LncRNA-LncDACH1 mediated phenotypic switching of smooth muscle cells during neointimal hyperplasia in male arteriovenous fistulas.

Arteriovenous fistulas (AVFs) are the most common vascular access points for hemodialysis (HD), but they have a high incidence of postoperative dysfunction, mainly due to excessive neointimal hyperplasia (NIH). Our previous studies have revealed a highly conserved LncRNA-LncDACH1 as an important regulator of cardiomyocyte and fibroblast proliferation. Herein, we find that LncDACH1 regulates NIH in AVF in male mice with conditional knockout of smooth muscle cell-specific LncDACH1 and in male mice model of AVF with LncDACH1 overexpression by adeno-associated virus. Mechanistically, silence of LncDACH1 activates p-AKT through promoting the expression of heat shock protein 90 (HSP90) and serine/arginine-rich splicing factor protein kinase 1 (SRPK1). Moreover, LncDACH1 is transcriptionally activated by transcription factor KLF9 that binds directly to the promoter region of the LncDACH1 gene. In this work, during AVF NIH, LncDACH1 is downregulated by KLF9 and promotes NIH through the HSP90/ SRPK1/ AKT signaling axis.

Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV.

Background: Postoperative pneumonia (POP) is one of the primary complications after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative mortality, extended hospital stay, and increased medical fee. Early identification of pneumonia and more aggressive treatment can improve patient outcomes. We aimed to develop a model to predict POP in aSAH patients using machine learning (ML) methods. Methods: This internal cohort study included 706 patients with aSAH undergoing intracranial aneurysm embolization or aneurysm clipping. The cohort was randomly split into a train set (80%) and a testing set (20%). Perioperative information was collected from participants to establish 6 machine learning models for predicting POP after surgical treatment. The area under the receiver operating characteristic curve (AUC), precision-recall curve were used to assess the accuracy, discriminative power, and clinical validity of the predictions. The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Results: In this study, 15.01% of patients in the training set and 12.06% in the testing set with POP after underwent surgery. Multivariate logistic regression analysis showed that mechanical ventilation time (MVT), Glasgow Coma Scale (GCS), Smoking history, albumin level, neutrophil-to-albumin Ratio (NAR), c-reactive protein (CRP)-to-albumin ratio (CAR) were independent predictors of POP. The logistic regression (LR) model presented significantly better predictive performance (AUC: 0.91) than other models and also performed well in the external validation set (AUC: 0.89). Conclusion: A machine learning model for predicting POP in aSAH patients was successfully developed using a machine learning algorithm based on six perioperative variables, which could guide high-risk POP patients to take appropriate preventive measures.

Characterization of OsPIN2 Mutants Reveal Novel Roles for Reactive Oxygen Species in Modulating Not Only Root Gravitropism but Also Hypoxia Tolerance in Rice Seedlings.

Tolerance to submergence-induced hypoxia is an important agronomic trait especially for crops in lowland and flooding-affected areas. Although rice (Oryza sativa) is considered a flood-tolerant crop, only limited cultivars display strong tolerance to prolonged submergence and/or hypoxic stress. Therefore, characterization of hypoxic resistant genes and/or germplasms have important theoretical and practical significance for rice breeding and sustained improvements. Previous investigations have demonstrated that loss-of-function of OsPIN2, a gene encoding an auxin efflux transporter, results in the loss of root gravitropism due to disrupted auxin transport in the root tip. In this study, we revealed a novel connection between OsPIN2 and reactive oxygen species (ROS) in modulating root gravitropism and hypoxia tolerance in rice. It is shown that the OsPIN2 mutant had decreased accumulation of ROS in root tip, due to the downregulation of glycolate oxidase encoding gene OsGOX6, one of the main H2O2 sources. The morphological defects of root including waved rooting and agravitropism in OsPIN2 mutant may be rescued partly by exogenous application of H2O2. The OsPIN2 mutant exhibited increased resistance to ROS toxicity in roots due to treatment with H2O2. Furthermore, it is shown that the OsPIN2 mutant had increased tolerance to hypoxic stress accompanied by lower ROS accumulation in roots, because the hypoxia stress led to over production of ROS in the roots of the wild type but not in that of OsPIN2 mutant. Accordingly, the anoxic resistance-related gene SUB1B showed differential expression in the root of the WT and OsPIN2 mutant in response to hypoxic conditions. Notably, compared with the wild type, the OsPIN2 mutant displayed a different pattern of auxin distribution in the root under hypoxia stress. It was shown that hypoxia stress caused a significant increase in auxin distribution in the root tip of the WT but not in that of the war1 mutant. In summary, these results suggested that OsPIN2 may play a role in regulating ROS accumulation probably via mediating auxin transport and distribution in the root tip, affecting root gravitropism and hypoxic tolerance in rice seedlings. These findings may contribute to the genetic improvement and identification of potential hypoxic tolerant lines in rice.

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.

The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening.

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

A Potent SOS1 PROTAC Degrader with Synergistic Efficacy in Combination with KRASG12C Inhibitor.

AMG510, as the first approved inhibitor for KRASG12C mutation, has shown promising efficacy in nonsmall-cell lung cancer and colorectal cancer harboring KRASG12C mutation. However, the moderate response rate and the rapid emergence of acquired resistance limit the therapeutic potential of AMG510, highlighting the need for the development of combination strategies. Here, we observed the suppression of RAS-MAPK signaling induced by AMG510 was prolonged and enhanced by SOS1 knockdown. Thus, we design, synthesize, and characterize a potent and specific SOS1 degrader 23. Compound 23 showed efficient SOS1 degradation in KRAS-driven cancer cells and achieved significant antiproliferative potency. Importantly, the combination of 23 with AMG510 suppressed RAS signaling feedback activation, showing synergistic effects against KRASG12C mutant cells in vitro and in vivo. Our findings demonstrated that KRASG12C inhibition plus SOS1 degradation as a potential therapeutic strategy to improve antitumor response and overcome acquired resistance to KRASG12C inhibitor.

Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer.

PURPOSE: To established an AI system to make the pathological diagnosis of prostate cancer. METHODS: Prostate histopathological whole mount (WM) sections from patients underwent robot-assisted laparoscopic prostatectomy were prepared. All the prostate WM pathological sections were converted to digital image data and marked with different colors on the basis of the ISUP Gleason grade group. The image was then fed into a segmentation algorithm. We chose modified U-Net as our fundamental network architecture. RESULTS: 172 patients were involved in this study. 896 pieces of prostate WM pathological sections from 160 patients, in which 826 pieces of WM sections from 148 patients were assigned to the training set randomly. After image segmentation there were totally 2,138,895 patches, of which 1,646,535 patches were valid for training. The other WM section was arranged for testing. Based on the whole image testing, AI and pathologists presented the same answers among 21 of 22 pieces of sections. To evaluate the diagnostic results at the pixel level, we anticipated correct cancer or non-cancer diagnose from this AI system. The area under the ROC curve as 96.8%. The value of pixel accuracy of three methods (binary analysis, clinically oriented analysis and analysis for different ISUP Gleason grade) were 96.93%, 95.43% and 93.88%, respectively. The value of frequency weighted IoU were 94.32%, 92.13% and 90.21%, respectively. CONCLUSIONS: This AI system is able to assist pathologists to make a final diagnosis, indicating the great potential and a wide-range of applications of AI in the medical field.

The pathogenic role of succinate-SUCNR1: a critical function that induces renal fibrosis via M2 macrophage.

BACKGROUND: Renal fibrosis significantly contributes to the progressive loss of kidney function in chronic kidney disease (CKD), with alternatively activated M2 macrophages playing a crucial role in this progression. The serum succinate level is consistently elevated in individuals with diabetes and obesity, both of which are critical factors contributing to CKD. However, it remains unclear whether elevated succinate levels can mediate M2 polarization of macrophages and contribute to renal interstitial fibrosis. METHODS: Male C57/BL6 mice were administered water supplemented with 4% succinate for 12 weeks to assess its impact on renal interstitial fibrosis. Additionally, the significance of macrophages was confirmed in vivo by using clodronate liposomes to deplete them. Furthermore, we employed RAW 264.7 and NRK-49F cells to investigate the underlying molecular mechanisms. RESULTS: Succinate caused renal interstitial macrophage infiltration, activation of profibrotic M2 phenotype, upregulation of profibrotic factors, and interstitial fibrosis. Treatment of clodronate liposomes markedly depleted macrophages and prevented the succinate-induced increase in profibrotic factors and fibrosis. Mechanically, succinate promoted CTGF transcription via triggering SUCNR1-p-Akt/p-GSK3ß/ß-catenin signaling, which was inhibited by SUCNR1 siRNA. The knockdown of succinate receptor (SUCNR1) or pretreatment of anti-CTGF(connective tissue growth factor) antibody suppressed the stimulating effects of succinate on RAW 264.7 and NRK-49F cells. CONCLUSIONS: The causative effects of succinate on renal interstitial fibrosis were mediated by the activation of profibrotic M2 macrophages. Succinate-SUCNR1 played a role in activating p-Akt/p-GSK3ß/ß-catenin, CTGF expression, and facilitating crosstalk between macrophages and fibroblasts. Our findings suggest a promising strategy to prevent the progression of metabolic CKD by promoting the excretion of succinate in urine and/or using selective antagonists for SUCNR1.

Breaking CHIPS-Mediated immune evasion with tripterin to promote neutrophil chemotaxis against MRSA infection.

Neutrophils are the most important innate immune cells in host defense against methicillin-resistant Staphylococcus aureus (MRSA). However, MRSA orchestrates precise and timely expression of a series of virulence factors, especially the chemotaxis inhibitory protein of Staphylococcus aureus (CHIPS), to evade neutrophil-mediated host defenses. Here, we demonstrated that tripterin, a plant-derived bioactive pentacyclic triterpenoid, had a low minimum inhibitory concentration (MIC) of 1.28 µg/mL and displayed excellent anti-MRSA activity in vitro and in vivo. RNA-seq and further knockdown experiments revealed that tripterin could dramatically downregulate the expression of CHIPS by regulating the SaeRS two-component regulatory system, thereby enhancing the chemotactic response of neutrophils. Furthermore, tripterin also displayed a potential inhibitory effect on biofilm components to enhance neutrophil infiltration into the interior of the biofilm. In a mouse bacteremia model, tripterin could still maintain an excellent therapeutic effect that was significantly better than that of the traditional antibiotic vancomycin. Overall, these results suggest that tripterin possesses a superior antibacterial activity via breaking CHIPS-mediated immune evasion to promote neutrophil chemotaxis, thus providing a novel strategy for combating serious pathogenic infections.

Revolutionizing the structural design and determination of covalent-organic frameworks: principles, methods, and techniques.

Covalent organic frameworks (COFs) represent an important class of crystalline porous materials with designable structures and functions. The interconnected organic monomers, featuring pre-designed symmetries and connectivities, dictate the structures of COFs, endowing them with high thermal and chemical stability, large surface area, and tunable micropores. Furthermore, by utilizing pre-functionalization or post-synthetic functionalization strategies, COFs can acquire multifunctionalities, leading to their versatile applications in gas separation/storage, catalysis, and optoelectronic devices. Our review provides a comprehensive account of the latest advancements in the principles, methods, and techniques for structural design and determination of COFs. These cutting-edge approaches enable the rational design and precise elucidation of COF structures, addressing fundamental physicochemical challenges associated with host-guest interactions, topological transformations, network interpenetration, and defect-mediated catalysis.

Recent advances in melittin-based nanoparticles for antitumor treatment: from mechanisms to targeted delivery strategies.

As a naturally occurring cytolytic peptide, melittin (MLT) not only exhibits a potent direct tumor cell-killing effect but also possesses various immunomodulatory functions. MLT shows minimal chances for developing resistance and has been recognized as a promising broad-spectrum antitumor drug because of this unique dual mechanism of action. However, MLT still displays obvious toxic side effects during treatment, such as nonspecific cytolytic activity, hemolytic toxicity, coagulation disorders, and allergic reactions, seriously hampering its broad clinical applications. With thorough research on antitumor mechanisms and the rapid development of nanotechnology, significant effort has been devoted to shielding against toxicity and achieving tumor-directed drug delivery to improve the therapeutic efficacy of MLT. Herein, we mainly summarize the potential antitumor mechanisms of MLT and recent progress in the targeted delivery strategies for tumor therapy, such as passive targeting, active targeting and stimulus-responsive targeting. Additionally, we also highlight the prospects and challenges of realizing the full potential of MLT in the field of tumor therapy. By exploring the antitumor molecular mechanisms and delivery strategies of MLT, this comprehensive review may inspire new ideas for tumor multimechanism synergistic therapy.

Biomimetic nanoplatform with selectively positioned indocyanine green for accurate sentinel lymph node imaging.

The status of draining lymph nodes (LNs) is critical for determining the treatment and prognosis of cancer that spreads through the lymphatic system. Indocyanine green (ICG) fluorescence imaging has been widely used in sentinel LN (SLN) biopsy technology and has shown favorable effects. However, this too has its own limitations, such as fluorescence instability and diffusion imaging. In this study, we developed macrophage cell membrane-camouflaged ICG-loaded biomimetic nanoparticles (M@F127-ICG) for accurate SLN imaging. ICG selectively positioned at the hydrophobic-hydrophilic interfaces of pluronic F127 micelles protected itself from quenching in aqueous solution, thereby maintaining fluorescence stability and improving fluorescence intensity. In addition, to further improve the aggregation in SLN, the micellar surface was coated with a layer of biomimetic macrophage cell membrane to target LN-resident macrophages. In vivo fluorescence imaging demonstrated that M@F127-ICG significantly enhanced the fluorescence signal and improved the imaging efficiency of SLN. Thus, selectively positioning ICG in the biomimetic nanoplatform enhanced the fluorescence intensity and stability, providing a novel tracer for timely and accurate SLN imaging.

Learning Self-Corrective Network via Adaptive Self-Labeling and Dynamic NMS for High-Performance Long-Term Tracking.

This article presents a self-corrective network-based long-term tracker (SCLT) including a self-modulated tracking reliability evaluator (STRE) and a self-adjusting proposal postprocessor (SPPP). The targets in the long-term sequences often suffer from severe appearance variations. Existing long-term trackers often online update their models to adapt the variations, but the inaccurate tracking results introduce cumulative error into the updated model that may cause severe drift issue. To this end, a robust long-term tracker should have the self-corrective capability that can judge whether the tracking result is reliable or not, and then it is able to recapture the target when severe drift happens caused by serious challenges (e.g., full occlusion and out-of-view). To address the first issue, the STRE designs an effective tracking reliability classifier that is built on a modulation subnetwork. The classifier is trained using the samples with pseudo labels generated by an adaptive self-labeling strategy. The adaptive self-labeling can automatically label the hard negative samples that are often neglected in existing trackers according to the statistical characteristics of target state, and the network modulation mechanism can guide the backbone network to learn more discriminative features without extra training data. To address the second issue, after the STRE has been triggered, the SPPP follows it with a dynamic NMS to recapture the target in time and accurately. In addition, the STRE and the SPPP demonstrate good transportability ability, and their performance is improved when combined with multiple baselines. Compared to the commonly used greedy NMS, the proposed dynamic NMS leverages an adaptive strategy to effectively handle the different conditions of in view and out of view, thereby being able to select the most probable object box that is essential to accurately online update the basic tracker. Extensive evaluations on four large-scale and challenging benchmark datasets including VOT2021LT, OxUvALT, TLP, and LaSOT demonstrate superiority of the proposed SCLT to a variety of state-of-the-art long-term trackers in terms of all measures. Source codes and demos can be found at https://github.com/TJUT-CV/SCLT.

Effects of the pre-existing coronary heart disease on the prognosis of COVID-19 patients: A systematic review and meta-analysis.

Although studies have shown severe Coronavirus disease 2019 (COVID-19) outcomes in patients with pre-existing coronary heart disease (CHD), the prognosis of COVID-19 patients with pre-existing CHD remains uncertain primarily due to the limited number of patients in existing studies. This study aimed to investigate the impacts of pre-existing CHD on the prognosis of COVID-19 patients. Five electronic databases were searched for eligible studies. This article focused on cohort and case-control studies involving the prognosis of COVID-19 patients with pre-existing CHD. The meta-analysis was performed using a random effects model. The odds ratios (ORs) and 95% confidence intervals (CIs) were used as valid indicators. The study was registered in PROSPERO with the identifier: CRD42022352853. A total of 81 studies, involving 157,439 COVID-19 patients, were included. The results showed that COVID-19 patients with pre-existing CHD exhibited an elevated risk of mortality (OR = 2.45; 95%CI: [2.04, 2.94], P < 0.001), severe/critical COVID-19 (OR = 2.57; 95%CI: [1.98, 3.33], P < 0.001), Intensive Care Unit or Coronary Care Unit (ICU/CCU) admission: (OR = 2.75, 95%CI: [1.61, 4.72], P = 0.002), and reduced odds of discharge/recovery (OR = 0.43, 95%CI: [0.28, 0.66], P < 0.001) compared to COVID-19 patients without pre-existing CHD. Subgroup analyses indicated that the prognosis of COVID-19 patients with pre-existing CHD was influenced by publication year, follow-up duration, gender, and hypertension. In conclusion, pre-existing CHD significantly increases the risk of poor prognosis in patients with COVID-19, particularly in those male or hypertensive patients.

Development and external validation of a nomogram for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Background: Postoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP. Methods: An internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Results: Among patients in our internal cohort, 15.66% (n = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively. Conclusion: We found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population.

Kallistatin Deficiency Induces the Oxidative Stress-Related Epithelial-Mesenchymal Transition of Retinal Pigment Epithelial Cells: A Novel Protagonist in Age-Related Macular Degeneration.

Purpose: Retinal pigment epithelium (RPE) dysfunction induced by oxidative stress-related epithelial-mesenchymal transition (EMT) of RPE is the primary underlying mechanism of age-related macular degeneration (AMD). Kallistatin (KAL) is a secreted protein with an antioxidative stress effect. However, the relationship between KAL and EMT in RPE has not been determined. Therefore we aimed to explore the impact and mechanism of KAL in oxidative stress-induced EMT of RPE. Methods: Sodium iodate (SI) was injected intraperitoneally to construct the AMD rat model and investigate the changes in RPE morphology and KAL expression. KAL knockout rats and KAL transgenic mice were used to explain the effects of KAL on EMT and oxidative stress. In addition, Snail overexpressed adenovirus and si-RNA transfected ARPE19 cells to verify the involvement of Snail in mediating KAL-suppressed EMT of RPE. Results: AMD rats induced by SI expressed less KAL in the retina, and KAL knockout rats showed RPE dysfunction spontaneously where EMT and reactive oxygen species (ROS) production increased in RPE. In contrast, KAL overexpression attenuated EMT and ROS levels in RPE, even in TGF-ß treatment. Mechanistically, Snail reversed the beneficial effect of KAL on EMT and ROS reduction. Moreover, KAL ameliorated SI-induced AMD-like pathological changes. Conclusions: Our findings demonstrated that KAL inhibits oxidative stress-induced EMT by downregulating the transcription factor Snail. Herein, KAL knockout rats may be an appropriate animal model for observing spontaneous RPE dysfunction for AMD-like retinopathy, and KAL may represent a novel therapeutic target for treating dry AMD.

The toxic mechanism of tetracycline on root tips in hulless barley (Hordeum vulgare L. var. nudum).

Tetracycline (TC) is a commonly used antibiotic that affects various physiological processes in plants. However, its negative effects on plants remain poorly understood at the molecular level. To ascertain the TC toxicity in the roots, transcriptomic, cytological, and physiological analyses were performed to explore the molecular mechanisms of TC influencing the growth of hulless barley root. At a low concentration (1 mg/L), TC promoted root growth by upregulating the genes related to the flavonoid pathway. At high concentrations (10, 100, and 200 mg/L), TC downregulated genes related to homologous recombination in the root meristem zone and inhibited the mitosis index by 16.4%. Disruption of the DNA repair process can lead to chromosomal aberrations, resulting in a 6.8% C-mitosis rate in the most severe cases. Finally, root growth was inhibited by TC, as evidenced by a reduction in root viability, an increase in reactive oxygen species content, and an inhibition of root length. Cross-comparison of physiological and cytological characterizations and transcriptomic information revealed changes in genetic processes under TC stress. Overall, we present an early genetic strategy to study the significant influence of TC stress on roots.

Prognostic nomogram for predicting lower extremity deep venous thrombosis in ruptured intracranial aneurysm patients who underwent endovascular treatment.

Background: Lower extremity deep vein thrombosis (DVT) is one of the major postoperative complications in patients with ruptured intracranial aneurysms (RIA) who underwent endovascular treatment (EVT). However, patient-specific predictive models are still lacking. This study aimed to construct and validate a nomogram model for estimating the risk of lower extremity DVT for RIA patients who underwent EVT. Methods: This cohort study enrolled 471 RIA patients who received EVT in our institution between 1 January 2020 to 4 February 2022. Perioperative information on participants is collected to develop and validate a nomogram for predicting lower extremity DVT in RIA patients after EVT. Predictive accuracy, discriminatory capability, and clinical effectiveness were evaluated by concordance index (C-index), calibration curves, and decision curve analysis. Result: Multivariate logistic regression analysis showed that age, albumin, D-dimer, GCS score, middle cerebral artery aneurysm, and delayed cerebral ischemia were independent predictors for lower extremity DVT. The nomogram for assessing individual risk of lower extremity DVT indicated good predictive accuracy in the primary cohort (c-index, 0.92) and the validation cohort (c-index, 0.85), with a wide threshold probability range (4-82%) and superior net benefit. Conclusion: The present study provided a reliable and convenient nomogram model developed with six optimal predictors to assess postoperative lower extremity DVT in RIA patients, which may benefit to strengthen the awareness of lower extremity DVT control and supply appropriate resources to forecast patients at high risk of RIA-related lower extremity DVT.

Experiments on the Capillary Force of a Clam-Shell Droplet on Fibers by 3-D Model Reconstruction.

The capillary force is critical to the moving and breaking of droplets on fibers. This study brings forward a 3-D model reconstruction method for a clam-shell droplet on fibers and obtains the capillary force by the surface integral of Laplace pressure on the whole droplet. The capillary force results are verified by the droplet gravity and axial drag force, respectively. Moreover, the tensile tangential stresses are analyzed to illustrate the top limits of Laplace pressure against droplet breaking or sliding on the fiber. The experiment shows that the capillary force obtained by the 3-D model accurately describes the vertical and tangential forces of the clam-shell droplet on the fiber. Sharp shrinking of the cross-section on the droplet's upper part results in an exponential increase in tensile and tangential stresses, which makes the droplet break or move on the fiber.

Versatile potassium vanadium fluorophosphate (KVPO4F) composites as Dual-Function cathode and anode materials for Potassium-Ion hybrid capacitors.

Potassium-based energy storage has emerged as a promising alternative for advanced energy storage systems, driven by the abundance of potassium, fast ion migration, and low standard electrode potential. Hybrid capacitors, which combine the desirable characteristics of batteries and supercapacitors, offer a compelling solution for efficient energy storage. In this study, we present the development of versatile composite materials, specifically potassium vanadium fluorophosphate (KVPO4F) composites, utilizing a sol-gel method. These composites enable tunable potassium storage and charge transport kinetics within regulated voltage windows, serving as both cathode and anode materials. The anode composite, composed of KVPO4F and hierarchical porous carbon (HPC), exhibited exceptional stability over 400 cycles within a low-voltage window. On the other hand, the cathode composite, consisting of battery-like KVPO4F and physisorption activated carbon (AC), demonstrated great potential as a cathode material, striking a balance between specific energy and cycle life within a regulated high-voltage window. By integrating KVPO4F/C as the anode and KVPO4F/AC as the cathode, we successfully created potassium-ion hybrid capacitors (PIHCs) that showcased an impressive capacity retention of 83% after 10,000 cycles within a high voltage window of 0.5-4.3 V. Furthermore, to explore the application of these materials in miniaturized energy storage, we fabricated potassium-ion micro hybrid capacitors (PIMHCs) with interdigitated electrodes. These devices exhibited a high areal energy density of 18.8 µWh cm-2 at a power density of 111.6 µW cm-2, indicating their potential for compact energy storage systems. The results of this study demonstrate the versatility and efficacy of the developed KVPO4F composite materials, highlighting their potential for future advancements in potassium-based energy storage technologies.