational cost needed during simulation. The primary objective of this paper is to present a novel approach for reservoir simulation that uses Random Forest (RF) which is one of the widely used Machine learning (ML) algorithm to reduce the number of iterations at each time step and speed up the process. This study investigated the benefits of employing the novel approach created using RF with an application to a conventional single-phase gas reservoir. The study's novelty is in developing a new ML-based reservoir simulator that will make reservoir simulation much faster and computationally more efficient. The standard physics-based system of equations has been included while the traditional reservoir simulation algorithm is modified.

Application of machine learning in predicting oil rate decline for Bakken shale oil wells.

Commercial reservoir simulators are required to solve discretized mass-balance equations. When the reservoir becomes heterogeneous and complex, more grid blocks can be used, which requires detailed and accurate reservoir information, for e.g. porosity, permeability, and other parameters that are not always available in the field. Predicting the EUR (Estimated Ultimate Recovery) and rate decline for a single well can therefore take hours or days, making them computationally expensive and time-consuming. In contrast, decline curve models are a simpler and speedier option because they only require a few variables in the equation that can be easily gathered from the wells' current data. The well data for this study was gathered from the Montana Board of Oil and Gas Conservation's publicly accessible databases. The SEDM (Stretched Exponential Decline Model) decline curve equation variables specifically designed for unconventional reservoirs variables were correlated to the predictor parameters in a random oil field well data set. The study examined the relative influences of several well parameters. The study's novelty comes from developing an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. The successful application of this study relies highly on the availability of good quality and quantity of the dataset.

Weather extremes associated with increased Ross River virus and Barmah Forest virus notifications in NSW: learnings for public health response.

OBJECTIVE: To examine the sequence of environmental and entomological events prior to a substantial increase in Ross River virus (RRV) and Barmah Forest virus (BFV) notifications with a view to informing future public health response. METHODS: Rainfall, tidal, mosquito and human arboviral notification data were analysed to determine the temporality of events. RESULTS: Following two extremely dry years, there was a substantial increase in the abundance of mosquitoes along coastal New South Wales (NSW) two weeks after a significant rainfall event and high tides in February 2020. Subsequently, RRV and BFV notifications in north east NSW began to increase eight and nine weeks respectively after the high rainfall, with RRV notifications peaking 12 weeks after the high rainfall. CONCLUSIONS: Mosquito bite avoidance messaging should be instigated within two weeks of high summer rainfall, especially after an extended dry period. IMPLICATIONS FOR PUBLIC HEALTH: Intense summertime rain events, which are expected to increase in frequency in south-east Australia with climate change, can lead to significant increases in arboviral disease. These events need to be recognised by public health practitioners to facilitate timely public health response. This has taken on added importance since the emergence of Japanese encephalitis virus in southeastern Australia in 2022.

Chronic Cannabis Intoxication and Propofol-Induced Salivation: Causes and Considerations.

Legalization/decriminalization of cannabis will increase the numbers of patients who have had recent exposure to recreational or medical cannabis. Currently, little has been reported about potential interactions between cannabis use and Propofol anesthesia e.g., for oropharyngeal procedures. We describe three cases of 'cannabis-induced hypersalivation after propofol' (CHAP) and present our institutions' experience with this unique pharmacological combination. Increased hypersalivation may complicate procedures and represent a procedural risk of suffocation. We evaluate possible pharmacological interactions that might underlie this phenomenon and consider management options going forward.

A Study of Association of Plasma Procalcitonin with Various Components of Metabolic Syndrome and Insulin Resistance.

The metabolic syndrome is a cluster of risk factors that are associated with increased risk of cardiovascular diseases and Type 2 diabetes mellitus. The aim was to study the association of plasma procalcitonin with various components of metabolic syndrome (abdominal obsesity, dyslipidemia, hypertension and hyperglycemia) and insulin resistance and compare with healthy controls. In addition, we tried to gauge the association of plasma procalcitonin with complications of metabolic syndrome at presentation. MATERIAL: This was a hospital based observational comparative study on 30 cases of metabolic syndrome in hospital wards and OPD of SMS Medical College, Jaipur and 30 accompanying healthy controls. INCLUSION CRITERIA: Age more than 18 years, and newly diagnosed or old cases of metabolic syndrome using the definition given by International Diabetes Federation (2006). EXCLUSION CRITERIA: Individuals with active infection, trauma, surgery, neoplasms, cirrhosis, pancreatitis and autoimmune diseases or taking medications which can alter the biochemical profile. Detailed history was taken, subjects clinically examined and anthropometric measurements were taken. Required investigations were obtained and statistical analysis done. OBSERVATION: Plasma procalcitonin was significantly higher in cases (mean 0.11 ng/ml) compared to controls (mean 0.002 ng/ml). Waist and neck circumference (102.87±5.19 and 42.03±3.08) values were higher in case group compared to control group (79.67 ±7.98 and 37.10±1.35). Plasma procalcitonin significantly (p<0.05) correlated with level of insulin resistance (HOMA IR), waist circumference, neck circumference, S. Triglycerides, S. VLDL, fasting blood glucose and fasting insulin level in the case group when analyzed by linear regression analysis. We also found that out of 30 subjects in cases, 16.7% had history of MI, whereas in control group no subject was reported with MI. In case of history of CVA, 16.7% had CVA in case group, whereas in control group, no patient was reported with CVA., Conclusion: Our findings based on community-based data showed that higher plasma procalcitonin levels in the normal range are associated with increased measures of obesity, components of the metabolic syndrome, and greater risk of having metabolic syndrome and insulin resistance. Because associations only partly depend on BMI, plasma procalcitonin may serve as a new marker for adipocyte dysfunction, chronic low-grade inflammation, or both.

A Comparative Study between Mean Platelet Volume and Hba1c Levels in Patients of Type 2 Diabetes Mellitus.

Diabetes mellitus is a major global health problem, increasingly affecting the population across the world. Diabetic patients have an increased risk of developing micro and macro vascular diseases, and platelets may be involved as a causative agent with respect to altered platelet morphology and function. There are studies evaluating the association between Mean Platelet Volume (MPV) and HbA1c and its role in predicting glycaemic control with conflicting results. Thus the present study was conducted to assess the relationship between HbA1c levels and platelet activity (MPV), determine the association among MPV, glycemic control, and diabetic vascular complications and to evaluate the influence of improved glycemic control on MPV in type 2 diabetic patients. MATERIAL: This was a hospital based observational comparative study on 100 cases of diabetes mellitus divided in 2 groups i.e Group A (HbA1c <7) and Group B (HbA1c >7) and 50 healthy controls in Group C in hospital wards and OPD of SMS Medical College, Jaipur. INCLUSION CRITERIA: Age more than 18 years, and newly diagnosed or old cases of diabetes mellitus using the definition given by American Diabetes Association. EXCLUSION CRITERIA: Abnormal platelet count (<100 and >450×103/µL), Acute febrile illness, Use of drugs affecting platelet function, Male patients with Hb<12.5mg/dl and females with Hb<11.5 mg/dl and Pregnant females. OBSERVATION: It was observed that mean MPV(fl) was maximum in Group B (13.35±1.27), followed by Group A (10.77±.77) and Group C (9.09±.85) and a significant (p-value<0.05) relation was found statistically. We also observed that mean HbA1c (%) was maximum in Group B (8.82±1.41), followed by Group A (6.66±.004) and Group C (5.67±.45) and a significant (p-value<0.05) relation was found statistically in these 3 groups. In group B, at baseline MPV(fl) levels were more (13.35±1.26) than at follow up after glycemic control of 3mths (12.13±1.20) and this was found to be statistically significant. CONCLUSION: We found that Mean platelet volume in diabetic mellitus type 2 patients was significantly higher than non-diabetic group. We also found that the mean platelet volume in uncontrolled diabetic group (HbA1c more than 7 percent) was significantly higher than controlled diabetic group (HbA1c less than 7 percent). Our study showed that in diabetes mellitus, platelets become more reactive and aggregable and their mean volume (MPV) is increased. We also found that increase in HbA1c concentration was directly proportional to increased MPV.

Climate change, health and wellbeing: challenges and opportunities in NSW, Australia.

The NSW (New South Wales) Climate Change Policy Framework, launched by the NSW Government in 2016, recognises that climate change presents risks to health and wellbeing. Risks to health and wellbeing come from direct impacts of extreme weather events, and from indirect impacts through effects on air, water, food and ecosystems. Responding to these challenges offers an opportunity to protect and promote health by enhancing environmental amenities, and building adaptive capacity and resilience in populations and systems. To develop policy that effectively protects and promotes health in the face of climate change in NSW it is necessary to define the expected impacts of climate change on health and wellbeing in NSW.

Building an environmentally accountable medical curriculum through international collaboration.

BACKGROUND: Global environmental change is associated with significant health threats. The medical profession can address this challenge through advocacy, health system adaptation and workforce preparedness. Stewardship of health systems with attention to their environmental impacts can contribute to mitigation of and adaptation to negative health impacts of environmental change. Medical schools have an integral role in training doctors who understand the interdependence of ecosystems and human health. Yet integrating environmental perspectives into busy medical curricula is not a simple task. CONTENT: At the 2016 Association for Medical Education in Europe conference, medical educators, students and clinicians from six continents discussed these challenges in a participatory workshop. Here we reflect on emerging themes from the workshop and how to plan for curricular change. Firstly, we outline recent developments in environmental health and associated medical education. Secondly, we reflect on our process and outcomes during this innovative approach to international collaboration. Thirdly, we present learning objectives which cover core content for environmentally accountable medical curricula, developed through a reflective process of international collaboration integrating current literature and the workshop outcomes. CONCLUSIONS: International collaboration can bring together diverse perspectives and provide critical insights for the inclusion of environmental health into basic education for medical practitioners.

Influenza outbreak preparedness: lessons from outbreaks in residential care facilities in 2014.

This report describes 6 influenza outbreaks in residential care facilities during the 2014 influenza season in the Sydney Local Health District. Vaccination rates were high among residents (95%) and low among staff (39%). The majority of residents with laboratory confirmed influenza (67%) did not meet the influenza-like illness case definition.Positive influenza specimens were subtyped as H3N2 (40%), H1N1 (5%) or not subtyped (55%). We illustrate the implications of low vaccine effectiveness and antigenic drift, and provide recommendations for the effective management of future influenza outbreaks.

The effect of temperature on exhaled breath condensate collection.

Exhaled breath condensate (EBC) collection is an innovative method of non-invasively sampling the lung, and can detect a variety of volatile and non-volatile biomarkers, but the disadvantage is the small volume of sample collected. It was hypothesized that a collection system at a lower temperature would increase the volume collected, but may alter the relative concentration of the biomarkers of interest. EBC was collected in a cross-over study using a custom-made collection system, cooled using either wet (4 °C) or dry ice (-20 °C) in randomized order in normal non-smoking volunteers. The volume of the EBC collected per unit time was determined as were conductivity, the concentrations and total amount of protein, hydrogen peroxide, and nitrite/nitrate concentrations. Dry ice was associated with a 79% greater volume of EBC than the wet ice (1387 ± 612 µL; 773 ± 448 µL respectively, p < 0.0001). Conductivity was influenced by the temperature of collection (18.78 ± 6.71 µS cm(-1) for wet ice and 15.32 ± 6.28 µS cm(-1) for dry ice, p = 0.02) as was hydrogen peroxide (1.34 ± 0.88 µg mL(-1) for wet ice and 0.68 ± 0.32 µg mL(-1) for dry ice, p = 0.009) while the concentrations and total values for protein and nitrate/nitrite were not significantly different (p > 0.05). This pilot study suggests that lower collection temperatures facilitate the collection of a larger sample volume. This larger volume is not simply more dilute, with increased water content, nor is there a simple correction factor that can be applied to the EBC biomarkers to correct for the different methods.