Control Compounds for Preclinical Drug-Induced Liver Injury Assessment: Consensus-driven systematic review by the ProEuroDILI Network.

BACKGROUND & AIMS: Idiosyncratic drug-induced liver injury (DILI) is a complex and unpredictable event caused by drugs, herbal or dietary supplements. Early identification of human hepatotoxicity at preclinical stages remains a major challenge, in which the selection of validated in vitro systems and test drugs has a significant impact. This systematic review analyzed the compounds used in hepatotoxicity assays and established a list of DILI positive and negative control drugs for validation of in vitro models of DILI, supported by literature and clinical evidence and endorsed by an expert committee from COST Action ProEuroDILI Network (CA17112). METHODS: Following 2020 PRISMA guidelines, original research articles focusing on DILI which used in vitro human models and performed at least one hepatotoxicity assay with positive and negative control compounds, were included. Bias of the studies was assessed by a modified 'Toxicological Data Reliability Assessment Tool'. RESULTS: 51 studies (out of 2,936) met the inclusion criteria, with 30 categorized as reliable without restrictions. Although there was a broad consensus on positive compounds, the selection of negative compounds lacked clarity. 2D monoculture, short exposure times and cytotoxicity endpoints were the most tested, although there was no consensus on the drug concentrations. CONCLUSIONS: The extensive analysis highlighted the lack of agreement on control compounds for in vitro DILI assessment. Following comprehensive in vitro and clinical data analysis together with input from the expert committee, an evidence-based consensus-driven list of 10 positive and negative drugs is proposed for validating in vitro models for improving preclinical drug safety testing regimes. IMPACT AND IMPLICATIONS: Prediction of human toxicity early in the drug development process remains a major challenge. For this, human in vitro models are becoming increasingly important, however, the development of more physiologically relevant liver models and careful selection of control DILI+ and DILI- drugs are requisites to better predict DILI liability of new drug candidates. Thus, this systematic study holds critical implications for standardizing validation of new in vitro models for studying drug-induced liver injury (DILI). By establishing a consensus-driven list of positive and negative control drugs, the study provides a scientifically justified framework for enhancing the consistency of preclinical testing, thereby addressing a significant challenge in early hepatotoxicity identification. The results are of paramount importance to all the actors involved in the drug development process, offering a standardized approach to assess hepatotoxic risks. Practically, these findings can guide researchers in evaluating safety profiles of new drugs, refining in vitro models, and informing regulatory agencies on potential improvements to regulatory guidelines, ensuring a more systematic and efficient approach to drug safety assessment.

Improving predictions: Enhancing in-hospital mortality forecast for ICU patients with sepsis-induced coagulopathy using a stacking ensemble model.

The incidence of sepsis-induced coagulopathy (SIC) is high, leading to increased mortality rates and prolonged hospitalization and intensive care unit (ICU) stays. Early identification of SIC patients at risk of in-hospital mortality can improve patient prognosis. The objective of this study is to develop and validate machine learning (ML) models to dynamically predict in-hospital mortality risk in SIC patients. A ML model is established based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict in-hospital mortality in SIC patients. Utilizing univariate feature selection for feature screening. The optimal model was determined by calculating the area under the curve (AUC) with a 95% confidence interval (CI). The optimal model was interpreted using Shapley Additive Explanation (SHAP) values. Among the 3112 SIC patients included in MIMIC-IV, a total of 757 (25%) patients experienced mortality during their ICU stay. Univariate feature selection helps us to pick out the 20 most critical variables from the original feature. Among the 10 developed machine learning models, the stacking ensemble model exhibited the highest AUC (0.795, 95% CI: 0.763-0.827). Anion gap and age emerged as the most significant features for predicting the mortality risk in SIC. In this study, an ML model was constructed that exhibited excellent performance in predicting in-hospital mortality risk in SIC patients. Specifically, the stacking ensemble model demonstrated superior predictive ability.

Probe-Type Multi-Core Fiber Optic Sensor for Simultaneous Measurement of Seawater Salinity, Pressure, and Temperature.

In this article, we propose and demonstrate a probe-type multi-core fiber (MCF) sensor for the multi-parameter measurement of seawater. The sensor comprises an MCF and two capillary optical fibers (COFs) with distinct inner diameters, in which a 45° symmetric core reflection (SCR) structure and a step-like inner diameter capillary (SIDC) structure filled with polydimethylsiloxane (PDMS) are fabricated at the fiber end. The sensor is equipped with three channels for different measurements. The surface plasmon resonance (SPR) channel (CHSPR) based on the side-polished MCF is utilized for salinity measurement. The fiber end air cavity, forming the Fabry-Pérot interference (FPI) channel (CHFPI), is utilized for pressure and temperature measurement. Additionally, the fiber Bragg grating (FBG) channel (CHFBG), which is inscribed in the central core, serves as temperature compensation for the measurement results. By combining three sensing principles with space division multiplexing (SDM) technology, the sensor overcomes the common challenges faced by multi-parameter sensors, such as channel crosstalk and signal demodulation difficulties. The experimental results indicate that the sensor has sensitivities of 0.36 nm/‱, -10.62 nm/MPa, and -0.19 nm/°C for salinity, pressure, and temperature, respectively. As a highly integrated and easily demodulated probe-type optical fiber sensor, it can serve as a valuable reference for the development of multi-parameter fiber optic sensors.

Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy.

OBJECTIVE: We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE). METHODS: In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14.74% (MIMIC IV: 1112, eICU: 594). Using the least absolute shrinkage and selection operator (LASSO), we built nine ML models and a stacking ensemble model and determined the optimal model based on the area under the receiver operating characteristic curve (AUC). We used the Shapley additive explanations (SHAP) algorithm to determine the optimal model. RESULTS: The study included 9943 patients. LASSO identified 15 variables. The stacking ensemble model achieved the highest AUC on the test set (0.807) and 0.671 on external validation. SHAP analysis highlighted Glasgow Coma Scale (GCS) and age as key variables. The model (https://sic1.shinyapps.io/SSAAEE/) can predict in-hospital mortality risk for patients with SAE. CONCLUSIONS: We developed a stacked ensemble model with enhanced generalization capabilities using novel data to predict mortality risk in patients with SAE.

PTI-ETI synergistic signal mechanisms in plant immunity.

Plants face a relentless onslaught from a diverse array of pathogens in their natural environment, to which they have evolved a myriad of strategies that unfold across various temporal scales. Cell surface pattern recognition receptors (PRRs) detect conserved elicitors from pathogens or endogenous molecules released during pathogen invasion, initiating the first line of defence in plants, known as pattern-triggered immunity (PTI), which imparts a baseline level of disease resistance. Inside host cells, pathogen effectors are sensed by the nucleotide-binding/leucine-rich repeat (NLR) receptors, which then activate the second line of defence: effector-triggered immunity (ETI), offering a more potent and enduring defence mechanism. Moreover, PTI and ETI collaborate synergistically to bolster disease resistance and collectively trigger a cascade of downstream defence responses. This article provides a comprehensive review of plant defence responses, offering an overview of the stepwise activation of plant immunity and the interactions between PTI-ETI synergistic signal transduction.

A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation.

Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named [Formula: see text]-net. We provide a proof of convergence for the [Formula: see text]-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.

In Situ Generation of H2O2 over MoOx Decorated on Cu2O@CuO Core-Shell Particle Nanoarchitectonics for Boosting Photocatalytic Oxidative Desulfurization.

Photocatalytic oxidation desulfurization (PODS) has emerged as a promising, ecofriendly alternative to traditional, energy-intensive fuel desulfurization methods. Nevertheless, its progress is still hindered due to the slow sulfide oxidation kinetics in the current catalytic systems. Herein, we present a MoOx decorated on a Cu2O@CuO core-shell catalyst, which enables a new, efficient PODS pathway by in situ generation of hydrogen peroxide (H2O2) with saturated moist air as the oxidant source. The photocatalyst delivers remarkable specific activity in oxidizing dibenzothiophene (DBT), achieving a superior rate of 7.8 mmol g-1 h-1, while maintaining a consistent performance across consecutive reuses. Experimental investigations reveal that H2O2 is produced through the two-electron oxygen reduction reaction (ORR), and both H2O2 and the hydroxyl radicals (•OH) generated from it act as the primary reactive species responsible for sulfide oxidation. Importantly, our catalyst accomplishes complete PODS of real diesel fuel, underscoring an appealing industrial prospect for our photocatalyst.

Research on Genotype Markers for Plant Height and Assisted Breeding of Key Sorghum Resources in China.

Dwarfing and the selection of optimal plant types constitute the primary focus of sorghum breeding. However, the lack of clarity regarding the gene types associated with plant height genes Dw1-Dw4 in the primary breeding materials has led to increased plant heights in improved offspring of the same plant height type, resulting in unsatisfactory morphological traits. This study aimed to elucidate the gene types related to plant height in breeding materials, validate the regulatory mechanisms, and establish a material improvement system. The goal was to achieve molecular-marker-assisted dwarf breeding through the detection of plant height genes and the test cross verification of main Chinese sorghum materials. Using 38 main male sterile lines and 57 main restorer lines of grain sorghum as materials, three plant height genes were detected and classified. Ninety-five F1 generation hybrids of these materials, along with typical materials, were measured at the wax maturity stage. Test cross results demonstrated that the variation in dw1-dw3 genes in the breeding materials significantly influenced the plant height of hybrid offspring. The main male sterile lines in Chinese sorghum predominantly exhibited the "three-dwarf" type of Kafir and its improved lines, characterized by the genotype (Dw1-Dw2-dw3-dw4). On the other hand, restorer lines mainly showcased the improved "two-dwarf" (Dw1-Dw2-dw3-dw4) genotype of the Kaoliang/Caudatum subspecies, along with the "three-dwarf" type of some Kafir and its improved lines. The test materials predominantly contained dw3 genes, with relatively fewer dw1 genes in the restorer lines. The primary restorer materials lacked the dw2 gene, and dw2 significantly influenced plant type. The increased plant height in improved offspring of the same plant height type material was attributed to differences in gene types. Therefore, the enhancement of plant height in breeding materials should prioritize the use of different methods in conjunction with Dw1 and Dw2 classification.

Prior drug allergies are associated with worse outcome in patients with idiosyncratic drug-induced liver injury: A machine learning approach for risk stratification.

The impact of prior drug allergies (PDA) on the clinical features and outcomes of patients who develop idiosyncratic drug-induced liver injury (DILI) is largely unknown. We aimed to assess the clinical presentation and outcomes of DILI patients based on the presence or absence of PDA and explore the association between culprit drugs responsible for DILI and allergy. We analysed a well-vetted cohort of DILI cases enrolled from the Spanish DILI Registry. Bootstrap-enhanced least absolute shrinkage operator procedure was used in variable selection, and a multivariable logistic model was fitted to predict poor outcomes in DILI. Of 912 cases with a first episode of DILI, 61 (6.7%) had documented PDA. Patients with PDA were older (p = 0.009), had higher aspartate aminotransferase (AST) levels (p = 0.047), lower platelet count (p = 0.011) and higher liver-related mortality than those without a history of drug allergies (11% vs. 1.6%, p < 0.001). Penicillin was the most common drug associated with PDA in DILI patients (32%). A model including PDA, nR-based type of liver injury, female sex, AST, total bilirubin, and platelet count showed an excellent performance in predicting poor outcome in patients from the Spanish DILI Registry (area under the ROC curve [AUC] 0.887; 95% confidence interval [CI] 0.794 - 0.981) and the LATINDILI Network (AUC 0.932; 95% CI 0.884 - 0.981). Patients with suspected DILI should be screened for PDA as they would require a close monitoring for early detection of worsening clinical course.

Flow diverters versus stent-assisted coiling in unruptured intracranial vertebral artery dissecting aneurysms.

OBJECTIVE: Flow diverters (FDs) have been used in unruptured intracranial vertebral artery dissecting aneurysms (IVADAs) with seemingly more favorable outcomes compared with stent-assisted coiling (SAC). However, the benefits of FDs over SAC in unruptured IVADAs need further evaluation. METHODS: This was a propensity score-matched, retrospective cohort study. Consecutive patients with unruptured IVADAs treated with FDs or SAC at the authors' hospital between January 2016 and December 2020 were reviewed. Propensity score matching at 1:1 was based on age, significant stenosis adjacent to aneurysmal dilatation, maximum diameter, and posterior inferior cerebellar artery involvement. Periprocedural cerebrovascular complications and angiographic and clinical outcomes were compared between the two matched groups. RESULTS: A total of 124 unruptured IVADAs in 123 patients (median age 53 [interquartile range 47-59] years; 101 men) were included. The FD and SAC groups included 65 and 59 IVADAs, respectively. Propensity score matching resulted in 47 matched pairs. The rates of immediate complete occlusion were significantly lower in the matched FD group than in the matched SAC group (6.4% vs 68.1%, p < 0.001). The rates of periprocedural cerebrovascular complications were comparable between the two matched groups (6.4% vs 6.4%, p > 0.99). At last follow-up, the rates of complete occlusion (89.4% vs 80.9%, p = 0.39) and favorable clinical outcomes (100.0% vs 97.9%, p > 0.99) were comparable, whereas the rate of recanalization was significantly lower in the matched FD group than in the matched SAC group (0.0% vs 12.8%, p = 0.03). Although the difference between the rates of in-stent stenosis was not statistically significant (17.0% vs 6.4%, p = 0.18), the difference in the effect measures was considerable. CONCLUSIONS: In unruptured IVADAs and compared with SAC, FDs provide comparable rates of periprocedural cerebrovascular complications, favorable clinical outcomes, and follow-up complete occlusion, lower rates of immediate complete occlusion and follow-up recanalization, and likely higher rates of in-stent stenosis.

METTL3-Mediated STING Upregulation and Activation in Kupffer Cells Contribute to Radiation-Induced Liver Disease via Pyroptosis.

PURPOSE: Radiation therapy is a vital adjuvant treatment for liver cancer, although the challenge of radiation-induced liver diseases (RILDs) limits its implementation. Kupffer cells (KCs) are a crucial cell population of the hepatic immune system, and their biologic function can be modulated by multiple epigenetic RNA modifications, including N6-methyladenosine (m6A) methylation. However, the mechanism for m6A methylation in KC-induced inflammatory responses in RILD remains unclear. The present study investigated the function of m6A modification in KCs contributing to RILD. METHODS AND MATERIALS: Methylated RNA-immunoprecipitation sequencing and RNA transcriptome sequencing were used to explore the m6A methylation profile of primary KCs isolated from mice after irradiation with 3 × 8 Gy. Western blotting and quantitative real-time PCR were used to evaluate gene expression. DNA pulldown and chromatin immunoprecipitation assays were performed to verify target gene binding and identify binding sites. RESULTS: Methylated RNA-immunoprecipitation sequencing revealed significantly increased m6A modification levels in human KCs after irradiation, suggesting the potential role of upregulated m6A in RILD. In addition, the study results corroborated that methyltransferase-like 3 (METTL3) acts as a main modulator to promote the methylation and gene expression of TEAD1, leading to STING-NLRP3 signaling activation. Importantly, it was shown that IGF2BP2 functions as an m6A "reader" to recognize methylated TEAD1 mRNA and promote its stability. METTL3/TEAD1 knockdown abolished the activation of STING-NLRP3 signaling, protected against RILD, and suppressed inflammatory cytokines and hepatocyte apoptosis. Moreover, clinical human normal liver tissue samples collected after irradiation showed increased expression of STING and interleukin-1ß in KCs compared with nonirradiated samples. Notably, STING pharmacologic inhibition alleviated irradiation-induced liver injury in mice, indicating its potential therapeutic role in RILD. CONCLUSIONS: The results of our study reveal that TEAD1-STING-NLRP3 signaling activation contributes to RILD via METTL3-dependent m6A modification.

Characterization of drug-induced liver injury associated with drug reaction with eosinophilia and systemic symptoms in two prospective DILI registries.

Idiosyncratic drug-induced liver injury (DILI) associated with drug reactions with eosinophilia and systemic symptoms (DRESS) is poorly characterized among patients of Western countries. We aimed to comprehensively assess the clinical characteristics, outcomes, and causative agents in a prospective, well-vetted cohort of DILI patients with DRESS (DILI-DRESS). We identified 53 DILI-DRESS cases from the Spanish DILI Registry and the Latin American DILI Network. For comparison purposes, we defined a group of DILI patients (n = 881). DILI-DRESS cases were younger (47 vs. 53 years, respectively; p = 0.042) and presented more frequently with cholestatic/mixed damage (p = 0.018). Most DILI-DRESS patients showed moderate liver injury, 13% developed severe damage, and only one patient (with hepatocellular injury due to anti-tuberculosis drugs) progressed to acute liver failure and died. DILI-DRESS cases showed a distinctive causative drug pattern compared to DILI cases. The most frequent drugs were carbamazepine (13%), anti-tuberculosis drugs (13%), amoxicillin-clavulanate (11%), and allopurinol and lamotrigine (7.6% each). Among all cases of DILI due to allopurinol and lamotrigine, 67% presented with a DILI-DRESS phenotype, respectively. Higher total bilirubin (TBL) levels at DILI recognition (odds ratio [OR] 1.23; 95% confidence interval [CI] 1.04-1.45) and absence of eosinophilia (OR 8.77; 95% CI 1.11-69.20) increased the risk for developing a severe-fatal injury in DILI-DRESS patients. DILI-DRESS patients have a more frequent cholestasis/mixed pattern of injury at presentation, with antiepileptics as distinctive causative drug class. Most of the lamotrigine and allopurinol cases present with this phenotype. Higher TBL levels and absence of eosinophilia at DILI recognition are markers of poor outcomes.

Identification of WUSCHEL-related homeobox gene and truncated small peptides in transformation efficiency improvement in Eucalyptus.

BACKGROUND: The WUSCHEL-related Homeobox (WOX) genes, which encode plant-specific homeobox (HB) transcription factors, play crucial roles in regulating plant growth and development. However, the functions of WOX genes are little known in Eucalyptus, one of the fastest-growing tree resources with considerable widespread cultivation worldwide. RESULTS: A total of nine WOX genes named EgWOX1-EgWOX9 were retrieved and designated from Eucalyptus grandis. From the three divided clades marked as Modern/WUS, Intermediate and Ancient, the largest group Modern/WUS (6 EgWOXs) contains a specific domain with 8 amino acids: TLQLFPLR. The collinearity, cis-regulatory elements, protein-protein interaction network and gene expression analysis reveal that the WUS proteins in E. grandis involve in regulating meristems development and regeneration. Furthermore, by externally adding of truncated peptides isolated from WUS specific domain, the transformation efficiency in E. urophylla × E. grandis DH32-29 was significant enhanced. The transcriptomics data further reveals that the use of small peptides activates metabolism pathways such as starch and sucrose metabolism, phenylpropanoid biosynthesis and flavonoid biosynthesis. CONCLUSIONS: Peptides isolated from WUS protein can be utilized to enhance the transformation efficiency in Eucalyptus, thereby contributing to the high-efficiency breeding of Eucalyptus.

Regulatory controls of duplicated gene expression during fiber development in allotetraploid cotton.

Polyploidy complicates transcriptional regulation and increases phenotypic diversity in organisms. The dynamics of genetic regulation of gene expression between coresident subgenomes in polyploids remains to be understood. Here we document the genetic regulation of fiber development in allotetraploid cotton Gossypium hirsutum by sequencing 376 genomes and 2,215 time-series transcriptomes. We characterize 1,258 genes comprising 36 genetic modules that control staged fiber development and uncover genetic components governing their partitioned expression relative to subgenomic duplicated genes (homoeologs). Only about 30% of fiber quality-related homoeologs show phenotypically favorable allele aggregation in cultivars, highlighting the potential for subgenome additivity in fiber improvement. We envision a genome-enabled breeding strategy, with particular attention to 48 favorable alleles related to fiber phenotypes that have been subjected to purifying selection during domestication. Our work delineates the dynamics of gene regulation during fiber development and highlights the potential of subgenomic coordination underpinning phenotypes in polyploid plants.

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

OBJECTIVES: To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework. METHODS: A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework. RESULTS: Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts. CONCLUSION: The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods. CLINICAL RELEVANCE STATEMENT: Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification. KEY POINTS: •There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms. •The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images. •The deep learning framework is robust and has the potential to be applied in a clinical setting.

Synthesis of isobutanol using acetate as sole carbon source in Escherichia coli.

BACKGROUND: With concerns about depletion of fossil fuel and environmental pollution, synthesis of biofuels such as isobutanol from low-cost substrate by microbial cell factories has attracted more and more attention. As one of the most promising carbon sources instead of food resources, acetate can be utilized by versatile microbes and converted into numerous valuable chemicals. RESULTS: An isobutanol synthetic pathway using acetate as sole carbon source was constructed in E. coli. Pyruvate was designed to be generated via acetyl-CoA by pyruvate-ferredoxin oxidoreductase YdbK or anaplerotic pathway. Overexpression of transhydrogenase and NAD kinase increased the isobutanol titer of recombinant E. coli from 121.21 mg/L to 131.5 mg/L under batch cultivation. Further optimization of acetate supplement concentration achieved 157.05 mg/L isobutanol accumulation in WY002, representing the highest isobutanol titer by using acetate as sole carbon source. CONCLUSIONS: The utilization of acetate as carbon source for microbial production of valuable chemicals such as isobutanol could reduce the consumption of food-based substrates and save production cost. Engineering strategies applied in this study will provide a useful reference for microbial production of pyruvate derived chemical compounds from acetate.

Case report: Unveiling the unforeseen: a catastrophic encounter of giant aortic aneurysm rupture during re-sternotomy in a patient with bicuspid aortic valve and previous surgical aortic valve replacement.

Due to structural abnormalities in the leaflets, patients with bicuspid aortic valve (BAV) may develop isolated aortic valve disease, such as aortic regurgitation, aortic stenosis, or a combination of both. In addition to valvular pathology, numerous studies have indicated that approximately 40% of BAV patients exhibit aortic pathologies characterized by aortic dilatation. According to guidelines for valvular diseases, patients with BAV who require surgical aortic valve replacement (SAVR) and have a diameter of the aortic sinuses or ascending aorta ≥4.5 cm are recommended to undergo concomitant replacement of the aortic sinuses or ascending aorta. However, we encountered a case in 2020 involving a patient with severe aortic regurgitation due to BAV and an ascending aortic diameter of 4.2 cm. This patient underwent SAVR and ascending aortoplasty surgery at our center. Remarkably, three years postoperatively, the patient's aortic diameter rapidly expanded by nearly threefold, which also suggests the risk of encountering a giant aortic root aneurysm during reoperation. Unfortunately, a fatal rupture of a giant aortic root aneurysm was encountered during re-sternotomy. Fortunately, with adequate preoperative planning, we successfully managed to avert this perilous situation. The patient recovered without complications and was discharged on the 8th day. Individualized surgical plans were formulated based on a comprehensive evaluation of the perioperative conditions.

Statins may Decrease Aneurysm wall Enhancement of Unruptured Fusiform Intracranial Aneurysms: A high-resolution 3T MRI Study.

Inflammation plays an integral role in the formation, growth, and progression to rupture of unruptured intracranial aneurysms. Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) has emerged as a surrogate biomarker of vessel wall inflammation and unruptured intracranial aneurysm instability. We investigated the correlation between anti-inflammatory drug use and three-dimensional AWE of fusiform intracranial aneurysms (FIAs). We retrospectively analyzed consecutive patients with FIAs in our database who underwent 3T HR-MRI at three Chinese centers. FIAs were classified as fusiform-type, dolichoectatic-type, or transitional-type. AWE was objectively defined using the aneurysm-to-pituitary stalk contrast ratio in three-dimensional space by determining the contrast ratio of the average signal intensity in the aneurysmal wall and pituitary stalk on post-contrast T1-weighted images. Data on aneurysm size, morphology, and location, as well as patient demographics and comorbidities, were collected. Univariate and multivariate logistic regression analyses were performed to determine factors independently associated with AWE of FIAs on HR-MRI. In total, 127 FIAs were included. In multivariate analysis, statin use (ß = -0.236, P = 0.007) was the only independent factor significantly associated with decreased AWE. In the analysis of three FIA subtypes, the fusiform and transitional types were significantly associated with statin use (rs = -0.230, P = 0.035; and rs = -0.551, P = 0.010; respectively). It establishes an incidental correlation between the use of statins daily for ≥ 6 months and decreased AWE of FIAs. The findings also indicate that the pathophysiology may differ among the three FIA subtypes.

Photoelectric characteristics of hydrogen-terminated polycrystalline diamond MESFETs.

In the field of diamond MESFETs, this work is what we believe to be the first to investigate the optoelectronic properties of hydrogen-terminated polycrystalline diamond MESFETs under visible and near-UV light irradiation. It is shown that the diamond MESFETs are well suited for weak light detection in the near-ultraviolet region around the wavelength of 368 nm, with a responsivity of 6.14 × 106 A/W and an external quantum efficiency of 2.1 × 107 when the incident light power at 368.7 nm is only 0.75 µW/cm2. For incident light at 275.1 nm, the device's sensitivity and EQE increase as the incident light power increases; at an incident light power of 175.32 µW/cm2 and a VGS of -1 V, the device's sensitivity is 2.9 × 105 A/W and the EQE is 1.3 × 106. For incident light in the wavelength range of 660 nm to 404 nm with an optical power of 70 µW/cm2, the device achieves an average responsivity of 1.21 × 105 A/W. This indicates that hydrogen-terminated polycrystalline diamond MESFETs are suitable for visible and near-UV light detection, especially for weak near-UV light detection. However, the transient response test of the device shows a long relaxation time of about 0.2 s, so it is not yet suitable for high-speed UV communication or detection.