SESLA suppresses the activation of macrophages and dendritic cells after Gram-positive bacterial challenge.

BACKGROUND: Secoeudesma sesquiterpenes lactone A (SESLA) is a sesquiterpene derived from Inula japonica Thunb. and is known to possess many pharmacological properties, e.g. anti-tumor and anti-inflammatory activities. However, the immunomodulatory role of SESLA in gram-positive (G+) bacterial infection is not clear. MATERIALS AND METHODS: To set up a G+ bacterial infection model in vitro, we carried out a bacterial mimic (PGN or Pam3CSK4) or Methicillin-resistant Staphylococcus aureus (MRSA) stimulated experiment using macrophages or dendritic cells (DCs). ELISA and qPCR were performed to measure the expression of inflammatory cytokines. Flow cytometry was used to detect the expression of MHC II and co-stimulatory molecules on the surface of DCs. The network pharmacology was used to identify the molecular mechanism and potential targets of SESLA that are predicted to be involved in the MRSA-elicited inflammation. Western blot and dual luciferase reporter assay were adopted to certify possible molecular mechanism of SESLA. RESULTS: This study demonstrated that SESLA treatment significantly reduced the levels of inflammatory cytokines stimulated by PGN, Pam3CSK4 or even MRSA in vitro, and it also reduced PGN-induced expression of MHC II and co-stimulatory molecules on the surface of DCs. Mechanistically, the inhibition of IκBα phosphorylation and the suppression of T cells activation could account for its anti-inflammatory activity. CONCLUSION: The present study validated the notable anti-inflammatory activity of SESLA and discovered its previously uncharacterized immunoregulatory role and the underlying mechanism in G+ bacterial infections. Overall, SESLA has a potential to be an antibiotic adjuvant for the treatment of G+ bacterial infections.

Digital Gonioscopy Based on Three-dimensional Anterior-Segment OCT: An International Multicenter Study.

PURPOSE: To develop and evaluate the performance of a 3-dimensional (3D) deep-learning-based automated digital gonioscopy system (DGS) in detecting 2 major characteristics in eyes with suspected primary angle-closure glaucoma (PACG): (1) narrow iridocorneal angles (static gonioscopy, Task I) and (2) peripheral anterior synechiae (PAS) (dynamic gonioscopy, Task II) on OCT scans. DESIGN: International, cross-sectional, multicenter study. PARTICIPANTS: A total of 1.112 million images of 8694 volume scans (2294 patients) from 3 centers were included in this study (Task I, training/internal validation/external testing: 4515, 1101, and 2222 volume scans, respectively; Task II, training/internal validation/external testing: 378, 376, and 102 volume scans, respectively). METHODS: For Task I, a narrow angle was defined as an eye in which the posterior pigmented trabecular meshwork was not visible in more than 180° without indentation in the primary position captured in the dark room from the scans. For Task II, PAS was defined as the adhesion of the iris to the trabecular meshwork. The diagnostic performance of the 3D DGS was evaluated in both tasks with gonioscopic records as reference. MAIN OUTCOME MEASURES: The area under the curve (AUC), sensitivity, and specificity of the 3D DGS were calculated. RESULTS: In Task I, 29.4% of patients had a narrow angle. The AUC, sensitivity, and specificity of 3D DGS on the external testing datasets were 0.943 (0.933-0.953), 0.867 (0.838-0.895), and 0.878 (0.859-0.896), respectively. For Task II, 13.8% of patients had PAS. The AUC, sensitivity, and specificity of 3D DGS were 0.902 (0.818-0.985), 0.900 (0.714-1.000), and 0.890 (0.841-0.938), respectively, on the external testing set at quadrant level following normal clinical practice; and 0.885 (0.836-0.933), 0.912 (0.816-1.000), and 0.700 (0.660-0.741), respectively, on the external testing set at clock-hour level. CONCLUSIONS: The 3D DGS is effective in detecting eyes with suspected PACG. It has the potential to be used widely in the primary eye care community for screening of subjects at high risk of developing PACG.

Weighing features of lung and heart regions for thoracic disease classification.

BACKGROUND: Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological information, which is vital for thoracic disease classification. Inspired by this, we propose a novel deep learning framework to explore discriminative information from lung and heart regions. RESULT: We design a feature extractor equipped with a multi-scale attention module to learn global attention maps from global images. To exploit disease-specific cues effectively, we locate lung and heart regions containing pathological information by a well-trained pixel-wise segmentation model to generate binarization masks. By introducing element-wise logical AND operator on the learned global attention maps and the binarization masks, we obtain local attention maps in which pixels are are 1 for lung and heart region and 0 for other regions. By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions. Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions. Our method with pixel-wise segmentation can help overcome the deviation of locating local regions. Evaluated by the benchmark split on the publicly available chest X-ray14 dataset, the comprehensive experiments show that our method achieves superior performance compared to the state-of-the-art methods. CONCLUSION: We propose a novel deep framework for the multi-label classification of thoracic diseases in chest X-ray images. The proposed network aims to effectively exploit pathological regions containing the main cues for chest X-ray screening. Our proposed network has been used in clinic screening to assist the radiologists. Chest X-ray accounts for a significant proportion of radiological examinations. It is valuable to explore more methods for improving performance.

The Agonist of Inward Rectifier Potassium Channel (IK1) Attenuates Rat Reperfusion Arrhythmias Linked to CaMKII Signaling.

Inward rectifier potassium channels (IK1, Kir) are known to play critical roles in arrhythmogenesis. Thus, how IK1 agonist affects reperfusion arrhythmias needs to be clarified, and its underlying mechanisms should be determined. Reperfusion arrhythmias were modeled by coronary ligation (ischemia, 15 minutes) and release (reperfusion, 15 minutes). Zacopride (1.5-50 µg/kg in vivo, or 0.1-10 µmol/Lex vivo) was applied in the settings of pretreatment (3 minutes before coronary ligation) and posttreatment (5 minutes after coronary ligation). Hypoxia (45 minutes) /reoxygenation (30 minutes) model was established in cultured H9c2 (2-1) cardiomyocytes. Zacopride or KN93 was applied before hypoxia (pretreatment). In the setting of pre- or posttreatment, zacopride at 15 µg/kg in vivo or 1 µmol/Lin vitro exhibited superlative protections on reperfusion arrhythmias or intracellular calcium overload. Western blot data from ex vivo hearts or H9c2 (2-1) cardiomyocytes showed that I/R (H/R) induced the inhibition of Kir2.1 (the dominant subunit of IK1 channel in ventricle), phosphorylation and oxidation of CaMKII, downregulation of SERCA2, phosphorylation of phospholamban (at Thr17), and activation of caspase-3. Zacopride treatment (1 µmol/L) was noted to strikingly restore the expression of Kir2.1 and SERCA2 and decrease the activity of CaMKII, phospholamban, and caspase-3. These effects were largely eliminated by co-application of IK1 blocker BaCl2. CaMKII inhibitor KN93 attenuated calcium overload and p-PLB (Thr17) in an IK1-independent manner. IK1-depedent inhibition of CaMKII activity is found to be a key cardiac salvage signaling under Ca2+ dyshomeostasis and reactive oxygen species (ROS) stress. IK1 might be a novel target for pharmacological conditioning of reperfusion arrhythmia, especially for the application after unpredictable ischemia.

MicroRNA-136-3p inhibits glioma tumorigenesis in vitro and in vivo by targeting KLF7.

BACKGROUND: Malignant brain tumors have been a serious threat to human health worldwide. This study aims to investigate the role of miR-136-3p in glioma development. METHODS: Hematoxylin-eosin staining (H&E) staining was used to determine the pathologic alterations of glioma tissues. Quantitative real-time PCR (qRT-PCR) analysis and GEO2R analysis was performed to examine the expression of miRNAs and genes. Western blot was applied to detect the protein expression. Cell counting kit-8 (CCK-8) and colony formation were used to analyze the glioma cell growth. Trans-well assay was used to determine the cell migration. Annexin V-FITC/PI staining was conducted to determine the cell apoptosis of transfected glioma cells. The dual-luciferase reporter assay was carried out to confirm the binding sites of miR-136-3p on 3' untranslated regions (3' UTR) of Kruppel-like factor 7 (KLF7). Tumor-bearing experiment in nude mice was performed to comprehensively investigate the role of miR-136-3p/KLF7 axis in gliomas. RESULTS: Firstly, the results showed that miR-136-3p was decreased in glioma tissues compared with adjacent tissues. Overexpression of miR-136-3p significantly inhibited cell growth of LN-229 and U251 by decreasing expression of Cyclin A1 and PCNA (proliferating cell nuclear antigen), and it suppressed glioma cell migration by downregulating N-cadherin and elevating E-cadherin levels, and it also promotes glioma cell apoptosis by promoting Bcl2-associated X (Bax) expression but suppressing Bcl-2 expression. Furthermore, we observed that KLF7 was a direct target of miR-136-3p, and KLF7 was negatively regulated by miR-136-3p in glioma cells. Finally, overexpression of KLF7 partly blocked miR-136-3p-induced inhibition of tumor growth in vitro and in vivo. CONCLUSIONS: Targeting miR-136-3p/KLF7 axis might be a novel manner to counter against gliomas.

20(S)-protopanaxadiol induces apoptosis in human umbilical vein endothelial cells by activating the PERK-eIF2alpha-ATF4 signaling pathway.

20(S)-protopanaxadiol (PPD)-type ginsenosides are generally believed to be the most pharmacologically active components of Panax ginseng. These compounds induce apoptotic cell death in various cancer cells, which suggests that they have anti-cancer activity. Anti-angiogenesis is a promising therapeutic approach for controlling angiogenesis-related diseases such as malignant tumors, age-related macular degeneration, and atherosclerosis. Studies showed that 20(S)-PPD at low concentrations induces endothelial cell growth, but in our present study, we found 20(S)-PPD at high concentrations inhibited cell growth and mediated apoptosis in human umbilical vein endothelial cells (HUVECs). The mechanism by which high concentrations of 20(S)-PPD mediate endothelial cell apoptosis remains elusive. The present current study investigated how 20(S)-PPD induces apoptosis in HUVECs for the first time. We found that caspase-9 and its downstream caspase, caspase-3, were cleaved into their active forms after 20(S)-PPD treatment. Treatment with 20(S)-PPD decreased the level of Bcl-2 expression but did not change the level of Bax expression. 20(S)-PPD induced endoplasmic reticulum stress in HUVECs and stimulated UPR signaling, initiated by protein kinase R-like endoplasmic reticulum kinase (PERK) activation. Total protein expression and ATF4 nuclear import were increased, and CEBP-homologous protein (CHOP) expression increased after treatment with 20(S)-PPD. Furthermore, siRNA-mediated knockdown of PERK or ATF4 inhibited the induction of CHOP expression and 20(s)-PPD-induced apoptosis. Collectively, our findings show that 20(S)-PPD inhibits HUVEC growth by inducing apoptosis and that ATF4 expression activated by the PERK-eIF2α signaling pathway is essential for this process. These findings suggest that high concentrations of 20(S)-PPD could be used to treat angiogenesis-related diseases.

Exosomes derived from hypoxia-preconditioned mesenchymal stromal cells ameliorate cognitive decline by rescuing synaptic dysfunction and regulating inflammatory responses in APP/PS1 mice.

Administration of exosomes derived from mesenchymal stromal cells (MSCs) could improve some neurologic conditions by transferring functional biomolecules to recipient cells. Furthermore, exosomes from hypoxic progenitor cells exerted better therapeutic effects in organ injury through specific cargoes. However, there are no related reports about whether exosomes derived from MSCs or hypoxia-preconditioned MSCs (PC-MSCs) could prevent memory deficits in Alzheimer disease (AD). In this study, the exosomes derived from MSCs or PC-MSCs were systemically administered to transgenic APP/PS1 mice. The expression of miR-21 in MSCs was significantly increased after hypoxic treatment. Injection of exosomes from normoxic MSCs could rescue cognition and memory impairment according to results of the Morris water maze test, reduced plaque deposition, and Aß levels in the brain; could decrease the activation of astrocytes and microglia; could down-regulate proinflammatory cytokines (TNF-α and IL-1ß); and could up-regulate anti-inflammatory cytokines (IL-4 and -10) in AD mice, as well as reduce the activation of signal transducer and activator of transcription 3 (STAT3) and NF-κB. Compared to the group administered exosomes from normoxic MSCs, in the group administered exosomes from PC-MSCs, learning and memory capabilities were significantly improved; the plaque deposition and Aß levels were lower, and expression of growth-associated protein 43, synapsin 1, and IL-10 was increased; and the levels of glial fibrillary acidic protein, ionized calcium-binding adaptor molecule 1, TNF-α, IL-1ß, and activation of STAT3 and NF-κB were sharply decreased. More importantly, exosomes from PC-MSCs effectively increased the level of miR-21 in the brain of AD mice. Additionally, replenishment of miR-21 restored the cognitive deficits in APP/PS1 mice and prevented pathologic features. Taken together, these findings suggest that exosomes from PC-MSCs could improve the learning and memory capabilities of APP/PS1 mice, and that the underlying mechanism may lie in the restoration of synaptic dysfunction and regulation of inflammatory responses through regulation of miR-21.-Cui, G.-H., Wu, J., Mou, F.-F., Xie, W.-H., Wang, F.-B., Wang, Q.-L., Fang, J., Xu, Y.-W., Dong, Y.-R., Liu, J.-R., Guo, H.-D. Exosomes derived from hypoxia-preconditioned mesenchymal stromal cells ameliorate cognitive decline by rescuing synaptic dysfunction and regulating inflammatory responses in APP/PS1 mice.

Stachydrine Protects Against Pressure Overload-Induced Cardiac Hypertrophy by Suppressing Autophagy.

BACKGROUND: Autophagy is required for the maintenance of cardiomyocyte homeostasis. However, excessive autophagy plays a maladaptive role in pressure overload-induced heart failure. To identify mechanisms by which Stachydrine inhibits pressure overload-induced cardiac hypertrophy, we determined inhibitory activities against activation of NADPH oxidase, reactive oxygen species(ROS) production and excessive activation of autophagy. METHODS: Stachydrine was administered intragastrically to Wistar rats after Transverse aortic constriction(TAC) and H9c2 cells were treated with Stachydrine after Angiotension II stimulation. The activation of NADPH oxidase2 required the membrane translocation of p47phox and p67phox. Cell membrane fraction was isolated by ultracentrifuge in sucrose. The expression of p67phox, p47phox, gp91phox subunit in the cell membrane were determined by western blot. The combination of p67phox and gp91 phox subunit was detected by immunofluorescence staining. The expression of phosphorylated p47phox subunit was determined by western blot. The intracellular ROS were measured with DCF-DA fluoresence. The autophagic flux was measured by recording the fluorescence emission of the fusion protein mRFP-GFP-LC3 by dynamic live-cell imaging. Reuslts: We report here that stachydrine, a major constituent of Leonurus heterophyllus Sweet, inhibited AngII-induced excessive autophagy within H9c2 cells. Stachydrine blocked the over phosphorylation of the p47phox subunit, decreased the translocation of p47phox and p67phox to the membrane, inhibited the activity of NOX2, and reduced the generation of ROS. We also demonstrated that stachydrine ameliorated TAC-induced cardiac hypertrophy, dysfunction and excessive autophagy in vivo. CONCLUSIONS: Our study highlights the importance of regulating NOX2 when autophagy is obviously activated. By inhibiting NOX2, Stachydrine inhibits ROS production, thus exerting a remarkable activity of inhibiting hypertrophy, which could have considerable effect on clinical practice.

Predicting Implantable Collamer Lens Vault Using Machine Learning Based on Various Preoperative Biometric Factors.

Purpose: To predict the vault size after Implantable Collamer Lens (ICL) V4c implantation using machine learning methods and to compare the predicted vault with the conventional manufacturer's nomogram. Methods: This study included 707 patients (707 eyes) who underwent ICL V4c implantation at the Department of Ophthalmology, Peking Union Medical College Hospital, from September 2019 to January 2022. Random Forest Regression (RFR), XGBoost, and linear regression (LR) were used to predict the vault size 1 week after ICL V4c implantation. The mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Bland-Altman plot were utilized to compare the prediction performance of these machine learning methods. Results: The dataset was divided into a training set of 180 patients (180 eyes) and a test set of 527 patients (527 eyes). XGBoost had the lowest prediction error, with mean MAE, RMSE, and SMAPE values of 121.70 µm, 148.87 µm, and 19.13%, respectively. The Bland‒Altman plots of RFR and XGBoost showed better prediction consistency than LR. However, XGBoost showed narrower 95% limits of agreement (LoA) than RFR, ranging from -307.12 to 256.59 µm. Conclusions: XGBoost demonstrated better predictive performance than RFR and LR, as it had the lowest prediction error and the narrowest 95% LoA. Machine learning may be applicable for vault prediction, and it might be helpful for reducing the complications and the secondary surgery rate. Translational Relevance: Using the proposed machine learning model, surgeons can consider the postoperative vault to reduce the surgical complications.

Towards Lightweight Super-Resolution with Dual Regression Learning.

Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large since there may exist many different HR images that can be super-resolved from the same LR image. As a result, it is hard to directly learn a promising SR mapping from such a large space. Second, it is often inevitable to develop very large models with extremely high computational cost to yield promising SR performance. In practice, one can use model compression techniques to obtain compact models by reducing model redundancy. Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space. To alleviate the first challenge, we propose a dual regression learning scheme to reduce the space of possible SR mappings. Specifically, in addition to the mapping from LR to HR images, we learn an additional dual regression mapping to estimate the downsampling kernel and reconstruct LR images. In this way, the dual mapping acts as a constraint to reduce the space of possible mappings. To address the second challenge, we propose a dual regression compression (DRC) method to reduce model redundancy in both layer-level and channel-level based on channel pruning. Specifically, we first develop a channel number search method that minimizes the dual regression loss to determine the redundancy of each layer. Given the searched channel numbers, we further exploit the dual regression manner to evaluate the importance of channels and prune the redundant ones. Extensive experiments show the effectiveness of our method in obtaining accurate and efficient SR models.

Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.

Purpose: Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients' long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants. Methods: A total of 22,569 infants who underwent routine ROP screening in Shenzhen Eye Hospital from March 2003 to September 2023 were collected, including 3335 infants with ROP and 1234 infants with TR-ROP among ROP infants. Two machine learning methods of logistic regression and decision tree and a deep learning method of multi-layer perceptron were trained by using the relevant combination of risk factors such as birth weight (BW), gestational age (GA), gender, whether multiple births (MB) and mode of delivery (MD) to achieve the risk prediction of ROP and TR-ROP. We used five evaluation metrics to evaluate the performance of the risk prediction model. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) were the main measurement metrics. Results: In the risk prediction for ROP, the BW + GA demonstrated the optimal performance (mean ± SD, AUCPR: 0.4849 ± 0.0175, AUC: 0.8124 ± 0.0033). In the risk prediction of TR-ROP, reasonable performance can be achieved by using GA + BW + Gender + MD + MB (AUCPR: 0.2713 ± 0.0214, AUC: 0.8328 ± 0.0088). Conclusions: Combining risk factors with AI in screening programs for ROP could achieve risk prediction of ROP and TR-ROP, detect TR-ROP earlier and reduce the number of ROP examinations and unnecessary physiological stress in low-risk infants. Therefore, combining ROP-related biometric information with AI is a cost-effective strategy for predictive diagnostic, targeted prevention, and personalization of medical services in early screening and treatment of ROP.

Bibliometric analysis of hotspots and trends of global myopia research.

AIM: To gain insights into the global research hotspots and trends of myopia. METHODS: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS: A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION: Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images.

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. Algorithmic comparative assessments and blind evaluations conducted by 10 board-certified radiologists indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines and airways. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.

Platycodin D facilitates antiviral immunity through inhibiting cytokine storm via targeting K63-linked TRAF6 ubiquitination.

Influenza virus infection is a worldwide challenge that causes heavy burdens on public health. The mortality rate of severe influenza patients is often associated with hyperactive immunological abnormalities characterized by hypercytokinemia. Due to the continuous mutations and the occurrence of drug-resistant influenza virus strains, the development of host-directed immunoregulatory drugs is urgently required. Platycodon grandiflorum is among the top 10 herbs of traditional Chinese medicine used to treat pulmonary diseases. As one of the major terpenoid saponins extracted from Platycodon grandiflorum, Platycodin D (PD) has been reported to play several roles, including anti-inflammation, analgesia, anti-cancer, hepatoprotection, and immunoregulation. However, the therapeutic roles of PD to treat influenza virus infection remains unknown. Here, we show that PD can protect the body weight loss in severely infected influenza mice, alleviate lung damage, and thus improve the survival rate. More specifically, PD protects flu mice via decreasing the immune cell infiltration into lungs and downregulating the overactivated inflammatory response. Western blot and immunofluorescence assays exhibited that PD could inhibit the activation of TAK1/IKK/NF-κB and MAPK pathways. Besides that, CETSA, SPR and immunoprecipitation assays indicated that PD binds with TRAF6 to decrease its K63 ubiquitination after R837 stimulation. Additionally, siRNA interference experiments exhibited that PD could inhibit the secretion of IL-1ß and TNF-α in TRAF6-dependent manner. Altogether, our results suggested that PD is a promising drug candidate for treating influenza. Our study also offered a scientific explanation for the commonly used Platycodon grandiflorum in many anti-epidemic classic formulas. Due to its host-directed regulatory role, PD may serve as an adjuvant therapeutic drug in conjunction with other antiviral drugs to treat the flu.

Calibrate the Inter-observer Segmentation Uncertainty via Diagnosis-first Principle.

Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis-First segmentation Framework (DiFF) is proposed. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis-First Ground-truth (DF-GT). Then, the Take and Give Model (T&G Model) to segment DF-GT from the raw image is proposed. With the T&G Model, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

Deep segmentation of OCTA for evaluation and association of changes of retinal microvasculature with Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Optical coherence tomography angiography (OCTA) enables fast and non-invasive high-resolution imaging of retinal microvasculature and is suggested as a potential tool in the early detection of retinal microvascular changes in Alzheimer's Disease (AD). We developed a standardised OCTA analysis framework and compared their extracted parameters among controls and AD/mild cognitive impairment (MCI) in a cross-section study. METHODS: We defined and extracted geometrical parameters of retinal microvasculature at different retinal layers and in the foveal avascular zone (FAZ) from segmented OCTA images obtained using well-validated state-of-the-art deep learning models. We studied these parameters in 158 subjects (62 healthy control, 55 AD and 41 MCI) using logistic regression to determine their potential in predicting the status of our subjects. RESULTS: In the AD group, there was a significant decrease in vessel area and length densities in the inner vascular complexes (IVC) compared with controls. The number of vascular bifurcations in AD is also significantly lower than that of healthy people. The MCI group demonstrated a decrease in vascular area, length densities, vascular fractal dimension and the number of bifurcations in both the superficial vascular complexes (SVC) and the IVC compared with controls. A larger vascular tortuosity in the IVC, and a larger roundness of FAZ in the SVC, can also be observed in MCI compared with controls. CONCLUSION: Our study demonstrates the applicability of OCTA for the diagnosis of AD and MCI, and provides a standard tool for future clinical service and research. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of AD and MCI.

Fei-Yan-Qing-Hua decoction decreases hyperinflammation by inhibiting HMGB1/RAGE signaling and promotes bacterial phagocytosis in the treatment of sepsis.

ETHNOPHARMACOLOGICAL RELEVANCE: Fei-Yan-Qing-Hua decoction (FYQHD), derived from the renowned formula Ma Xing Shi Gan tang documented in Zhang Zhong Jing's "Treatise on Exogenous Febrile Disease" during the Han Dynasty, has demonstrated notable efficacy in the clinical treatment of pneumonia resulting from bacterial infection. However, its molecular mechanisms underlying the therapeutic effects remains elusive. AIM OF THE STUDY: This study aimed to investigate the protective effects of FYQHD against lipopolysaccharide (LPS) and carbapenem-resistant Klebsiella pneumoniae (CRKP)-induced sepsis in mice and to elucidate its specific mechanism of action. MATERIALS AND METHODS: Sepsis models were established in mice through intraperitoneal injection of LPS or CRKP. FYQHD was administered via gavage at low and high doses. Serum cytokines, bacterial load, and pathological damage were assessed using enzyme-linked immunosorbent assay (ELISA), minimal inhibitory concentration (MIC) detection, and hematoxylin and eosin staining (H&E), respectively. In vitro, the immunoregulatory effects of FYQHD on macrophages were investigated through ELISA, MIC, quantitative real-time PCR (Q-PCR), immunofluorescence, Western blot, and a network pharmacological approach. RESULTS: The application of FYQHD in the treatment of LPS or CRKP-induced septic mouse models revealed significant outcomes. FYQHD increased the survival rate of mice exposed to a lethal dose of LPS to 33.3%, prevented hypothermia (with a rise of 3.58 °C), reduced pro-inflammatory variables (including TNF-α, IL-6, and MCP-1), and mitigated tissue damage in LPS or CRKP-induced septic mice. Additionally, FYQHD decreased bacterial load in CRKP-infected mice. In vitro, FYQHD suppressed the expression of inflammatory cytokines in macrophages activated by LPS or HK-CRKP. Mechanistically, FYQHD inhibited the PI3K/AKT/mTOR/4E-BP1 signaling pathway, thereby suppressing the translational level of inflammatory cytokines. Furthermore, it reduced the expression of HMGB1/RAGE, a positive feedback loop in the inflammatory response. Moreover, FYQHD was found to enhance the phagocytic activity of macrophages by upregulating the expression of phagocytic receptors such as CD169 and SR-A1. CONCLUSION: FYQHD provides protection against bacterial sepsis by concurrently inhibiting the inflammatory response and augmenting the phagocytic ability of immune cells.

Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration.

BACKGROUND: The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus. METHODS: First we trained and internally validated the nuclear age predictor with a deep-learning algorithm, using 12 904 anterior segment optical coherence tomography (AS-OCT) images from four diverse Asian and American cohorts: Zhongshan Ophthalmic Center with Machine0 (ZOM0), Tomey Corporation (TOMEY), University of California San Francisco and the Chinese University of Hong Kong. External testing was done on three independent datasets: Tokyo University (TU), ZOM1 and Shenzhen People's Hospital (SPH). We also demonstrate the possibility of detecting nuclear cataracts (NCs) from the nuclear age gap. FINDINGS: In the internal validation dataset, the nuclear age could be predicted with a mean absolute error (MAE) of 2.570 years (95% CI 1.886 to 2.863). Across the three external testing datasets, the algorithm achieved MAEs of 4.261 years (95% CI 3.391 to 5.094) in TU, 3.920 years (95% CI 3.332 to 4.637) in ZOM1-NonCata and 4.380 years (95% CI 3.730 to 5.061) in SPH-NonCata. The MAEs for NC eyes were 8.490 years (95% CI 7.219 to 9.766) in ZOM1-NC and 9.998 years (95% CI 5.673 to 14.642) in SPH-NC. The nuclear age gap outperformed both ophthalmologists in detecting NCs, with areas under the receiver operating characteristic curves of 0.853 years (95% CI 0.787 to 0.917) in ZOM1 and 0.909 years (95% CI 0.828 to 0.978) in SPH. INTERPRETATION: The nuclear age predictor shows good performance, validating the feasibility of using AS-OCT images as an effective screening tool for nucleus degeneration. Our work also demonstrates the potential use of the nuclear age gap to detect NCs.

Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).

With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.