Correlation between vertebral bone mineral density and multi-level virtual non-calcium imaging parameters from dual-layer spectral detector computed tomography.

Background: Virtual non-calcium (VNCa) imaging based on dual-energy computed tomography (CT) plays an increasingly important role in diagnosing spinal diseases. However, the utility of VNCa technology in the measurement of vertebral bone mineral density (BMD) is limited, especially the VNCa CT value at multiple calcium suppression levels and the slope of VNCa curve. This retrospective cross-sectional study aimed to explore the correlation between vertebral BMD and new VNCa parameters from dual-layer spectral detector CT. Methods: The dual-layer spectral detector CT and quantitative CT (QCT) data of 4 hydroxyapatite (HAP) inserts and 667 vertebrae of 234 patients (132 male and 102 female) who visited a university teaching hospital between April and May 2023 were retrospectively analyzed. The BMD values of 3 vertebrae (T12, L1, and L2) and inserts were measured using QCT, defined as QCT-BMD. The VNCa CT values and the slope λ of the VNCa attenuation curve of vertebrae and inserts were recorded. The correlations between VNCa parameters (VNCa CT value, slope λ) and QCT-BMD were analyzed. Results: For the vertebrae, the correlation coefficient ranged from -0.904 to 0.712 (all P<0.05). As the calcium suppression index (CaSI) increased, the correlation degree exhibited a decrease first and then increased, with the best correlation (r=-0.904, P<0.001) observed at the index of 25%. In contrast, the correlation coefficient for the inserts remained relatively stable (r=-0.899 to -1, all P<0.05). For the vertebrae, the values of 3 slopes λ (λ1, λ2, and λ3) derived from the VNCa attenuation curve were 6.50±1.99, 3.75±1.15, and 2.04±0.62, respectively. Regarding the inserts, the λ1, λ2, and λ3 values were 11.56 [interquartile range (IQR): 2.40-22.62], 6.68 (IQR: 1.39-13.49), and 3.63 (IQR: 0.75-7.8), respectively. For the vertebrae, all 3 correlation coefficients between 3 slopes λ and QCT-BMD were 0.956 (all P<0.05). For the inserts, the 3 correlation coefficients were 0.996, 0.998, and 1 (all P<0.05), respectively. Conclusions: A promising correlation was detected between VNCa CT parameters and QCT-BMD in vertebrae, warranting further investigation to explore the possibility of VNCa imaging to assess BMD.

Crystal structure characterization, Hirshfeld surface analysis, and DFT calculation studies of 1-(6-amino-5-nitro-naphthalen-2-yl)ethanone.

The title compound, C12H10N2O3, was obtained by the de-acetyl-ation reaction of 1-(6-amino-5-nitro-naphthalen-2-yl)ethanone in a concentrated sulfuric acid methanol solution. The mol-ecule comprises a naphthalene ring system bearing an acetyl group (C-3), an amino group (C-7), and a nitro group (C-8). In the crystal, the mol-ecules are assembled into a two-dimensional network by N⋯H/H⋯N and O⋯H/H⋯O hydrogen-bonding inter-actions. n-π and π-π stacking inter-actions are the dominant inter-actions in the three-dimensional crystal packing. Hirshfeld surface analysis indicates that the most important contributions are from O⋯H/H⋯O (34.9%), H⋯H (33.7%), and C⋯H/H⋯C (11.0%) contacts. The energies of the frontier mol-ecular orbitals were computed using density functional theory (DFT) calculations at the B3LYP-D3BJ/def2-TZVP level of theory and the LUMO-HOMO energy gap of the mol-ecule is 3.765 eV.

A reversible implantable memristor for health monitoring applications.

Conventional implantable electronics based on von Neumann architectures encounter significant limitations in computing and processing vast biological information due to computational bottlenecks. The memristor with integrated memory-computing and low power consumption offer a promising solution to overcome the computational bottleneck and Moore's law limitations of traditional silicon-based implantable devices, making them the most promising candidates for next-generation implantable devices. In this work, a highly stable memristor with an Ag/BaTiO3/MnO2/FTO structure was fabricated, demonstrating retention characteristics exceeding 1200 cycles and endurance above 1000 s. The device successfully exhibited three-stage responses to biological signals after implantation in SD (Sprague-Dawley) rats. Importantly, the memristor perform remarkable reversibility, maintaining over 100 cycles of stable repetition even after extraction from the rat. This study provides a new perspective on the biomedical application of memristors, expanding the potential of implantable memristive devices in intelligent medical fields such as health monitoring and auxiliary diagnostics.

Revolution in Cancer Treatment: How Are Intelligently Designed Nanostructures Changing the Game?

Nanoparticles (NPs) are extremely important tools to overcome the limitations imposed by therapeutic agents and effectively overcome biological barriers. Smart designed/tuned nanostructures can be extremely effective for cancer treatment. The selection and design of nanostructures and the adjustment of size and surface properties are extremely important, especially for some precision treatments and drug delivery (DD). By designing specific methods, an important era can be opened in the biomedical field for personalized and precise treatment. Here, we focus on advances in the selection and design of nanostructures, as well as on how the structure and shape, size, charge, and surface properties of nanostructures in biological fluids (BFs) can be affected. We discussed the applications of specialized nanostructures in the therapy of head and neck cancer (HNC), which is a difficult and aggressive type of cancer to treat, to give an impetus for novel treatment approaches in this field. We also comprehensively touched on the shortcomings, current trends, and future perspectives when using nanostructures in the treatment of cancer.

Recovering carbon fibers from waste CFRPs via pyrolysis-oxidation method: Implications for reuse in remanufactured materials.

Carbon fiber-reinforced polymer composites (CFRPs) have gained widespread usage due to their promising physiochemical properties, while this causes large amounts of waste CFRPs worldwide. In this study, carbon fibers were successfully recovered from waste CFRPs through the pyrolysis-oxidation method, and the recovered fibers were reused in remanufacturing the secondary generation CFRPs. Moreover, the individual and interactive effects of pyrolysis-oxidation recovering parameters on the mechanical strength of the resulting remanufactured CFRPs (reCFRPs) were investigated. The recovered carbon fibers displayed surface chemical structures similar to virgin fibers but with high contents of oxygen-containing bonds. The tensile strength retention (TSR) of the reCFRPs was primarily influenced by oxidation temperature. Notably, a higher oxidation temperature, especially exceeding 560 °C, amplified the impact of oxidation duration on the TSR value. Similarly, concerning interlaminar shear strength retention (ISSR), the oxidation stage had a more substantial effect compared to the pyrolysis stage. As the oxidation temperature increased from 500 °C to 600 °C, the ISSR value initially increased and then decreased, irrespective of variations in pyrolysis parameters. Additionally, through integrating the response surface methodology (RSM) analysis and multi-island genetic algorithm (MIGA) global optimization, three recovery strategies, along with the corresponding processing parameters, were proposed to meet diverse requirements. The conclusions could provide valuable insights for optimizing the recovery and reuse of carbon fibers from waste CFRPs.

Coupling Relationships between the Brain and the Central Pattern Generator Based on a Fractional-Order Extended Hindmarsh-Rose Model.

BACKGROUND: The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system. METHODS: In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli. RESULTS AND CONCLUSIONS: These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.

Assessing the efficacy of aluminum metal clusters Al13 and Al15 in mitigating NO2 and SO2 pollutants: a DFT investigation.

The present investigation delves into the adverse environmental impact of atmospheric pollutant gases, specifically nitrogen dioxide (NO2) and sulfur dioxide (SO2), which necessitates the identification and implementation of effective control measures. The central objective of this study is to explore the eradication of these pollutants through the utilization of aluminum Al13 and Al15 metal clusters, distinguished by their unique properties. The comprehensive evaluation of gas/cluster interactions is undertaken employing density functional theory (DFT). Geometric optimization calculations for all structures are executed using the ωB97XD functional and the Def2-svp basis set. To probe various interaction modalities, gas molecule distribution around the metal clusters is sampled using the bee colony algorithm. Frequency calculations employing identical model chemistry validate the precision of the optimization calculations. The quantum theory of atoms in molecules (QTAIM) and natural bond orbital (NBO) methodologies are applied for the analysis of intermolecular interactions. This research establishes the robust formation of van der Waals attractions between the investigated gas molecules, affirming aluminum metal clusters as viable candidates for the removal and control of these gases.

Synthesis, crystal structure and Hirshfeld surface analysis of N-(6-acetyl-1-nitro-naphthalen-2-yl)acetamide.

The title compound, C14H12N2O4, was obtained from 2-acetyl-6-amino-naphthalene through two-step reactions of acetyl-ation and nitration. The mol-ecule comprises the naphthalene ring system consisting of functional systems bearing a acetyl group (C-2), a nitro group (C-5), and an acetyl-amino group (C-6). In the crystal, the mol-ecules are assembled into two-dimensional sheet-like structures by inter-molecular N-H⋯O and C-H⋯O hydrogen-bonding inter-actions. Hirshfeld surface analysis illustrates that the most important contributions to the crystal packing are from O⋯H/H⋯O (43.7%), H⋯H (31.0%), and C⋯H/H⋯C (8.5%) contacts.

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server.

Background: Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa. Methods: We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers. Results: Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups. Conclusion: Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Nanoparticles in tumor microenvironment remodeling and cancer immunotherapy.

Cancer immunotherapy and vaccine development have significantly improved the fight against cancers. Despite these advancements, challenges remain, particularly in the clinical delivery of immunomodulatory compounds. The tumor microenvironment (TME), comprising macrophages, fibroblasts, and immune cells, plays a crucial role in immune response modulation. Nanoparticles, engineered to reshape the TME, have shown promising results in enhancing immunotherapy by facilitating targeted delivery and immune modulation. These nanoparticles can suppress fibroblast activation, promote M1 macrophage polarization, aid dendritic cell maturation, and encourage T cell infiltration. Biomimetic nanoparticles further enhance immunotherapy by increasing the internalization of immunomodulatory agents in immune cells such as dendritic cells. Moreover, exosomes, whether naturally secreted by cells in the body or bioengineered, have been explored to regulate the TME and immune-related cells to affect cancer immunotherapy. Stimuli-responsive nanocarriers, activated by pH, redox, and light conditions, exhibit the potential to accelerate immunotherapy. The co-application of nanoparticles with immune checkpoint inhibitors is an emerging strategy to boost anti-tumor immunity. With their ability to induce long-term immunity, nanoarchitectures are promising structures in vaccine development. This review underscores the critical role of nanoparticles in overcoming current challenges and driving the advancement of cancer immunotherapy and TME modification.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

Comprehensive analysis of exosome gene LYPD3 and prognosis/immune cell infiltration in lung cancer.

Background: Lung cancer (LC) is a leading cause of cancer-associated mortality worldwide, with high incidence and mortality rates. Ly6/PLAUR domain containing 3 (LYPD3) is a tumorigenic and highly glycosylated cell surface protein that has been rarely reported in LC. This study aimed to explore the prognostic role and immune cell infiltration of LYPD3 in LC. Methods: We used ExoCarta, a database of exosomal proteins and RNA, to select exosomes in LC. The Tumor Immune Estimation Resource (TIMER) and Human Protein Atlas (HPA) databases were utilized to compare the expression of LYPD3 in LC. We applied Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and Kaplan-Meier (KM) plotter to evaluate the prognostic prediction performance of LYPD3. Biological processes (BPs), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and gene set enrichment analysis (GSEA) analyses were performed to illustrate the possible role of LYPD3 in LC. The correlations between LYPD3 and immune cell infiltration were explored using Tumor and Immune System Interaction Database (TISIDB), GEPIA2, and TIMER. R software was used for statistical analysis and mapping. Results: A total of 904 exosome molecules were screened in LC. Further analysis showed that the up-regulation of LYPD3 in these 904 exosome molecules was associated with poor prognosis in LC. Pan-cancer analyses revealed that the expression of LYPD3 varied in many cancers, particularly in LC. Clinical correlation analysis indicated that LYPD3 was associated with stage and T classification in LC. We observed that LYPD3 co-expression genes were associated with cell cycle, DNA replication, proteasome, and regulation of the actin cytoskeleton by GSEA. Moreover, LYPD3 was associated with immune modulators. Immunophenoscores (IPS) and IPS-CTLA4 were significantly different between the high LYPD3 group and low LYPD3 group. Additionally, the median half maximal inhibitory concentration (IC50) of bexarotene, cyclopamine, etoposide, and paclitaxel in LYPD3 high group was significantly lower than that in LYPD3 low group. Conclusions: LYPD3 is involved in many BPs of LC, such as regulating immune cell infiltration and affecting prognosis. Therefore, LYPD3 may have potential value as a biomarker for prognosis and immunotherapy in LC.

Single-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma.

BACKGROUND: The efficacy of neoadjuvant chemo-immunotherapy (NAT) in esophageal squamous cell carcinoma (ESCC) is challenged by the intricate interplay within the tumor microenvironment (TME). Unveiling the immune landscape of ESCC in the context of NAT could shed light on heterogeneity and optimize therapeutic strategies for patients. METHODS: We analyzed single cells from 22 baseline and 24 post-NAT treatment samples of stage II/III ESCC patients to explore the association between the immune landscape and pathological response to neoadjuvant anti-PD-1 combination therapy, including pathological complete response (pCR), major pathological response (MPR), and incomplete pathological response (IPR). RESULTS: Single-cell profiling identified 14 major cell subsets of cancer, immune, and stromal cells. Trajectory analysis unveiled an interesting link between cancer cell differentiation and pathological response to NAT. ESCC tumors enriched with less differentiated cancer cells exhibited a potentially favorable pathological response to NAT, while tumors enriched with clusters of more differentiated cancer cells may resist treatment. Deconvolution of transcriptomes in pre-treatment tumors identified gene signatures in response to NAT contributed by specific immune cell populations. Upregulated genes associated with better pathological responses in CD8 + effector T cells primarily involved interferon-gamma (IFNγ) signaling, neutrophil degranulation, and negative regulation of the T cell apoptotic process, whereas downregulated genes were dominated by those in the immune response-activating cell surface receptor signaling pathway. Natural killer cells in pre-treatment tumors from pCR patients showed a similar upregulation of gene expression in response to IFNγ but a downregulation of genes in the neutrophil-mediated immunity pathways. A decreased cellular contexture of regulatory T cells in ESCC TME indicated a potentially favorable pathological response to NAT. Cell-cell communication analysis revealed extensive interactions between CCL5 and its receptor CCR5 in various immune cells of baseline pCR tumors. Immune checkpoint interaction pairs, including CTLA4-CD86, TIGIT-PVR, LGALS9-HAVCR2, and TNFSF4-TNFRSF4, might serve as additional therapeutic targets for ICI therapy in ESCC. CONCLUSIONS: This pioneering study unveiled an intriguing association between cancer cell differentiation and pathological response in esophageal cancer patients, revealing distinct subgroups of tumors for which neoadjuvant chemo-immunotherapy might be effective. We also delineated the immune landscape of ESCC tumors in the context of clinical response to NAT, which provides clinical insights for better understanding how patients respond to the treatment and further identifying novel therapeutic targets for ESCC patients in the future.

Effects of Water and Nitrogen Control on the Growth Physiology, Yields, and Economic Benefits of Lycium barbarum Plants in a Lycium barbarum + Alfalfa System.

In the production of economic forests, there are common issues such as excessive application of water and fertilizer, redundant plant growth, and low economic benefits. Reasonable water and fertilizer management can not only help address these problems but also improve the absorption and use efficiency of water and fertilizer resources by plants, promoting the green and efficient development of the fruit and forestry industry. In order to explore a suitable water and nitrogen management mode for Lycium barbarum, field experiments were conducted in this study from 2021 to 2022. Specifically, four irrigation modes (according to the proportion ratio of soil moisture content to field moisture capacity θf, 45-55% θf (W1, severe water deficiency), 55-65% θf (W2, moderate water deficiency), 65-75% θf (W3, mild water deficiency), and 75-85% θf (W4, sufficient irrigation)) and four nitrogen application levels (0 kg·ha-1 (N0, no nitrogen application), 150 kg·ha-1 (N1, low nitrogen application level), 300 kg·ha-1 (N2, medium nitrogen application level), and 450 kg·ha-1 (N3, high nitrogen application level)) were set up to analyze the influences of water and nitrogen control on the plant height, stem diameter, chlorophyll content, photosynthetic characteristics and yield, and economic benefits of Lycium barbarum in the Lycium barbarum + Alfalfa system. The study results show that the plant height and stem diameter increment of Lycium barbarum increase with the irrigation amount, increasing first and then decreasing with the increase in the nitrogen application level. Meanwhile, the chlorophyll contents in Lycium barbarum continuously increase throughout their growth periods, with Lycium barbarum treated with W4N2 during all growth periods presenting the highest contents of chlorophyll. In a Lycium barbarum + Alfalfa system, the daily variation curve of the Lycium barbarum net photosynthetic rate presents a unimodal pattern, with maximum values of the daily average net photosynthetic rate and daily carboxylation rate appearing among W4N2-treated plants (19.56 µmol·m-2·s-1 and 157.06 mmol·m-2·s-1). Meanwhile, the transpiration rates of Lycium barbarum plants continuously decrease with the increased degree of water deficiency and decreased nitrogen application level. W1N2-treated plants exhibit the highest leaf daily average water use efficiency (3.31 µmol·s-1), presenting an increase of 0.50-10.47% in efficiency compared with plants under other treatments. The coupling of water and nitrogen has significantly improved the yields and economic benefits of Lycium barbarum plants, with W4N2-treated and W3N2-treated plants presenting the highest dried fruit yield (2623.07 kg·ha-1) and net income (50,700 CNY·ha-1), respectively. Furthermore, compared with other treatment methods, these two treatment methods (W4N2 and W3N2) exhibit increases of 4.04-84.08% and 3.89-123.35% in dried fruit yield and net income indexes, respectively. Regression analysis shows that, in a Lycium barbarum + Alfalfa system, both high yields and economic benefits of Lycium barbarum plants can be achieved using an irrigation amount of 4367.33-4415.07 m3·ha-1 and a nitrogen application level of 339.80-367.35 kg·ha-1. This study can provide a reference for improving the productivity of Lycium barbarum plants and achieving a rational supply of water and nitrogen in Lyciun barbarum + Alfalfa systems in the Yellow River Irrigation Area of Gansu, China, and other similar ecological areas.

Mechanical biomimetic silk nano fiber-magnesium ion complex/hydroxyethylcellulose/glycerol hydrogel dressing with angiogenic capacity for accelerating scarless diabetic wound healing.

Quick scarless healing remains a key issue for diabetic wounds. Here, a stretchable elastomeric hydrogel dressing composed of hydroxyethylcellulose (HEC), silk nano fiber-magnesium ion complex (Mg2+-SNF) and glycerol (Gly) was developed to optimize mechanical niche, anti-inflammatory and angiogenic behavior simultaneously. The composite hydrogel dressing exhibited skin-like elasticity (175.1 ± 23.9 %) and modulus (156.7 ± 2.5 KPa) while Mg2+-SNF complex endowed the dressing with angiogenesis, both favoring quick scarless skin regeneration. In vitro cell studies revealed that the hydrogel dressing stimulated fibroblast proliferation, endothelial cell migration and vessel-like tube formation, and also induced anti-inflammatory behavior of macrophages. In vivo results revealed accelerated healing of diabetic wounds. The improved granulation ingrowth and collagen deposition suggested high quality repair. Both thinner epidermal layer and low collagen I/III ratio of the regenerated skin confirmed scarless tissue formation. This bioactive hydrogel dressing has promising potential to address the multifaceted challenges of diabetic wound management.

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) and 166 094 non-IED 4-second video-EEG segments. The video data is processed by the proposed patient detection method, with frame difference and Simple Keypoints (SKPS) capturing patients' movements. EEG data is processed with EfficientNetV2. The video and EEG features are fused via a multilayer perceptron. We developed a comparative model, termed nEpiNet, to test the effectiveness of the video feature in vEpiNet. The 10-fold cross-validation was used for testing. The 10-fold cross-validation showed high areas under the receiver operating characteristic curve (AUROC) in both models, with a slightly superior AUROC (0.9902) in vEpiNet compared to nEpiNet (0.9878). Moreover, to test the model performance in real-world scenarios, we set a prospective test dataset, containing 215 h of raw video-EEG data from 50 patients. The result shows that the vEpiNet achieves an area under the precision-recall curve (AUPRC) of 0.8623, surpassing nEpiNet's 0.8316. Incorporating video data raises precision from 70% (95% CI, 69.8%-70.2%) to 76.6% (95% CI, 74.9%-78.2%) at 80% sensitivity and reduces false positives by nearly a third, with vEpiNet processing one-hour video-EEG data in 5.7 min on average. Our findings indicate that video data can significantly improve the performance and precision of IED detection, especially in prospective real clinic testing. It suggests that vEpiNet is a clinically viable and effective tool for IED analysis in real-world applications.

First-Trimester Triglyceride-Glucose Index and Triglyceride/High-Density Lipoprotein Cholesterol are Predictors of Gestational Diabetes Mellitus Among the Four Surrogate Biomarkers of Insulin Resistance.

Purpose: This study seeks to assess the potential of early pregnancy Triglyceride Glucose Index (TyG), triglyceride to High-Density Lipoprotein Cholesterol ratio (TG/HDL-c), Low-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol ratio (LDL-C/HDL-C), and Total Cholesterol to High-Density Lipoprotein Cholesterol ratio (TC/HDL-C) in predicting Gestational Diabetes Mellitus (GDM). Patients and Methods: A total of 1073 adults singleton pregnant women were enrolled from June 2017 to September 2019. Complete anthropometric data and lipid profiles were measured in the first trimester (before 12 weeks gestation) and a 75g oral glucose tolerance test (OGTT) at 24-28 weeks was performed. Based on OGTT results, participants were categorised into Normal Glucose Tolerance (NGT) group (n=872) and GDM group (n=201). General data, laboratory test results, and surrogate insulin resistance indicators such as TyG index, TG/HDL-C, LDL-C/HDL-C, and TC/HDL-C were documented and compared. To compare differences between the two groups, t-test was used, Spearman correlation analysis and linear regression analysis were performed to establish associations between these indicators and insulin resistance in GDM. Receiver Operating Characteristic (ROC) curves were generated to compare the thresholds of these indicators for predicting GDM during pregnancy and to quantify overall diagnostic accuracy. Results: Individuals with GDM had higher TyG, TG/HDL-C, and LDL-C/HDL-C levels (P < 0.001), but with no significant difference observed in TC/HDL-C. All four ratios were positively correlated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), yet only TyG emerged as an independent risk factor for HOMA-IR. The Area under the Curve (AUC) of TyG index (0.692) was comparable to that of HOMA-IR (0.703). The cut-off points for TyG index, TG/HDL-C, and HOMA-IR in predicting GDM were 7.088, 0.831, and 1.8, respectively. HOMA-IR exhibited the highest sensitivity (79.1%), while TyG index (64.3%) and TG/HDL-C ratio (64.3%) demonstrated better specificity compared to HOMA-IR (56.3%). LDL-C/HDL-C and TC/HDL-C offered no discernible predictive advantage. Conclusion: Early pregnancy TyG index and TG/HDL-C can aid in identifying pregnant women at risk for GDM, potentially facilitating early and effective intervention to improve prognosis. TyG index exhibited superior predictive capability compared to TG/HDL-C.

The complete chloroplast genome sequence of a cultivar of Chrysanthemum, Chrysanthemum × morifolium 'Hangbaiju' (Asteraceae).

Chrysanthemum × morifolium Ramat 1792 cultivar 'Hangbaiju', also known as 'Hangzhou White Chrysanthemum', originates from Tongxiang City, Zhejiang Province, China. It is celebrated as one of Zhejiang's 'eight flavors'. In this study, we reported the complete chloroplast genome of Chrysanthemum × morifolium cultivar 'Hangbaiju'. The genome has a circular structure of 151,110 bp containing a large single-copy region (LSC) of 82,851 bp, a small copy region (SSC) of 18,351 bp, and two inverted repeats (IR) of 24,936 and 24,972 bp in length. It comprises 128 genes: 85 protein-coding gene, 8 ribosomal RNA (rRNA) genes, and 35 transfer RNA (tRNA) genes. Phylogenetic analysis, based on complete chloroplast genomes, demonstrates that Chrysanthemum × morifolium 'Hangbaiju' shares a close genetic cluster with Chrysanthemum × morifolium 'Fubaiju' (MT1919691.1). Notably, 'Fubaiju' was introduced to Macheng, Hubei Province from Tongxiang in 1968 according to public information. The chloroplast genome data, coupled with morphological and historical records, strongly suggest that they are the same variety known by different names based on their cultivation locations.

A Combined Method of Seismic Monitoring and Transient Electromagnetic Detection for the Evaluation of Hydraulic Fracturing Effect in Coal Burst Prevention.

In order to mitigate the risk of roof-dominated coal burst in underground coal mining, horizontal long borehole staged hydraulic fracturing technology has been prevailingly employed to facilitate the weakening treatment of the hard roof in advance. Such weakening effect, however, can hardly be evaluated, which leads to a lack of a basis in which to design the schemes and parameters of hydraulic fracturing. In this study, a combined underground-ground integrated microseismic monitoring and transient electromagnetic detection method was utilized to carry out simultaneous evaluations of the seismic responses to each staged fracturing and the apparent resistivity changes before and after all finished fracturing. On this basis, the comparable and applicable fracturing effects on coal burst prevention were evaluated and validated by the distribution of microseismic events and their energy magnitude during the mining process. Results show that the observed mining-induced seismic events are consistent with the evaluation results obtained from the combined seismic-electromagnetic detection method. However, there is a limited reduction effect on resistivity near the fractured section that induces far-field seismic events. Mining-induced seismic events are concentrated primarily within specific areas, while microseismic events in the fractured area exhibit high frequency but low energy overall. This study validates the rationality of combined seismic-electromagnetic detection results and provides valuable insights for optimizing fracturing construction schemes as well as comprehensively evaluating outcomes associated with underground directional long borehole staged hydraulic fracturing.