Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba.

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism "masks out" redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout.

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Large-Kernel Attention for 3D Medical Image Segmentation.

Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.

Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method.

In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple with other dual challenges: the inherent opacity of 'black box' models and the ongoing pursuit of performance enhancement. In response to these intertwined challenges, the core concept of our Human-Computer Interaction (HCI) based learning models (RS_UNet, LC_UNet, UUNet and WD_UNet) hinge on the versatile combination of diverse query strategies and an array of deep learning models. We train four HCI models based on the initial training dataset and sequentially repeat the following steps 1-4: (1) Query Strategy: Our proposed HCI models selects those samples which contribute the most additional representative information when labeled in each iteration of the query strategy (showing the names and sequence numbers of the samples to be annotated). Additionally, in this phase, the model selects the unlabeled samples with the greatest predictive disparity by calculating the Wasserstein Distance, Least Confidence, Entropy Sampling, and Random Sampling. (2) Central line correction: The selected samples in previous stage are then used for domain expert correction of the system-generated tracheal central lines in each training round. (3) Update training dataset: When domain experts are involved in each epoch of the DL model's training iterations, they update the training dataset with greater precision after each epoch, thereby enhancing the trustworthiness of the 'black box' DL model and improving the performance of models. (4) Model training: Proposed HCI model is trained using the updated training dataset and an enhanced version of existing UNet. Experimental results validate the effectiveness of this Human-Computer Interaction-based approaches, demonstrating that our proposed WD-UNet, LC-UNet, UUNet, RS-UNet achieve comparable or even superior performance than the state-of-the-art DL models, such as WD-UNet with only 15 %-35 % of the training data, leading to substantial reductions (65 %-85 % reduction of annotation effort) in physician annotation time.

Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.

In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could support these efforts, to bring learning healthcare system (LHS) from guidelines to real-world implementation. This report chronicles the trajectory of the COVID-19 pandemic, emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance the functioning of LHS. The challenges faced by patients and healthcare systems around the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools that not only detect potential pandemic-prone diseases early on but also assist in patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts, monitor viral mutations and variant emergence, and assess vaccine and treatment efficacy in real-time. A patient-centric approach remains paramount, ensuring patients are both informed and actively involved in disease mitigation strategies.

First-principles studies on the process of electron transfer between hydrophobic liquids and water.

Using the density functional theory, we conducted a study on the electrification upon contact between hydrophobic liquid molecules and water molecules, revealing localized characteristics of contact-electrification. These "localized features" refer to the specific microscale characteristics where electron transfer predominantly occurs at the contact regions, influenced by factors such as atomic distances and molecular orientations. Although the electrostatic potential and the highest occupied molecular orbital-lowest unoccupied molecular orbital gap offer substantial predictive insights for electron transfer across polymer interfaces, they fall short in capturing the complexities associated with the interaction between hydrophobic liquids and water molecules. The electronegativity of elements at the interface and the localization of molecular orbitals play a decisive role in electron transfer. Simultaneously, for liquid molecules with irregular structures, there is no correlation between the "contact area" and the amount of electron transfer. The "contact area" refers to the surface region where two different liquid molecules come into close proximity. It is defined by the surface area of atoms with interatomic distances smaller than the van der Waals radius. This study challenges traditional assumptions about contact-electrification, particularly in liquid-liquid interfaces, providing new insights into the localized nature of this phenomenon.

FDA Approval Summary: Fruquintinib for the Treatment of Refractory Metastatic Colorectal Cancer.

On November 8, 2023, the FDA approved fruquintinib, an inhibitor of vascular endothelial growth factor receptor (VEGFR)-1, -2, and -3, for the treatment of patients with metastatic colorectal cancer (mCRC) who have been previously treated with fluoropyrimidine-, oxaliplatin-, and irinotecan-based chemotherapy, an anti-VEGF therapy, and if RAS wild-type and medically appropriate, an anti-EGFR therapy. Approval was based on Study FRESCO-2, a globally conducted, double-blind, placebo-controlled randomized trial. The primary endpoint was overall survival (OS). The key secondary endpoint was progression-free survival. A total of 691 patients were randomly assigned (461 and 230 into the fruquintinib and placebo arms, respectively). Fruquintinib provided a statistically significant improvement in OS with a hazard ratio (HR) of 0.66 [95% confidence interval (CI), 0.55, 0.80; P < 0.001]. The median OS was 7.4 months (95% CI, 6.7, 8.2) in the fruquintinib arm and 4.8 months (95% CI, 4.0, 5.8) for the placebo arm. Adverse events observed were generally consistent with the known safety profile associated with the inhibition of VEGFR. The results of FRESCO-2 were supported by the FRESCO study, a double-blind, single-country, placebo-controlled, randomized trial in patients with refractory mCRC who have been previously treated with fluoropyrimidine-, oxaliplatin-, and irinotecan-based chemotherapy. In FRESCO, the OS HR was 0.65 (95% CI, 0.51, 0.83; P < 0.001). FDA concluded that the totality of the evidence from FRESCO-2 and FRESCO supported an indication for patients with mCRC with prior treatment with fluoropyrimidine, oxaliplatin-, and irinotecan-based chemotherapy, an anti-VEGF biological therapy, and if RAS wild-type and medically appropriate, an anti-EGFR therapy.

Constructing Chainmail-Structured CoP/C Nanospheres as Highly Active Anodic Electrocatalysts for Oxygen Evolution Reaction.

Constructing highly active and noble metal-free electrocatalysts is significant for the anodic oxygen evolution reaction (OER). Herein, uniform carbon-coated CoP nanospheres (CoP/C) are developed by a direct impregnation coupling phosphorization approach. Importantly, CoP/C only takes a small overpotential of 230 mV at the current density of 10 mA cm-2 and displays a Tafel slope of 56.87 mV dec-1. Furthermore, the intrinsic activity of CoP/C is 21.44 times better than that of commercial RuO2 under an overpotential of 260 mV. In situ Raman spectroscopy studies revealed that a large number of generated Co-O and Co-OH species could facilitate the *OH adsorption, effectively accelerating the reaction kinetics. Meanwhile, the carbon shell with a large number of mesoporous pores acts as the chainmail of CoP, which could improve the active surface area of the catalyst and prevent the Co sites from oxidative dissolution. This work provides a facile and effective reference for the development of highly active and stable OER catalysts.

Preoperative anemia: impact on short- and long-term outcomes following curative-intent resection of gastroenteropancreatic neuroendocrine tumors.

BACKGROUND: The effect of preoperative anemia on clinical outcomes of patients undergoing resection of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) has not been previously investigated. This study aimed to characterize how preoperative anemia affected short- and long-term outcomes of patients undergoing curative-intent resection of GEP-NETs. METHODS: Patients who underwent curative-intent resection for GEP-NETs between January 1990 and December 2020 were identified from 8 major institutions. The last preoperative hemoglobin level was recorded; anemia was defined as <13.5 g/dL in males or <12.0 g/dL in females based on the guides of the American Society of Hematology. The effect of anemia on postoperative outcomes was assessed on uni- and multivariate analyses. RESULTS: Among 1559 patients, the median age was 58 years (IQR, 48-66), and roughly one-half of the cohort was male (796 [51.1%]). Most patients had a pancreatic tumor (1040 [66.7%]), followed by small bowel (259 [16.6%]), duodenum (103 [6.6%]), stomach (66 [4.2%]), appendix (53 [3.4%]), and other locations (38 [2.6%]). The median preoperative hemoglobin level was 13.4 g/dL (IQR, 12.2-14.5). Overall, 101 (6.7%) and 119 (8.5%) patients received an intra- or postoperative packed red blood cell (pRBC) transfusion, respectively. A total of 972 patients (44.5%) experienced a postoperative complication. Although the overall incidence of complications was no different among patients who did (anemic: 48.7%) vs patients who did not (nonanemic: 47.3%) have anemia (P = .597), patients with preoperative anemia were more likely to develop a major (Clavien-Dindo grade ≥IIIa: 48.9% [anemic] vs 38.0% [nonanemic]; P = .006) and multiple (≥3 types of complications: 32.2% [anemic] vs 19.7% [anemic]; P < .001) complications. Of note, 1-, 3-, and 5-year overall survival (OS) rates were 96.7%, 90.5%, and 86.6%, respectively. On multivariable analysis, anemia (hazard ratio, 2.0; 95% CI, 1.2-3.2; P = .006) remained associated with worse OS; postoperative pRBC transfusion was associated with an OS (5-year OS: 75.0% vs 87.7%; P = .017) and recurrence-free survival (RFS; 5-year RFS: 66.9% vs 76.5%; P = .047). CONCLUSION: Preoperative anemia was commonly identified in roughly 1 in 3 patients who underwent curative-intent resection for GEP-NETs. Preoperative anemia was strongly associated with a higher risk of postoperative morbidity and worse long-term outcomes.

Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach.

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile.

IKBKE promotes the ZEB2-mediated EMT process by phosphorylating HMGA1a in glioblastoma.

IKBKE (Inhibitor of Nuclear Factor Kappa-B Kinase Subunit Epsilon) is an important oncogenic protein in a variety of tumors, which can promote tumor growth, proliferation, invasion and drug resistance, and plays a critical regulatory role in the occurrence and progression of malignant tumors. HMGA1a (High Mobility Group AT-hook 1a) functions as a cofactor for proper transcriptional regulation and is highly expressed in multiple types of tumors. ZEB2 (Zinc finger E-box Binding homeobox 2) exerts active functions in epithelial mesenchymal transformation (EMT). In our current study, we confirmed that IKBKE can increase the proliferation, invasion and migration of glioblastoma cells. We then found that IKBKE can phosphorylate HMGA1a at Ser 36 and/or Ser 44 sites and inhibit the degradation process of HMGA1a, and regulate the nuclear translocation of HMGA1a. Crucially, we observed that HMGA1a can regulate ZEB2 gene expression by interacting with ZEB2 promoter region. Hence, HMGA1a was found to promote the ZEB2-related metastasis. Consequently, we demonstrated that IKBKE can exert its oncogenic functions via the IKBKE/HMGA1a/ZEB2 signalling axis, and IKBKE may be a prominent biomarker for the treatment of glioblastoma in the future.

Investigation and analysis of the incidence and influencing factors of thyroid dysfunction in patients with chronic kidney disease / 中华内分泌外科杂志

Objective:To investigate the incidence of thyroid dysfunction in patients with chronic kidney disease and analyze the influencing factors.Methods:One hundred and ninety-eight patients with chronic kidney disease who were treated in Chronic Disease Management Department of the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine from Apr. 2021 to Apr. 2022 were selected, including 71 patients with abnormal thyroid function and 127 patients with normal thyroid function. The differences in TT3, FT3, TT4, FT4, and TSH between patients with abnormal thyroid function and those with normal thyroid function were analyzed. At the same time, the abnormal thyroid function of patients with different clinical characteristics and the influencing factors were analyzed. The intergroup differences were analyzed using t-test or χ2-test, and the influencing factors were analyzed using logistic regression analysis. Results:In one hundred and ninety-eight patients with chronic kidney disease, thyroid function abnormality occurred in 71 patients (35.86%), including two or more abnormal thyroid function indicators in 35 patients (49.30%). The total triiodothyronine (TT3), free triiodothyronine (FT3), total thyroxine (TT4) and free thyroxine (FT4) in patients with abnormal thyroid function were (1.02 ± 0.29) nmol/mL, (3.03 ± 0.88) pmol/L, (77.93 ± 20.02) nmol/mL and (11.02 ± 1.95) pmol/L respectively, which was significantly lower in patients with normal thyroid function (1.32±0.25) nmol/mL, (4.20±0.92) pmol/L, (93.30±19.28) nmol/mL and (13.54±1.82) pmol/ ( P<0.05), while thyroid stimulating hormone (TSH) was (3.14 ± 0.96) mIU/L, which was significantly higher than that in patients with normal thyroid function (1.84±0.89) mIU/L ( P<0.05). The incidence of thyroid dysfunction in female patients was 50.59% (43/85), It was significantly higher than 24.78% (28/113) of male patients (P <0.05) ; The incidence of thyroid dysfunction in patients aged 60 years was 49.55% (55/111), It was significantly higher than 18.39% (16/87) of the patients aged <60 years ( P<0.05) ; The incidence of thyroid dysfunction in patients with 1-year duration of disease was 71.43% (30/42), It was significantly higher than 25.28% (41/156) of patients with a course of disease <1 year ( P<0.05) ; The incidence of thyroid dysfunction in patients with clinical stage G 4 to 5 was 50.62% (41/81), It was significantly higher than 25.64% (30/117) of patients in G1~3 stages ( P<0.05) ; The incidence of thyroid dysfunction in patients with diabetes was 74.36% (29/39), This was significantly higher than 26.42% (42/159) in patients without diabetes mellitus ( P<0.05). Logistic regression analysis showed that gender, age, course of disease and clinical stage were the influencing factors of thyroid dysfunction in patients with chronic kidney disease ( P<0.05) . Conclusion:A high proportion of patients with chronic kidney disease have abnormal thyroid function, which is affected by the patient's sex, age, course of disease and clinical stage,clinical attention should be paid to targeted intervention to prevent the incidence of thyroid dysfunction in chronic kidney disease population.

Regulation of chondrocyte autophagy by acupotomy to promote chondrocyte homeostasis in osteoarthritis / 中国组织工程研究

BACKGROUND:Acupotomy is an effective method for the clinical treatment of osteoarthritis,with affirmed clinical outcomes,but the specific mechanisms remain unclear OBJECTIVE:To investigate the role of acupotomy in modulating chondrocyte autophagy to promote chondrocyte homeostasis in osteoarthritis. METHODS:Twenty-eight New Zealand rabbits were randomly divided into control group,osteoarthritis group,acupotomy group,and hyaluronic acid group,with seven rabbits in each group.The knee osteoarthritis rabbit model was prepared using the Videman method in the latter three groups.After modeling,the control group and osteoarthritis group received no interventions.The acupotomy group received acupotomy treatment 15 minutes per time,once a week,while the hyaluronic acid group received intra-articular injection of hyaluronic acid once a week,with a continuous treatment duration of 5 weeks.The day after the final intervention,knee joint macrostructure was observed using DR imaging,chondrocyte ultrastructure was examined through transmission electron microscopy,apoptosis of chondrocytes was assessed using Tunel staining,and western blot analysis was used to detect the expression of proteins related to the PI3K/Akt/mTOR pathway. RESULTS AND CONCLUSION:The DR imaging results revealed that the osteoarthritis group exhibited narrowed knee joint spaces and the formation of periarticular osteophytes,while the hyaluronic acid group and acupotomy group showed widened knee joint spaces with a reduction in periarticular osteophytes.Transmission electron microscopy results demonstrated a decreased number of autophagosomes in chondrocytes in the osteoarthritis group,along with nuclear shrinkage,nuclear membrane rupture,incomplete organelle morphology,and a clear tendency towards cell death.In contrast,both the hyaluronic acid group and acupotomy group exhibited a significant increase in autophagosomes,intact nuclear membranes,and a well-preserved cellular state.Tunel staining results indicated a considerable decrease in the number of apoptotic cells in the hyaluronic acid group and acupotomy group compared with the osteoarthritis group.Western blot results revealed that,compared with the control group,the expression levels of Beclin1,Cath D,and LC3II/LC3I were significantly decreased in the osteoarthritis group(P<0.05),while the expression levels of p-Akt/Akt and p-mTOR/mTOR were significantly increased(P<0.05);compared with the osteoarthritis group,the expression levels of Beclin1,Cath D,and LC3II/LC3I were significantly increased in both the hyaluronic acid group and acupotomy group(P<0.05),while the expression levels of p-Akt/Akt and p-mTOR/mTOR were significantly decreased(P<0.05).To conclude,acupotomy intervention can modulate the PI3K/Akt/mTOR signaling pathway to enhance the autophagic level in chondrocytes,thereby maintaining chondrocyte homeostasis.This ultimately leads to a slowdown in cartilage degeneration.

Application of self-designed collateral circulation quantitative score based on multi-task learning vascular segmentation in sCTA assessment of collateral circulation in acute ischemic stroke / 西安交通大学学报(医学版)

【Objective】 To evaluate the clinical use of the baseline CT angiography (CTA) quantitative score (self-designed collateral circulation quantitative, SD-CCQ) in determining the collateral circulation compensation status in patients with acute ischemic stroke (AIS), as well as the reliability and accuracy of the SD-CCQ score and the Alberta Stroke Program Early CT Score (ASPECTS). 【Methods】 Retrospective analysis was made on the clinical and imaging data, including CT, CTA and DWI image data, of 84 patients who were admitted for acute ischemic stroke to the Department of Neurorehabilitation of Zhongshan Hospital of Traditional Chinese Medicine from January 2020 to December 2022.Their CTA source images were annotated using a multi-task deep learning method for vascular segmentation. The ASPECTS score and SD-CCQ score were then applied to the CTA images following vascular segmentation in order to assess the collateral circulation compensation of AIS patients. The Kappa test was used to assess the consistency of the two methods used to assess collateral circulation, and the multifactorial Logistic regression analysis was used to examine the relationship between the SD-CCQ and the prognosis of the AIS patients. 【Results】 ASPECTS score had good consistency with SD-CCQ score in evaluating collateral circulation in AIS patients (κ=0.65, P<0.001), and the diagnostic accuracy of the latter for benign collateral circulation in AIS was 96.15%. Logistic regression analysis showed that the new collateral circulation score, baseline NIHSS, and DWI infarct volume were the main factors affecting the long-term prognosis of AIS patients. 【Conclusion】 The new scoring system SD-CCQ can be used to evaluate the compensatory status of collateral circulation in AIS patients, which may help in clinical treatment decision-making and prognosis prediction.

Three 2,3-diketoquinoxaline alkaloids with hepatoprotective activity from Heterosmilax yunnanensis / 药学学报

Three 2,3-diketoquinoxaline alkaloids were isolated from Heterosmilax yunnanensis Gagnep. Their structures were determined through 1D and 2D NMR, HR-ESI-MS, UV, and IR as 1-[5′-(3″-hydroxy-3″-methyl) glutaryl] ribityl-2,3-diketo-1,2,3,4-tetrahydro-6,7-dimethylquinoxaline (1), 1-[2′-(3″-hydroxy-3″-methyl) glutaryl]ribityl-2,3-diketo-1,2,3,4-tetrahydro-6,7-dimethylquinoxaline (2), and 1-ribityl-2,3-diketo-1,2,3,4-tetrahydro-6,7-dimethylquinoxaline (3). Compounds 1 and 2 are novel compounds, and 3 was isolated from H. yunnanensis for the first time. The hepatoprotective activity of these three compounds was evaluated, with compound 3 showing promising hepatoprotective activity.

Improvement effect and mechanism of “Layers adjusting external application”paste on synovial fibrosis in rats with knee osteoarthritis / 中国药房

OBJECTIVE To investigate the improvement effect and potential mechanism of “Layers adjusting external application” paste on synovial fibrosis （SF） in rats with knee osteoarthritis （KOA）. METHODS Male SD rats were randomly divided into sham operation group， KOA group and Layers adjusting external application group， with 8 rats in each group. KOA model was induced by the anterior cruciate ligament disruption method in KOA group and Layers adjusting external application group. Fourteen days after modeling， the Layers adjusting external application group was given “Layers adjusting external application” paste [Sanse powder （8 g for every 100 cm2）， Compound sanhuang ointment （5 g for every 100 cm2）] on the knee joint， 8 h every day， for 28 d in total. After the last administration， the degree of synovitis and fibrosis in rats was observed， and Krenn scoring was performed in each group. The expressions of collagen Ⅰ， high mobility group protein B1 （HMGB1） and phosphorylated nuclear factor-κB p65 （p-NF-κB p65） were detected in the synovial membrane； the contents of interleukin-1β （IL- 1β）， IL-6 and tumor necrosis factor-α （TNF-α） in serum as well as the expressions of fibrosis-related and HMGB1/Toll-like receptor 4 （TLR4）/NF-κB signaling pathway-related proteins and mRNA were detected in synovial tissue. RESULTS Compared with the sham operation group， the synovial lining cells in the KOA group showed significant proliferation and disordered arrangement， the inflammatory cell infiltration and collagen fiber deposition were obvious； the positive expressing cells of collagen Ⅰ， HMGB1 and p-NF-κB p65 were increased significantly； the contents of IL-1β， IL-6 and TNF-α in serum， the expressions of fibrosis-related protein （transforming growth factor-β， collagen Ⅰ， tissue inhibitor of metalloproteinase 1， α-smooth muscle actin） and their mRNA as well as theexpressions of HMGB1， TLR4 protein and their mRNA， the expressions of p-NF-κB p65 protein and NF-κB p65 mRNA were all increased significantly in synovial tissues of rats （P＜0.01）. Compared with the KOA group， the pathological changes in the synovial tissue of rats in Layers adjusting external application group were significantly improved， and the above quantitative indicators were significantly reversed （P＜0.05 or P＜0.01）. CONCLUSIONS “Layers adjusting external application” paste could significantly improve SF in KOA rats， the mechanism of which may be associated with the inhibition of the activation of HMGB1/ TLR4/NF-κB signaling pathway.

Research progress of dysthyroid optic neuropathy / 国际眼科杂志(Guoji Yanke Zazhi)

Dysthyroid optic neuropathy is an important secondary pathological condition of thyroid-associated ophthalmopathy, characterized clinically by several clinical manifestations, including reduced visual acuity, impairment of color vision, relative afferent pupillary defect, and optic disk edema or atrophy. Ophthalmological auxiliary examination shows abnormal vision field and visual evoked potential, etc., and imagining examination shows orbital apex crowding, which can assist diagnosis. The pathogenesis of this disease is still unclear. With previous studies proposing that it was related to optic nerve compression, stretch, and ischemia. Treatment methods include high-dose intravenous glucocorticoid, orbital decompression, orbital radiation therapy, and biological agent. This article systematically reviews the research progress on the epidemiological characteristics, pathogenesis, diagnosis, and treatment of this disease, with a view to providing useful reference for future in-depth clinical practice and scientific research.

Lipid profiling reveals Leymus Chinensis root insensitivity to Ca limitation.

BACKGROUND: Leymus chinensis (L. chinensis) is a perennial native forage grass widely distributed in the steppe of Inner Mongolia as the dominant species. Calcium (Ca) is an essential mineral element important for plant adaptation to the growth environment. Ca limitation was previously shown to strongly inhibit Arabidopsis (Arabidopsis thaliana) seedling growth and disrupt plasma membrane stability and selectivity, increasing fluid-phase-based endocytosis and contents of all major membrane lipids. RESULTS: In this study, we investigated the significance of Ca for L. chinensis growth and membrane stability relative to Arabidopsis. Our results showed that Ca limitation did not affect L. chinensis seedling growth and endocytosis in roots. Moreover, the plasma membrane maintained high selectivity. The lipid phosphatidylcholine (PC): phosphatidylethanolamine (PE) ratio, an indicator of the membrane stability, was five times higher in L. chinensis than in Arabidopsis. Furthermore, in L. chinensis, Ca limitation did not affect the content of any major lipid types, decreased malondialdehyde (MDA) content, and increased superoxide dismutase (SOD) activity, showing an opposite pattern to that in Arabidopsis. L. chinensis roots accumulated higher contents of PC, phosphatidylinositol (PI), monogalactosyldiacylglycerol (MGDG), phosphatidylglycerol (PG), cardiolipin (CL), digalactosyldiacylglycerol (DGDG), and lysophosphatidylcholine (LPC) but less phosphatidylethanolamine (PE), diacylglycerol (DAG), triacylglycerolv (TAG), phosphatidylserine (PS), lysobisphosphatidic acids (LPAs), lysophosphatidylethanolamine (LPE), and lysophosphatidylserine (LPS) than Arabidopsis roots. Moreover, we detected 31 and 66 unique lipids in L. chinensis and Arabidopsis, respectively. CONCLUSIONS: This study revealed that L. chinensis roots have unique membrane lipid composition that was not sensitive to Ca limitation, which might contribute to the wider natural distribution of this species.

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).