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The atherogenic index of plasma (AIP) is used to evaluate the risk of atherosclerosis, while the systemic immune-inflammation index (SII) measures inflammation. The AIP and SII are indicators used to predict diseases in various areas. This study aims to explore the relationship between AIP and SII. A cross-sectional study design was used to recruit 70,190 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018, excluding AIP missing data, SII missing data, participants under 20 years of age, and participants with missing covariates to eventually include 8163 participants. We used weighted multiple linear regression analysis, trend test, smooth curve fitting and threshold effect analysis to examine the relationship between AIP and SII. Among the 8163 participants included in the study, the mean (± SD) age was 48.412 ± 16.842 years. The mean SII (± SD) for all participants was 519.910 ± 316.974. In a model adjusted for all covariates (Model 3), AIP showed a significant positive correlation with SII [β (95% CI) 32.497 (5.425, 59.569), P  = 0.021]. The smooth curve fitting results of AIP and SII are an “inverted U-shape” non-linear relationship, and the inflection point is at AIP = 0.82. This positive association between AIP and SII was found only in females and participants under 50. Specifically, for females, the positive correlation between AIP and SII was linear [β (95% CI) 80.791 (44.625, 116.958); P  < 0.001]. In participants under 50, the positive correlation between AIP and SII was [β (95% CI) 34.198 (3.087, 65.310); P  = 0.034], and there was also an “inverted U-shape” non-linear relationship with an inflection point of AIP = 0.549. For participants aged 20–50 years and males, the smooth curve showed a “down-flat-down” non-linear relationship. There is a significant positive correlation between AIP and SII. A positive association between AIP and SII was observed exclusively in females and among participants under 50. Furthermore, AIP and SII demonstrated a nonlinear relationship that resembles an “inverted U-shape”. These findings offer new insights into the prevention, treatment, and management of cardiovascular disease. However, further comprehensive cohort studies are necessary to validate the relationship between AIP and SII.

Multi-Object Tracking (MOT) aims to associate multiple objects across consecutive video sequences and maintain continuous and stable trajectories. Currently, much attention has been paid to data association problems, where many methods filter detection boxes for object matching based on the confidence scores (CS) of the detectors without fully utilizing the detection results. Kalman filter (KF) is a traditional means for sequential frame processing, which has been widely adopted in MOT. It matches and updates a predicted trajectory with a detection box in video. However, under crowded scenes, the noise will create low-confidence detection boxes, causing identity switch (IDS) and tracking failure. In this paper, we thoroughly investigate the limitations of existing trajectory prediction schemes in MOT and prove that KF can still achieve competitive results in video sequence processing if proper care is taken to handle the noise. We propose a confidence-based trajectory prediction scheme (dubbed ConfMOT) based on KF. The CS of the detection results is used to adjust the noise during updating KF and to predict the trajectories of the tracked objects in videos. While a cost matrix (CM) is constructed to measure the cost of successful matching of unreliable objects. Meanwhile, each trajectory is labeled with a unique CS, while the lost trajectories that have not been updated for a long time will be removed. Our tracker is simple yet efficient. Extensive experiments have been conducted on mainstream datasets, where our tracker has exhibited superior performance to other advanced competitors.

Based on the panel data of 123 city commercial banks in China from 2007 to 2017, we use the dynamic panel system GMM estimation method to empirically test the impact of private capital holdings on the stability of city commercial banks. The results show that private capital holding improves the operating performance of city commercial banks, reduces the volatility of return on total assets, and is conducive to the stability of city commercial banks. Furthermore, the lack of financial awareness of local governments has led to the negative impact of private capital, that is, the stability of the banks has declined.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

Introduction: This study aimed to identify whether interleukin-6 (IL-6) is better than C-reactive protein (CRP) for the prediction of severe acute pancreatitis (SAP), infected pancreatic necrosis (IPN), and mortality.Methods: Sixty-seven patients with acute pancreatitis (AP) who were hospitalized within 48 h of onset and received serum CRP and IL-6 tests from September 2018 to September 2019 were included. Spearman’s correlation was performed to assess their associations with severity. The areas under the curve (AUCs) for the prediction of SAP, organ failure, pancreatic necrosis, IPN, and mortality were estimated using receiver operating characteristic curves.Result: Serum CRP and IL-6 levels were significantly positively correlated with the severity of AP (p < 0.05). The AUC for the prediction of SAP based on the CRP level was 0.78 (95% CI, 0.66–0.89) and that based on the IL-6 level was 0.69 (95% CI, 0.56–0.82). For the prediction of organ failure and pancreatic necrosis, CRP was more accurate than IL-6 (AUC 0.80 vs. 0.72 and 0.75 vs. 0.68, respectively). However, CRP was less accurate than IL-6 for predicting mortality and IPN (AUC 0.70 vs. 0.75 and 0.65 vs. 0.81, respectively). Systemic inflammatory response syndrome plus CRP was more accurate than systemic inflammatory response syndrome plus IL-6 (AUC 0.79 vs. 0.72) for the prediction of SAP.Conclusions: IL-6 was more accurate than CRP for predicting mortality and IPN in patients with AP.

Due to the random fluctuations in power experienced by high-temperature green electric hydrogen production systems, further deterioration of spatial distribution characteristics such as temperature, voltage/current, and material concentration inside the solid oxide electrolysis cell (SOEC) stack may occur. This has a negative impact on the system’s flexibility and the corresponding control capabilities. In this paper, based on the SOEC electrolytic cell model, a comprehensive optimization method using an adaptive incremental Kriging surrogate model is proposed. The reliability of this method is verified by accurately analyzing the dynamic performance of the SOEC and the spatial characteristics of various physical quantities. Additionally, a thermal dynamic analysis is performed on the SOEC, and an adaptive time-varying LPV-MPC optimization control method is established to ensure the temperature stability of the electrolysis cell stack, aiming to maintain a stable, efficient, and sustainable SOEC operation. The simulation analysis of SOEC hydrogen production adopting a variable load operation has demonstrated the advantages of this method over conventional PID control in stabilizing the temperature of the stack. It allows for a rapid adjustment in the electrolysis voltage and current and improves electrolysis efficiency. The results highlighted that the increase in the electrolysis load increases the current density, while the water vapor, electrolysis voltage, and H 2 flow rate significantly decrease. Finally, the SOEC electrolytic hydrogen production module is introduced for optimization scheduling of energy consumption in Xinjiang, China. The findings not only confirmed that the SOEC can transition to the current load operating point at each scheduling period but also demonstrated higher effectiveness in stabilizing the stack temperature and improving electrolysis efficiency.

Background Inflammatory myofibroblastic tumor (IMT) is an uncommon cardiac tumor that primarily affects infants, children, and young adults. While complete surgical resection generally leads to a favorable prognosis, accurate diagnostic tests remain limited. Case presentation We describe the case of a 26-year-old female who had a dual tumor inside and outside the heart and was misdiagnosed by echocardiography and MRI. We also review 71 cases of cardiac IMTs from the literature regarding their epidemiology, clinical presentation, and outcome. Conclusion Early detection of this rare disorder is essential for optimal surgical management.

Advances in spatial omics enable deeper insights into tissue microenvironments while posing computational challenges. Therefore, we developed SOAPy, a comprehensive tool for analyzing spatial omics data, which offers methods for spatial domain identification, spatial expression tendency, spatiotemporal expression pattern, cellular co-localization, multi-cellular niches, cell–cell communication, and so on. SOAPy can be applied to diverse spatial omics technologies and multiple areas in physiological and pathological contexts, such as tumor biology and developmental biology. Its versatility and robust performance make it a universal platform for spatial omics analysis, providing diverse insights into the dynamics and architecture of tissue microenvironments.

Background Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN’s activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA–logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results Our PCA–logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion The PCA–logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.

This paper concerns a class of coupled competitive neural networks, subject to disturbance and discontinuous activation functions. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results.