Random Forest Regression Analysis Research Articles

Improvement of Saline–Alkali Soil and Straw Degradation Efficiency in Cold and Arid Areas Using Klebsiella sp. and Pseudomonas sp.

Corn straw is an important renewable resource, which could improve the quality of saline–alkali cultivated land. However, the slow decomposition of crop residues in cold, arid, and saline–alkali soils can lead to serious resource waste and ecological crises. The use of beneficial microorganisms with degradation functions could solve these problems. In this study, three types of saline–alkali soil with low, medium, and high salinity levels were used in the straw-returning experiment. The experiment was conducted with four treatments: GF2 (Klebsiella sp.), GF7 (Pseudomonas sp.), GF2+GF7, and CK (control without bacteria). Microbial characteristics, straw degradation efficiency, element release rate, and soil factors were compared, and random forest linear regression and partial least squares path modeling analysis methods were utilized. The results indicated that the degradation of bacterial metabolites, the efficiency of corn stover degradation, the efficiency of component degradation, and the release rates of elements (C, N, P, and K) initially increased and then decreased with the increase in salinity. At the maximum value of moderately saline–alkali soil, the effect of GF2+GF7 treatment was significantly better than that of other treatments (p < 0.05). Given the interactive effects of saline–alkali soil and microbial factors, the application of exogenous degrading bacteria could significantly increase soil enzyme activity and soil available nutrients, as well as regulate the salt–alkali ion balance in soil. The cation exchange capacity (9.13%, p < 0.01) was the primary driving force for the degradation rate of straw in saline–alkali soil with different degrees of salinization under the influence of exogenous degrading bacteria. Straw decomposition directly affected the soil chemical properties and indirectly affected soil enzyme activity. The results of this study would provide new strategies and insights into the utilization of microbial resources to promote straw degradation.

Rare bacterial taxa drive the ecosystem multifunctionality in lake water-level-fluctuating zone during seasonal water level fluctuations

Complex microbial community plays a crucial role in driving soil ecosystem multifunctionality. Water-level-fluctuating zone (WLFZ) of lakes serves as vital transitional areas between land and water, exhibiting crucial ecological functions. However, the relationship between soil moisture-sensitive bacterial communities and ecosystem multifunctionality remains poorly understood. In this study, we conducted a two-year seasonal field survey in the WLFZ of one large flooding lake. By using amplicon sequencing, we explored the changing characteristics of soil bacterial communities during water level fluctuations and their driving role in ecosystem multifunctionality. A simultaneous decrease in soil bacterial diversity and ecosystem multifunctionality was observed as the soil transitioned from drought to flooding states. Soil bacterial diversity was positively correlated with ecosystem multifunctionality, and rare bacterial sub-communities showed a higher correlation with multifunctionality than the overall bacterial community. Random forest regression analysis demonstrated that rare bacterial sub-communities were the optimal predictor variable for ecosystem multifunctionality in the WLFZ. Additionally, Actinobacteriota and Methylomirabilota held significance in predicting multifunctionality under drought and flooding states, respectively. This study highlighted the predominant role of soil rare bacterial sub-communities in driving ecosystem functionality in the WLFZ.

Synergistic co-evolution of rhizosphere bacteria in response to acidification amelioration strategies: impacts on the alleviation of tobacco wilt and underlying mechanisms.

Soil acidification represents a severe threat to tobacco cultivation regions in South China, exacerbating bacterial wilt caused by Ralstonia solanacearum. The comprehension of the underlying mechanisms that facilitate the restoration of rhizosphere microbial communities in "healthy soils" is imperative for ecologically managing tobacco bacterial wilt. This study focuses on acidified tobacco soils that have been subjected to continuous cultivation for 20 years. The experimental treatments included lime (L), biochar (B), and a combination of lime and biochar (L+B), in addition to a control group (CK). Utilizing rhizosphere biology and niche theory, we assessed disease suppression effects, changes in soil properties, and the co-evolution of the rhizosphere bacterial community. Each treatment significantly reduced tobacco bacterial wilt by 16.67% to 20.14% compared to the control group (CK) (p < 0.05) and increased yield by 7.86% to 27.46% (p < 0.05). The biochar treatment (B) proved to be the most effective, followed by the lime-biochar combination (L+B). The key factors controlling wilt were identified through random forest regression analysis as an increase in soil pH and exchangeable bases, along with a decrease in exchangeable acidity. However, lime treatment alone led to an increase in soil bulk density and a decrease in available nutrients, whereas both biochar and lime-biochar treatments significantly improved these parameters (p < 0.05). No significant correlation was found between the abundance of Ralstonia and wilt incidence. Nonetheless, all treatments significantly expanded the ecological niche breadth and average variation degree (AVD), enhanced positive interactions and cohesion within the community, and intensified negative interactions involving Ralstonia. This study suggests that optimizing community niches and enhancing pathogen antagonism are key mechanisms for mitigating tobacco wilt in acidified soils. It recommends using lime-biochar mixtures as soil amendments due to their potential ecological and economic benefits. This study offers valuable insights for disease control strategies and presents a novel perspective for research on Solanaceous crops.

Driving factors of the distribution of microplastics in the surface soil of the typical uninhabited and habited areas in the Qinghai–Tibet plateau, China

Microplastics (MPs) are widely detected in the soil of the Qinghai–Tibet Plateau with increasing economic activities. However, studies concerning the driving factors affecting the presence of these surface soil MPs for the typical regions with different geographic conditions are still lacking. Here we chose three representative regions (Ali, Yushu, and Haixi) from east to west across the plateau to investigate the distribution and further explore the contributing factors of surface soil MPs. The Spearman rank correlation, Geodetector, Random Forest Regression and Principal Component Analysis were used to unveil how the driving factors influence MPs distribution across the plateau. The results revealed that the MPs abundance, type, size, color and polymer across the Ali, Yushu, and Haixi were different. Microplastic abundance was inversely correlated with the distance from roads and residential areas, but was positively related to precipitation. Moreover, traffic elements were the primary source of MPs pollution in the Ali and Yushu but residential activities were the leading source of MPs contamination in the Haixi. Besides, backward trajectory simulations suggested that atmospheric transport may also contribute to the presence of soil MPs in the representative regions. These results further indicated that different regions may require different measures for controlling MPs pollution in surface soil. This study provides new insights into the distribution and source of MPs and further offers valuable methodology for future research aimed at uncover driving factors contributing MPs pollution across different regions with various geographical conditions.

Spatiotemporal variability of leaf critical senescence age across northern lands and its key drivers

Leaf senescence, a pivotal phenological event, signifies the aging of vegetation canopies and triggers abrupt shifts in various biogeochemical processes. However, the spatiotemporal pattern of leaf senescence age and its primary driving factors across northern lands remains unclear. In this study, we introduced a concept termed leaf critical senescence age (CSA) to characterize the initiation of senescence stage, which quantifies the time span between the onset dates of vegetation growth and senescence. Then, utilizing long-term remote sensing vegetation index data, we investigated the spatiotemporal variations of leaf CSA over northern lands (>30°N). Spatially, leaf CSA displayed extensive variability (ranging from 42 to 263 days), with an average of 146 ± 32 days. Deciduous broadleaf forests exhibited the longest CSA (177 ± 28 days), while shrublands demonstrated the shortest (121 ± 22 days). Temporally, most plant functional types experienced a reversal in leaf CSA trends around 2010, leading to the contrasting trends between 1982–2010 (+0.21 days/year) and 2010–2015 (−2.36 days/year) across northern lands. Further random-forest regression and partial correlation analysis together indicated that temperature was the dominant factor driving spatiotemporal variations in leaf CSA. These findings suggest that climate warming is reshaping the geographical pattern of leaf senescence age, posing great uncertainty to future projections of terrestrial feedback to climate change.

Neutrophil Extracellular Trap Scores Predict 90-Day Mortality in Hepatitis B-Related Acute-on-Chronic Liver Failure.

Hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF) is associated with pronounced systemic inflammation, and neutrophil extracellular traps (NETs) are key components of this response. The primary objective of this study was to establish an NET-related scoring system for patients with HBV-ACLF. A prospective training cohort of 81 patients from the Beijing Ditan Hospital was included. The concentrations of NET markers (cell-free DNA, myeloperoxidase DNA [MPO-DNA], and citrullinated histone H3) in peripheral blood were quantified. Random survival forest, LASSO regression, and multivariate Cox regression analyses were used to identify prognostic factors associated with 90-day mortality in ACLF patients and develop a nomogram for visualization, which was followed by evaluation in a validation cohort (n = 40). NET-related marker levels were significantly higher in the non-survival group than in the survival group (p < 0.05). The NET score was constructed by combining MPO-DNA, neutrophil-to-lymphocyte ratio, and age data. The score's diagnostic effectiveness, assessed by the area under the curve, yielded values of 0.83 and 0.77 in the training and validation sets, respectively, markedly surpassing those of other established models (p < 0.05). In both groups, the 90-day mortality rates were 88.8% and 75.0%, respectively, for patients categorized as high risk and 18.0% and 12.5%, respectively, for those classified as low risk.

Growth Simulation of Lyophyllum decastes and Coprinus comatus and Their Influencing Factors in a Forested Catchment

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d−1, 0.54 cm d−1, and 4.54 cm d−1, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson’s correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research.

Effects of season and water quality on community structure of planktonic eukaryotes in the Chaohu Lake Basin.

Analyzing the correlation between planktonic eukaryotic communities (PECs) and aquatic physicochemical parameters (APPs) provides important references for predicting the impact of climate change and human activities on aquatic ecosystems. To assess the influence of seasons and APPs on PEC structures in lakes and rivers, we utilized high-throughput sequencing of the 18S rRNA gene to analyze PEC structures in a lake and seven rivers in the Chaohu Lake Basin and analyzed their correlations with APPs. Our results revealed that PEC structure was significantly affected by season, with the highest α-diversity observed in summer. Furthermore, we identified several APPs, including water temperature, conductivity, dissolved oxygen, pH, phosphate, total phosphorus, trophic level index (TLI), nitrate, ammonia nitrogen, and total nitrogen, that significantly influenced PEC structures. Specifically, we found that Stephanodiscus hantzschii, Simocephalus serrulatus, Cryptomonas sp. CCAC_0109, Pedospumella encystans, Actinochloris sphaerica, Chlamydomonas angulosa, Gonyostomum semen, Skeletonema potamos, Chlamydomonas klinobasis, Pedospumella sp., and Neochlorosarcina negevensis were significantly correlated to TLI, while Limnoithona tetraspina, Theileria sp., and Pseudophyllomitus vesiculosus were significantly correlated to the water quality index (WQI). However, our random forest regression analysis using the top 100 species was unable to accurately predict the WQI and TLI. These results provide valuable data for evaluating the impact of APPs on PEC and for protecting water resource in the Chaohu Lake Basin.

Acceptance level of advance care planning and its associated factors among the public: A nationwide survey

BackgroundAdvance care planning (ACP) can contribute to individuals making decisions about their healthcare preferences in advance of serious illness. Up to now, the acceptance level and associated factors of ACP among the public in China remain unclear. This study aims to investigate the acceptance level of ACP in China and identify factors associated with it based on the socioecological model.MethodsA total of 19,738 participants were included in this survey. We employed a random forest regression analysis to select factors derived from the socioecological model. Multivariate generalized linear model analysis was then conducted to explore the factors that were associated with the acceptance level of ACP.ResultsOn a scale ranging from 0 to 100, the median score for acceptance level of ACP was 64.00 (IQR: 48.00–83.00) points. The results of the multivariate generalized linear model analysis revealed that participants who scored higher on measures of openness and neuroticism personality traits, as well as those who had greater perceptions of social support, higher levels of health literacy, better neighborly relationships, family health, and family social status, were more likely to accept ACP. Conversely, participants who reported higher levels of subjective well-being and greater family communication levels demonstrated a lower likelihood of accepting ACP.ConclusionsThis study identified multiple factors associated with the acceptance level of ACP. The findings offer valuable insights that can inform the design and implementation of targeted interventions aimed at facilitating a good death and may have significant implications for the formulation of end-of-life care policies and practices in other countries facing similar challenges.

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma.

Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance. Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed. Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed. Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.

A novel mitochondrial function-associated programmed cell death-related prognostic signature for predicting the prognosis of early breast cancer.

Purpose: To screen mitochondrial function-associated PCD-related biomarkers and construct a risk model for predicting the prognosis of early breast cancer. Methods: Data on gene expression levels and clinical information were obtained from the TCGA database, and GSE42568 and GSE58812 datasets were obtained from GEO database. The mitochondrial function-associated programmed cell death (PCD) related genes in early breast cancer were identified, then LASSO logistic regression, SVM-RFE, random forest (RF), and multiple Cox logistic regression analysis were employed to construct a prognostic risk model. Differences in immune infiltration, drug sensitivity, and immunotherapy response were evaluated between groups. Lastly, the qRT-PCR was employed to confirm the key genes. Results: Total 1,478 DEGs were screened between normal and early breast cancer groups, and these DEGs were involved in PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interaction pathways. Then total 178 mitochondrial function-associated PCD related genes were obtained, followed by a four mitochondrial function-associated PCD related genes prognostic model and nomogram were built. In addition, total 2 immune checkpoint genes were lowly expressed in the high-risk group, including CD47 and LAG3, and the fraction of some immune cells in high- and low-risk groups had significant difference, such as macrophage, eosinophil, mast cell, etc., and the Top3 chemotherapeutics with significant differences were included FH535, MK.2206, and bicalutamide. Finally, the qRT-qPCR results shown that the CREB3L1, CAPG, SPINT1 and GRK3 mRNA expression were in line with the bioinformatics analysis results. Conclusion: Four mitochondrial function-associated PCD-related genes were identified, including CREB3L1, CAPG, SPINT1, and GRK3, and the prognostic risk model and nomogram were established for predicting the survival of early breast cancer patient. The chemotherapeutics, containing FH535, MK.2206, and bicalutamide, might be used for early breast cancer.

Stemness subtypes in lower-grade glioma with prognostic biomarkers, tumor microenvironment, and treatment response

Our research endeavors are directed towards unraveling the stem cell characteristics of lower-grade glioma patients, with the ultimate goal of formulating personalized treatment strategies. We computed enrichment stemness scores and performed consensus clustering to categorize phenotypes. Subsequently, we constructed a prognostic risk model using weighted gene correlation network analysis (WGCNA), random survival forest regression analysis as well as full subset regression analysis. To validate the expression differences of key genes, we employed experimental methods such as quantitative Polymerase Chain Reaction (qPCR) and assessed cell line proliferation, migration, and invasion. Three subtypes were assigned to patients diagnosed with LGG. Notably, Cluster 2 (C2), exhibiting the poorest survival outcomes, manifested characteristics indicative of the subtype characterized by immunosuppression. This was marked by elevated levels of M1 macrophages, activated mast cells, along with higher immune and stromal scores. Four hub genes—CDCA8, ORC1, DLGAP5, and SMC4—were identified and validated through cell experiments and qPCR. Subsequently, these validated genes were utilized to construct a stemness risk signature. Which revealed that Lower-Grade Glioma (LGG) patients with lower scores were more inclined to demonstrate favorable responses to immune therapy. Our study illuminates the stemness characteristics of gliomas, which lays the foundation for developing therapeutic approaches targeting CSCs and enhancing the efficacy of current immunotherapies. By identifying the stemness subtype and its correlation with prognosis and TME patterns in glioma patients, we aim to advance the development of personalized treatments, enhancing the ability to predict and improve overall patient prognosis.

Enzyme Activity Stoichiometry Suggests That Fertilization, Especially Nitrogen Fertilization, Alleviates Nutrient Limitation of Soil Microorganisms in Moso Bamboo Forests

Rational application of N fertilizer is essential for maintaining the long-term productivity of Moso bamboo forests. Microbial activity is a crucial indicator of soil quality. Changes in soil nutrient resources due to N addition can lead to microbial nutrient limitations, thereby impeding the maintenance of soil quality. Currently, there is limited research on the effects of N application on microbial nutrient limitations in Moso bamboo forest soils. To examine the changes in extracellular enzyme activity and microbial nutrient limitations in Moso bamboo forest soils following N application, we conducted an N application experiment in northern Guizhou. The findings revealed that the N3 treatment (726 kg·N·hm−2·yr−1) significantly reduced β-glucosidase (BG) activity by 27.61% compared to the control group (no fertilization). The N1 (242 kg·N·hm−2·yr−1), N2 (484 kg·N·hm−2·yr−1), and N3 treatments notably increased the activities of leucine aminopeptidase (LAP) and N-acetyl-β-D-glucosidase (NAG) by 11.45% to 15.79%. Acid phosphatase (ACP) activity remained unaffected by fertilization. N application treatments significantly decreased the C:Ne and C:Pe ratios, while the N:Pe ratio was less influenced by N fertilizer application. Scatter plots and vector characteristics of enzyme activity stoichiometry suggested that microorganisms in the study area were limited by C and N, and N fertilizer application reduced the vector length and increased the vector angle, indicating that N application alleviated the C and N limitation of microorganisms in Moso bamboo forests. Redundancy Analysis (RDA) demonstrated that microbial biomass phosphorus (MBP) was the most critical factor affecting extracellular enzyme activity and stoichiometry. Furthermore, Random Forest Regression analysis identified MBP and the N:Pm ratio as the most significant factors influencing microbial C and N limitation, respectively. The study demonstrated that N application modulates the microbial nutrient acquisition strategy by altering soil nutrient resources in Moso bamboo forests. Formulating fertilizer application strategies based on microbial nutrient requirements is more beneficial for maintaining soil quality and sustainably managing Moso bamboo forests. Additionally, our study offers a theoretical reference for understanding carbon cycling in bamboo forest ecosystems in the context of substantial N inputs.

Deciphering the key soil microbial taxa that contribute to saponin accumulation in a geo-authentic Sanqi ginseng production area: Evidence from four different varieties

Sanqi ginseng (Panax notoginseng) is a typical geo-authentic medicinal herb and the accumulation of saponins heavily relies on its growing environment. However, the effects of soil factors on saponin accumulation are still not well characterized. In this study, four different varieties (improved varieties: KZ, HD and PK; common variety: PT) and two typical soils (geo-authentic soil: QB; non-geo-authentic soil: SL) were selected for pot experiments, and the root biomass and saponin content, as well as the soil microbiome were investigated after one-year of cultivation. Results showed that soil, rather than variety, was the determinant of the root development and saponin accumulation of Sanqi ginseng. Sanqi ginseng cultivated in the QB soil had better root morphology (with long and dense fibrous roots) and higher saponin accumulation. Compared to the physicochemical properties, the soil microbiome exhibited a higher explanatory power (24.68 %) for the biomass and saponin of Sanqi ginseng, and its α-diversity and β-diversity were strongly related to biomass, saponin content and saponin accumulation. Random forest regression analysis further revealed that those having high and positive contributions to biomass and saponin accumulation, such as bacterial genera Arthrobacter, Gaiella, Granulicella, Bradyrhizobium, Rhizomicrobium, Rhizobium and Streptomyces, and fungal genera Fusarium, Plenodomus, Setophoma and Trichoderma, were significantly enriched in QB soil. This study revealed that soil is one of the key factors for producing geo-authentic Sanqi ginseng and highlighted the importance of key soil microbial taxa in the saponin accumulation of Sanqi ginseng, thus providing a theoretical basis for quality improvement through microbiome regulation.

Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study.

Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.

Unraveling Endometrial Cancer Survival Predictors Through Advanced Machine Learning Techniques.

This study explores endometrial cancer (EC) within the broader context of oncogynecology, focusing on 3,845 EC patients at the Almazov National Research Center. The research analyzes clinical data, employing machine learning techniques like random forest regression and decision tree analysis. Key findings include age-dependent impacts on EC outcomes, unexpected correlations between dietary habits and recurrence risk (e.g., higher risk for vegans), and intriguing associations like soft drink consumption influencing relapse. Despite limitations like a retrospective design and self-reported data, the study's extended eight-year follow-up and robust database enhance its credibility. The nuanced insights into EC risk factors, influenced by factors like physical activity and diet, open avenues for targeted diagnostics and prevention strategies, showcasing the potential of machine learning in predicting outcomes.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis.Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established.Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model.Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Integrated analysis reveals critical cisplatin-resistance regulators E2F7 contributed to tumor progression and metastasis in lung adenocarcinoma

BackgroundDrug resistance poses a significant challenge in cancer treatment, particularly as a leading cause of therapy failure. Cisplatin, the primary drug for lung adenocarcinoma (LUAD) chemotherapy, shows effective treatment outcomes. However, the development of resistance against cisplatin is a major obstacle. Therefore, identifying genes resistant to cisplatin and adopting personalized treatment could significantly improve patient outcomes.MethodsBy examining transcriptome data of cisplatin-resistant LUAD cells from the GEO database, 181 genes associated with cisplatin resistance were identified. Using univariate regression analysis, random forest and multivariate regression analyses, two prognostic genes, E2F7 and FAM83A, were identified. This study developed a prognostic model utilizing E2F7 and FAM83A as key indicators. The Cell Counting Kit 8 assay, Transwell assay, and flow cytometry were used to detect the effects of E2F7 on the proliferation, migration, invasiveness and apoptosis of A549/PC9 cells. Western blotting was used to determine the effect of E2F7 on AKT/mTOR signaling pathway.ResultsThis study has pinpointed two crucial genes associated with cisplatin resistance, E2F7 and FAM83A, and developed a comprehensive model to assist in the diagnosis, prognosis, and evaluation of relapse risk in LUAD. Analysis revealed that patients at higher risk, according to these genetic markers, had elevated levels of immune checkpoints (PD-L1 and PD-L2). The prognostic and diagnosis values of E2F7 and FAM83A were further confirmed in clinical data. Furthermore, inhibiting E2F7 in lung cancer cells markedly reduced their proliferation, migration, invasion, and increased apoptosis. In vivo experiments corroborated these findings, showing reduced tumor growth and lung metastasis upon E2F7 suppression in lung cancer models.ConclusionOur study affirms the prognostic value of a model based on two DEGs, offering a reliable method for predicting the success of tumor immunotherapy in patients with LUAD. The diagnostic and predictive model based on these genes demonstrates excellent performance. In vitro, reducing E2F7 levels shows antitumor effects by blocking LUAD growth and progression. Further investigation into the molecular mechanisms has highlighted E2F7’s effect on the AKT/mTOR signaling pathway, underscoring its therapeutic potential. In the era of personalized medicine, this DEG-based model promises to guide clinical practice.

Melatonin promotes the recovery of apple plants after waterlogging by shaping the structure and function of the rhizosphere microbiome.

The dynamics of the physiological adaptability of plants and the rhizosphere soil environment after waterlogging remain unclear. Here we investigated the mechanisms regulating plant condition and shaping of the rhizosphere microbiome in a pot experiment. In the experiment, we added melatonin to waterlogged plants, which promoted waterlogging relief. The treatment significantly enhanced photosynthesis and the antioxidant capacity of apple plants, and significantly promoted nitrogen (N) utilization efficiency by upregulating genes related to N transport and metabolism. Multiperiod soil microbiome analysis showed the dynamic effects of melatonin on the diversity of the microbial community during waterlogging recovery. Random forest and linear regression analyses were used to screen for potential beneficial bacteria (e.g., Azoarcus, Pseudomonas and Nocardioides) specifically regulated by melatonin and revealed a positive correlation with soil nutrient levels and plant growth. Furthermore, metagenomic analyses revealed the regulatory effects of melatonin on genes involved in N cycling in soil. Melatonin positively contributed to the accumulation of plant dry weight by upregulating the expression of nifD and nifK (N fixation). In summary, melatonin positively regulates physiological functions in plants and the structure and function of the microbial community; it promoted the recovery of apple plants after waterlogging stress.

Root Respiration–Trait Relationships Are Influenced by Leaf Habit in Tropical Plants

Root respiration is a critical physiological trait that significantly influences root system activity. Recent studies have associated root respiration with the economic functioning of roots; however, research on root respiration in tropical plants remains limited. This study examined the fine root respiration and the relationship between root respiration and root chemical and morphological traits in 16 tropical plant species, including both evergreen and deciduous species. Findings revealed that deciduous species exhibited higher root respiration compared to evergreen species. Root respiration positively correlated with root nitrogen concentration and specific root length and correlated negatively with root diameter and root tissue density across all species. The root respiration patterns in evergreen species aligned with those seen in all tree species, while deciduous species showed a distinct negative correlation with root tissue density and no significant correlations with other root traits. Principal component analysis revealed that the patterns of root variation in both evergreen and deciduous trees were multidimensional, with deciduous trees exhibiting acquisitive traits and evergreen trees displaying conservative traits. Random forest and multiple regression analysis showed that specific root length exerted the most significant influence on root respiration in both evergreen and deciduous trees. These findings are ecologically significant, enhancing our understanding of root respiration in tropical plants and its impact on ecosystem functions. They contribute valuable insights and support the conservation and management of tropical vegetation.