Lasso-Cox interpretable model of AFP-negative hepatocellular carcinoma.

BACKGROUND: In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction. METHODS: We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model's effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system. RESULTS: The AUC values of the Lasso-Cox model at 1, 2, and 3 years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3 years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups. CONCLUSION: The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.

Knowledge Mapping of Macrophages in Osteoporosis: A Bibliometric Analysis (1999-2023).

BACKGROUND: Osteoporosis is a common metabolic disorder that significantly impacts quality of life in the elderly population. Macrophages play a crucial role in the development of osteoporosis by regulating bone metabolism through cytokine secretion. However, there is a lack of scholarly literature in the field of bibliometrics on this topic. OBJECTIVE: This study provides a detailed analysis of the research focus and knowledge structure of macrophage studies in osteoporosis using bibliometrics. METHODS: The scientific literature on macrophage research in the context of osteoporosis, retrieved from the Web of Science Core Collection (WoSCC) database spanning from January 1999 to December 2023, has been incorporated for bibliometric examination. The data is methodically analyzed and visually represented using analytical and visualization tools including VOSviewer, CiteSpace, Scimago Graphica, the Bibliometrix R package, and Pajek. RESULTS AND CONCLUSIONS: In the last quarter-century, there has been a consistent rise in the quantity of scholarly publications focusing on the relationship between macrophages and osteoporosis, resulting in a total of 1499 research documents. These studies have originated from 45 different countries, with China, South Korea, and the United States being the most prominent contributors, and the United States having the highest frequency of citations. Noteworthy research institutions involved in this field include Shanghai Jiao Tong University, Wonkwang University, Huazhong University of Science and Technology, and Seoul National University. The Journal of Bone and Mineral Research is widely regarded as the premier and most frequently referenced publication in the field. These publications involve the collaboration of 8744 authors, with Lee Myeung Su contributing the most articles, and Takayanagi being the most co-cited author. Key emerging research focal points are encapsulated in keywords such as "mTOR," "BMSCs," "bone regeneration," and "exosome." The relationships between exosome from macrophage sources and those from BMSCs, along with the regulatory role of the mTOR signaling pathway on macrophages, represent crucial directions for future development in this field. This study represents the inaugural comprehensive bibliometric analysis detailing trends and advancements in macrophage research within the osteoporosis domain. It delineates recent frontiers and hotspots, providing valuable insights for researchers in this particular area of study.

A novel member of miR169 family negatively regulates maize resistance against Bipolaris maydis.

MicroRNAs (miRNAs) have been confirmed to play important roles in plant defense response. However, the key maize miRNAs involved in the defense response against Bipolaris maydis are very limited. In this study, a novel member of the miR169 family in response to B. maydis, named zma-miR169s, was discovered and investigated. The expression levels of pre-miR169s and zma-miR169s were significantly repressed during B. maydis infection. CRISPR/Cas9-induced zma-miR169s mutant exhibited more resistance against B. maydis, whereas overexpression zma-miR169s enhanced susceptibility, supporting that zma-miR169s might play a negative role in maize resistance. Moreover, RNA-seq and GO analysis showed that differentially expressed genes were highly enriched in the oxidation-reduction process and plant hormone pathway. Hence, reactive oxygen species (ROS) and plant hormone levels were further investigated. ROS detection confirmed that zma-miR169s mutant accumulated more ROS, while less ROS was detected in transgenic maize OE-miR169s. Furthermore, more remarkable changes in PR-1 expression levels and salicylic acid (SA) contents were detected in zma-miR169s mutant compared to wild-type and transgenic maize during B. maydis infection. Additionally, nuclear transcription factors (NF-YA1 and NF-YA13) were identified as targets regulated by zma-miR169s through the agrobacterium-mediated transient expression method. Overexpression of ZmNF-YA13 enhanced Arabidopsis resistance to Pseudomonas syringae pv. tomato DC3000. Taken together, our results suggest that zma-miR169s negatively regulate maize defense responses by influencing ROS accumulation and the SA-dependent signaling pathway.

Long-term Trends of Dissolved Organic Carbon Dynamics in a Subtropical Forest Responding to Environmental Changes.

Dissolved organic carbon (DOC) dynamics are critical to carbon cycling in forest ecosystems and sensitive to global change. Our study, spanning from 2001 to 2020 in a headwater catchment in subtropical China, analyzed DOC and water chemistry of throughfall, litter leachate, soil waters at various depths, and streamwater. We focused on DOC transport through hydrological pathways and assessed the long-term trends in DOC dynamics amidst environmental and climatic changes. Our results showed that the annual DOC deposition via throughfall and stream outflow was 14.2 ± 2.2 and 1.87 ± 0.83 g C m-2 year-1, respectively. Notably, there was a long-term declining trend in DOC deposition via throughfall (-0.195 mg C L-1 year-1), attributed to reduced organic carbon emissions from clean air actions. Conversely, DOC concentrations in soil waters and stream waters showed increasing trends, primarily due to mitigated acid deposition. Moreover, elevated temperature and precipitation could partly explain the long-term rise in DOC leaching. These trends in DOC dynamics have significant implications for the stability of carbon sink in terrestrial, aquatic, and even oceanic ecosystems at regional scales.

Highly sensitive antibiotic sensing based on optical weak value amplification: A case study of chloramphenicol.

The public health concern of antibiotic residues in animal-origin food has been a long-standing issue. In this work, we present a novel method for antibiotic detection, leveraging optical weak value amplification and harnessing an indirect competitive inhibition assay, which significantly boosts the system's sensitivity in identifying small molecule antibiotics. We chose chloramphenicol as a model compound and mixed it with chloramphenicol-bovine serum albumin conjugates to bind to the chloramphenicol antibody competitively. We achieved a broad linear detection range of up to 3.24 ng/mL and a high concentration resolution of 33.20 pg/mL. To further validate the universality of our proposed detection methodology, we successfully applied it to testing gibberellin and tetracycline. Moreover, we conducted regeneration experiments and real-sample correlation studies. This study introduces a novel strategy for the label-free optical sensing of small molecule antibiotics, greatly expanding the range of applications for sensors utilizing optical weak value amplification.

Design, synthesis and biological evaluation of piperine derivatives as potent antitumor agents.

A series of piperine derivatives were designed and successfully synthesized. The antitumor activities of these compounds against 293 T human normal cells, as well as MDA-MB-231 (breast) and Hela (cervical) cancer cell lines, were assessed through the MTT assay. Notably, compound H7 exhibited moderate activity, displaying reduced toxicity towards non-tumor 293 T cells while potently enhancing the antiproliferative effects in Hela and MDA-MB-231 cells. The IC50 values were determined to be 147.45 ± 6.05 µM, 11.86 ± 0.32 µM, and 10.50 ± 3.74 µM for the respective cell lines. In subsequent mechanistic investigations, compound H7 demonstrated a dose-dependent inhibition of clone formation, migration, and adhesion in Hela cells. At a concentration of 15 µM, its inhibitory effect on Hela cell function surpassed that of both piperine and 5-Fu. Furthermore, compound H7 exhibited promising antitumor activity in vivo, as evidenced by significant inhibition of tumor angiogenesis and reduction in tumor weight in a chicken embryo model. These findings provide a valuable scientific foundation for the development of novel and efficacious antitumor agents, particularly highlighting the potential of compound H7 as a therapeutic candidate for cervical cancer and breast cancer.

Genome-wide Characterization of Small Secreted Peptides in Nicotiana tabacum and Functional Assessment of NtLTP25 in Plant Immunity.

Small secreted peptides (SSPs), serving as signaling molecules for intercellular communication, play significant regulatory roles in plant growth, development, pathogen immunity, and responses to abiotic stress. Despite several SSPs, such as PIP, PSK, and PSY having been identified to participate in plant immunity, the majority of SSPs remain understudied, necessitating the exploration and identification of SSPs regulating plant immunity from vast genomic resources. Here we systematically characterized 756 putative SSPs across the genome of Nicotiana tabacum. 173 SSPs were further annotated as established SSPs, such as nsLTP, CAPE, and CEP. Furthermore, we detected the expression of 484 putative SSP genes in five tissues, with 83 SSPs displaying tissue-specific expression. Transcriptomic analysis of tobacco roots under plant defense hormones revealed that 46 SSPs exhibited specific responsiveness to salicylic acid (SA), and such response was antagonistically regulated by methyl jasmonate. It's worth noting that among these 46 SSPs, 16 members belong to nsLTP family, and one of them, NtLTP25, was discovered to enhance tobacco's resistance against Phytophthora nicotianae. Overexpression of NtLTP25 in tobacco enhanced the expression of ICS1, subsequently stimulating the biosynthesis of SA and the expression of NPR1 and pathogenesis-related genes. Concurrently, NtLTP25 overexpression activated genes associated with ROS scavenging, consequently mitigating the accumulation of ROS during the subsequent phases of pathogenesis. These discoveries indicate that these 46 SSPs, especially the 16 nsLTPs, might have a vital role in governing plant immunity that relies on SA signaling. This offers a valuable source for pinpointing SSPs involved in regulating plant immunity.

[Evolution Trend of Water Quality in the Inner Mongolia Section of the Yellow River from 2003 to 2020].

The aim of this study was to comprehensively understand the water environment quality status and its change trend in the Inner Mongolia section of the Yellow River Basin. To analyze the water quality in recent years,the water quality data in the Yellow River basin from 2003 to 2020 were firstly collected from five typical monitoring stations．Various data analysis methods, including principal component analysis, cluster analysis, and a long short-term memory model, were used along with an improved comprehensive water quality identification index to explore the spatiotemporal characteristics of water quality in the Yellow River Basin. The results showed that the overall water quality in the basin has improved and stabilized over time. In terms of temporal variation, there was a distinction between the wet season and dry season, with a better status observed during the wet season due to increased agricultural irrigation and higher water volume. Spatially, the five monitoring sections could be divided into three categories based on strong natural factors that maintained their temporal characteristics during the wet season； however, significant differences were observed during the dry season due to urban water usage patterns. Analysis using LSTM models revealed that ammonia nitrogen will continue to decline and have a decreasing impact on the comprehensive water quality. These findings provide valuable insights for the comprehensive management of water quality in Inner Mongolia's Yellow River Basin.

In situ co-deposition synthesis for collagen-Astragalus polysaccharide composite with intrafibrillar mineralization as potential biomimetic-bone repair materials.

A hybrid material possessing both componential and structural imitation of bone tissue is the preferable composites for bone defect repair. Inspired by the microarchitecture of native bone, this work synthesized in vitro a functional mineralized collagen fibril (MCF) material by utilizing the method of in situ co-precipitation, which was designed to proceed in the presence of Astragalus polysaccharide (APS), thus achieving APS load within the biomineralized collagen-Astragalus polysaccharide (MCAPS) fibrils. Transmission electron microscope (TEM), selected area electron diffraction (SAED) and scanning electronic microscopy (SEM) identified the details of the intrafibrillar mineralization of the MCAPS fibrils, almost mimicking the secondary level of bone tissue microstructure. A relatively uniform and continuous mineral layer formed on and within all collagen fibrils and the mineral phase was identified as typical weak-crystalline hydroxyapatite (HA) with a Ca/P ratio of about 1.53. The proliferation of bone marrow-derived mesenchymal stem cells (BMSC) and mouse embryo osteoblast precursor cells (MC3T3-E1) obtained a significant promotion by MCAPS. As for the osteogenic properties of MCAPS, a distinct increase in the alkaline phosphatase (ALP) activity and the number of calcium nodules (CN) in BMSC and MC3T3-E1 was detected. The up-regulation of three osteogenic-related genes of RUNX-2, BMP-2 and OCN were confirmed via reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to further verify the osteogenic performance promotion of MCAPS. A period of 14 days of culture demonstrated that MCAPS-L exhibited a preferable efficacy in enhancing ALP activity and CN quantity, as well as in promoting the expression of osteogenic-related genes over MCAPS-M and MCAPS-H, indicating that a lower dose of APS within the material of MCAPS is more appropriate for its osteogenesis promotion properties.

Crystal structure and characterization of monascin from the extracts of Monascus purpureus-fermented rice.

We present a novel solid form of monascin, an azaphilonoid derivative extracted from Monascus purpureus-fermented rice. The crystal structure, C21H26O5, was characterized by single-crystal X-ray diffraction and belongs to the orthorhombic space group P212121. To gain insight into the electronic properties of the short contacts in the crystalline state of monascin, we utilized the Experimental Library of Multipolar Atom Model 2 (ELMAM2) database to transfer the electron density of monascin in its crystalline state. Hirshfeld surface analysis, fingerprint analysis, electronic properties and energetic characterization reveal that intermolecular C-H...O hydrogen bonds play a crucial role in the noncovalent bonding interactions by connecting molecules into two- and three-dimensional networks. The molecular electrostatic potential (MEP) map of the monascin molecule demonstrates that negatively charged regions located at four O atoms are favoured binding sites for more positively charged amino acid residues during molecular recognition. In addition, powder X-ray diffraction confirms that no transformation occurs during the crystallization of monascin.

CREB3 suppresses hepatocellular carcinoma progression by depressing AKT signaling through competitively binding with insulin receptor and transcriptionally activating RNA-binding motif protein 38.

cAMP responsive element binding protein 3 (CREB3), belonging to bZIP family, was reported to play multiple roles in various cancers, but its role in hepatocellular carcinoma (HCC) is still unclear. cAMP responsive element binding protein 3 like 3 (CREB3L3), another member of bZIP family, was thought to be transcription factor (TF) to regulate hepatic metabolism. Nevertheless, except for being TFs, other function of bZIP family were poorly understood. In this study, we found CREB3 inhibited growth and metastasis of HCC in vitro and in vivo. RNA sequencing indicated CREB3 regulated AKT signaling to influence HCC progression. Mass spectrometry analysis revealed CREB3 interacted with insulin receptor (INSR). Mechanistically, CREB3 suppressed AKT phosphorylation by inhibiting the interaction of INSR with insulin receptor substrate 1 (IRS1). In our study, CREB3 was firstly proved to affect activation of substrates by interacting with tyrosine kinase receptor. Besides, CREB3 could act as a TF to transactivate RNA-binding motif protein 38 (RBM38) expression, leading to suppressed AKT phosphorylation. Rescue experiments further confirmed the independence between the two functional manners. In conclusion, CREB3 acted as a tumor suppressor in HCC, which inhibited AKT phosphorylation through independently interfering interaction of INSR with IRS1, and transcriptionally activating RBM38.

Confounding amplifies the effect of environmental factors on COVID-19.

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.

Increasing research has shown that the abnormal expression of microRNA (miRNA) is associated with many complex diseases. However, biological experiments have many limitations in identifying the potential disease-miRNA associations. Therefore, we developed a computational model of Three-Layer Heterogeneous Network based on the Integration of CircRNA information for MiRNA-Disease Association prediction (TLHNICMDA). In the model, a disease-miRNA-circRNA heterogeneous network is built by known disease-miRNA associations, known miRNA-circRNA interactions, disease similarity, miRNA similarity, and circRNA similarity. Then, the potential disease-miRNA associations are identified by an update algorithm based on the global network. Finally, based on global and local leave-one-out cross validation (LOOCV), the values of AUCs in TLHNICMDA are 0.8795 and 0.7774. Moreover, the mean and standard deviation of AUC in 5-fold cross-validations is 0.8777+/-0.0010. Especially, the two types of case studies illustrated the usefulness of TLHNICMDA in predicting disease-miRNA interactions.

MRI Assessment of Diastolic Dysfunction in People Living With the Human Immunodeficiency Virus: Correlation With Markers of Disease Activity.

BACKGROUND: Despite the advent of combination antiretroviral therapy, people living with human immunodeficiency virus (PLWH) are at an increased risk for cardiac disease. PURPOSE: To explore the presence and extent of diastolic atrial and left ventricular dysfunction in PLWH using cardiac MRI in correlation with clinical markers of disease activity. STUDY TYPE: Prospective. POPULATION: A total of 163 participants comprising 101 HIV-infected individuals (age: 52 years [42-62 years]; 92% male) and 62 age- and sex-matched healthy volunteers (age: 51 years [30-72 years]; 85% male). FIELD STRENGTH/SEQUENCE: 3.0 T, cardiac MRI including balanced steady-state free precession (SSFP) for the short-axis, two-, three-, and four-chamber views were performed. ASSESSMENT: Assessment of cardiac function and strain analysis were accomplished by CVI42 software. Blood samples for CD4+ T cells and cardiac risk factors were also collected before MRI. STATISTICAL TESTS: Independent t tests, Mann-Whitney U test, Pearson's correlation analysis, and multivariate linear analyses (significance level: P < 0.05). RESULTS: PLWH had a significantly larger left atrial volume maximum index (LAVImax: 32.6 ± 8.7 vs. 28.7 ± 8.1 mL/m2), minimum (LAVImin: 14.8 ± 5.5 vs. 11.5 ± 5.4 mL/m2,), and prior to atrial contraction (LAVIpre-a: 23.4 ± 6.7 vs. 19.7 ± 7.2 mL/m2) as compared to healthy volunteers. The LA reservoir (LAtEF: 55.0 ± 10.2 vs. 61.4 ± 10.4; Sls: 29.0 ± 8.1 vs. 33.8 ± 11.8), conduit (LApEF: 28.4 ± 8.2 vs. 32.3 ± 11.3, P = 0.01; Sle: 16.3 ± 6.5 vs. 18.9 ± 8.2), and booster pump function (LAaEF: 37.4 ± 12.4 vs. 42.7 ± 13.1, P = 0.01, Sla: 12.7 ± 5.1 vs. 14.9 ± 5.7) were all significant impaired in PLWH. Global circumferential left ventricular diastolic strain rate (LVGCS-d) was significantly lower in the HIV patients. Multivariate analysis results showed that Nadir CD4+ T cells had a significant adverse association with LVGCS-d (ß = 0.51). CONCLUSION: LA structure abnormalities and LV diastolic dysfunction were manifested in PLWH, with Nadir CD4+ T cell counts potentially serving as a risk factor for early cardiac diastolic dysfunction. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

S3T-Net: A novel electroencephalogram signals-oriented emotion recognition model.

In this paper, a novel skipping spatial-spectral-temporal network (S3T-Net) is developed to handle intra-individual differences in electroencephalogram (EEG) signals for accurate, robust, and generalized emotion recognition. In particular, aiming at the 4D features extracted from the raw EEG signals, a multi-branch architecture is proposed to learn spatial-spectral cross-domain representations, which benefits enhancing the model generalization ability. Time dependency among different spatial-spectral features is further captured via a bi-directional long-short term memory module, which employs an attention mechanism to integrate context information. Moreover, a skip-change unit is designed to add another auxiliary pathway for updating model parameters, which alleviates the vanishing gradient problem in complex spatial-temporal network. Evaluation results show that the proposed S3T-Net outperforms other advanced models in terms of the emotion recognition accuracy, which yields an performance improvement of 0.23% , 0.13%, and 0.43% as compared to the sub-optimal model in three test scenes, respectively. In addition, the effectiveness and superiority of the key components of S3T-Net are demonstrated from various experiments. As a reliable and competent emotion recognition model, the proposed S3T-Net contributes to the development of intelligent sentiment analysis in human-computer interaction (HCI) realm.

Easy Access to Bright Oxygen Defects in Biocompatible Single-Walled Carbon Nanotubes via a Fenton-like Reaction.

The covalent functionalization of single-walled carbon nanotubes (SWNTs) with luminescent oxygen defects increases their brightness and enables their application as optical biosensors or fluorescent probes for in vivo imaging in the second-biological window (NIR-II). However, obtaining luminescent defects with high brightness is challenging with the current functionalization methods due to a restricted window of reaction conditions or the necessity for controlled irradiation with ultraviolet light. Here, we report a method for introducing luminescent oxygen defects via a Fenton-like reaction that uses benign and inexpensive chemicals without light irradiation. (6,5) SWNTs in aqueous dispersion functionalized with this method show bright E11* emission (1105 nm) with 3.2 times higher peak intensities than the pristine E11 emission and a reproducible photoluminescence quantum yield of 3%. The functionalization can be performed within a wide range of reaction parameters and even with unsorted nanotube raw material at high concentrations (100 mg L-1), giving access to large amounts of brightly luminescent SWNTs. We further find that the introduced oxygen defects rearrange under light irradiation, which gives additional insights into the structure and dynamics of oxygen defects. Finally, the functionalization of ultrashort SWNTs with oxygen defects also enables high photoluminescence quantum yields. Their excellent emission properties are retained after surfactant exchange with biocompatible pegylated phospholipids or single-stranded DNA to make them suitable for in vivo NIR-II imaging and dopamine sensing.

Spatiotemporal Transformation-Based Neural Network With Interpretable Structure for Modeling Distributed Parameter Systems.

Many industrial processes can be described by distributed parameter systems (DPSs) governed by partial differential equations (PDEs). In this research, a spatiotemporal network is proposed for DPS modeling without any process knowledge. Since traditional linear modeling methods may not work well for nonlinear DPSs, the proposed method considers the nonlinear space-time separation, which is transformed into a Lagrange dual optimization problem under the orthogonal constraint. The optimization problem can be solved by the proposed neural network with good structural interpretability. The spatial construction method is employed to derive the continuous spatial basis functions (SBFs) based on the discrete spatial features. The nonlinear temporal model is derived by the Gaussian process regression (GPR). Benefiting from spatial construction and GPR, the proposed method enables spatially continuous modeling and provides a reliable output range under the given confidence level. Experiments on a catalytic reaction process and a battery thermal process demonstrate the effectiveness and superiority of the proposed method.

Relationship of triglyceride-glucose index to gallstone prevalence and age at first gallstone surgery in American adults.

The triglyceride-glucose (TyG) index is a novel marker of insulin resistance that has been strongly associated with many diseases related to metabolic disorders, such as diabetes, coronary heart disease, myocardial infarction, obesity, nonalcoholic fatty liver disease, and stroke. However, whether the TyG index is associated with the prevalence of gallstones has not been determined. Therefore, the purpose of this study was to evaluate the relationship between the TyG index and the prevalence of gallstones in American adults, as well as the age at which adults in America undergo their first gallstone surgery. We selected individuals from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to March 2020. Based on the goal of our study, comprehensive inclusion and exclusion criteria were created. A logistic regression analysis, dose-response curve, and subgroup analysis were computed to assess the relationship between the TyG index and gallstone prevalence and age at first surgery for gallstone. A total of 3905 participants aged > 20 years were included in our study, of whom 421 had a self-reported history of gallstones. A total of 1884 (48.2%) males and 2021 (51.8%) females were included. After confounders adjustment, it was found single-unit increases in the TyG index were linked with a 25.0% increase in gallstone prevalence (odds ratio [OR] = 1.25, 95% confidence interval [95%CI]: 1.04, 1.51). After conversion of the TyG index values from continuous to categorical variables with tertiles, a marked 48% increase in gallstone incidence was found in tertile 3 relative to tertile 1 (OR = 1.48, 95% CI: 1.09, 1.99). The dose-response curve results indicated positive associations between gallstone prevalence and the TyG index, while the latter was negatively associated with age at first gallstone surgery. Based on subgroup analysis, the positive association between TyG index and high-incidence of gallstones was more significant in females (OR = 1.39, 95% CI: 1.09, 1.77), age < 40 years (OR = 2.02, 95% CI: 1.23, 3.29), and other race (OR = 1.46, 95% CI: 1.06, 2.02). A higher TyG index is associated with a higher incidence of gallstones and may lead to an earlier age of first gallstone surgery. However, a causal relationship between TyG and gallstones cannot be established.

Adaptive control for circulating cooling water system using deep reinforcement learning.

Due to the complex internal working process of circulating cooling water systems, most traditional control methods struggle to achieve stable and precise control. Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). The structure is based on the Markov decision process of the recirculating cooling water system. Initially, the TD3 algorithm is employed to construct a deep reinforcement learning agent. Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. The agent updates its network based on different reward values obtained through interactions with the system, thereby gradually aligning the action values with the optimal policy. The TD3-RTM method introduces a reference trajectory model to accelerate the convergence speed of the agent and reduce oscillations and instability in the control system. Subsequently, simulation experiments were conducted in MATLAB/Simulink. The results show that compared to PID, fuzzy PID, DDPG and TD3, the TD3-RTM method improved the transient time in the flow loop by 6.09s, 5.29s, 0.57s, and 0.77s, respectively, and the Integral of Absolute Error(IAE) indexes decreased by 710.54, 335.1, 135.97, and 89.96, respectively, and the transient time in the temperature loop improved by 25.84s, 13.65s, 15.05s, and 0.81s, and the IAE metrics were reduced by 143.9, 59.13, 31.79, and 1.77, respectively. In addition, the overshooting of the TD3-RTM method in the flow loop was reduced by 17.64, 7.79, and 1.29 per cent, respectively, in comparison with the PID, the fuzzy PID, and the TD3.