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Abstract Gynecologic cancers develop from the female reproductive organs. Microbial dysbiosis in the gut and oral cavity can communicate with each other through various ways, leading to mucosal destruction, inflammatory response, genomic instability, and ultimately inducing cancer and worsening. Here, we introduce the mechanisms of interactions between gut and oral microbiota and their changes in the development of gynecologic tumors. In addition, new therapeutic approaches based on microbiota modulation are discussed.

Abstract Background Cognitive impairment is a core symptom of schizophrenia. Metabolic abnormalities impact cognition, and although the influence of blood lipids on cognition has been documented, it remains unclear. We conducted a small cross-sectional study to investigate the relationship between blood lipids and cognition in patients with stable-phase schizophrenia. Using Olink proteomics, we explored the potential mechanisms through which blood lipids might affect cognition from an inflammatory perspective. Methods A total of 107 patients with stable-phase schizophrenia and cognitive impairment were strictly included. Comprehensive data collection included basic patient information, blood glucose, blood lipids, and body mass index. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and the MATRICS Consensus Cognitive Battery (MCCB). After controlling for confounding factors, we identified differential metabolic indicators between patients with mild and severe cognitive impairment and conducted correlation and regression analyses. Furthermore, we matched two small sample groups of patients with lipid metabolism abnormalities and used Olink proteomics to analyze inflammation-related differential proteins, aiming to further explore the association between lipid metabolism abnormalities and cognition. Results The proportion of patients with severe cognitive impairment (SCI) was 34.58%. Compared to patients with mild cognitive impairment (MCI), those with SCI performed worse in the Attention/Alertness (t = 2.668, p = 0.009) and Working Memory (t = 2.496, p = 0.014) cognitive dimensions. Blood lipid metabolism indicators were correlated with cognitive function, specifically showing that higher levels of TG (r = -0.447, p

Abstract In rare earth nickelates (RENiO3), electron-lattice coupling drives a concurrent metal-to-insulator and bond disproportionation phase transition whose microscopic origin has long been the subject of active debate. Of several proposed mechanisms, here we test the hypothesis that pairs of self-doped ligand holes spatially condense to provide local spin moments that are antiferromagnetically coupled to Ni spins. These singlet-like states provide a basis for long-range bond and spiral spin order. Using magnetic resonant X-ray scattering on NdNiO3 thin films, we observe the chiral nature of the spin-disproportionated state, with spin spirals propagating along the crystallographic (101)ortho direction. These spin spirals are found to preferentially couple to X-ray helicity, establishing the presence of a hitherto-unobserved macroscopic chirality. The presence of this chiral magnetic configuration suggests a potential multiferroic coupling between the noncollinear magnetic arrangement and improper ferroelectric behavior as observed in prior studies on NdNiO3 (101)ortho films and RENiO3 single crystals. Experimentally-constrained theoretical double-cluster calculations confirm the presence of an energetically stable spin-disproportionated state with Zhang-Rice singlet-like combinations of Ni and ligand moments.

Abstract Background Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types. Methods Participants, aged 18–45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models—1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)—were implemented using Python to classify health status. Saliency maps were generated to provide model explainability. Results The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy. Conclusions The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

Abstract Background Natural compounds can positively impact health, and various studies suggest that they regulate glucose‒lipid metabolism by influencing short-chain fatty acids (SCFAs). This metabolism is key to maintaining energy balance and normal physiological functions in the body. This review explores how SCFAs regulate glucose and lipid metabolism and the natural compounds that can modulate these processes through SCFAs. This provides a healthier approach to treating glucose and lipid metabolism disorders in the future. Methods This article reviews relevant literature on SCFAs and glycolipid metabolism from PubMed and the Web of Science Core Collection (WoSCC). It also highlights a range of natural compounds, including polysaccharides, anthocyanins, quercetins, resveratrols, carotenoids, and betaines, that can regulate glycolipid metabolism through modulation of the SCFA pathway. Results Natural compounds enrich SCFA-producing bacteria, inhibit harmful bacteria, and regulate operational taxonomic unit (OTU) abundance and the intestinal transport rate in the gut microbiota to affect SCFA content in the intestine. However, most studies have been conducted in animals, lack clinical trials, and involve fewer natural compounds that target SCFAs. More research is needed to support the conclusions and to develop healthier interventions. Conclusions SCFAs are crucial for human health and are produced mainly by the gut microbiota via dietary fiber fermentation. Eating foods rich in natural compounds, including fruits, vegetables, tea, and coarse fiber foods, can hinder harmful intestinal bacterial growth and promote beneficial bacterial proliferation, thus increasing SCFA levels and regulating glucose and lipid metabolism. By investigating how these compounds impact glycolipid metabolism via the SCFA pathway, novel insights and directions for treating glucolipid metabolism disorders can be provided.

Abstract Strongly correlated materials respond sensitively to external perturbations such as strain, pressure, and doping. In the recently discovered superconducting infinite-layer nickelates, the superconducting transition temperature can be enhanced via only ~ 1% compressive strain-tuning with the root of such enhancement still being elusive. Using resonant inelastic x-ray scattering (RIXS), we investigate the magnetic excitations in infinite-layer PrNiO2 thin films grown on two different substrates, namely SrTiO3 (STO) and (LaAlO3)0.3(Sr2TaAlO6)0.7 (LSAT) enforcing different strain on the nickelates films. The magnon bandwidth of PrNiO2 shows only marginal response to strain-tuning, in sharp contrast to the enhancement of the superconducting transition temperature T c in the doped superconducting samples. These results suggest the bandwidth of spin excitations of the parent compounds is similar under strain while T c in the doped ones is not, and thus provide important empirics for the understanding of superconductivity in infinite-layer nickelates.

Abstract Recent theoretical studies have suggested that transition metal perovskite oxide membranes can enable surface phonon polaritons in the infrared range with low loss and much stronger subwavelength confinement than bulk crystals. Such modes, however, have not been experimentally observed so far. Here, using a combination of far-field Fourier-transform infrared (FTIR) spectroscopy and near-field synchrotron infrared nanospectroscopy (SINS) imaging, we study the phonon polaritons in a 100 nm thick freestanding crystalline membrane of SrTiO3 transferred on metallic and dielectric substrates. We observe a symmetric-antisymmetric mode splitting giving rise to epsilon-near-zero and Berreman modes as well as highly confined (by a factor of 10) propagating phonon polaritons, both of which result from the deep-subwavelength thickness of the membranes. Theoretical modeling based on the analytical finite-dipole model and numerical finite-difference methods fully corroborate the experimental results. Our work reveals the potential of oxide membranes as a promising platform for infrared photonics and polaritonics.

BackgroundThe impact of a protein-rich diet and protein supplements on athletic performance remains a topic of debate. Does protein intake offer benefits for athletes? If so, which specific aspects of athletic performance are most influenced by protein?MethodsThis study aimed to explore the relationship between protein intake and athletic performance. A systematic database search was conducted to identify randomized controlled trials (RCTs) examining the effects of protein intake on athletes’ performance. The databases searched included PubMed, Scopus, Web of Science, EBSCO, and Ovid. The meta-analysis included a total of 28 studies involving 373 athletes. The meta-analysis employed both the fixed-effects model and the random-effects model to investigate the impact of protein intake on sports performance. Subgroup analyses were conducted to provide solid evidence to explain the results of the meta-analysis. Sensitive analysis and funnel plots were used to assess the risk of bias and data robustness.ResultsOverall, protein intake did not show a statistically significant improvement in athletic performance (standardized mean difference [SMD] = 0.12, 95% confidence interval [CI]: −0.01 to 0.25). However, in subgroup analysis, the protein group demonstrated a statistically significant improvement in endurance performance, as indicated by the forest plot of final values (SMD = 0.17, 95% CI: 0.02 to 0.32). Additionally, the change value in the forest plot for endurance performance showed even greater statistical significance than the final value (SMD = 0.31, 95% CI: 0.15 to 0.46). In the subgroup analysis based on physiological indices, muscle glycogen showed a statistically significant improvement in the protein group (standardized mean difference [SMD] = 0.74, 95% confidence interval [CI]: 0.02 to 0.32). Furthermore, subgroup analyses based on protein supplementation strategies revealed that co-ingestion of protein and carbohydrates (CHO) demonstrated statistically significant improvements in endurance performance (SMD = 0.36, 95% CI: 0.11 to 0.61), whereas high protein intake alone did not.ConclusionProtein intake appears to provide modest benefits to athletes in improving their performance, particularly by enhancing endurance. Subgroup analysis suggests that protein intake improves muscle glycogen levels and that the co-ingestion of protein with CHO is more effective for endurance athletes than high protein intake alone.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, Identifier CRD42024508021.

BackgroundThe five-year recurrence rate for patients with hepatocellular carcinoma (HCC) is as high as 70%. Patients with high-risk recurrence factors experience significantly poorer prognosis. Local regional therapies, including transarterial chemoembolisation (TACE), hepatic arterial infusion chemotherapy (HAIC), radiotherapy, and emerging immunotherapy, are commonly used adjuvant treatment options. We conducted an indirect comparison of these adjuvant therapies for such patients.MethodsWe conducted a systematic search in public databases for relevant studies and assessed the efficacy and safety of the corresponding therapies by consolidating disease-free survival (DFS), overall survival (OS), and adverse events (AEs).ResultsA total of eight randomised controlled trials were ultimately included. The Gelman-Rubin plot and kernel density estimation indicate that the stability of the combined model is satisfactory.Conclusionimmunotherapy is not inferior to local regional therapies in delaying tumour recurrence, however, the higher incidence of AEs remains a significant concern. Adjuvant radiotherapy demonstrated superior efficacy in delaying tumour recurrence compared to adjuvant TACE, although further support from phase III clinical trial evidence is required.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024576316.

The softening layer formed by the ferrite in the welding interface can significantly reduce the mechanical properties of traditional flash butt welding joints. This study proposed a flash-friction hybrid welding method to address the issue of the ferrite softening layer and comprehensively investigated the impact of vibration friction speed on joint performance. The results showed that increasing the vibration friction speed could effectively inhibit the formation of the ferrite softening layer in the weld center zone (WCZ) and enhance the mechanical properties of the joint. When the vibration friction speed was 3 mm/s, the tensile strength of the joint reached its maximum value of 982.9 MPa, approximately equal to 97.3 % of that of the base material (BM). The fracture occurred in the heat-affected zone (HAZ) and was characterized by a ductile fracture mechanism. Moreover, the microstructural analysis revealed that dynamic recrystallization (DRX) occurred in the WCZ, thermo-mechanically affected zone (TMAZ), and fine-grained zone (FGZ)-HAZ of the joint. Compared to the BM, the grain size significantly decreased, and the thickness and spacing of lamellar cementite also reduced. These microstructural evolutions also contributed to the enhanced mechanical properties of the joints. This study provided new insights for welding in the rail transportation field.

BackgroundEpidemiological and observational studies demonstrate a comorbid relationship between hypothyroidism and atherosclerotic cardiovascular disease (ASCVD). The nature and direction of this causal relationship, however, remain unclear.ObjectiveThis study aims to elucidate the causal relationship between hypothyroidism and ASCVD using a bidirectional Mendelian randomization approach.MethodSingle nucleotide polymorphisms (SNPs) associated with hypothyroidism were identified and selected as genetic instrumental variables from aggregated data of genome-wide association studies (GWAS). The outcome of interest, ASCVD, included seven conditions: coronary artery disease (CAD), angina pectoris (AP), myocardial infarction (MI), ischemic stroke (IS), and subtypes IS-large artery atherosclerosis (IS-LAA), IS-small vessels (IS-SV), and peripheral artery disease (PAD). MR analysis employed multiple methods—chiefly inverse variance weighting (IVW), along with MR Egger, weighted median, and weighted mode—to assess causality. Cochrane's Q test was utilized to evaluate heterogeneity in the MR findings. Causal association reliability was assessed using the MR-Egger intercept, MR-PRESSO tests, and leave-one-out analysis. Reverse MR analysis ensued if forward MR identified a positive exposure-outcome association. Moreover, the DAVID database facilitated GO functional and KEGG pathway enrichment analyses of neighboring genes to instrumental variables, exploring potential disease mechanisms.ResultGWAS pooled data yielded 122 SNPs as potential instrumental variables for hypothyroidism. Forward MR analysis, using the IVW method, indicated hypothyroidism as a risk factor for CAD (OR = 2.34, 95% CI = 1.39–3.94, P = 0.001), AP (OR = 2.01, 95% CI = 1.28–3.16, P = 0.002), MI (OR = 1.02, 95% CI = 1.01–1.04, P = 0.004), and IS-SV (OR = 6.92, 95% CI = 2.45–19.55, P

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Background: Huashi Baidu granule (HSBD), an approved herbal formula for treating COVID-19, demonstrates safety and efficacy. Despite its market approval, the detailed methodology and identification of its active components remain unexplored, leaving its bioactive constituents and action mechanisms unclear. Methods: This study investigated the potential mechanisms of HSBD’s active ingredient in treating COVID-19. Our approach integrated various techniques, including the UHPLC-QqQ-MS/MS method, analysis of the GEO database, network pharmacology, surface plasmon resonance, molecular docking and molecular dynamics simulations, to formulate a comprehensive research strategy. Results: The UHPLC-QqQ-MS/MS method employed for HSBD analysis proved stable, reliable, and reproducible. We identified 25 principal components in HSBD, with 7 compounds detected in plasma, namely Pogostone, P-Hydroxybenzoic acid, Paeoniflorin, Rhein, Emodin, Ephedrine hydrochloride, and Pseudoephedrine hydrochloride. Protein-Protein Interaction (PPI) network analysis identified MMP9 as a pivotal target. Surface plasmon resonance analysis revealed that Paeoniflorin and Rhein exert their antiviral effects by interacting with RBD and ACE2. In contrast, Emodin’s antiviral mechanism predominantly involves binding to MMP9. Molecular docking results indicated strong binding affinities of Rhein and Paeoniflorin to the hACE2 protein, and high binding affinities of Emodin to the MMP9 protein, all of which were corroborated by molecular dynamics simulations. Conclusion: We investigated the methodology and identified the active components of HSBD, focusing on those absorbed into the plasma, to elucidate the effective material basis of HSBD in the treatment of COVID-19, our research offered insightful exploration into its mechanisms of action against COVID-19.

Abstract Background Sorafenib is a standard first-line treatment for advanced hepatocellular carcinoma (HCC), yet its effectiveness is often constrained. Emerging studies reveal that sorafenib triggers ferroptosis, an iron-dependent regulated cell death (RCD) mechanism characterized by lipid peroxidation. Our findings isolate the principal target responsible for ferroptosis in HCC cells and outline an approach to potentially augment sorafenib's therapeutic impact on HCC. Methods We investigated the gene expression alterations following sgRNA-mediated knockdown induced by erastin and sorafenib in HCC cells using CRISPR screening-based bioinformatics analysis. Gene set enrichment analysis (GSEA) and the "GDCRNATools" package facilitated the correlation studies. We employed tissue microarrays and cDNA microarrays for validation. Ubiquitination assay, Chromatin immunoprecipitation (ChIP) assay, RNA immunoprecipitation (RIP) assay, and dual-luciferase reporter assay were utilized to delineate the specific mechanisms underlying ferroptosis in HCC cells. Results Our study has revealed that pleiomorphic adenoma gene 1 (PLAG1), a gene implicated in pleomorphic adenoma, confers resistance to ferroptosis in HCC cells treated with sorafenib. Sorafenib leads to the opposite trend of protein and mRNA levels of PLAG1, which is not caused by affecting the stability or ubiquitination of PLAG1 protein, but by the regulation of PLAG1 at the transcriptional level by its upstream competitive endogenous long non-coding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1). Data from 139 HCC patients showed a significant positive correlation between PLAG1 and GPX4 levels in tumor samples, and PLAG1 is instrumental in redox homeostasis by driving the expression of glutathione peroxidase 4 (GPX4), the enzyme that reduces lipid peroxides (LPOs), which further leads to ferroptosis inhibition. Conclusions Ferroptosis is a promising target for cancer therapy, especially for patients resistant to standard chemotherapy or immunotherapy. Our findings indicate that PLAG1 holds therapeutic promise and may enhance the efficacy of sorafenib in treating HCC.

ABSTRACTBackground: Initial evidence proposes that exposure to childhood adversity may induce avoidance or withdrawal behaviour. However, it remains unclear whether childhood adversity results in avoidance from externally directed thinking to both deliberate and spontaneous mind wandering, i.e. intentionally or unintentionally diverting attention from ongoing task to task-independent thoughts.Objective: To assess the associations between childhood adversity, and mind wandering, and to evaluate the mediating roles of cognitive flexibility, and habit tendencies.Methods: A total of 601 Chinese subjects (378 females, Mage = 19.37) participated in the current study. The participants completed a series of questionnaires including demographics, childhood maltreatment, cognitive flexibility, habitual tendencies, and mind wandering.Results: Hierarchical regression analyses showed childhood adversity, the control facet of cognitive flexibility, and the automaticity facet of habitual tendencies had significant contributions to deliberate mind wandering (β = 0.10, β = −0.40, and β = 0.06) and spontaneous mind wandering (β = 0.09, β = −0.28, and β = 0.07). Serial mediation analyses revealed that the control and automaticity partially mediated associations between childhood adversity and mind wandering (deliberate mind wandering: 95% CIs = [0.037 0.078], and spontaneous mind wandering: 95% CIs = [0.023, 0.062]).Conclusions: The findings underscore the pivotal role of mediators in delineating the relationship between childhood adversity and mind wandering in everyday life. Interventions geared toward augmenting the control component of cognitive flexibility and regulating the automatic component of habitual tendencies show the potential to ameliorate the propensity of individuals affected by childhood adversity to disengage cognitively from the present moment.

The association between blood flow rate (BFR) and clinical outcomes in patients undergoing maintenance hemodialysis (MHD) is inconclusive. This retrospective study included 175 patients undergoing MHD treatment between July 2015 and March 2022, divided into two groups based on time-averaged effective blood flow rate (eBFR) median value. We investigated arteriovenous fistula (AVF) outcomes and the association of eBFR with all-cause mortality and new major adverse cardiovascular events (MACE). Mean ± SD and median time-averaged eBFR values were 276 ± 24 and 275 mL/min, respectively. After adjusting for relevant factors including age, sex, vintage, diabetes, CVD, receiving hemodiafiltration (HDF) treatment and spKt/V, Cox models indicated a low time-averaged eBFR (≤ 275 ml/min) was associated with increased risks of all-cause mortality (hazard ratio [HR] 14.18; 95% confidence interval [CI], 3.14–64.1) and new MACE (HR 3.76; 95% CI, 1.91–7.40) in MHD patients. Continuous Cox models demonstrated each 20 ml/min increase in eBFR linked to a 63% decrease in the risk of all-cause mortality (HR: 0.37, 95% CI: 0.23–0.59) and a 38% decrease in the occurrence of new MACE (HR: 0.62, 95% CI: 0.46–0.84). There was no significant difference in AVF outcomes between the two groups. Our study noted higher eBFR (>275 mL/min) is associated with lower risks of both all-cause mortality and new MACE compared with low eBFR (≤275 mL/min). Increased eBFR is not associated with a higher risk of AVF failure.

ImportanceUnderstanding treatment expectations of patients and their clinicians is of great importance in improving personalized medical services and enhancing patient safety systems.ObjectiveTo investigate treatment expectations of patients and their clinicians and compare differences between both, by using a pair of validated structured assessment tools covering three key aspects/dimensions of clinical interests.Design, setting, and participantsThis single-center cross-sectional study was conducted at Peking Union Medical College Hospital in China. The study enrolled patients aged 16 years and older receiving inpatient care and their clinicians. Patient recruitment was conducted from March 2023 to November 2023.AssessmentsIn addition to demographic and clinical characteristics, this study employed two validated structured assessment tools to evaluate treatment expectations among patients and their clinicians: the Hospitalized Patients’ Expectations for Treatment Scale-Patient version (HOPE-P) and its counterpart, the Hospitalized Patients’ Expectations for Treatment Scale-Clinician version (HOPE-C).ResultsA total of 233 patients (mean [SD] age, 52.3 [15.1] years; 108 [46.4%] female) along with their clinicians, who numbered 75 in total were enrolled in this study. The distribution of total scores for HOPE-P and HOPE-C displayed similar patterns, with most scores concentrated in the higher range (above 50% of the full score). The mean HOPE-P total score was higher than that of HOPE-C (mean [SD] score, 38.78 [4.86] vs 37.49 [4.32]; t = 3.12, P = 0.002). In Dimension 2, the HOPE-P score was higher than HOPE-C (23.67 [3.20] vs 21.72 [3.03]; t = 6.98, P < 0.001). However, in Dimensions 1 and 3, HOPE-P scored lower than HOPE-C (13.37 [2.44] vs 13.84 [1.73]; t = -2.384, P < 0.018; 1.74 [1.14] vs 1.94 [1.00]; t = -2.00, P = 0.047). Certain demographic and clinical characteristics led to variations in patients’ treatment expectations, including marital status, monthly family income, and smoking history.Conclusions and relevanceThis cross-sectional study revealed significant differences between patients’ and doctors’ treatment expectations. Notably, it highlighted the need for clinicians to focus on rationalizing patients’ expectations concerning treatment outcomes.Trial Registration Chinese Clinical Trial Registry IdentifierChiCTR2300075262.

Objectives: To comprehensively analyze the randomized controlled clinical trials of integrated traditional Chinese medicine (TCM) and western medicine in the treatment of metabolic syndrome (MetS), and to explore the clinical efficacy and safety of different TCM combined with western medicine for MetS. The purpose of this study is to provide specific suggestions for clinical guidance in the treatment of MetS. Methods: A comprehensive literature review was conducted across several databases, including China Knowledge Network, Wanfang Data, VIP Information, China Biomedical Literature Service System, Embase, PubMed, and Web of Science, up to October 2023. The scope of this review was confined to RCTs focusing on the treatment of metabolic syndrome through an integrated approach of TCM and Western medicine. The primary efficacy endpoints analyzed were clinical efficacy, fasting blood glucose (FBG), triglyceride (TG), and high-density lipoprotein (HDL). Data synthesis and analysis were performed using Stata 16 and RevMan 5.4 for both traditional and network meta-analyses. Results: The findings from both traditional and network meta-analyses reveal that the combination of JiangZhiHuoXue pills (JZHX) + Conventional Western Medicine (CWM) significantly reduces FBG levels. Similarly, the AnShenNingXin capsules (ASNX) + CWM combination markedly lowers TG levels, while the FuFangQiMa capsules (FFQM) + CWM combination shows enhanced efficacy in elevating HDL levels. Notably, the combination of KangNing capsules (KNJN) + CWM demonstrates a more pronounced clinical effect compared to CWM/placebo alone. Conclusions: The study concludes that the synergistic combination of TCM and Western medicine exhibits superior therapeutic benefits in treating MetS compared to CWM/Placebo treatments alone. The combinations of JZHX, AXNX, FFQM, and KNJN with CWM emerge as potentially effective treatments.

Multivariate time series clustering has become an important research topic in the task of time series analysis. Compared with univariate time series, the research of multivariate time series is more complex and difficult. Although many clustering algorithms for multivariate time series have been proposed, these algorithms still have difficulties in solving the accuracy and interpretation at the same time. Firstly, most of the current work does not consider the length redundancy and variable correlation of multivariable time series, resulting in large errors in the final similarity matrix. Secondly, the data are commonly used in the clustering process with the division paradigm, when the numerical space presents a complex distribution, this idea does not perform well, and it does not have the explanatory power of each variable and space. To address the above problems, this paper proposes a multivariate time series adaptive weight density clustering algorithm using Shapelet (high information-rich continuous subsequence) space (MDCS). This algorithm firstly performs a Shapelet search for each variable, and obtains its own Shapelet space through an adaptive strategy. Then, it weights the numerical distribution generated by each variable to obtain a similarity matrix that is more consistent with the characteristics of data distribution. Finally, the data are finally allocated using the shared nearest neighbor density peak clustering algorithm with improved density calculation and secondary allocation. Experimental results on several real datasets demonstrate that MDCS has better clustering results compared with current state-of-the-art clustering algorithms, with an average increase of 0.344 and 0.09 in the normalized mutual information and Rand index, balancing performance and interpretability.

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

Lenvatinib, a second-generation multi-receptor tyrosine kinase inhibitor approved by the FDA for first-line treatment of advanced liver cancer, facing limitations due to drug resistance. Here, we applied a multidimensional, high-throughput screening platform comprising patient-derived resistant liver tumor cells (PDCs), organoids (PDOs), and xenografts (PDXs) to identify drug susceptibilities for conquering lenvatinib resistance in clinically relevant settings. Expansion and passaging of PDCs and PDOs from resistant patient liver tumors retained functional fidelity to lenvatinib treatment, expediting drug repurposing screens. Pharmacological screening identified romidepsin, YM155, apitolisib, NVP-TAE684 and dasatinib as potential antitumor agents in lenvatinib-resistant PDC and PDO models. Notably, romidepsin treatment enhanced antitumor response in syngeneic mouse models by triggering immunogenic tumor cell death and blocking the EGFR signaling pathway. A combination of romidepsin and immunotherapy achieved robust and synergistic antitumor effects against lenvatinib resistance in humanized immunocompetent PDX models. Collectively, our findings suggest that patient-derived liver cancer models effectively recapitulate lenvatinib resistance observed in clinical settings and expedite drug discovery for advanced liver cancer, providing a feasible multidimensional platform for personalized medicine.

Abstract Objective The doctor-patient relationship (DPR) plays a crucial role in the Chinese healthcare system, functioning to improve medical quality and reduce medical costs. This study examined the psychometric properties of the Chinese version of the Difficult Doctor-Patient Relationship Questionnaire (DDPRQ-10) among general hospital inpatients in China. Methods The research recruited 38 resident doctors responsible for 120 participants, and factor analyses were used to assess the construct validity of the scale. Convergent validity was evaluated by examining the correlation between DDPRQ-10 and depressive symptoms, burnout, and self-efficacy, using the Patient Health Questionnaire Depression Scale-9 item (PHQ-9), and the Maslach Burnout Inventory (MBI). Both multidimensional item response theory (MIRT) and unidimensional item response theory (IRT) frameworks were used to estimate the parameters of each item. Results The Chinese version of DDPRQ-10 showed satisfactory internal consistency (Cronbach's alpha = 0.931), and fitted in a modified two-factor model of positive feelings and negative feelings (χ2/df = 1.494, GFI = 0.925, RMSEA = 0.071, SRMR = 0.008, CFI = 0.985, NFI = 0.958, NNFI = 0.980, TLI = 0.980, IFI = 0.986). Significant correlations with PHQ-9 with DDPRQ-10 and both subscales were revealed (r = 0.293 ~ 0.333, p

BackgroundThe prevalence of Type 2 Diabetes Mellitus (T2D) and its significant role in increasing Coronary Heart Disease (CHD) risk highlights the urgent need for effective CHD screening within this population. Despite current advancements in T2D management, the complexity of cardiovascular complications persists. Our study aims to develop a comprehensive CHD screening model for T2D patients, employing multimodal data to improve early detection and management, addressing a critical gap in clinical practice.MethodsWe analyzed data from 699 patients, including 471 with CHD (221 of these also had T2D) and a control group of 228 without CHD. Employing strict diagnostic criteria, we conducted significance testing and multivariate analysis to identify key indicators for T2D-CHD diagnosis. This led to the creation of a neural network model using 21 indicators and a logistic regression model based on an 8-indicator subset. External validation was performed with an independent dataset from an additional 212 patients to confirm the models’ generalizability.ResultsThe neural network model achieved an accuracy of 90.7%, recall of 90.78%, precision of 90.83%, and an F-1 score of 0.908. The logistic regression model demonstrated an accuracy of 90.13%, recall of 90.1%, precision of 90.22%, and an F-1 score of 0.9016. External validation reinforced the models’ reliability and effectiveness in broader clinical settings.ConclusionOur AI-driven diagnostic models significantly enhance early CHD detection and management in T2D patients, offering a novel, efficient approach to addressing the complex interplay between these conditions. By leveraging advanced analytics and comprehensive patient data, we present a scalable solution for improving clinical outcomes in this high-risk population, potentially setting a new standard in personalized care and preventative medicine.

ABSTRACTHorizontal gene transfer (HGT) is the process through which genetic information is transferred between different genomes and that played a crucial role in bacterial evolution. HGT can enable bacteria to rapidly acquire antibiotic resistance and bacteria that have acquired resistance is spreading within the microbiome. Conventional methods of characterizing HGT patterns include short-read metagenomic sequencing (short-reads mNGS), long-read sequencing, and single-cell sequencing. These approaches present several limitations, such as short-read fragments, high amounts of input DNA, and sequencing costs, respectively. Here, we attempt to circumvent present limitations to detect HGT by developing a metagenomics co-barcode sequencing workflow (MECOS) and applying it to the human and mouse gut microbiomes. In addition to that, we have over 10-fold increased contig length compared to short-reads mNGS; we also obtained exceeding 30 million paired reads with co-barcode information. Applying the novel bioinformatic pipeline, we integrated this co-barcoding information and the context information from long reads, and observed over 50-fold HGT events after we corrected the potential wrong HGT events. Specifically, we detected approximately 3,000 HGT blocks in individual samples, encompassing ~6,000 genes and ~100 taxonomic groups, including loci conferring tetracycline resistance through ribosomal protection. MECOS provides a valuable tool for investigating HGT and advance our understanding on the evolution of natural microbial communities within hosts.IMPORTANCEIn this study, to better identify horizontal gene transfer (HGT) in individual samples, we introduce a new co-barcoding sequencing system called metagenomics co-barcoding sequencing (MECOS), which has three significant improvements: (i) long DNA fragment extraction, (ii) a special transposome insertion, (iii) hybridization of DNA to barcode beads, and (4) an integrated bioinformatic pipeline. Using our approach, we have over 10-fold increased contig length compared to short-reads mNGS, and observed over 50-fold HGT events after we corrected the potential wrong HGT events. Our results indicate the presence of approximately 3,000 HGT blocks, involving roughly 6,000 genes and 100 taxonomic groups in individual samples. Notably, these HGT events are predominantly enriched in genes that confer tetracycline resistance via ribosomal protection. MECOS is a useful tool for investigating HGT and the evolution of natural microbial communities within hosts, thereby advancing our understanding of microbial ecology and evolution.

Ischemic stroke is a cerebrovascular disease associated with high mortality and disability rates. Since the inflammation and immune response play a central role in driving ischemic damage, it becomes essential to modulate excessive inflammatory reactions to promote cell survival and facilitate tissue repair around the injury site. Various cell types are involved in the inflammatory response, including microglia, astrocytes, and neutrophils, each exhibiting distinct phenotypic profiles upon stimulation. They display either proinflammatory or anti-inflammatory states, a phenomenon known as ‘cell polarization.’ There are two cell polarization therapy strategies. The first involves inducing cells into a neuroprotective phenotype in vitro, then reintroducing them autologously. The second approach utilizes small molecular substances to directly affect cells in vivo. In this review, we elucidate the polarization dynamics of the three reactive cell populations (microglia, astrocytes, and neutrophils) in the context of ischemic stroke, and provide a comprehensive summary of the molecular mechanisms involved in their phenotypic switching. By unraveling the complexity of cell polarization, we hope to offer insights for future research on neuroinflammation and novel therapeutic strategies for ischemic stroke.

Abstract The mechanism of unconventional superconductivity in correlated materials remains a great challenge in condensed matter physics. The recent discovery of superconductivity in infinite-layer nickelates, as an analog to high-T c cuprates, has opened a new route to tackle this challenge. By growing 8 nm Pr0.8Sr0.2NiO2 films on the (LaAlO3)0.3(Sr2AlTaO6)0.7 substrate, we successfully raise the superconducting onset transition temperature T c in the widely studied SrTiO3-substrated nickelates from 9 K into 15 K, which indicates compressive strain is an efficient protocol to further enhance superconductivity in infinite-layer nickelates. Additionally, the x-ray absorption spectroscopy, combined with the first-principles and many-body simulations, suggest a crucial role of the hybridization between Ni and O orbitals in the unconventional pairing. These results also suggest the increase of T c be driven by the change of charge-transfer nature that would narrow the origin of general unconventional superconductivity in correlated materials to the covalence of transition metals and ligands.

Transradial access (TRA) is a safe and comfortable approach and the preferred access for percutaneous coronary intervention. However, TRA is not widely used for peripheral interventions. Currently, there is a lack of data on patient selection, appropriate medical devices, complication prevention, and TRA adoption. Therefore, the Chinese Society of Interventional Oncology of the China Anti-Cancer Association organized nationwide experts to establish a Working Group of China Expert Consensus on TRA in percutaneous peripheral interventions in 2022, and jointly formulated this consensus to better promote the application of TRA in peripheral interventions to guide clinicians on patient selection, technical recommendations, and physician training. This consensus mainly focuses on the current situation, advantages and limitations of TRA in peripheral interventions, anatomical characteristics of the radial artery, patient selection, technical aspects, prevention and management of complications, radiation dose, and learning curve. A consensus was reached through a literature evaluation and by referring to the opinions of the expert group.

Abstract Purpose Stem cell therapy is a promising therapeutic approach for inherited retinal diseases (IRDs). This study aims to quantitatively examine the effectiveness and safety of stem cell therapy for patients with IRDs, including retinitis pigmentosa and Stargardt disease (STGD). Methods We searched PubMed, EMBASE, Web of Science, Cochrane Library databases, and the ClinicalTrials.gov website. The latest retrieval time was August 20, 2023. The primary outcomes were rates and mean difference (MD) of best-corrected visual acuity (BCVA) improvement. Subgroup analyses were conducted according to administration routes and stem cell types. This study was registered with PROSPERO (CRD42022349271). Results Twenty-one prospective studies, involving 496 eyes (404 RP and 92 STGD) of 382 patients (306 RP and 76 STGD), were included in this study. For RP, the rate of BCVA improvement was 49% and 30% at 6 months and 12 months, respectively, and the BCVA was significantly improved in the operative eyes at 6 months post-treatment (MD = − 0.12 logMAR, 95% CI .17 to − 0.06 logMAR; P

Inverse synthetic aperture LiDAR (ISAL) can create high-resolution images within a few milliseconds, which are employed for long-range airspace target identification. However, its optical signal characteristics incur the non-negligible higher-order kinematic parameters of the target and phase errors due to atmospheric turbulence. These higher-order parameters and phase errors make it challenging for imaging the ISAL signals. In this paper, we propose an approach integrating the RD algorithm with an image translation network. Unlike the conventional methods, our approach does not require high accuracy in estimating each target motion and atmospheric parameter. The phase error of the RD image is fitted by an image translation network, which greatly simplifies the computational difficulty of the ISAL imaging model. The experimental results demonstrate that our model has good generalization performance. Specifically, our method consistently performs well in capturing the target information under different types of noise and sparsity aperture (SA) rates compared to other conventional methods. In addition, our approach can be applied to the measured data after training the network by using simulated data.

This research aimed to explore the diverse phenotypic characteristics of moso bamboo in China and pinpoint essential characteristics of moso bamboo. In this study, 63 grids were selected using the grid method to investigate 28 phenotypic traits of moso bamboo across the entire distribution area of China. The results suggest that the phenotypic traits of moso bamboo exhibit rich diversity, with coefficients of variation ranging from 5.87% to 36.57%. The phenotypic traits of moso bamboo showed varying degrees of correlation. A principal component analysis was used to identify seven main phenotypic trait indicators: diameter at breast height (DBH), leaf area (LA), leaf weight (LW), branch-to-leaf ratio (BLr), leaf moisture content (Lmc), wall-to-cavity ratio (WCr), and node length at breast height (LN), which accounted for 81.64% of the total information. A random forest model was used, which gave good results to validate the results. The average combined phenotypic trait value (D-value) of most germplasm was 0.563. The highest D-value was found in Wuyi 1 moso in Fujian (0.803), while the lowest D-value was observed in Pingle 2 moso in Guangxi (0.317). The clustering analysis of phenotypic traits classified China’s moso bamboo germplasm into four groups. Group I had the highest D-value and is an important candidate germplasm for excellent germplasm screening.

Abstract Background Cervical cancer is and will remain to be an important health problem in China, especially with an increasing proportion of younger patients who has more specific needs. In China, surgery to remove tumor burden followed by postoperative treatment with radiotherapy and chemotherapy based on clinicopathologic factors may be the best choice for stages IB3 and IIA2 patients. Radical hysterectomy in cervical cancer has been a classic landmark surgery in gynecology. The current trial is designed to evaluate whether there is a difference between laparoscopic RH and abdominal RH in cervical cancer (stages IB3 and IIA2) patient survival under stringent operation standards and consistent surgical oncologic principles. This paper reports the rationale, design, and implementation of the trial. Methods/design This is an investigator-initiated, prospective, randomized, open, blinded endpoint (PROBE) controlled trial. A total of 1104 patients with stage IB3 and IIA2 cervical cancer will be enrolled over a period of 3 years. Patients are randomized (1:1) to either the laparoscopic RH or the abdominal RH group. Patients will then be followed up for at least 5 years. The primary end point will be 5-year overall survival, and secondary endpoints include 5-year progression-free survival, recurrence, and quality of life measurements. Discussion The study results will provide more convincing evidence-based information for stages IB3 and IIA2 cervical cancer patients and their gynecologic cancer surgeons in their choice of surgical method. Trial registration ClinicalTrials.gov, NCT04939831 , retrospectively registered on 25 June 2021.

The characteristics and controlling factors of shale oil occurrence in Permian Fengcheng Formation of the Mahu Sag, Junggar Basin are complex, making occurrence state identification and quantitative characterization difficult. It is of great significance to clarify the occurrence characteristics and controlling factors of shale oil for selecting sweet spots. In order to characterize the content of shale oil in different occurrence states, some shale samples in the central and slope areas of the sag were extracted step by step, and a quantitative characte-rization experiment method of shale oil in different occurrence states was established, obtaining the contents of free light hydrocarbon, free heavy hydrocarbon and adsorbed hydrocarbon, respectively. Based on the study of organic geochemistry and reservoir micro-characteristics, the following conclusions are obtained. Due to the low TOC and clay contents in the study area, the shale oil in the Fengcheng Formation mainly occurs in dissociative state and has the least adsorbed hydrocarbon content. Shale oil occurs mostly on mineral surface and in kerogen in oil film state, heavy hydrocarbons usually occur in shale with large micropore specific surface area, and macropores are the main occurrence space of free oil. With the increase of organic matter abundance and pore size, the shale oil content in different occurrence states shows an increasing trend, indicating that the higher organic matter abundance and larger pore size are conducive to shale oil enrichment. Maturity can greatly affect the adsorption capacity of fluid in shale, and the higher the maturity of organic matter, the higher the proportion of free hydrocarbons and the lower the proportion of adsorbed hydrocarbons. The increase of specific surface area promotes the enrichment of adsorbed oil and free heavy hydrocarbons, and restricts the enrichment of free light hydrocarbons, indicating that the specific surface area of mesoporous pores has a controlling effect on the adsorption capacity of shale oil, i.e., the larger the specific surface area, the stronger the adsorption capacity of shale oil. The relevant research and recognition can provide a basis for selecting sweet spots and evaluating the benefits of shale oil exploitation in the study area.

Summary: Type 1 innate lymphoid cells (ILC1s) possess adaptive immune features, which confer antigen-specific memory responses against haptens and viruses. However, the transcriptional regulation of memory ILC1 responses is currently not known. We show that retinoic acid receptor-related orphan receptor alpha (RORα) has high expression in memory ILC1s in murine contact hypersensitivity (CHS) models. RORα deficiency diminishes ILC1-mediated CHS responses significantly but has no effect on memory T cell-mediated CHS responses. During sensitization, RORα promotes sensitized-ILC1 expansion by suppressing expression of cell-cycle repressors in draining lymph nodes. RORα programs gene-expression patterns related to cell survival and is required for the long-term maintenance of memory ILC1s in the liver. Our findings reveal RORα to be a key transcriptional factor for sensitized-ILC1 expansion and long-term maintenance of memory ILC1s.

Heterogeneity in ion flux of electrolyte materials largely dictates the growth of lithium dendrites and performance of lithium metal anode, which is considered “holy grail” to enable safe rechargeable batteries with ultrahigh energy density. This challenge is even more prominent in polymer-based electrolytes, which thereby leads to uneven ion distribution and mobility of ionic charge carriers, disturbing electrodeposition. In this work, we propose an ion-immobilization strategy achieved by an in-situ grafted nanohybrid fluoropolymer electrolyte, which takes advantages of unique weak solvation of fluoropolymers to tether anions to solvation structure of Li+. Such regulation in conjunction with the well dispersed nanometric aggregates, enables polymer electrolytes serving as ion reservoirs with strengthened weakly-solvating effect. As such, we suppressed formation of dendritic lithium by inducing uniform ion distribution, which significantly extended cell lifetime to over 1900 h in electrodeposition and prolonged charge-discharge cycles of in-situ lithium metal batteries up to 4.6 V at ambient temperature. This work demonstrates a new approach to regulate interfacial chemistry of Li anode and access high-performance polymer-based electrolytes.

BackgroundProviding comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. ObjectiveThis review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. MethodsWe conducted a scoping review following the Arksey and O’Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. ResultsOf 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients’ blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. ConclusionsCDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. International Registered Report Identifier (IRRID)RR2-10.37766/inplasy2022.9.0061

Li2TiO3/Li4SiO4 ceramics have been regarded as promising tritium breeders due to their numerous merits. In this work, chitosan was employed as an effective binder to provide adhesion of Li2TiO3 particles to Li4SiO4 particles, resulting in Li2TiO3/Li4SiO4 ceramics with improved performance. Chitosan could be removed during the sintering process and had little influence on the phase composition. The resulting Li2TiO3/Li4SiO4 ceramics had higher density (92.5%T.D.), greater crushing loads (115.2 ± 20.8 N) and superior thermal properties (3.61 W/m∙K) when compared to the samples prepared without chitosan. SEM analyses displayed a dual-scale structure and the inter- and trans-granular fracture mode. Thermal cycling tests revealed that the phase composition was stable but the crushing load fell slightly. The crushing load remained at 92.4 ± 17.1 N after 12 cycles. Overall, chitosan plays an important role in the amelioration of interface combination between Li4SiO4 and Li2TiO3, and thus significantly improve density, crushing load and thermal conductivity of Li2TiO3/Li4SiO4.