Karst carbon sink mechanism and its contribution to carbon neutralization under land- use management.

The chemical weathering process of carbonate rocks consumes a large quantity of CO2. This has great potential as a carbon sink, and it is one of a significant pathway for achieving carbon neutrality. However, the control mechanisms of karst carbon sink fluxes are unclear, and there is a lack of effective and accurate accounting. We took the Puding Shawan karst water­carbon cycle test site in China, which has identical initial conditions but different land use types, as the research subject. We used controlled experiments over six years to evaluate the mechanisms for the differences in hydrology, water chemistry, concentrations and fluxes of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC). We found that the transition from rock to bare soil to grassland led to increases in the DIC concentration by 0.08-0.62 mmol⋅L-1. The inorganic carbon sink flux (CSF) increased by 3.01-5.26 t⋅C⋅km-2⋅a-1, an increase amplitude of 30-70 %. The flux of dissolved organic carbon (FDOC) increase by 0.28 to 0.52 t⋅C⋅km-2⋅a-1, an increase amplitude of 34-90 %. We also assessed the contribution of land use modifications to regional carbon neutrality, it indicate that positive land use modification can significantly regulate the karst carbon sink, with grassland having the greatest carbon sequestration ability. Moreover, in addition to DOC from soil organic matter degradation, DOC production by chemoautotrophic microorganisms utilizing DIC in groundwater may also be a potential source. Thus, coupled studies of the conversion of DIC to DOC processes in groundwater are an important step in assessing karst carbon sink fluxes.

PECSS: Pulmonary Embolism Comprehensive Screening Score to safely rule out pulmonary embolism among suspected patients presenting to emergency department.

BACKGROUND: Pulmonary embolism is a severe cardiovascular disease and can be life-threatening if left untreated. However, the detection rate of pulmonary embolism using existing pretest probability scores remained relatively low and clinical rule out often relied on excessive use of computed tomographic pulmonary angiography. METHODS: We retrospectively collected data from pulmonary embolism suspected patients in Zhongshan Hospital from July 2018 to October 2022. Pulmonary embolism diagnosis and severity grades were confirmed by computed tomographic pulmonary angiography. Patients were randomly divided into derivation and validation set. To construct the Pulmonary Embolism Comprehensive Screening Score (PECSS), we first screened for candidate clinical predictors using univariate logistic regression models. These predictors were then included in a searching algorithm with indicators of Wells score, where a series of points were assigned to each predictor. Optimal D-Dimer cutoff values were investigated and incorporated with PECSS to rule out pulmonary embolism. RESULTS: In addition to Wells score, PECSS identified seven clinical predictors (anhelation, abnormal blood pressure, in critical condition when admitted, age > 65 years and high levels of pro-BNP, CRP and UA,) strongly associated with pulmonary embolism. Patients can be safely ruled out of pulmonary embolism if PECSS ≤ 4, or if 4 < PECSS ≤ 6 and D-Dimer ≤ 2.5 mg/L. Comparing with Wells approach, PECSS achieved lower failure rates across all pulmonary embolism severity grades. These findings were validated in the held-out validation set. CONCLUSIONS: Compared to Wells score, PECSS approaches achieved lower failure rates and better compromise between sensitivity and specificity. Calculation of PECSS is easy and all predictors are readily available upon emergency department admission, making it widely applicable in clinical settings. TRAIL REGISTRATION: The study was retrospectively registered (No. CJ0647) and approved by Human Genetic Resources in China in April 2022. Ethical approval was received from the Medical Ethics Committee of Zhongshan Hospital (NO.B2021-839R).

Response of OC, TN, and TP deposition mediated by aquatic photosynthetic community structures in shallow karst surface waters under different land uses.

Land use change alters the hydrochemical features, nutrient outputs, and community structure of aquatic photosynthetic organisms in watersheds and has an important impact on C, N, and P biogeochemical processes. In shallow water environments, sediments are the most important burial sites for C, N, and P; however, the factors underlying the control of their deposition by land use changes remain unclear. In this study, the relationship among hydrochemical features, aquatic photosynthetic organism community structure, and C, N, and P deposition in surface waters associated with different land uses was studied at the Shawan Karst Water-Carbon Cycle Test Site, Puding, SW China, by combining field monitoring and laboratory experiments performed over a complete hydrological year from September 2018 to August 2019. The results indicate that (1) OC and TN deposition showed small differences among ponds associated with five land uses, while TP was significantly higher in ponds associated with shrubland and grassland than in ponds of cultivated land, bare soil, and bare rock. (2) Cultivated land increased OC and TN deposition by increasing N and P output and planktonic algae biomass in surface waters, while grassland and shrubland ponds mainly by increasing DIC output and macrophyte biomass. (3) Compared with cultivated land, grassland and shrubland significantly enhanced TP deposition by promoting the deposition of calcium-bound P and biogenic P from macrophytes and their epiphytic algae in surface waters. In conclusion, the shift of cultivated land and bare soil to grassland and shrubland may be conducive to the formation of benign aquatic ecosystems and stabilization of C, N, and P sinks in karst shallow surface waters.

Study of the mechanism underlying the role of PINK1/Parkin in the formic acid-induced autophagy of PC12 cells.

This study aimed to explore PINK1/Parkin's role in methanol metabolite formic acid-induced autophagy in PC12 cells and provide a theoretical basis for elucidating methanol-induced neurotoxicity. After treatment with different formic acid concentrations, we observed the morphology and mitochondria of PC12 cells. We used an ultra-micro enzyme kit to detect the mitochondrial Na+ -K+ -ATPase and Ca2+ -Mg2+ -ATPase activities; a JC-1 kit to detect changes in the mitochondrial membrane potential (MMP); MDC staining to detect the autophagy levels; and western blotting to measure the expression levels of the mitochondrial marker protein COX IV and the autophagy-related proteins Beclin1, P62 and LC3II/LC3I, and the mitochondrial and cytoplasmic levels of PINK1, Parkin and P-Parkin. Compared with the control group, the mitochondrial diameters, the mitochondrial Na+ -K+ -ATP and Ca2+ -Mg2+ -ATPase activities, the MMP, and the COX IV expression levels decreased significantly (P < 0.05). The fluorescence signal intensity (indicating autophagy); relative Beclin1 and LC3II/LC3I protein expression levels; and relative mitochondrial PINK1, Parkin and P-Parkin levels increased significantly, and the relative P62 protein expression levels and relative cytoplasmic PINK1, Parkin and P-Parkin levels decreased significantly (P < 0.05) compared with the control group. Thus, formic acid alters mitochondrial morphology, causes mitochondrial dysfunction, affects the PINK/Parkin pathway and, thus, activates the process of mitochondrial autophagy.

Effectiveness of eHealth Self-management Interventions in Patients With Heart Failure: Systematic Review and Meta-analysis.

BACKGROUND: Heart failure (HF) is a common clinical syndrome associated with substantial morbidity, a heavy economic burden, and high risk of readmission. eHealth self-management interventions may be an effective way to improve HF clinical outcomes. OBJECTIVE: The aim of this study was to systematically review the evidence for the effectiveness of eHealth self-management in patients with HF. METHODS: This study included only randomized controlled trials (RCTs) that compared the effects of eHealth interventions with usual care in adult patients with HF using searches of the EMBASE, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and CINAHL databases from January 1, 2011, to July 12, 2022. The Cochrane Risk of Bias tool (RoB 2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence for each outcome of interest. Meta-analyses were performed using Review Manager (RevMan v.5.4) and R (v.4.1.0 x64) software. RESULTS: In total, 24 RCTs with 9634 participants met the inclusion criteria. Compared with the usual-care group, eHealth self-management interventions could significantly reduce all-cause mortality (odds ratio [OR] 0.83, 95% CI 0.71-0.98, P=.03; GRADE: low quality) and cardiovascular mortality (OR 0.74, 95% CI 0.59-0.92, P=.008; GRADE: moderate quality), as well as all-cause readmissions (OR 0.82, 95% CI 0.73-0.93, P=.002; GRADE: low quality) and HF-related readmissions (OR 0.77, 95% CI 0.66-0.90, P<.001; GRADE: moderate quality). The meta-analyses also showed that eHealth interventions could increase patients' knowledge of HF and improve their quality of life, but there were no statistically significant effects. However, eHealth interventions could significantly increase medication adherence (OR 1.82, 95% CI 1.42-2.34, P<.001; GRADE: low quality) and improve self-care behaviors (standardized mean difference -1.34, 95% CI -2.46 to -0.22, P=.02; GRADE: very low quality). A subgroup analysis of primary outcomes regarding the enrolled population setting found that eHealth interventions were more effective in patients with HF after discharge compared with those in the ambulatory clinic setting. CONCLUSIONS: eHealth self-management interventions could benefit the health of patients with HF in various ways. However, the clinical effects of eHealth interventions in patients with HF are affected by multiple aspects, and more high-quality studies are needed to demonstrate effectiveness.

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.

BACKGROUND: Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice. OBJECTIVE: We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units (ICUs) and used the SHapley Additive exPlanation (SHAP) method to explain the extreme gradient boosting (XGBoost) model and explore prognostic factors for HF. METHODS: In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70% used for model training and 30% used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model. RESULTS: A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97%. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95% CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95% CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10%~28% threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable. CONCLUSIONS: The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.

High stability of autochthonous dissolved organic matter in karst aquatic ecosystems: Evidence from fluorescence.

Biological carbon pump (BCP) in karst areas has received intensive attention for years due to their significant contribution to the global missing carbon sink. The stability of autochthonous dissolved organic matter (Auto-DOM) produced by BCP in karst aquatic ecosystems may play a critical role in the missing carbon sink. However, the source of dissolved organic matter (DOM) in inland waters and its consumption by planktonic bacteria have not been thoroughly examined. Recalcitrant dissolved organic matter (RDOM) may exist in karst aquatic ecosystem as in the ocean. Through the study of the chromophoric dissolved organic matter (CDOM) and the interaction between CDOM and the planktonic bacterial community under different land uses at the Shawan Karst Water-carbon Cycle Test Site, SW China, we found that C2, as the fluorescence component of Auto-DOM mineralised by planktonic bacteria, may have some of the characteristics of RDOM and is an important DOM source in karst aquatic ecosystems. The stability ratio (Fmax(C2/(C1+C2))) of Auto-DOM reached 89.6 ± 6.71% in winter and 64.1 ± 7.19% in spring. Moreover, correlation-based network analysis determined that the planktonic bacterial communities were controlled by different fluorescence types of CDOM, of which C1 (fresh Auto-DOM), C3 (conventional allochthonous DOM (Allo-DOM)) and C4 (the Allo-DOM mineralised by bacteria) were clustered in one module together with prevalent organic-degrading planktonic bacteria; C2 was clustered in another tightly combined module, suggesting specific microbial utilization strategies for the C2 component. In addition, some important planktonic bacterium and functional genes (including chemotrophic heterotrophs and photosynthetic bacteria) were found to be affected by high Ca2+ and dissolved inorganic carbon (DIC) concentrations in karst aquatic ecosystems. Our research showed that Auto-DOM may be as an important carbon sink as the Allo-DOM in karst ecosystems, the former generally being neglected based on a posit that it is easily and first mineralized by planktonic bacteria.

Role of carbon and nutrient exports from different land uses in the aquatic carbon sequestration and eutrophication process.

The hydrochemical features affected by differing land uses play a key role in regulating both the primary production of aquatic photosynthetic organisms and the formation of autochthonous organic carbon (AOC); this impacts eutrophication and the global carbon cycle. In shallow water environments where phytoplankton and submerged plants coexist, the C-N-P limitations on the primary production of these aquatic organisms, and the mechanisms by which they promote the formation of AOC are poorly understood. In this study, over the hydrological year September 2018 to August 2019, a large-scale field simulation experiment at the Shawan Karst Test Site (SW China) with various types of land use was systematically conducted to investigate the C-N-P limitations on the primary production of phytoplankton and submerged plants. The results indicate that (1) phytoplankton are co-limited by nitrogen (N) and phosphorus (P) but with the N more important, while submerged plants are limited by carbon (C); (2) Chlorophyta and Bacillariophyta display a stronger competitive advantage than Cyanophyta in aqueous environments with high C but low N-P; (3) there is a seasonal difference in the contribution of phytoplankton and submerged plants to the formation of AOC, however, throughout the year, the contributions of phytoplankton (27%) and submerged plants biomass (28%) to AOC concentrations in the water were similar, combinedly accounting for approximately 17% of the formed AOC. It is concluded that natural restoration of vegetation, or injecting CO2 into water, which results in higher C but lower N-P loadings, may simultaneously help to mitigate eutrophication (with changes in biological structure and species) and increase C sequestration in surface waters.