RESEARCH AIMS: To (1) describe exploratory estimates of greenhouse gas emission factors for all infant and young child milk formula products and (2) estimate national greenhouse gas emission association with commercial milk formulas sold in selected countries in the Asia Pacific region.
METHOD: We used a secondary data analysis descriptive design incorporating a Life Cycle Assessment (LCA) concepts and methodology to estimate kg CO2 eq. emissions per kg of milk formula, using greenhouse gas emission factors for milk powder, vegetable oils, and sugars identified from a literature review. Proportions of ingredients were calculated using FAO Codex Alimentarius guidance on milk formula products. Estimates were calculated for production and processing of individual ingredients from cradle to factory gate. Annual retail sales data for 2012-2017 was sourced from Euromonitor International for six purposively selected countries; Australia, South Korea, China, Malaysia, India, Philippines.
RESULTS: Annual emissions for milk formula products ranged from 3.95-4.04 kg CO2 eq. Milk formula sold in the six countries in 2012 contributed 2,893,030 tons CO2 eq. to global greenhouse gas emissions. Aggregate emissions were highest for products (e.g., toddler formula), which dominated sales growth. Projected 2017 emissions for milk formula retailed in China alone were 4,219,052 tons CO2 eq.
CONCLUSIONS: Policies, programs and investments to shift infant and young child diets towards less manufactured milk formula and more breastfeeding are "Triple Duty Actions" that help improve dietary quality and population health and improve the sustainability of the global food system.
Methods: Information on all confirmed COVID-19 cases in Selangor between 25 January and 28 April 2020 was obtained. Clusters were identified, and cases were disaggregated into linked, unlinked and imported cases. Epidemic curves were constructed, and the timing of movement control orders was compared with the numbers of cases reported.
Results: During the study period, 1395 confirmed COVID-19 cases were reported to the Selangor Health Department, of which 15.8% were imported, 79.5% were linked and 4.7% were unlinked cases. For two main clusters, the number of cases decreased after control measures were instituted, by contact-tracing followed by isolation and home quarantine for the first cluster (n = 126), and with the addition of the movement control order for the second, much larger cluster (n = 559).
Discussion: The findings suggest that appropriate, timely public health interventions and movement control measures have a synergistic effect on controlling COVID-19 outbreaks.
METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit.
RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful.
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.
METHODS AND RESULTS: We queried the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research database among patients ≥15 years old from 1999 to 2020. VHD and its subtypes were listed as the underlying cause of death. We calculated age-adjusted mortality rate (AAMR) per 100 000 individuals and determined overall trends by estimating the average annual percent change using the Joinpoint regression program. Subgroup analyses were performed based on demographic and geographic factors. In the 22-year study, there were 446 096 VHD deaths, accounting for 0.80% of all-cause mortality (56 014 102 people) and 2.38% of the total cardiovascular mortality (18 759 451 people). Aortic stenosis recorded the highest mortality of VHD-related death in both male (109 529, 61.74%) and female (166 930, 62.13%) populations. The AAMR of VHD has declined from 8.4 (95% CI, 8.2-8.5) to 6.6 (95% CI, 6.5-6.7) per 100 000 population. Similar decreasing AAMR trends were also seen for the VHD subtypes. Men recorded higher AAMR for aortic stenosis and aortic regurgitation, whereas women had higher AAMR for mitral stenosis and mitral regurgitation. Mitral regurgitation had the highest change in average annual percent change in AAMR.
CONCLUSIONS: The mortality rate of VHD among the US population has declined over the past 2 decades. This highlights the likely efficacy of increasing surveillance and advancement in the management of VHD, resulting in improved outcomes.
Method: Field data collected during the COVID-19 outbreak in Selangor, Malaysia, up to 13 April 2020 were used, comprising socio-demographic characteristics, comorbidities and presenting symptoms of COVID-19 cases. ICU admission was determined from medical records. Multiple logistic regression analysis was performed to identify factors associated with ICU admission requiring intubation/mechanical ventilation among COVID-19 cases.
Results: A total of 1287 COVID-19-positive cases were included for analysis. The most common comorbidities were hypertension (15.5%) and diabetes (11.0%). More than one third of cases presented with fever (43.8%) or cough (37.1%). Of the 25 cases that required intubation/mechanical ventilation, 68.0% had hypertension, 88.0% had fever, 40.0% had dyspnoea and 44.0% were lethargic. Multivariate regression showed that cases that required intubation/mechanical ventilation had significantly higher odds of being older (aged 360 years) [adjusted odds ratio (aOR) = 3.9] and having hypertension (aOR = 5.7), fever (aOR = 9.8), dyspnoea (aOR = 9.6) or lethargy (aOR = 7.9) than cases that did not require intubation/mechanical ventilation.
Conclusion: The COVID-19 cases in Selangor, Malaysia requiring intubation/mechanical ventilation were significantly older, with a higher proportion of hypertension and symptoms of fever, dyspnoea and lethargy. These risk factors have been reported previously for severe COVID-19 cases, and highlight the role that ageing and underlying comorbidities play in severe outcomes to respiratory disease.