Machine Learning Predictive Modeling for the Identification of Moderate Coronavirus Disease 2019 During the Pandemic: A Retrospective Study.

BACKGROUND: Timely differentiation of moderate COVID-19 cases from mild cases is beneficial for early treatment and saves medical resources during the pandemic. We attempted to construct a model to predict the occurrence of moderate COVID-19 through a retrospective study. METHODS: In this retrospective study, clinical data from patients with COVID-19 admitted to Hainan Western Central Hospital in Danzhou, China, between August 1, 2022, and August 31, 2022, was collected, including sex, age, signs on admission, comorbidities, imaging data, post-admission treatment, length of stay, and the results of laboratory tests on admission. The patients were classified into a mild-to-moderate-type group according to WHO guidance. Factors that differed between groups were included in machine learning models such as Bernoulli Naïve Bayes (BNB), linear discriminant analysis, support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR) models. These models were compared to select the optimal model with the best predictive efficacy for moderate COVID-19. The predictive performance of the models was assessed using the area under the curve (AUC), sensitivity, specificity, and calibration plot. RESULTS: A total of 231 patients with COVID-19 were included in this retrospective analysis. Among them, 152 (68.83%) were mild types, 72 (31.17%) were moderate types, and there were no patients with severe or critical types. A logistic regression model combined with age, respiratory rate (RR), lactate dehydrogenase (LDH), D-dimer, and albumin was selected to predict the occurrence of moderate COVID-19. The receiver operating characteristic curve (ROC) showed that AUC, sensitivity, and specificity in the model were 0.719, 0.681, and 0.635, respectively, in predicting moderate COVID-19. Calibration curve analysis revealed that the predicted probability of the model was in good agreement with the true probability. Stratified analysis showed better predictive efficacy after modeling for people aged ≤66 years (AUC = 0.7656) and a better calibration curve. CONCLUSION: The LR model, combined with age, RR, D-dimer, LDH, and albumin, can predict the occurrence of moderate COVID-19 well, especially for patients aged ≤66 years.

Relocate 10 billion livestock to reduce harmful nitrogen pollution exposure for 90% of China's population.

Livestock production in China is increasingly located near urban areas, exposing human populations to nitrogen pollution via air and water. Here we analyse livestock and human population data across 2,300 Chinese counties to project the impact of alternative livestock distributions on nitrogen emissions. In 2012 almost half of China's livestock production occurred in peri-urban regions, exposing 60% of the Chinese population to ammonia emissions exceeding UN guidelines. Relocating 5 billion animals by 2050 according to crop-livestock integration criteria could reduce nitrogen emissions by two-thirds and halve the number of people exposed to high ammonia emissions. Relocating 10 billion animals away from southern and eastern China could reduce ammonia exposure for 90% of China's population. Spatial planning can therefore serve as a powerful policy instrument to tackle nitrogen pollution and exposure of humans to ammonia.

Strategies to reduce ammonia emissions from livestock and their cost-benefit analysis: A case study of Sheyang county.

Ammonia (NH3) emissions, the majority of which arise from livestock production, are linked to high concentration of PM2.5 and lower air quality in China. NH3 mitigation options were well studied at the small-scale (laboratory or pilot), however, they lack of a large-scale test in China. This study fills this crucial gap by evaluating the cost-benefit of pioneering NH3 mitigation projects carried out for a whole county - Sheyang, Jiangsu province, China. Measures were implemented in 2019 following two distinct strategies, improved manure treatment for industrial livestock farms, and collection and central treatment for traditional livestock farms. Emission reductions of 16% were achieved in a short time. While this is remarkable, it falls short of expectations from small-scale studies. If measures were fully implemented according to purpose and meet expectations from the small scale, higher emission reductions of 42% would be possible. The cost benefit analysis presented in this study demonstrated advantages of central manure treatment over in-farm facilities. With improved implementation of mitigation strategies in industrial livestock farms, traditional livestock farms may play an increasing role in total NH3 emissions, which means such farms either need to be included in future NH3 mitigation policies or gradually replaced by industrial livestock farms. The study found an agricultural NH3 reduction technology route suitable for China's national conditions (such as the "Sheyang Model").

Nitrogen budgets of contrasting crop-livestock systems in China.

The crop-livestock system is responsible for a large proportion of global reactive nitrogen (Nr) losses, especially from China. There are diverse livestock systems with contrasting differences in feed, livestock and manure management. However, it is not yet well understood which factors greatly impact on the nitrogen (N) budgets and losses of each system. In this study, we systematically evaluated the N budgets of the crop-livestock production system from 1980 to 2050 in China by identifying the differences of 20 distinct livestock systems. During 1980 to 2010, the total N flow through the crop-livestock system increased from 21.4 to 49.7 Tg, with large variations in different input/output pathways, due to the strong livestock transitions of production towards to a monogastric and landless industrial system. Different systems contributed differently to the total N budgets in 2010. For example, the landless industrial system contributed 67% of livestock product N output, but accounted for 80% of total mineral N fertilizer use and feed N imports by the whole crop-livestock system. The mixed system had the highest rate of N use efficiency at system level due to high dependence on recycled N. N losses were diversely distributed by different systems, with the mixed ruminant system responsible for the majority of NH3-N emission in livestock production, and the grazing ruminant system dominant in NO3-N losses in feed production. The total N entering the crop-livestock system is estimated to be 53.9 Tg with total N losses of 41.3 Tg in 2050 under a business-as-usual scenario. However, this amount could be significantly decreased through combined measures that indicate a considerable potential for future improvements. Overall, our results provide new insights into N use and the management of livestock production.

miR-16-5p Regulates PTPN4 and Affects Cardiomyocyte Apoptosis and Autophagy Induced by Hypoxia/Reoxygenation.

OBJECTIVES: To explore the effects of miR-16-5p and PTPN4 on the apoptosis and autophagy of AC16 cardiomyocytes after hypoxia/reoxygenation treatment. METHODS: AC16 cells were divided into the control group (NC), hypoxia/reoxygenation group (H/R), knockdown miR-16-5p negative control group (NC inhibitor), knockdown miR-16-5p group (miR-16-5p inhibitor), overexpression miR-16-5p negative control group (NC mimics), overexpression miR-16-5p group (miR-16-5p mimics), silent PTPN4 negative control group (sh-NC), silent PTPN4 group (sh-PTPN4), and silent PTPN4 + knockdown miR-16-5p group (sh-PTPN4 + miR-16-5p inhibitor). Real-time fluorescent quantitative PCR (RT-qPCR) and western blotting (WB) were used to measure the expression level of miR-16-3p, miR-16-5p, protein tyrosine phosphatase nonreceptor type 4 (PTPN4), and autophagy-related proteins (beclin-1, LC3 II/I, and P26) in AC16 cells. The apoptosis level of AC16 cells in each group was measured by flow cytometry and TUNEL. The dual-luciferase reporter gene experiment was also used to verify the targeting relationship between miR-16-5p and PTPN4. RESULTS: After H/R treatment, the levels of myocardial injury markers including LDH and CK-MB in AC16 cells were increased significantly (P < 0.05), and the levels of cell apoptosis and autophagy also increased significantly (P < 0.05). The level of miR-16-3p in AC16 cells did not change significantly after H/R treatment, whereas the level of miR-16-5p was increased significantly (P < 0.05). After miR-16-5p was knocked down, the levels of LDH and CK-MB in AC16 cells treated with H/R were significantly reduced (P < 0.05), and the rates of cell apoptosis and autophagy were also significantly reduced (P < 0.05). miR-16-5p negatively regulated the expression level of PTPN4 protein in AC16 cells (P < 0.05), and the dual-luciferase reporter gene experiment confirmed that PTPN4 was the downstream target of miR-16-5p. Silencing of PTPN4 significantly increased the damage of AC16 cells induced by H/R treatment (P < 0.05), but simultaneously inhibiting the expression of PTPN4 and miR-16-5p reversed the protective effect of miR-16-5p knockdown on AC16 cells (P < 0.05). CONCLUSIONS: The expression of miR-16-5p is upregulated in AC16 cells after H/R treatment and the knockdown which can protect AC16 cells from H/R-induced cell damage that may be due to its regulation on the expression of PTPN4.

Global environmental costs of China's thirst for milk.

China has an ever-increasing thirst for milk, with a predicted 3.2-fold increase in demand by 2050 compared to the production level in 2010. What are the environmental implications of meeting this demand, and what is the preferred pathway? We addressed these questions by using a nexus approach, to examine the interdependencies of increasing milk consumption in China by 2050 and its global impacts, under different scenarios of domestic milk production and importation. Meeting China's milk demand in a business as usual scenario will increase global dairy-related (China and the leading milk exporting regions) greenhouse gas (GHG) emissions by 35% (from 565 to 764 Tg CO2eq ) and land use for dairy feed production by 32% (from 84 to 111 million ha) compared to 2010, while reactive nitrogen losses from the dairy sector will increase by 48% (from 3.6 to 5.4 Tg nitrogen). Producing all additional milk in China with current technology will greatly increase animal feed import; from 1.9 to 8.5 Tg for concentrates and from 1.0 to 6.2 Tg for forage (alfalfa). In addition, it will increase domestic dairy related GHG emissions by 2.2 times compared to 2010 levels. Importing the extra milk will transfer the environmental burden from China to milk exporting countries; current dairy exporting countries may be unable to produce all additional milk due to physical limitations or environmental preferences/legislation. For example, the farmland area for cattle-feed production in New Zealand would have to increase by more than 57% (1.3 million ha) and that in Europe by more than 39% (15 million ha), while GHG emissions and nitrogen losses would increase roughly proportionally with the increase of farmland in both regions. We propose that a more sustainable dairy future will rely on high milk demanding regions (such as China) improving their domestic milk and feed production efficiencies up to the level of leading milk producing countries. This will decrease the global dairy related GHG emissions and land use by 12% (90 Tg CO2eq reduction) and 30% (34 million ha land reduction) compared to the business as usual scenario, respectively. However, this still represents an increase in total GHG emissions of 19% whereas land use will decrease by 8% when compared with 2010 levels, respectively.