Altered resting-state brain function in endurance athletes.

Previous research has confirmed significant differences in regional brain activity and functional connectivity between endurance athletes and non-athletes. However, no studies have investigated the differences in topological efficiency of the brain functional network between endurance athletes and non-athletes. Here, we compared differences in regional activities, functional connectivity, and topological properties to explore the functional basis associated with endurance training. The results showed significant correlations between Regional Homogeneity in the motor cortex, visual cortex, cerebellum, and the training intensity parameters. Alterations in functional connectivity among the motor cortex, visual cortex, cerebellum, and the inferior frontal gyrus and cingulate gyrus were significantly correlated with training intensity parameters. In addition, the graph theoretical analysis results revealed a significant reduction in global efficiency among athletes. This decline is mainly caused by decreased nodal efficiency and nodal local efficiency of the cerebellar regions. Notably, the sensorimotor regions, such as the precentral gyrus and supplementary motor areas, still exhibit increased nodal efficiency and nodal local efficiency. This study not only confirms the improvement of regional activity in brain regions related to endurance training, but also offers novel insights into the mechanisms through which endurance athletes undergo changes in the topological efficiency of the brain functional network.

The proactive and reactive mechanisms of learned spatial suppression.

Selection history refers to the notion that previous allocations of attention or suppression have the potential to elicit lingering and enduring selection biases that are isolated from goal-driven or stimulus-driven attention. However, in the singleton detection mode task, manipulating the selection history of distractors cannot give rise to pure proactive inhibition. Therefore, we employed a combination of a working memory task and a feature search mode task, simultaneously recording cortical activity using EEG, to investigate the mechanisms of suppression guided by selection history. The results from event-related potential and reaction times showed an enhanced inhibitory performance when the distractor was presented at the high-probability location, along with instances where the target appeared at the high-probability location of distractors. These findings demonstrate that a generalized proactive inhibition bias is learned and processed independent of cognitive resources, which is supported by selection history. In contrast, reactive rejection toward the low-probability location was evident through the Pd component under varying cognitive resource conditions. Taken together, our findings indicated that participants learned proactive inhibition when the distractor was at the high-probability location, whereas reactive rejection was involved at low-probability location.

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population.

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

Happy people are always similar: The evidence from brain morphological and functional inter-subject correlations.

A fundamental question in the study of happiness is whether there is neural evidence to support a well-known hypothesis that happy people are always similar while unfortunate people have their own misfortunes. To investigate this, we employed several happiness-related questionnaires to identify potential components of happiness, and further investigated and confirmed their associations with personality, mood, aggressive behaviors, and amygdala reactivity to fearful faces within a substantial sample size of college students (n = 570). Additionally, we examined the functional and morphological similarities and differences among happy individuals using the inter-subject representational similarity analysis (IS-RSA). IS-RSA emphasizes the geometric properties in a high-dimensional space constructed by brain or behavioral patterns and focuses on individual subjects. Our behavioral findings unveiled two factors of happiness: individual and social, both of which mediated the effect of personality traits on individual aggression. Subsequently, mood mediated the impact of happiness on aggressive behaviors across two subgroup splits. Functional imaging data revealed that individuals with higher levels of happiness exhibited reduced amygdala reactivity to fearful faces, as evidenced by a conventional face-matching task (n = 104). Moreover, IS-RSA demonstrated that these participants manifested similar neural activation patterns when processing fearful faces within the visual pathway, but not within the emotional network (e.g., amygdala). Morphological observations (n = 425) indicated that individuals with similar high happiness levels exhibited comparable gray matter volume patterns within several networks, including the default mode network, fronto-parietal network, visual network, and attention network. Collectively, these findings offer early neural evidence supporting the proposition that happy individuals may share common neural characteristics.

The computational and neural substrates of individual differences in impulsivity under loss framework.

Numerous neuroimaging studies have identified significant individual variability in intertemporal choice, often attributed to three neural mechanisms: (1) increased reward circuit activity, (2) decreased cognitive control, and (3) prospection ability. These mechanisms that explain impulsivity, however, have been primarily studied in the gain domain. This study extends this investigation to the loss domain. We employed a hierarchical Bayesian drift-diffusion model (DDM) and the inter-subject representational similarity approach (IS-RSA) to investigate the potential computational neural substrates underlying impulsivity in loss domain across two experiments (n = 155). These experiments utilized a revised intertemporal task that independently manipulated the amounts of immediate and delayed-loss options. Behavioral results demonstrated positive correlations between the drift rate, measured by the DDM, and the impulsivity index K in Exp. 1 (n = 97) and were replicated in Exp. 2 (n = 58). Imaging analyses further revealed that the drift rate significantly mediated the relations between brain properties (e.g., prefrontal cortex activations and gray matter volume in the orbitofrontal cortex and precuneus) and K in Exp. 1. IS-RSA analyses indicated that variability in the drift rate also mediated the associations between inter-subject variations in activation patterns and individual differences in K. These findings suggest that individuals with similar impulsivity levels are likely to exhibit similar value processing patterns, providing a potential explanation for individual differences in impulsivity within a loss framework.

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.

BACKGROUND: The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments. METHODS: In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9. RESULTS: Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics. CONCLUSIONS: This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.

Genome-Wide Identification of B-Box Gene Family and Candidate Light-Related Member Analysis of Tung Tree (Vernicia fordii).

Light is one of the most important environmental factors for plant growth. In the production process of tung oil tree cultivation, due to the inappropriate growth of shading conditions, the lower branches are often dry and dead, which seriously affects the yield of tung oil trees. However, little is known about the key factors of light-induced tree photomorphogenesis. In this study, a total of 22 VfBBX family members were identified to provide a reference for candidate genes in tung tree seedlings. All members of the VfBBX family have different numbers of highly conserved B-box domains or CCT domains. Phylogenetic evolution clustered the VfBBX genes into four categories, and the highest density of members was on chromosome 6. Interspecific collinearity analysis suggested that there were six pairs of duplicate genes in VfBBX members, but the expression levels of all family members in different growth and development stages of the tung tree were significantly divergent. After different degrees of shading treatment and physiological data determination of tung tree seedlings, the differential expression level and chlorophyll synthesis genes correlation analysis revealed that VfBBX9 was a typical candidate nuclear localization transcription factor that was significantly differentially expressed in light response. This study systematically identified the VfBBX gene family and provided a reference for studying its molecular function, enhanced the theoretical basis for tung tree breeding, and identified excellent varieties.

Acute kidney injury after intracerebral hemorrhage: a mini review.

Intracerebral hemorrhage (ICH) stands as a prevalent and pivotal clinical condition. The potential cooccurrence of acute kidney injury (AKI) among afflicted individuals can profoundly influence their prognosis. In recent times, there has been a growing focus among clinical practitioners on researching the relationship between ICH and AKI. AKI occurring concurrently with ICH predominantly arises from both hemodynamic and non-hemodynamic mechanisms. The latter encompasses neurohumoral regulation, inflammatory response, oxidative stress, and iatrogenic factors such as contrast agents, dehydrating agents, antibiotics, and diuretics. Moreover, advanced age, hypertension, elevated baseline creatinine levels, chronic kidney disease, and larger hematomas predispose patients to AKI. Additionally, the current utilization of biomarkers and the development of predictive models appear promising in identifying patients at risk of AKI after ICH. This article aims to underscore the potential of the aforementioned insights to inspire novel approaches to early clinical intervention.

Nocardia farcinica brain abscess with torque teno virus co-infection: A case report.

Background: Brain abscesses caused by Nocardia are rare and difficult to diagnose. Nocardia farcinica is among the most common species; however, the conventional diagnosis of N. farcinica infection consists of cerebrospinal fluid (CSF) and blood culture and Gram staining. These procedures prolong the time to diagnosis and initiating treatment. Case presentation: A 69-year-old woman with diabetes mellitus presented with headaches and dizziness persisting for 2 weeks, which was initially diagnosed as a brain abscess. Due to the unusual presentation and rapid progression of symptoms, she underwent surgical resection of the brain abscess. No pathogens were detected in blood or CSF cultures. However, metagenomic next-generation sequencing (mNGS) identified N. farcinica and Torque teno virus in pus extracted from the abscesses. The patient received appropriate antibiotic therapy and recovered fully without any residual neurological deficits. Conclusion: mNGS useful for prompt diagnosis and selection of antibiotic therapy for brain abscesses caused by Nocardia. Surgical intervention is necessary in some cases.

Helicobacter pylori triggers inflammation and oncogenic transformation by perturbing the immune microenvironment.

The immune microenvironment plays a critical regulatory role in the pathogenesis of Helicobacter pylori (H. pylori). Understanding the mechanisms that drive the transition from chronic inflammation to cancer may provide new insights for early detection of gastric cancer. Although chronic inflammation is frequent in precancerous gastric conditions, the monitoring function of the inflammatory microenvironment in the progression from H. pylori-induced chronic inflammation to gastric cancer remains unclear. This literature review summarizes significant findings on how H. pylori triggers inflammatory responses and facilitates cancer development through the immune microenvironment. Furthermore, the implications for future research and clinical applications are also addressed. The review is divided into four main sections: inflammatory response and immune evasion mechanisms induced by H. pylori, immune dysregulation associated with gastric cancer, therapeutic implications, and future perspectives on H. pylori-induced gastric carcinogenesis with a focus on the immune microenvironment.

Optimization strategies for CO2 biological fixation.

Global sustainable development faces a significant challenge in effectively utilizing CO2. Meanwhile, CO2 biological fixation offers a promising solution. CO2 has the highest oxidation state (+4 valence state), whereas typical multi­carbon chemicals have lower valence states. The Gibbs free energy (ΔG) changes of CO2 reductive reactions are generally positive and this renders it necessary to input different forms of energy. Although biological carbon fixation processes are friendly to operate, the thermodynamic obstacles must be overcome. To make this reaction occur favorably and efficiently, diverse strategies to enhance CO2 biological fixation efficiency have been proposed by numerous researchers. This article reviews recent advances in optimizing CO2 biological fixation and intends to provide new insights into achieving efficient biological utilization of CO2. It first outlines the thermodynamic characteristics of diverse carbon fixation reactions and proposes optimization directions for CO2 biological fixation. A comprehensive overview of the catalytic mechanisms, optimization strategies, and challenges encountered by common carbon-fixing enzymes is then provided. Subsequently, potential routes for improving the efficiency of biological carbon fixation are discussed, including the ATP supply, reducing power supply, energy supply, reactor design, and carbon enrichment system modules. In addition, effective artificial carbon fixation pathways were summarized and analyzed. Finally, prospects are made for the research direction of continuously improving the efficiency of biological carbon fixation.

Supporting data-enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment.

A parallel hybrid ordinary differential equation (ODE) integrating the Activated Sludge Model No. 2d (ASM2d) and an artificial neural network (ANN) was developed to simulate biological phosphorus removal (BPR) with high accuracy and interpretability. Two novelties were introduced; first, the involved supporting data (i.e., phosphate-release activity) were incorporated as an input in the ANN. Second, the outputs of the ANN were selective. Three models were implemented using different ANN outputs, and all three outperformed ASM2d in phosphate estimation for anaerobic/aerobic sequencing batch reactor operation. In particular, the incorporation of four variables responsible for BPR into the ANN enabled the highest performance (R2 = 0.93) owing to the capture of increasing phosphate-accumulating organisms (PAOs). The ANN with the supporting data worked satisfactorily to compensate for ASM2d by adding proper PAOs, resulting in improvement in phosphate estimation. The novel parallel hybrid ODE can simulate BPR while maintaining physical meaning.

The clinical value of nutritional and inflammatory indicators in predicting pneumonia among patients with intracerebral hemorrhage.

Immunosuppression and malnutrition play pivotal roles in the complications of intracerebral hemorrhage (ICH) and are intricately linked to the development of stroke-associated pneumonia (SAP). Inflammatory markers, including NLR (neutrophil-to-lymphocyte ratio), SII (systemic immune inflammation index), SIRI (systemic inflammatory response index), and SIS (systemic inflammation score), along with nutritional indexes such as CONUT (controlling nutritional status) and PNI (prognostic nutritional index), are crucial indicators influencing the inflammatory state following ICH. In this study, our objective was to compare the predictive efficacy of inflammatory and nutritional indices for SAP in ICH patients, aiming to determine and explore their clinical utility in early pneumonia detection. Patients with severe ICH requiring ICU admission were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The outcomes included the occurrence of SAP and in-hospital death. Receiver operating characteristic (ROC) analysis, multivariate logistic regression, smooth curve analysis, and stratified analysis were employed to investigate the relationship between the CONUT index and the clinical outcomes of patients with severe ICH. A total of 348 patients were enrolled in the study. The incidence of SAP was 21.3%, and the in-hospital mortality rate was 17.0%. Among these indicators, multiple regression analysis revealed that CONUT, PNI, and SIRI were independently associated with SAP. Further ROC curve analysis demonstrated that CONUT (AUC 0.6743, 95% CI 0.6079-0.7408) exhibited the most robust predictive ability for SAP in patients with ICH. Threshold analysis revealed that when CONUT < 6, an increase of 1 point in CONUT was associated with a 1.39 times higher risk of SAP. Similarly, our findings indicate that CONUT has the potential to predict the prognosis of patients with ICH. Among the inflammatory and nutritional markers, CONUT stands out as the most reliable predictor of SAP in patients with ICH. Additionally, it proves to be a valuable indicator for assessing the prognosis of patients with ICH.

Obesity and lipid metabolism in the development of osteoporosis (Review).

Osteoporosis is a common bone metabolic disease that causes a heavy social burden and seriously threatens life. Improving osteogenic capacity is necessary to correct bone mass loss in the treatment of osteoporosis. Osteoblasts are derived from the differentiation of bone marrow mesenchymal stem cells, a process that opposes adipogenic differentiation. The peroxisome proliferator­activated receptor γ and Wnt/ß­catenin signaling pathways mediate the mutual regulation of osteogenesis and adipogenesis. Lipid substances play an important role in the occurrence and development of osteoporosis. The content and proportion of lipids modulate the activity of immunocytes, mainly macrophages, and the secretion of inflammatory factors, such as IL­1, IL­6 and TNF­α. These inflammatory effectors increase the activity and promote the differentiation of osteoclasts, which leads to bone imbalance and stronger bone resorption. Obesity also decreases the activity of antioxidases and leads to oxidative stress, thereby inhibiting osteogenesis. The present review starts by examining the bidirectional differentiation of BM­MSCs, describes in detail the mechanism by which lipids affect bone metabolism, and discusses the regulatory role of inflammation and oxidative stress in this process. The review concludes that a reasonable adjustment of the content and proportion of lipids, and the alleviation of inflammatory storms and oxidative damage induced by lipid imbalances, will improve bone mass and treat osteoporosis.

Adaptive Evolution for the Efficient Production of High-Quality d-Lactic Acid Using Engineered Klebsiella pneumoniae.

d-Lactic acid serves as a pivotal platform chemical in the production of poly d-lactic acid (PDLA) and other value-added products. This compound can be synthesized by certain bacteria, including Klebsiella pneumoniae. However, industrial-scale lactic acid production in Klebsiella pneumoniae faces challenges due to growth inhibition caused by lactic acid stress, which acts as a bottleneck in commercial microbial fermentation processes. To address this, we employed a combination of evolutionary and genetic engineering approaches to create an improved Klebsiella pneumoniae strain with enhanced lactic acid tolerance and production. In flask fermentation experiments, the engineered strain achieved an impressive accumulation of 19.56 g/L d-lactic acid, representing the highest production yield observed in Klebsiella pneumoniae to date. Consequently, this strain holds significant promise for applications in industrial bioprocessing. Notably, our genome sequencing and experimental analyses revealed a novel correlation between UTP-glucose-1-phosphate uridylyltransferase GalU and lactic acid resistance in Klebsiella pneumoniae. Further research is warranted to explore the potential of targeting GalU for enhancing d-lactic acid production.

Association between monocyte to high-density lipoprotein cholesterol ratio and kidney stone: insights from NHANES.

Purpose: The ratio of monocyte to high-density lipoprotein cholesterol (MHR) has surfaced as a novel biomarker indicative of inflammation and oxidative stress. The aim of our study was to evaluate the association between MHR and the risk of kidney stones. Methods: This study analyzed data from individuals aged 20-79 who participated in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018. The MHR was assessed as the exposure variable, while a self-reported history of kidney stones was used as the outcome variable. The independent relationship between MHR and the risk of kidney stones was thoroughly evaluated. Results: This study included 28,878 participants, and as the quartile range of the MHR increased, the proportion of kidney stones also rose progressively (7.20% to 8.89% to 10.88% to 12.05%, P<0.001). After adjusting for confounding factors, MHR was independently associated with an increased risk of kidney stones (OR=1.31, 95%CI=1.11-1.54, P=0.001), also independent of some common inflammatory indices. Subgroup analysis suggested that the relationship between MHR and kidney stones was more pronounced in female and individuals aged 20-49. Further restricted cubic spline (RCS) analysis indicated a nonlinear relationship between MHR and the risk of kidney stones. Conclusion: Our results indicate a positive correlation between MHR and an increased risk of kidney stones in US adults, underscoring the need for further large-scale prospective cohort studies to validate these findings.

The clinical value of inflammation index in predicting ICU mortality of critically ill patients with intracerebral hemorrhage.

Background: The inflammatory response holds paramount significance in the context of intracerebral hemorrhage (ICH) and exhibits a robust correlation with mortality rates. Biological markers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune inflammation index (SII), and systemic inflammatory response index (SIRI) play crucial roles in influencing the systemic inflammatory response following ICH. This study aims to compare the predictive efficacy of NLR, PLR, LMR, SII, and SIRI concerning the risk of mortality in the intensive care unit (ICU) among critically ill patients with ICH. Such a comparison seeks to elucidate their early warning capabilities in the management and treatment of ICH. Methods: Patients with severe ICH requiring admission to the ICU were screened from the Medical Information Marketplace for Intensive Care (MIMIC-IV) database. The outcomes studied included ICU mortality and 30 day ICU hospitalization rates, based on tertiles of the NLR index level. To explore the relationship between the NLR index and clinical outcomes in critically ill patients with ICH, we utilized receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and multivariate logistic regression analysis. Results: A total of 869 patients (51.9% male) were included in the study, with an ICU mortality rate of 22.9% and a 30 day ICU hospitalization rate of 98.4%. Among the five indicators examined, both the ROC curve and DCA indicated that NLR (AUC: 0.660, 95%CI: 0.617-0.703) had the highest predictive ability for ICU mortality. Moreover, this association remained significant even after adjusting for other confounding factors during multivariate analysis (HR: 3.520, 95%CI: 2.039-6.077). Based on the results of the multivariate analysis, incorporating age, albumin, lactic acid, NLR, and GCS score as variables, we developed a nomogram to predict ICU mortality in critically ill patients with ICH. Conclusion: NLR emerges as the most effective predictor of ICU mortality risk among critically ill patients grappling with ICH when compared to the other four indicators. Furthermore, the integration of albumin and lactic acid indicators into the NLR nomogram enhances the ability to promptly identify ICU mortality in individuals facing severe ICH.

Comprehensive assessment of multiple biomarker mechanisms in the brackish water clam Corbicula japonica exposed to polystyrene microplastics using structural equation modeling.

Using structural equation modeling (SEM), we investigated multiple biomarker mechanisms in terms of biochemical and individual marker responses in the brackish water clam Corbicula japonica following acute exposure to polystyrene microplastic (PS-MP). This study is the first to comprehensively explore multiple biomarker responses in bivalves using SEM. The model revealed that PS-MP accumulation was an independent biomarker, exhibiting significant direct effects on superoxide dismutase (SOD) and catalase (CAT) among the biochemical markers. Although CAT generally interacts closely with SOD, no significant relationship was identified between them, indicating that CAT may have independently responded to PS-MP stress. Among individual markers, significant indirect effects were observed on clearance rate (CR), reflecting feeding activity and valve open rate, indicating excretion activity via SOD and CAT. Finally, the carbon-based scope for growth was significantly influenced by CR. SEM is efficient and useful for identifying significant direct and indirect pathway relationships and for uncovering uncommon relationships in unified multiple biomarker mechanisms in aquatic studies.

Altitude explains insignificant autumn phenological changes across regions with large topography relief in the Tibetan Plateau.

The start of the growing season (SGS) and the end of the growing season (EGS) are widely employed in global change studies to represent the spring and autumn phenology, respectively. Despite the Tibetan Plateau (TP) experiencing significant warming in recent decades, EGS has exhibited only slight changes. Previous studies have concentrated on exploring the environmental regulation of phenology, ignoring the distinctive influences of elevation. Therefore, a more in-depth investigation into the underlying mechanism is warranted. In this study, we investigate the variability of EGS among alpine vegetation regions at different elevations and conduct an analysis based on satellite data. Phenology data of alpine vegetation are extracted from SPOT NDVI dataset spanning from 1999 to 2018, using a piecewise-logistic-maximum-ratio method. We analyze the factors influencing EGS trends at different elevations. The results show that the overall insignificant variation in EGS is mainly attributed to altitude. With the altitude increasing, the annual mean EGS experiences a delay of 0.28 days/100 m below 3500 m, while it advances by 0.2 days/100 m above 3500 m. The opposing shift in elevation below and above 3500 m leads to this counteraction. Elevation emerges as the predominant factor influencing EGS trends, explaining the highest variations (38 %), followed by SGS (22 %) and precipitation (22 %). The elevation effect is most pronounced in areas with substantial topography fluctuations. Moreover, the elevation lapse rate of EGS (ELR_EGS) exhibits an opposite trend with growing season (GS) temperature and a similar trend with GS precipitation between the regions below and above 3500 m, ultimately linking to this counteraction. This study underscores elevation is a critical regulator of vegetation EGS responses to climatic changes over the TP, revealing significant spatial heterogeneities in these responses.

Decadal soil total carbon loss in northern hinterland of Tibetan Plateau.

As the largest and highest plateau in the world, ecosystems on the Tibetan Plateau (TP) imply fundamental ecological significance to the globe. Among the variety, alpine grassland ecosystem on the TP forms a critical part of the global ecosystem and its soil carbon accounts over nine tenths of ecosystem carbon. Revealing soil carbon dynamics and the underlying driving forces is vital for clarifying ecosystem carbon sequestration capacity on the TP. By selecting northern TP, the core region of the TP, this study investigates spatiotemporal dynamics of soil total carbon and the driving forces based on two phases of soil sampling data from the 2010s and the 2020s. The research findings show that soil total carbon density (STCD) in total-surface (0-30 cm) in the 2010s (8.85 ± 3.08 kg C m-2) significantly decreased to the 2020s (7.15 ± 2.90 kg C m-2), with a decreasing rate (ΔSTCD) of -0.17 ± 0.39 kg C m-2 yr-1. Moreover, in both periods, STCD exhibited a gradual increase with soil depth deepening, while ΔSTCD loss was more apparent in top-surface and mid-surface than in sub-surface. Spatially, ΔSTCD loss in alpine desert grassland was -0.41 ± 0.48 kg C m-2 yr-1, which is significantly higher than that in alpine grassland (-0.11 ± 0.31 kg C m-2 yr-1) or alpine meadow (-0.04 ± 0.28 kg C m-2 yr-1). The STCD in 2010s explained >30 % of variances in ΔSTCD among the set of covariates. Moreover, rising temperature aggravates ΔSTCD loss in alpine desert grassland, while enhanced precipitation alleviates ΔSTCD loss in alpine meadow. This study sheds light on the influences of climate and background carbon on soil total carbon loss, which can be benchmark for predicting carbon dynamics under future climate change scenarios.