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The inconsistent findings concerning the effects of vitamin D supplementation on cardiometabolic risk factors and the large heterogeneity in the published literature call for further research to identify sources of heterogeneity and potential effect modifiers. We performed a meta-analysis of randomized controlled trials (RCTs) published until March 2024 that reported estimates for the effects of vitamin D supplementation on cardiometabolic factors and relevant baseline covariates of RCT participants. A total of 17 656 participants from 99 RCTs were analyzed, and weighted mean differences (95% confidence intervals (CI)) for the intervention status were derived using random-effects modeling. Overall, compared with the placebo, vitamin D supplementation (median dose: 3320 international unit (IU)·day−1; range 40–120 000 IU·day−1) had favorable effects on systolic blood pressure (SBP; −2.04 (95% CI, −3.50, −0.59) mmHg; 1 mmHg = 0.133 kPa), diastolic blood pressure (DBP; −3.00 (95% CI, −3.61, −2.39) mmHg), total cholesterol (TC; −0.12 (95% CI, −0.21, −0.03) mmol·L−1), fasting blood glucose (FBG; −0.13 (95% CI, −0.20, −0.05) mmol·L−1), hemoglobin A1C (A1C; −0.09% (95% CI, −0.13%, −0.05%)), and fasting blood insulin (FBI: −7.61 (95% CI, −11.93, −3.30) pmol·L−1). The benefits of vitamin D were most evident in trials performed in non-Westerners, participants with baseline 25-hydroxyvitamin D (25[OH]D) lower than 15.0 ng·mL−1, non-obese (body mass index (BMI) < 30 kg·m−2), and older (age ≥ 50 years). The findings of this study underscore the need for personalized vitamin D intervention strategies that comprehensively account for individual patient characteristics (such as ethnocultural background, age, BMI, and circulating 25[OH]D level), intervention dosage, and intervention duration to optimize cardiometabolic health outcomes.

Abstract Background Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. Methods We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs’ feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. Results The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term “high risk” of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs’ workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. Conclusions The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

Abstract The effect of bolt support in mining roadways with fractured or weak surrounding rock is poor, and the roof easily loses stability. Studying the support control technology of bolts on fractured surrounding rock is necessary. Based on the theories of pressure arches and combined arch support, the bolt anchorage and anchorage effect angle are proposed a model is established, and the optimal bolt anchorage is calculated. The influence of related factors on the bolt anchorage and the scope of bolt action are analysed via numerical simulation and experimental methods. The pretightening force of the bolts and the spacing between the rows of bolts are positively correlated and negatively correlated with the bolt anchorage, respectively. The compression zone conforms to the reinforced arch state when the bolt end is anchored. The experimental work shows that the maximum spacing a of the anchor rods and the length L of the anchor rods in the surrounding rock satisfy 4.28a

Abstract Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated.

Abstract This paper aims to address the issue of environmental pollution resulting from marine oil spills by evaluating the oil adsorption performance of commonly used fence materials. Conventional oil adsorption materials exhibit limited rates and capacities for oil adsorption. Existing methods have proven insufficient in meeting the requirements for efficient and rapid oil–water separation. A new oil-absorbing barrier was developed by utilizing high oil adsorption resin as the primary material and hydroxypropyl methyl cellulose (HPMC) as the binder, leveraging the exceptional oil adsorption and hydrophobic properties of P(BMA-SMA-St)/MIL-101(Fe) resin. The oil-absorbing fence was characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). The oil adsorption rates of carbon tetrachloride, toluene, diesel and gasoline by the oil adsorption fence with 25 g/L resin content were 101.26 g/m2, 68.12 g/m2, 35.19 g/m2, and 46.69 g/m2, respectively. After 120 h of UV irradiation, the coating’s oil absorption capacity remained nearly unchanged, and it demonstrated outstanding mechanical, chemical, and wear resistance. As a result, the oil adsorption fence possesses the capability to rapidly absorb oil from the water’s surface during the process of containing oil pollution, leading to positive social and economic impacts.

In order to investigate the effects of adding corn resistant starch (CRS) and sweet potato resistant starch (SRS) to starch noodles (SNs) on their physicochemical properties and structural quality, the changes in texture quality, breakage rate, sensory quality, microstructure, and in vitro digestibility of the two types of resistant starch noodles (RSNs) prepared with resistant starch (RS) added at 5.0, 7.5, 10.0, 12.5 and 15.0 g/100 g, respectively were measured. The results showed that the hardness and chewiness of corn resistant starch noodles (CRSNs) are higher than those of sweet potato resistant starch noodles (SRSNs) when the amounts of the two RSes added are the same, and the hardness and chewiness of both RSNs showed a decreasing trend when the addition amount of RS increases. The increase in the amount of RS added had a significant (P

IntroductionBenign multinodular goiter (BMNG) can grow very large and cause compression symptoms, making the operation procedure difficult. However, the recurrence rate of BMNG ranges from 3% to 43%. Reoperative thyroid surgery for BMNG is uncommon and can result in a high rate of complications, including hypoparathyroidism and recurrent laryngeal nerve palsy. Carbon nanoparticles (CNs) have been widely used as a protective agent for the parathyroid gland and as a tracer agent in central lymph node dissection. However, the protection effect of CNs in redoing BMNG has not been well illustrated. This study investigates whether CNs could protect parathyroid glands (PGs) during reoperation for patients with BMNG.MethodsBMNG patients who previously underwent thyroidectomy and received reoperation between January 2019 and January 2022 were retrospectively recruited. The Dunhill approach was employed for all patients. The patients were divided into two groups: the CNs group, who received injection CNs injection 1 hour before the operation (n = 24), and the control group, who underwent thyroid surgery without CNs injection (control group, n = 25). The numbers of PGs preserved in situ, autotransplantation, the accidental removal of the PGs, and the parathyroid hormone level were recorded and analyzed.ResultsThe results revealed that more PGs were preserved in situ in the CNs group compared to the control group (3.25±0.15 vs 2.60±0.16, P=.007). Moreover, fewer PGs were subjected to autotransplantation and were accidentally discovered in the specimen in the CNs group compared to the control group. Patients who had CNs injection exhibited a lower rate of transient (5/24 vs. 13/25, P=.024) and permanent hypoparathyroidism (2/24 vs. 9/25, P=.020) compared to the control group.

Recombinant adenovirus (rAdV) is a commonly used vector system for gene transfer. Efficient initial packaging and subsequent production of rAdV remains time-consuming and labor-intensive, possibly attributable to rAdV infection-associated oxidative stress and reactive oxygen species (ROS) production. Here, we show that exogenous GAPDH expression mitigates adenovirus-induced ROS-associated apoptosis in HEK293 cells, and expedites adenovirus production. By stably overexpressing GAPDH in HEK293 (293G) and 293pTP (293GP) cells, respectively, we demonstrated that rAdV-induced ROS production and cell apoptosis were significantly suppressed in 293G and 293GP cells. Transfection of 293G cells with adenoviral plasmid pAd-G2Luc yielded much higher titers of Ad-G2Luc at day 7 than that in HEK293 cells. Similarly, Ad-G2Luc was amplified more efficiently in 293G than in HEK293 cells. We further showed that transfection of 293GP cells with pAd-G2Luc produced much higher titers of Ad-G2Luc at day 5 than that of 293pTP cells. 293GP cells amplified the Ad-G2Luc much more efficiently than 293pTP cells, indicating that exogenous GAPDH can further augment pTP-enhanced adenovirus production. These results demonstrate that exogenous GAPDH can effectively suppress adenovirus-induced ROS and thus accelerate adenovirus production. Therefore, the engineered 293GP cells represent a superfast rAdV production system for adenovirus-based gene transfer and gene therapy.

In the 2023 hurricane season, the Hurricane Analysis and Forecast System (HAFS) based Ensemble Prediction System (EPS) was being ported to the Amazon Web Service cloud. This relocation aimed to provide real-time hurricane probabilistic forecast guidance for National Hurricane Center (NHC) forecasters. The system comprises Stochastically Perturbed Physics Tendencies (SPPT), Stochastically Kinetic Energy Backscatter (SKEB), and Stochastically Perturbed PBL Humidity (SHUM). Initial and boundary conditions are derived from the National Centers for Environmental Prediction (NCEP) operational Global Ensemble Forecast System (GEFS) 21-member forecast data. The performance of HAFS-EPS for 2023 Atlantic hurricane forecasts was compared with the global GEFS, global ECMWF ensemble, and operational HAFS-A/B forecasts. This comparison highlighted the advantages of higher-resolution regional ensemble forecasts for hurricane track, intensity, Rapid Intensification (RI) probability, and various hazards, including wind, wave, and storm surge probability guidance.

Abstract Through applying a 4‐MHz linear frequency modulation waveform, which has high range resolution and signal intensity, we successfully detected for the first time the ionospheric 150‐km echo enhancement at 430–450 MHz of the Ultra‐High‐Frequency (UHF) band using the newly built Sanya Incoherent Scatter Radar (SYISR). The obtained low signal enhancement (less than 0.5 dB) explains why previous UHF experiments did not detect them. We also found that our measured fine structure shows a much wider forbidden region than previous results and covers a much larger altitudinal and local time region. In comparison with recent upper‐hybrid instability theory and simulation, our results confirmed the predicted higher altitude occurrence, wider gaps between enhancements, the turn corner feature around sunrise, and perhaps the weak enhancement, which provide an independent evaluation of the newly proposed mechanism in UHF band. Future UHF experiments could further improve the physical understanding of 150‐km echo phenomenon.

Abstract Objectives Radiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival. Methods For radiomics texture selection, 3D Slicer 5.0.3 software and the least absolute shrinkage and selection operator (LASSO) algorithm were used. Subsequently, the radiomics model, computed tomography (CT) image, and clinical risk model were compared. The performance of the three models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration plots, and clinical impact curves (CICs). Results For the LNM prediction model, 224 patients with LNM information were used to construct a model that was applied to predict LNM. According to the CT data and clinical characteristics, we constructed a radiomics model, CT imaging model and clinical model. The radiomics model for evaluating LNM status showed excellent calibration and discrimination in the training cohort (AUC = 0.926, 95% CI = 0.869–0.982) and the validation cohort (AUC = 0.872, 95% CI = 0.802–0.941). DeLong’s test demonstrated that the difference among the three models was significant. Similarly, DCA and CIC showed that the radiomics model has better clinical utility than the CT imaging model and clinical model. Our model also exhibited good performance in predicting survival—in line with the findings of the model built with clinical risk factors. Conclusions CT radiomics models exhibited better predictive performance for LNM than models built based on clinical risk characteristics and CT imaging and had comparative clinical utility for predicting patient prognosis. Critical relevance statement The radiomics model showed excellent performance and discrimination for predicting LNM and survival of duodenal papillary carcinoma (DPC). Key Points LNM status determines the most appropriate treatment for DPC. Our radiomics model for evaluating the LNM status of DPC performed excellently. The radiomics model had high sensitivity and specificity for predicting survival, exhibiting great clinical value. Graphical Abstract

Abstract Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. Machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the development of targeted molecular therapy and immunotherapy for UC.

This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.

Abstract A novel highly stable aqueous foam was synthesized using CO2, sodium silicate (SS) and anionic surfactant of sodium dodecylbenzene sulfonate. The influence of CO2 foam on the mechanical properties and its underlying mechanisms of foamed backfill material was investigated. The experimental results revealed that the addition of CO2 and SS effectively reduced the drainage of the foam while strengthening the liquid film of the Plateau borders, which stabilizes the foam. The excellent stability is attributable to the gel network developed after SS exposed to CO2, that adhere to the foam surface. Furthermore, due to the interaction between encapsulated CO2 and hydration products, micro CaCO3 formed and filled the pore wall; thus, precast foam forms robust pore structures in the hardened foamed backfill.

Abstract Currently, endoscopic treatment for small gastrointestinal stromal tumors (GIST) has been widely accepted. However, for tumors larger than 5 cm, endoscopic treatment has not been recognized by national guidelines as the standard therapy due to concerns about safety and adverse tumor outcomes. Therefore, this study compares the long-term survival outcomes of endoscopic treatment and surgical treatment for GIST in the range of 5–10 cm. We selected patients with GIST from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Kaplan–Meier analysis and the log-rank test were employed to compare the long-term survival outcomes between endoscopic treatment and surgical treatment. A multivariate Cox proportional hazards model was used for analysis to identify risk factors influencing patient prognosis. To balance baseline data, we performed 1:1 propensity score matching (PSM). A total of 1223 GIST patients were included, with 144 patients (11.8%) received endoscopic treatment and 1079 patients (88.2%) received surgical treatment. Before PSM, there was no significant difference in the long-term survival rates between the two groups [5-year OS (86.5% vs. 83.5%, P = 0.42), 10-year OS (70.4% vs. 66.7%, P = 0.42)]. After adjusting for covariates, we found that the overall survival (HR = 1.26, 95% CI 0.89–1.77, P = 0.19) and cancer-specific survival (HR = 1.69, 95% CI 0.99–2.89, P = 0.053) risks were comparable between the endoscopic treatment group and the surgical treatment group. In the analysis after PSM, there was no significant difference between the endoscopic treatment group and the surgical treatment group. Our study found that for GIST patients with tumor sizes between 5 and 10 cm, the long-term OS and CSS outcomes were similar between the endoscopic treatment group and the surgical treatment group.

Effective bone regeneration through tissue engineering requires a combination of osteogenic progenitors, osteoinductive biofactors and biocompatible scaffold materials. Mesenchymal stem cells (MSCs) represent the most promising seed cells for bone tissue engineering. As multipotent stem cells that can self-renew and differentiate into multiple lineages including bone and fat, MSCs can be isolated from numerous tissues and exhibit varied differentiation potential. To identify an optimal progenitor cell source for bone tissue engineering, we analyzed the proliferative activity and osteogenic potential of four commonly-used mouse MSC sources, including immortalized mouse embryonic fibroblasts (iMEF), immortalized mouse bone marrow stromal stem cells (imBMSC), immortalized mouse calvarial mesenchymal progenitors (iCAL), and immortalized mouse adipose-derived mesenchymal stem cells (iMAD). We found that iMAD exhibited highest osteogenic and adipogenic capabilities upon BMP9 stimulation in vitro, whereas iMAD and iCAL exhibited highest osteogenic capability in BMP9-induced ectopic osteogenesis and critical-sized calvarial defect repair. Transcriptomic analysis revealed that, while each MSC line regulated a distinct set of target genes upon BMP9 stimulation, all MSC lines underwent osteogenic differentiation by regulating osteogenesis-related signaling including Wnt, TGF-β, PI3K/AKT, MAPK, Hippo and JAK-STAT pathways. Collectively, our results demonstrate that adipose-derived MSCs represent optimal progenitor sources for cell-based bone tissue engineering.

Karst is widely researched in the field of carbonate reservoirs. This study proposes a new formation mechanism and development model, specifically epigenetic karst, and asserts that high-quality reservoirs can be developed in the karst highlands and karst slopes of ancient landforms under the epigenetic karst with facies-controlled characteristics. The results indicate that there are three stages of karst in the study area. The first period is syngenetic karst, characterized by molds, intragranular dissolution pores, and micritization. Isotopic data reveal that the range of variation of carbonate rock 13C and 18O is similar to that of cement. The second period is epigenetic karst, the most significant karst in the study area. It undergoes a prolonged development time and features non-selective fabric dissolution. Oxygen and carbon isotopes exhibit negative values (18O ranges from −10.033‰ to −13.767‰ and 13C ranges from −2.56‰ to −0.52‰), which are significantly altered by atmospheric precipitation. The third period is buried karst, characterized by suture structure and asphaltene filling. The negative value of 18O in calcite veins is significantly

Abstract Although possessing well‐defined nanostructures and excellent multi‐electron redox properties, polyoxometalate clusters have poor intrinsic electrical conductivity and are prone to aggregation due to large surface energy, which makes them difficult to be fully utilized when applying as electrode materials for lithium‐ion batteries. In this paper, monodisperse K7MnV13O38 (MnV13) clusters are achieved by rationally utilizing nano‐sized high conductive carbon dots (CDs) as stabilizers. Benefiting from the fully exposed redox sites of MnV13 clusters (high utilization rate) and sufficient interfaces with carbon dots (extra interfacial energy storage), the optimized MnV13/10CDs anode delivers a high discharge capacity up to 1348 mAh g−1 at a current density of 0.1 A g−1 and exhibits superb rate/cycling capabilities. Density functional theory (DFT) calculations verify that ionic archway channels are formed between MnV13 and CDs, eliminating the bandgap and greatly improving the electron/ion conductivity of MnV13 and CDs. This paper paves a brand‐new way for synthesis of monodisperse clusters and maximization of extra interfacial energy storage.

It is increasingly clear that unconscious information impairs the performance of the corresponding action when the instruction to act is delayed. However, whether this impairment occurs at the response level or at the perceptual level remains controversial. This study used fMRI and a computational model with a pre-post design to address this elusive issue. The fMRI results showed that when the unconscious information containing strong stimulus-response associations was irrelevant to subsequent stimuli, the precuneus in the parietal lobe, which is thought to be involved in sensorimotor processing, was activated. In contrast, when the unconscious information was relevant to subsequent stimuli, regardless of the strength of the stimulus-response associations, some regions in the occipital and temporal cortices, which are thought to be involved in visual perceptual processing, were activated. In addition, the percent signal change in the regions of interest associated with motor inhibition was modulated by compatibility in the irrelevant but not in the relevant stimuli conditions. Modeling of behavioral data further supported that the irrelevant and relevant stimuli conditions involved fundamentally different mechanisms. Our finding reconciles the debate about the mechanism by which unconscious information impairs action performance and has important implications for understanding of unconscious cognition.

Abstract INTRODUCTION Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS We used EHRs from the University of Florida Health (UFHealth) system and created rule‐based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS Our best‐performing CP was “patient has at least 2 AD diagnoses and AD‐related keywords in AD encounters,” with an F1‐score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION We developed and validated rule‐based CPs for AD identification with good performance, which will be crucial for studies that aim to use real‐world data like EHRs. Highlights Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data. Utilized both structured and unstructured EHR data to enhance CP accuracy. Achieved a high F1‐score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN. Validated the CP across different demographics, ensuring robustness and fairness.

BackgroundPediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the key technology to address privacy concerns while preserving the accuracy and performance of ML algorithms. Federated learning could enable multisite development and implementation of ML algorithms to facilitate the translation of artificial intelligence into clinical practice. ObjectiveThe aim of this study is to develop a research protocol for implementation of federated ML across a large clinical research network to identify and discover pediatric asthma subtypes and their progression over time. MethodsThis mixed methods study uses data and clinicians from the OneFlorida+ clinical research network, which is a large regional network covering linked and longitudinal patient-level real-world data (RWD) of over 20 million patients from Florida, Georgia, and Alabama in the United States. To characterize the subtypes, we will use OneFlorida+ data from 2011 to 2023 and develop a research-grade pediatric asthma computable phenotype and clinical natural language processing pipeline to identify pediatric patients with asthma aged 2-18 years. We will then apply federated learning to characterize pediatric asthma subtypes and their temporal progression. Using the Promoting Action on Research Implementation in Health Services framework, we will conduct focus groups with practicing pediatric asthma clinicians within the OneFlorida+ network to investigate the clinical utility of the subtypes. With a user-centered design, we will create prototypes to visualize the subtypes in the EHR to best assist with the clinical management of children with asthma. ResultsOneFlorida+ data from 2011 to 2023 have been collected for 411,628 patients aged 2-18 years along with 11,156,148 clinical notes. We expect to complete the computable phenotyping within the first year of the project, followed by subtyping during the second and third years, and then will perform the focus groups and establish the user-centered design in the fourth and fifth years of the project. ConclusionsPediatric asthma subtypes incorporating RWD from diverse populations could improve patient outcomes by moving the field closer to precision pediatric asthma care. Our privacy-preserving federated learning methodology and qualitative implementation work will address several challenges of applying ML to large, multicenter RWD data. International Registered Report Identifier (IRRID)DERR1-10.2196/57981

Objective: To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS). Patients and Methods: Between 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients’ clinical notes and additional meta information. In addition, we designed an abbreviated NLP pipeline (aNLP) solely focusing on labels from TUS order requisitions to facilitate deployment at other health care systems. Our dataset was split into a training set of 468 (75%) and a test set of 160 (25%), using the former for rule development and the latter for performance evaluation. Results: There were 449 (95.9%) patients identified as aTUS and 19 (4.06%) as iTUS in the training set; there are 155 (96.88%) patients identified as aTUS and 5 (3.12%) were iTUS in the test set. In the training set, the pipeline achieved a sensitivity of 0.99, specificity of 0.95, and positive predictive value of 1.0 for detecting aTUS. The testing cohort revealed a sensitivity of 0.96, specificity of 0.80, and positive predictive value of 0.99. Similar performance metrics were observed in the aNLP pipeline. Conclusion: The NLP models can accurately identify the appropriateness of a thyroid ultrasound from clinical documentation and order requisition information, a critical initial step toward evaluating the drivers and outcomes of TUS use and subsequent thyroid cancer overdiagnosis.

To explore the effects of heat-moisture treatment on the physicochemical and structural properties of sweet potato starch, five types of sweet potato starch were used as the raw material, and their solubility, swelling power, water holding capacity, transmittance, and sedimentation were determined. Furthermore, the influence of heat-moisture treatment on the water absorption characteristics and crystal structure of sweet potato starch with different initial water contents was explored. The results showed that the solubility, swelling power, sedimentation, and transmittance of the five sweet potato starches treated with heat-moisture were all decreased when compared with the original starch, but their water holding capacity was increased, indicating that both sweet potato varieties and heat-moisture treatment had no significant effect on starch properties. There was no new diffraction characteristic peaks were found in sweet potato starch after heat-moisture treatment, the crystal type of those treated starch remained a C-type structure, but the diffraction intensity and crystallinity were decreased. The time required for the heat-moisture treated starch to reach equilibrium in water absorption was shorter than that of the original starch, and the saturated water absorption tended to decrease compared to the original starch. The soaking kinetic constants K1 and K2 from the Peleg model equation were calculated, and the water absorption kinetics equation of sweet potato starch after heat-moisture treatment was confirmed, achieving accurate prediction of its water content during the soaking process.

Abstract Background To investigate the clinical effects of a unilateral external fixator combined with bone transport and tibio-talar fusion in the treatment of severe postoperative infection of peri-ankle fractures. Methods The clinical data of 32 patients (22 men and 10 women) with severe postoperative infection of peri-ankle fractures were retrospectively analyzed. Patients’ age ranged from 26 to 62 (mean, 42 ± 9.5) years old. The types of fractures were distal tibia fracture (25 cases), distal tibia and fibula fracture (5 cases), and talus fracture (2 cases). All patients underwent treatment with unilateral external fixation combined with bone transport and tibio-talar fusion. 6 patients with severe infection received two-stage treatment involving focal debridement and external fixation, osteotomy, and bone transport. The remaining 26 patients underwent debridement, external fixation, and osteotomy simultaneously. The length of bone transport, total fixation time of the external fixator, and postoperative complications were recorded for all patients. The efficacy of the treatment was assessed using the American Association of Foot and Ankle Society (AOFAS) ankle–hindfoot score. Results Patients were followed up for 16–36 months, with an average follow-up time of 24 months. The length of tibia bone transport ranged from 5 to 15 cm, with a mean length of 8.5 cm. The external fixator was applied for 12–24 months, with an average duration of 16 months. One patient suffered from refracture at tibio-talar fusion site, and one patient had external fixation pin-tract infection. No complications, such as recurrent infections (especially the MRSA infection), poor mineralization, refracture, iatrogenic nerve damage or fusion failure, were found in the remaining patients. The preoperative AOFAS ankle–hindfoot function score was 40.0 ± 3.8 (range, 30–52) points, and it increased to 75.0 ± 3.0 (range, 67–78) points at the last follow-up. Conclusion A unilateral external fixator combined with bone transport and tibio-talar fusion is an effective method for treating severe postoperative infection of peri-ankle fractures. This approach is capable of reconstructing large bone defects that remain after clearing the infected lesion. Additionally, it provides stability to the ankle, enhances ankle–hindfoot function, and improves the patient’s quality of life.

In order to improve the stability of emulsions of rapeseed oil bodies under different environmental conditions. The soluble soybean polysaccharide (SSPS) with negative electric charge and konjac glucomannan (KGM) without electric charge were used to form the emulsions of rapeseed oil bodies. Laser scattering particle analyzer and microscope were used to analyze the particle size and observe the microstructure of rapeseed oil body emulsions. The stability of emulsions of rapeseed oil bodies with two different polysaccharides under different environment stresses (pH, salt concentrations and heat treatments) was investigated, and the emulsions of rapeseed oil bodies without polysaccharide were used as control. The results of experiment showed that the particle of emulsions of rapeseed oil bodies decreased along with the increase of SSPS and KGM concentrations. In addition to pH2.0, the particle size of emulsions of rapeseed oil bodies without polysaccharide at different pH was significantly higher (P0.05) for the particle size of emulsions of rapeseed oil bodies without polysaccharide at different salt concentrations, the particle size was significantly higher (P0.05) on the particle size of emulsions of rapeseed oil bodies with SSPS. Comprehensive above results, KGM and SSPS can significantly improve the stability of the emulsions of rapeseed oil bodies, SSPS shows a better effect.

Abstract Background Platelet count is associated with cardiovascular risk and mortality in several cardiovascular diseases, but the association of the nadir platelet counts post-septal myectomy with the cardiovascular complication risk in hypertrophic obstructive cardiomyopathy patients remains unclear. Methods This retrospective cohort study reviewed all adult patients who underwent septal myectomy at a single tertiary referral center over a 5-year period. Postoperative nadir platelet count was defined as the lowest platelet count in the first 4 postoperative days or until hospital discharge. The composite outcome included cardiovascular death, myocardial infarction, heart failure, malignant arrhythmia, cardiac tamponade, and major bleeding events within 30 days postoperatively. Univariable and multivariable logistic regression and restricted cubic spline models were used to assess the association between postoperative nadir platelet count and the 30-day postoperative cardiovascular complication risk. Results Among the 113 enrolled patients, 23 (20.4%) developed cardiovascular events within 30 days postoperatively. The incidence of postoperative cardiovascular complications was significantly higher in patients with a nadir platelet count ≤ 99 × 109/L than in those with a nadir platelet count > 99 × 109/L (33.3% vs. 7.1%, crude risk ratio: 4.67, 95% confidence interval: 1.69–12.85, P 0.05). Conclusion The postoperative nadir platelet count was significantly associated with the 30-day post-myectomy risk of cardiovascular complications in hypertrophic obstructive cardiomyopathy patients. Trial registration This trial was registered at ClinicalTrials.gov (NCT04275544).

Abstract Background Alzheimer’s disease (AD) is a degenerative neurological disorder. Recent studies have indicated that histone deacetylases (HDACs) are among the most prominent epigenetic therapy targets and that HDAC inhibitors have therapeutic effects on AD. Here, we identified sodium valproate (VPA), a pan-HDAC inhibitor, and WT161, a novel HDAC6 selective inhibitor, as potential therapeutic agents for AD. Underlying molecular mechanisms were investigated. Methods A cellular model, N2a-APPswe, was established via lentiviral infection, and the APPswe/PSEN1dE9 transgenic mouse model was employed in the study. LC–MS/MS was applied to quantify the concentration of WT161 in the mouse brain. Western blotting, immunohistochemical staining, thioflavin-S staining and ELISA were applied to detect protein expression in cells, tissues, or serum. RNA interference was utilized to knockdown the expression of specific genes in cells. The cognitive function of mice was assessed via the nest-building test, novel object recognition test and Morris water maze test. Results Previous studies have focused mainly on the impact of HDAC inhibitors on histone deacetylase activity. Our study discovered that VPA and WT161 can downregulate the expression of multiple HDACs, such as HDAC1 and HDAC6, in both AD cell and mouse models. Moreover, they also affect the expression of APP and APP secretases (BACE1, PSEN1, ADAM10). RNA interference and subsequent vitamin C induction further confirmed that the expression of APP and APP secretases is indeed regulated by HDAC1 and HDAC6, with the JNK pathway being the intermediate link in this regulatory process. Through the above pathways, VPA and WT161 effectively reduced Aβ deposition in both AD cell and mouse models and significantly improved cognitive function in AD mice. Conclusions In general, we have discovered that the HDAC6-JNK-APP secretases cascade is an important pathway for VPA and WT161 to exert their therapeutic effects on AD. Investigations into the safety and efficacy of VPA and WT161 were also conducted, providing essential preclinical evidence for assessing these two epigenetic drugs for the treatment of AD.

The backfilling mining technology for multi-source coal-based solid waste (MCSW) aligns with the key challenges and emerging paradigms in the coal industry, aiming at intelligent and cleaner coal mining, high efficiency, and low carbon utilization. This research area has gained considerable attention and significance for promoting high-quality coal mining, low environmental impact, and sustainable development. In this study, a response surface experiment was designed with desulfurization gypsum (DG), gasification slag (GS), and furnace bottom slag (FBS) as factors to analyze the performance of backfill materials with varying compositions. Parameters such as bleeding rate, compactness, viscosity, setting time, and age strength were tested. An optimal composition of backfill materials was obtained, with a MCSW content exceeding 90 %, 28-day strength exceeding 2 MPa, initial setting time between 240 and 480 min, and final setting time ranging from 300 to 600 min. The investigation of the optimal materials involved characterizing the pore structure, microstructure, composition, and silica gel structure through techniques such as X-ray diffraction (XRD) and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS). Additionally, the study explored micro-interface structures and enhancement mechanisms. After 28 days of curing, the backfill with a composition of DG 0.3, FBS 0.2, GS 0.1 exhibited a compressive strength of 5.18 MPa, with an initial setting time as short as 140 min. This can be attributed to the precipitation of silica gel on the grain surface, which mitigated the segregation effect. The silica gel promoted inter-particle bonding and rapid viscosity, preventing particle settling. This method not only ensures the safe disposal of coal-based solid waste but also contributes to the coordinated development of coal resource exploitation, environmental protection, and reduced carbon footprint.

Wnt signaling plays a major role in regulating cell proliferation and differentiation. The Wnt ligands are a family of 19 secreted glycoproteins that mediate their signaling effects via binding to Frizzled receptors and LRP5/6 coreceptors and transducing the signal either through β-catenin in the canonical pathway or through a series of other proteins in the noncanonical pathway. Many of the individual components of both canonical and noncanonical Wnt signaling have additional functions throughout the body, establishing the complex interplay between Wnt signaling and other signaling pathways. This crosstalk between Wnt signaling and other pathways gives Wnt signaling a vital role in many cellular and organ processes. Dysregulation of this system has been implicated in many diseases affecting a wide array of organ systems, including cancer and embryological defects, and can even cause embryonic lethality. The complexity of this system and its interacting proteins have made Wnt signaling a target for many therapeutic treatments. However, both stimulatory and inhibitory treatments come with potential risks that need to be addressed. This review synthesized much of the current knowledge on the Wnt signaling pathway, beginning with the history of Wnt signaling. It thoroughly described the different variants of Wnt signaling, including canonical, noncanonical Wnt/PCP, and the noncanonical Wnt/Ca2+ pathway. Further description involved each of its components and their involvement in other cellular processes. Finally, this review explained the various other pathways and processes that crosstalk with Wnt signaling.

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An 8-week feeding trial was conducted to investigate the effects of dietary carbohydrate levels on growth performance, body composition, and plasma biochemical parameters, liver and gut histology, and intestinal microbiota of Takifugu rubripes. Five isonitrogenous and isolipidic experimental diets were formulated with graded levels of cassava starch: 0% (C0), 8% (C8), 16% (C16), 24% (C24), and 32% (C32). The results showed that weight gain rate, specific growth rate (SGR) and protein efficiency ratio significantly increased in C16 and C24 groups (P < 0.05), while the feed conversion ratio expressed an opposite trend. Feeding rate is decreased with the increasing of carbohydrate levels (P < 0.05). Liver lipid content was dramatically increased in C24 treatment (P < 0.05). The intestinal amylase activity and muscle glycogen content were higher than other groups (P < 0.05). Plasma glucose levels were increased in C24 and C32 groups (P < 0.05). In addition, the abundance of intestinal potentially pathogenic bacteria (Staphylococcus) decreased, and the intestinal probiotics (Lactobacillus) increased with the increasing of dietary carbohydrate level (P < 0.05). The intestinal bacteria, indicated by Sobs, Chao and Shannon indexes, were more diverse with the increasing of the carbohydrate level (P < 0.05), meanwhile the abundance of transport and metabolism of carbohydrate, lipid and amino acid were enhanced with the increasing of carbohydrate levels (P < 0.05). In conclusion, appropriate supplementation of carbohydrate in diet promoted growth performance and feed utilization of juvenile Takifugu rubripes, and increased the liver lipid and muscle glycogen content, as well as intestinal microbial diversity. Based on the quadratic regression between SGR and carbohydrate levels, the optimal dietary carbohydrate level of juvenile Takifugu rubripes is 21.6%.

The biodiversity-ecosystem functioning (BEF) relationship is a critical topic in ecological research, as it guides the maintenance of ecosystem functioning in the face of climate change and human disturbance. However, the BEF relationship is complicated by the fact that biodiversity has multiple dimensions, and in particular functional diversity may be strongly correlated with community productivity. But the understanding is still lacking about how different functional traits determine community productivity. The present study tested the relationship between biodiversity and community productivity empirically in a demersal fish community, based on continuous bottom trawl surveys in Haizhou Bay, China from 2013 to 2022. Structural equation modeling (SEM) with composite variables was used to evaluate the relationships among environmental factors, species diversity (SD), functional diversity (FD), community weighted mean traits (CWM), and community productivity (CP), and the roles of different aspects of functional traits were further compared. The results showed that there were considerable heterogeneities in the temporal and spatial distributions of CP, and PCA revealed that the CP was weakly correlated with both species and functional diversity. SEM showed that CWM had the greatest influences on CP (r = 0.63), followed by SD (r = 0.52), and FD had the least impact (r = -0.42). This finding supported the mass ratio hypothesis, indicating that functional traits of dominant species determined community productivity. Environmental factors showed no direct impacts on CP but had indirect effects through biodiversity. Regarding the role of different functional traits, the trait of locomotion was the most important driver of CP in terms of CWM, while the food acquisition was more important than other components in terms of FD. Our study can contribute to an improved comprehension of the BEF linkages in marine fish communities, and serve as a guideline for fisheries management and biodiversity protection in the face of climate changes.

Long noncoding RNA nuclear-enriched abundant transcript 1 (NEAT1) is involved in the progression of Parkinson's disease (PD), but the specific regulatory role needs further exploration. This study showed that the expression of NEAT1 was upregulated in the cerebrospinal fluid (CSF) and peripheral blood of patients with different stages of PD. 1-Methyl-4-phenylpyridine (MPP)-treated PC 12 cells were transfected with si-NEAT1, and MPP treatment promoted cell apoptosis, oxidative stress and inflammatory factor secretion. Si-NEAT1 reversed the effects of MPP. NEAT1 silencing eliminated the effect of MPP on the protein expression levels of LC3-II and p62/SQSTM1. By using an online bioinformatics database, Fused in Sarcoma (FUS) was confirmed to be an RNA binding protein of NEAT1, and it was highly expressed in the CSF and peripheral blood of patients with PD. Si-FUS was transfected into MPP-treated PC 12 cells to detect cell apoptosis, oxidative stress, inflammatory factor secretion and autophagy, and the results were the same as those of transfection of si-NEAT1. Furthermore, MPP treatment reduced the phosphorylation levels of PI3K, Akt and mTOR, whereas si-FUS reversed the effects of MPP. In vivo, compared with the model group, the PD mice showed reduced NEAT1 and FUS expression levels and activated PI3K pathway after being injected with si-NEAT1. The brain tissue of NEAT1-silenced PD mice had decreased inflammatory infiltration and apoptosis and increased neurological scores. In conclusion, NEAT1 is involved in PD progression through FUS-mediated inhibition of the PI3K/AKT/mTOR signalling pathway.

IntroductionEphedra sinica polysaccharide (ESP) exerts substantial therapeutic effects on rheumatoid arthritis (RA). However, the mechanism through which ESP intervenes in RA remains unclear. A close correlation has been observed between enzymes and derivatives in the gut microbiota and the inflammatory immune response in RA.MethodsA type II collagen-induced arthritis (CIA) mice model was treated with Ephedra sinica polysaccharide. The therapeutic effect of ESP on collagen-induced arthritis mice was evaluated. The anti-inflammatory and cartilage-protective effects of ESP were also evaluated. Additionally, metagenomic sequencing was performed to identify changes in carbohydrate-active enzymes and resistance genes in the gut microbiota of the ESP-treated CIA mice. Liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry were performed to observe the levels of serum metabolites and short-chain fatty acids in the gut. Spearman’s correlational analysis revealed a correlation among the gut microbiota, antibiotic-resistance genes, and microbiota-derived metabolites.ResultsESP treatment significantly reduced inflammation levels and cartilage damage in the CIA mice. It also decreased the levels of pro-inflammatory cytokines interleukin (IL)-6, and IL-1-β and protected the intestinal mucosal epithelial barrier, inhibiting inflammatory cell infiltration and mucosal damage. Here, ESP reduced the TLR4, MyD88, and TRAF6 levels in the synovium, inhibited the p65 expression and pp65 phosphorylation in the NF-κB signaling pathway, and blocked histone deacetylase (HDAC1 and HDAC2) signals. ESP influenced the gut microbiota structure, microbial carbohydrate-active enzymes, and microbial resistance related to resistance genes. ESP increased the serum levels of L-tyrosine, sn-glycero-3-phosphocholine, octadecanoic acid, N-oleoyl taurine, and decreased N-palmitoyl taurine in the CIA mice.ConclusionESP exhibited an inhibitory effect on RA. Its action mechanism may be related to the ability of ESP to effectively reduce pro-inflammatory cytokines levels, protect the intestinal barrier, and regulate the interaction between mucosal immune systems and abnormal local microbiota. Accordingly, immune homeostasis was maintained and the inhibition of fibroblast-like synoviocyte (FLS) proliferation through the HDAC/TLR4/NF-κB pathway was mediated, thereby contributing to its anti-inflammatory and immune-modulating effects.

IntroductionNeutrophil extracellular traps (NETs) constitute a crucial element of the immune system, and dysfunction in immune responses is implicated in the susceptibility and progression of Parkinson's disease (PD). Nevertheless, the mechanism connecting PD and NETs remains unclear. This study aims to uncover potential NETs-related immune biomarkers and elucidate their role in PD pathogenesis.MethodsThrough differential gene analysis of PD and NETs in GSE7621 datasets, we identified two PD subtypes and explored potential biological pathways. Subsequently, using ClusterWGCNA, we pinpointed pertinent genes and developed clinical diagnostic models. We then optimized the chosen model and evaluated its association with immune infiltration. Validation was conducted using the GSE20163 dataset. Screening the single-cell dataset GSE132758 revealed cell populations associated with the identified gene. ResultsOur findings identified XGB as the optimal diagnostic model, with CAP2 identified as a pivotal gene. The risk model effectively predicted overall diagnosis rates, demonstrating a robust correlation between infiltrating immune cells and genes related to the XGB model. DiscussionIn conclusions, we identified PD subtypes and diagnostic genes associated with NETs, highlighting CAP2 as a pivotal gene. These findings have significant implications for understanding potential molecular mechanisms and treatments for PD.

The evolutionarily conserved Wnt signaling pathway plays a central role in development and adult tissue homeostasis across species. Wnt proteins are secreted, lipid-modified signaling molecules that activate the canonical (β-catenin dependent) and non-canonical (β-catenin independent) Wnt signaling pathways. Cellular behaviors such as proliferation, differentiation, maturation, and proper body-axis specification are carried out by the canonical pathway, which is the best characterized of the known Wnt signaling paths. Wnt signaling has emerged as an important factor in stem cell biology and is known to affect the self-renewal of stem cells in various tissues. This includes but is not limited to embryonic, hematopoietic, mesenchymal, gut, neural, and epidermal stem cells. Wnt signaling has also been implicated in tumor cells that exhibit stem cell-like properties. Wnt signaling is crucial for bone formation and presents a potential target for the development of therapeutics for bone disorders. Not surprisingly, aberrant Wnt signaling is also associated with a wide variety of diseases, including cancer. Mutations of Wnt pathway members in cancer can lead to unchecked cell proliferation, epithelial–mesenchymal transition, and metastasis. Altogether, advances in the understanding of dysregulated Wnt signaling in disease have paved the way for the development of novel therapeutics that target components of the Wnt pathway. Beginning with a brief overview of the mechanisms of canonical and non-canonical Wnt, this review aims to summarize the current knowledge of Wnt signaling in stem cells, aberrations to the Wnt pathway associated with diseases, and novel therapeutics targeting the Wnt pathway in preclinical and clinical studies.

In order to solve the severe damage and difficult support of lower group coal roadways under the influence of mining in upper group coal seams near Malan Mine, a field survey, numerical simulation, and theoretical analysis were conducted to analyze the stress environment of roadway surrounding rock during tunnel excavation and upper group coal mining. Based on this analysis, a strengthening support plan with combination structure of original plan and pipe cable was proposed, comparing the development of plastic zones and surrounding rock deformation between the original support plan and the strengthening support plan. The results showed that under the strengthening support plan, the vertical displacement and horizontal displacement of roadway surrounding rock reduced by 12.76%, 11.09%, and 17.06%, 16.63% respectively under tunnel excavation and mining influence, and the development of plastic zones was also significantly improved compared to the original support; the combination structure of pipe cable strengthening support plan has good supporting effect and can effectively control roadway surrounding rock development.

AbstractContext Protocatechuic acid (PCA) has a protective effect on alcoholic liver injury, but the role of PCA in type 2 diabetes-induced liver injury is not well known.Objectives This study explores the therapeutic effect and potential mechanism of PCA on type 2 diabetes-induced liver injury.Materials and methods An insulin resistance/type 2 diabetic (IR/D) model was established by high-fat diet for 4 weeks + streptozotocin (35 mg/kg; i.p) in male Wistar rats pretreated with or without PCA (15 or 30 mg/kg for 6 d).Results PCA at 15 and 30 mg/kg significantly upregulated the levels of body weight (BW; 230.2, 257.8 g), high density lipids (22.68, 34.78 mg/dL), glutathione (10.24, 16.21 nmol/mg), superoxide dismutase (21.62, 29.34 U/mg), glucagon-like peptide-1, glucose transporter-4, Wnt1, and β-catenin, while downregulating those of liver weight (LW; 9.4, 6.7 g), BW/LW (4.1, 2.6%), serum glucose (165, 120 mg/dL), serum insulin (13.46, 8.67 μIU/mL), homeostatic model assessment of insulin resistance (5.48, 2.57), total cholesterol (68.52, 54.31 mg/dL), triglycerides (72.15, 59.64 mg/dL), low density lipids (42.18, 30.71), aspartate aminotransferase (54.34 and 38.68 U/L), alanine aminotransferase (42.87, 29.98 U/L), alkaline phosphatase (210.16, 126.47 U/L), malondialdehyde (16.52, 10.35), pro-inflammatory markers (tumor necrosis factor α (TNF-α (149.67, 120.33 pg/mg)) , IL-6 (89.79, 73.69 pg/mg) and IL-1β (49.67, 38.73 pg/mg)), nuclear factor kappa B (NF-κB), and interleukin-1β, and ameliorated the abnormal pathological changes in IR/D rats.Discussion and conclusion PCA mitigates the IR, lipid accumulation, oxidative stress, and inflammation in liver tissues of IR/D rats by modulating the NF-κB and Wnt1/β-catenin pathways.

Abstract There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians’ Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p

Abstract Background The simulated three-dimensional (3D) printed anatomical model of the aorta, which has become the norm in medical education, has poor authenticity, tactility, feasibility, and interactivity. Therefore, this study explored the educational value and effect of mixed reality (MR) combined with a 3D printed model of aortic disease in training surgical residents. Method Fifty-one resident physicians who rotated in vascular surgery were selected and divided into traditional (27) and experimental (24) teaching groups using the random number table method. After undergoing the experimental and traditional training routines on aortic disease, both the groups took a theoretical test on aortic disease and an assessment of the simulation based on the Michigan Standard Simulation Experience Scale (MiSSES) template. Their scores and assessment results were compared. The study was conducted at the Department of Vascular Surgery of Peking University People’s Hospital, Beijing, China. Results In the theoretical test on aortic disease, the experimental teaching group obtained higher mean total scores (79.0 ± 9.1 vs. 72.6 ± 7.5, P = 0.013) and higher scores in anatomy/ pathophysiology (30.8 ± 5.4 vs. 24.8 ± 5.8; P

Face recognition is a widely used computer vision, which plays an increasingly important role in user authentication systems, security systems, and consumer electronics. The models for most current applications are based on high-definition digital cameras. In this paper, we focus on digital images derived from historical motion picture films. Historical motion picture films often have poorer resolution than modern digital imagery, making face detection a more challenging task. To approach this problem, we first propose a trunk–branch concatenated multi-task cascaded convolutional neural network (TB-MTCNN), which efficiently extracts facial features from blurry historical films by combining the trunk with branch networks and employing various sizes of kernels to enrich the multi-scale receptive field. Next, we build a deep neural network-integrated object-tracking algorithm to compensate for failed recognition over one or more video frames. The framework combines simple online and real-time tracking with deep data association (Deep SORT), and TB-MTCNN with the residual neural network (ResNet) model. Finally, a state-of-the-art image restoration method is employed to reduce the effect of noise and blurriness. The experimental results show that our proposed joint face recognition and tracking network can significantly reduce missed recognition in historical motion picture film frames.

In this study, we used the Sanya Incoherent Scatter Radar (SYISR) to observe the altitude profiles of traveling ionospheric disturbances (TIDs) during a moderate magnetic storm from 13 to 15 March 2022. Three TIDs were recorded, including two large-scale TIDs (LSTIDs) and one medium-scale TID (MSTID). These LSTIDs occurred during the storm recovery phase, characterized by periods of ~110–155 min, downward phase velocities of 22–60 m/s, and a relative amplitude of 17–25%. A nearly vertical front was noted at ~350–550 km, differing from AGW theory predictions. This structure is more attributed to the combined effects of sunrise-induced electron density changes and pre-sunrise uplift. Moreover, GNSS observations linked this LSTID to high-latitude origins, indicating a connection to polar magnetic storm excitation. However, the second LSTID was observed at lower altitudes (150–360 km) with a higher elevation angle (~17°). This LSTID, observed by the SYISR, was absent in the GNSS data from mainland China and Japan, suggesting a potential local source. The MSTID exhibited a larger relative amplitude of 29–36% at lower altitudes (130–210 km) with severe upward attenuation. The MSTID may be related to atmospheric gravity waves from the lower atmosphere. AGWs are considered to be the perturbation source for this MSTID event.

The first version operational Hurricane Analysis and Forecast System (HAFS) implemented the Vortex Initialization (VI) technique to optimize tropical cyclone structure and intensity, which was adopted from the Hurricane Weather Research and Forecasting system (HWRF) and does not initialize cloud hydrometeors and vertical velocity. This limitation in the VI caused the inconsistency issue between hurricane vortex and its cloud in the model initial condition. A new VI, which can relocate or cycle cloud hydrometeors and vertical velocity, has been developed to solve this issue. For the cold start, the VI simply relocates the cloud and vertical velocity fields of Global Forecasting System (GFS) analysis; for the warm start, the cloud and vertical velocity associated with a hurricane in the GFS analysis are replaced by the fields extracted from the 6 h HAFS forecast of a previous cycle. This new VI has been tested for the 2023 HAFS-A real-time experiment configuration, and another sensitivity experiment without relocating or cycling both cloud and vertical velocity is conducted to examine the effect of the new VI. A comparison of the results reveals that the new VI improves the intensity forecast and generates a very realistic initial cloud field in correct position. Validating the model initial conditions with observed radar data reveals that the new VI captures the secondary eyewall of major hurricanes and asymmetric convective structure of weak tropical storms. This improvement of the cloud field in the model initial condition through the new VI expects to provide a better background for further data assimilation. Additional sensitivity experiment that only relocates or cycles cloud hydrometeors without correcting the vertical velocity field results in poorer intensity forecasts, which highlights the importance of vertical velocity in the model initial condition.

Serine is a functional amino acid that effectively regulates the physiological functions of an organism. This study investigates the effects of adding exogenous serine to a culture medium to explore a feasible method for the rejuvenation of V. volvacea degenerated strains. The tissue isolation subcultured strains T6, T12, and T19 of V. volvacea were used as test strains, and the commercially cultivated strain V844 (T0) was used as a control. The results revealed that the addition of serine had no significant effect on non-degenerated strains T0 and T6, but could effectively restore the production characteristics of degenerated strains T12 and T19. Serine increased the biological efficiency of T12 and even helped the severely degenerated T19 to regrow its fruiting body. Moreover, exogenous serine up-regulated the expression of some antioxidant enzyme genes, improved antioxidase activity, reduced the accumulation of reactive oxygen species (ROS), lowered malondialdehyde (MDA) content, and restored mitochondrial membrane potential (MMP) and mitochondrial morphology. Meanwhile, serine treatment increased lignocellulase and mycelial energy levels. These findings form a theoretical basis and technical support for the rejuvenation of V. volvacea degenerated strains and other edible fungi.

CdS quantum dots (CdS QDs) are regarded as a promising photocatalyst due to their remarkable response to visible light and suitable placement of conduction bands and valence bands. However, the problem of photocorrosion severely restricts their application. Herein, the CdS QDs-Co9S8 hollow nanotube composite photocatalyst has been successfully prepared by loading Co9S8 nanotubes onto CdS QDs through an electrostatic self-assembly method. The experimental results show that the introduction of Co9S8 cocatalyst can form a stable structure with CdS QDs, and can effectively avoid the photocorrosion of CdS QDs. Compared with blank CdS QDs, the CdS QDs-Co9S8 composite exhibits obviously better photocatalytic hydrogen evolution performance. In particular, CdS QDs loaded with 30% Co9S8 (CdS QDs-30%Co9S8) demonstrate the best photocatalytic performance, and the H2 production rate reaches 9642.7 μmol·g−1·h−1, which is 60.3 times that of the blank CdS QDs. A series of characterizations confirm that the growth of CdS QDs on Co9S8 nanotubes effectively facilitates the separation and migration of photogenerated carriers, thereby improving the photocatalytic hydrogen production properties of the composite. We expect that this work will facilitate the rational design of CdS-based photocatalysts, thereby enabling the development of more low-cost, high-efficiency and high-stability composites for photocatalysis.

To research the effect of sweet potato leaf extract on the comprehensive quality of starch jelly, this study investigated the impact of several factors, including sodium carbonate, white granulated sugar, sweet potato leaf extract, sweet potato starch, and steaming time, on the sensory quality and texture characteristics of sweet potato leaf jelly, which was made using sweet potato leaf and sweet potato starch as the main ingredients. Response surface optimization experiments were conducted, using elasticity as the response value, to determine the optimal formula and process parameters for sweet potato leaf jelly. The optimal formula for sweet potato leaf jelly was: Sweet potato leaf extract 40 mL/100 g, white granulated sugar 1 g/100 g, sweet potato starch 19 g/100 g, purified water 40 mL/100 g. The raw materials were thoroughly mixed and steamed for 8 minutes, and then cooled. The resulting product had a green color, uniform texture, and a unique sweet potato leaf aroma, and the elasticity was verified to be 6.19. This study would provide a technical support for expanding the application of sweet potato leaf and improving the comprehensive value of the sweet potato industry.

ABSTRACT: Objectives: Treating patients with diabetic foot infection (DFI) is challenging because of high rates of antibiotic resistance. Therefore, to administer a suitable antibiotic treatment, it is necessary to know the antibiotic resistance patterns in DFIs. Methods: To explore this question, we selected metagenomic data of 36 tissue samples from patients with DFI in the National Center for Biotechnology Information Sequence Read Archive database. Results: A total of 229 antibiotic-resistant gene (ARG) subtypes belonging to 20 ARG types were detected. The antibiotic resistome of 229 different genes in the tissue samples of patients with DFI comprised 24 core and 205 accessory resistance genes. Among the core antibiotic resistome, multidrug, tetracycline, macrolide-lincosamide-streptogramin, and beta-lactam resistance genes were the dominant categories. Procrustes analysis indicated that both the microbial community composition and mobile genetic elements (MGEs) were determinants of the ARGs. In the network analysis, 29 species were speculated to be potential hosts of 28 ARGs based on the co-occurrence results. Plasmids and transposons were the most common elements that co-occurred with ARGs. Conclusions: Our study provided detailed information about antibiotic resistance patterns in DFI, which has practical implications for suggesting a more specific antibiotic choice.

Abstract Background It has been observed that Slo1 knockout mice have reduced motor function, and people with certain Slo1 mutations have movement problems, but there is no answer whether the movement disorder is caused by the loss of Slo1 in the nervous system, or skeletal muscle, or both. Here, to ascertain in which tissues Slo1 functions to regulate motor function and offer deeper insight in treating related movement disorder, we generated skeletal muscle‐specific Slo1 knockout mice, studied the functional changes in Slo1‐deficient skeletal muscle and explored the underlying mechanism. Methods We used skeletal muscle‐specific Slo1 knockout mice (Myf5‐Cre; Slo1flox/flox mice, called CKO) as in vivo models to examine the role of Slo1 in muscle growth and muscle regeneration. The forelimb grip strength test was used to assess skeletal muscle function and treadmill exhaustion test was used to test whole‐body endurance. Mouse primary myoblasts derived from CKO (myoblast/CKO) mice were used to extend the findings to in vitro effects on myoblast differentiation and fusion. Quantitative real‐time PCR, western blot and immunofluorescence approaches were used to analyse Slo1 expression during myoblast differentiation and muscle regeneration. To investigate the involvement of genes in the regulation of muscle dysfunction induced by Slo1 deletion, RNA‐seq analysis was performed in primary myoblasts. Immunoprecipitation and mass spectrometry were used to identify the protein interacting with Slo1. A dual‐luciferase reporter assay was used to identify whether Slo1 deletion affects NFAT activity. Results We found that the body weight and size of CKO mice were not significantly different from those of Slo1flox/flox mice (called WT). Deficiency of Slo1 in muscles leads to reduced endurance (~30% reduction, P

Objective: To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging. Participants and Methods: Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022. Results: Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation. Conclusion: The study evaluated a computable phenotype algorithm for the recruitment of individuals for a successful aging study using large-scale EHRs. Our study provides proof of concept of using big data and informatics as aids for the recruitment of individuals for prospective cohort studies.