Construction of Isoquinolone Scaffolds on DNA via Rhodium(III)-Catalyzed C-H Activation.

Isoquinolone is one of the most common heterocyclic core structures in countless natural products and many bioactive compounds. Here, a highly efficient approach to synthesize isoquinolone scaffolds on DNA via rhodium(III)-catalyzed C-H activation has been described. This chemistry transformation is robust and has shown good compatibility with DNA, which is suitable for DNA-encoded library synthesis.

Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning.

Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized zero-shot learning (GZSL) requires predicting both seen and unseen classes accurately. In the absence of target samples, many GZSL models may overfit training data and are inclined to predict individuals as categories that have been seen in training. To alleviate this problem, we develop a parameter-wise adversarial training process that promotes robust recognition of seen classes while designing during the test a novel model perturbation mechanism to ensure sufficient sensitivity to unseen classes. Concretely, adversarial perturbation is conducted on the model to obtain instance-specific parameters so that predictions can be biased to unseen classes in the test. Meanwhile, the robust training encourages the model robustness, leading to nearly unaffected prediction for seen classes. Moreover, perturbations in the parameter space, computed from multiple individuals simultaneously, can be used to avoid the effect of perturbations that are too extreme and ruin the predictions. Comparison results on four benchmark ZSL data sets show the effective improvement that the proposed framework made on zero-shot methods with learned metrics.

Oxidative Dehydroxycyclization of Catechols with o-Mercaptoanilines to Access 1-Hydroxyphenothiazines.

The protocol of aerobic oxidative dehydroxycyclization installed in the synthesis of rarely studied 1-hydroxyphenothiazines from catechols and o-mercaptoanilines is presented. Utilizing a natural renewable low-toxicity gallic acid as an organocatalyst, this established transformation proceeded smoothly in an aqueous ethanol solution under mild conditions with good functional group compatibility and up to a 94% isolated yield. This protocol is also characterized by its operational simple workup involving only recrystallization, revealing its sustainability and synthetic practicability.

Marine fungus Aspergillus c1. sp metabolite activates the HSF1/PGC-1α axis, inducing a thermogenic program for treating obesity.

Background and aims: Obesity is one of the most prevalent diseases worldwide with less ideal approved agents in clinic. Activating the HSF1/PGC-1α axis in adipose tissues has been reported to induce thermogenesis in mice, which presents a promising therapeutic avenue for obesity treatment. The present study aimed to identified novel natural HSF1 activator and evaluated the therapeutic effects of the newly discovered compound on obesity-associated metabolic disorders and the molecular mechanisms of these effects. Methods: Our previous reported HSF1/PGC-1α activator screening system was used to identify novel natural HSF1 activator. The PGC-1α luciferase activity, immunoblot, protein nuclear-translocation, immunofluorescence, chromatin immunoprecipitation assays were used to evaluate the activity of compound HN-001 in activating HSF1. The experiments of mitochondrial number measurement, TG assay and imaging, cellular metabolic assay, gene assays, and CRISPR/Cas 9 were applied for investigating the metabolic effect of HN-001 in C3H10-T1/2 adipocytes. The in vivo anti-obesity efficacies and beneficial metabolic effects of HN-001 were evaluated by performing body and fat mass quantification, plasma chemical analysis, GTT, ITT, cold tolerance test, thermogenesis analysis. Results: HN-001 dose- and time-dependently activated HSF1 and induced HSF1 nuclear translocation, resulting in an enhancement in binding with the gene Pgc-1α. This improvement induced activation of adipose thermogenesis and enhancement of mitochondrial oxidation capacity, thus inhibiting adipocyte maturation. Deletion of HSF1 in adipocytes impaired mitochondrial oxidation and abolished the above beneficial metabolic effects of HN-001, including adipocyte browning induction, improvements in mitogenesis and oxidation capacity, and lipid-lowering ability. In mice, HN-001 treatment efficiently alleviated diet-induced obesity and metabolic disorders. These changes were associated with increased body temperature in mice and activation of the HSF1/PGC-1α axis in adipose tissues. UCP1 expression and mitochondrial biogenesis were increased in both white and brown adipose tissues of HN-001-treated mice. Conclusion: These data indicate that HN-001 may have therapeutic potential for obesity-related metabolic diseases by increasing the capacity of energy expenditure in adipose tissues through a mechanism involving the HSF1/PGC-1α axis, which shed new light on the development of novel anti-obesity agents derived from marine sources.

Bioinspired Self-Assembly of Metalloporphyrins and Polyelectrolytes into Hierarchical Supramolecular Nanostructures for Enhanced Photocatalytic H2 Production in Water.

Polypeptides, as natural polyelectrolytes, are assembled into tailored proteins to integrate chromophores and catalytic sites for photosynthesis. Mimicking nature to create the water-soluble nanoassemblies from synthetic polyelectrolytes and photocatalytic molecular species for artificial photosynthesis is still rare. Here, we report the enhancement of the full-spectrum solar-light-driven H2 production within a supramolecular system built by the co-assembly of anionic metalloporphyrins with cationic polyelectrolytes in water. This supramolecular photocatalytic system achieves a H2 production rate of 793 and 685 µmol h-1 g-1 over 24 h with a combination of Mg or Zn porphyrin as photosensitizers and Cu porphyrin as a catalyst, which is more than 23 times higher than that of free molecular controls. With a photosensitizer to catalyst ratio of 10000 : 1, the highest H2 production rate of >51,700 µmol h-1 g-1 with a turnover number (TON) of >1,290 per molecular catalyst was achieved over 24 h irradiation. The hierarchical self-assembly not only enhances photostability through forming ordered stackings of the metalloporphyrins but also facilitates both energy and electron transfer from antenna molecules to catalysts, and therefore promotes the photocatalysis. This study provides structural and mechanistic insights into the self-assembly enhanced photostability and catalytic performance of supramolecular photocatalytic systems.

The failure behavior of coal-rock combined body under compression-shear loading.

As shallow coal reserves continue to deplete rapidly, deep mining has become an unavoidable course of action. In the process of deep coal mining, affected by blasting, mining, and excavation, the coal-rock interface often encounters the action of compression-shear composite load. The interface crack directly affects the stability of the coal-rock structure. Uniaxial compression experiments have been conducted on rock and coal-like material with pre-existing interfacial oblique cracks to study the crack propagation criterion of the rock and coal-like material interface. An image acquisition system is used to record the surface speckle field of the whole process of specimen failure. The strain fields and stress intensity factor of the sample at different times are obtained by digital image correlation. At the same time, the stress field near the crack tip is calculated. The results show that with the increase of loading, two strains of localization bands are formed on the surface of the specimen. One of the strain-localized bands starts at the lower tip of the prefabricated crack and propagates along the vertical interface. The other starts at the upper tip of the prefabricated damage and propagates along the interface direction. It can be seen that the lower tip of the prefabricated crack enters into the crack fracture process zone earlier than the upper tip. The strain localization band narrows gradually with the load increase, and then macro cracks appear. The initiation of two tips is suitable for different fracture criteria. The lower tip is dominated by the maximum circumferential tensile stress, and the upper tip is dominated by shear stress. The specimens eventually fracture along the interface. The evolution of the strain field during the failure process of rock and coal-like material can reflect the generation and propagation of the crack.

Identification of a natural PLA2 inhibitor from the marine fungus Aspergillus sp. c1 for MAFLD treatment that suppressed lipotoxicity by inhibiting the IRE-1α/XBP-1s axis and JNK signaling.

Lipotoxicity is a pivotal factor that initiates and exacerbates liver injury and is involved in the development of metabolic-associated fatty liver disease (MAFLD). However, there are few reported lipotoxicity inhibitors. Here, we identified a natural anti-lipotoxicity candidate, HN-001, from the marine fungus Aspergillus sp. C1. HN-001 dose- and time- dependently reversed palmitic acid (PA)-induced hepatocyte death. This protection was associated with IRE-1α-mediated XBP-1 splicing inhibition, which resulted in suppression of XBP-1s nuclear translocation and transcriptional regulation. Knockdown of XBP-1s attenuated lipotoxicity, but no additional ameliorative effect of HN-001 on lipotoxicity was observed in XBP-1s knockdown hepatocytes. Notably, the ER stress and lipotoxicity amelioration was associated with PLA2. Both HN-001 and the PLA2 inhibitor MAFP inhibited PLA2 activity, reduced lysophosphatidylcholine (LPC) level, subsequently ameliorated lipotoxicity. In contrast, overexpression of PLA2 caused exacerbation of lipotoxicity and weakened the anti-lipotoxic effects of HN-001. Additionally, HN-001 treatment suppressed the downstream pro-apoptotic JNK pathway. In vivo, chronic administration of HN-001 (i.p.) in mice alleviated all manifestations of MAFLD, including hepatic steatosis, liver injury, inflammation, and fibrogenesis. These effects were correlated with PLA2/IRE-1α/XBP-1s axis and JNK signaling suppression. These data indicate that HN-001 has therapeutic potential for MAFLD because it suppresses lipotoxicity, and provide a natural structural basis for developing anti-MAFLD candidates.

Joint ESPGHAN/NASPGHAN Guidelines on Childhood Eosinophilic Gastrointestinal Disorders Beyond Eosinophilic Esophagitis.

INTRODUCTION: Eosinophilic gastrointestinal disorders beyond eosinophilic esophagitis (non-EoE EGIDs) are rare chronic inflammatory disorders of the gastrointestinal (GI) tract. Diagnosis is based on clinical symptoms and histologic findings of eosinophilic inflammation after exclusion of a secondary cause or systemic disease. Currently, no guidelines exist for the evaluation of non-EoE EGIDs. Therefore, the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) formed a task force group to provide consensus guidelines for childhood non-EoE EGIDs. METHODS: The working group was composed of pediatric gastroenterologists, adult gastroenterologists, allergists/immunologists, and pathologists. An extensive electronic literature search of the MEDLINE, EMBASE, and Cochrane databases was conducted up to February 2022. General methodology was used in the formulation of recommendations according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to meet current standards of evidence assessment. RESULTS: The guidelines provide information on the current concept of non-EoE EGIDs, disease pathogenesis, epidemiology, clinical manifestations, diagnostic and disease surveillance procedures, and current treatment options. Thirty-four statements based on available evidence and 41 recommendations based on expert opinion and best clinical practices were developed. CONCLUSION: Non-EoE EGIDs literature is limited in scope and depth, making clear recommendations difficult. These consensus-based clinical practice guidelines are intended to assist clinicians caring for children affected by non-EoE EGIDs and to facilitate high-quality randomized controlled trials of various treatment modalities using standardized, uniform disease definitions.

STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+T CMR Segmentation.

The segmentation of cardiac structure in magnetic resonance images (CMR) is paramount in diagnosing and managing cardiovascular illnesses, given its 3D+Time (3D+T) sequence. The existing deep learning methods are constrained in their ability to 3D+T CMR segmentation, due to: (1) Limited motion perception. The complexity of heart beating renders the motion perception in 3D+T CMR, including the long-range and cross-slice motions. The existing methods' local perception and slice-fixed perception directly limit the performance of 3D+T CMR perception. (2) Lack of labels. Due to the expensive labeling cost of the 3D+T CMR sequence, the labels of 3D+T CMR only contain the end-diastolic and end-systolic frames. The incomplete labeling scheme causes inefficient supervision. Hence, we propose a novel spatio-temporal adaptation network with clinical prior embedding learning (STANet) to ensure efficient spatio-temporal perception and optimization on 3D+T CMR segmentation. (1) A spatio-temporal adaptive convolution (STAC) treats the 3D+T CMR sequence as a whole for perception. The long-distance motion correlation is embedded into the structural perception by learnable weight regularization to balance long-range motion perception. The structural similarity is measured by cross-attention to adaptively correlate the cross-slice motion. (2) A clinical prior embedding learning strategy (CPE) is proposed to optimize the partially labeled 3D+T CMR segmentation dynamically by embedding clinical priors into optimization. STANet achieves outstanding performance with Dice of 0.917 and 0.94 on two public datasets (ACDC and STACOM), which indicates STANet has the potential to be incorporated into computer-aided diagnosis tools for clinical application.

Enhancement of mitochondrial energy metabolism by melatonin promotes vascularized skeletal muscle regeneration in a volumetric muscle loss model.

Volumetric muscle loss (VML) is a condition that results in the extensive loss of 20 % or more of skeletal muscle due to trauma or tumor ablation, leading to severe functional impairment and permanent disability. The current surgical interventions have limited functional regeneration of skeletal muscle due to the compromised self-repair mechanism. Melatonin has been reported to protect skeletal muscle from exercise-induced oxidative damage and holds great potential to treat muscle diseases. In this study, we hypothesize that melatonin can enhance myoblast differentiation and promote effective recovery of skeletal muscle following VML. In vitro administration of melatonin resulted in a significant enhancement of myogenesis in C2C12 myoblast cells, as evidenced by the up-regulation of myogenic marker genes in a dose-dependent manner. Further experiments revealed that silent information of regulator type 3 (SIRT3) played a critical role in the melatonin-enhanced myoblast differentiation through enhancement of mitochondrial energy metabolism and activation of mitochondrial antioxidant enzymes such as superoxide dismutase 2 (SOD2). Silencing of Sirt3 completely abrogated the protective effect of melatonin on the mitochondrial function of myoblasts, evidenced by the increased reactive oxygen species, decreased adenosine triphosphate production, and down-regulated myoblast-specific marker gene expression. In order to attain a protracted and consistent release, liposome-encapsuled melatonin was integrated into gelatin methacryloyl hydrogel (GelMA-Lipo@MT). The implantation of GelMA-Lipo@MT into a tibialis anterior muscle defect in a VML model effectively stimulated the formation of myofibers and new blood vessels in situ, while concurrently inhibiting fibrotic collagen deposition. The findings of this study indicate that the incorporation of melatonin with GelMA hydrogel has facilitated the de novo vascularized skeletal muscle regeneration by augmenting mitochondrial energy metabolism. This represents a promising approach for the development of skeletal muscle tissue engineering, which could be utilized for the treatment of VML and other severe muscle injuries.

An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction.

Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods.Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM.Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts.Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.

A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy.

BACKGROUND AND OBJECTIVE: Cone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms. METHODS: The planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated. RESULTS: The images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05). CONCLUSIONS: The proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.

Joint ESPGHAN/NASPGHAN Guidelines on Childhood Eosinophilic Gastrointestinal Disorders beyond Eosinophilic Esophagitis.

INTRODUCTION: Eosinophilic Gastrointestinal Disorders beyond Eosinophilic Esophagitis (non-EoE EGIDs) are rare chronic inflammatory disorders of the gastrointestinal (GI) tract. Diagnosis is based on clinical symptoms and histologic findings of eosinophilic inflammation after exclusion of a secondary cause or systemic disease. Currently, no guidelines exist for the evaluation of non-EoE EGIDs. Therefore, the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) formed a task force group to provide consensus guidelines for childhood non-EoE EGIDs. METHODS: The working group was composed of pediatric gastroenterologists, adult gastroenterologists, allergists/immunologists, and pathologists. An extensive electronic literature search of the MEDLINE, EMBASE, and Cochrane databases was conducted up to February 2022. General methodology was used in the formulation of recommendations according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to meet current standards of evidence assessment. RESULTS: The guidelines provide information on the current concept of non-EoE EGIDs, disease pathogenesis, epidemiology, clinical manifestations, diagnostic and disease surveillance procedures, and current treatment options. Thirty-four statements based on available evidence and 41 recommendations based on expert opinion and best clinical practices were developed. CONCLUSION: Non-EoE EGIDs literature is limited in scope and depth, making clear recommendations difficult. These consensus-based clinical practice guidelines are intended to assist clinicians caring for children affected by non-EoE EGIDs and to facilitate high-quality randomized controlled trials of various treatment modalities using standardized, uniform disease definitions.

Rebalanced Zero-Shot Learning.

Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalanced semantic predictions, i.e. these models could perform precisely for some semantics, but may not for others. To address the drawback, we aim to introduce an imbalanced learning framework into ZSL. However, we find that imbalanced ZSL has two unique challenges: (1) Its imbalanced predictions are highly correlated with the value of semantic labels rather than the number of samples as typically considered in the traditional imbalanced learning; (2) Different semantics follow quite different error distributions between classes. To mitigate these issues, we first formalize ZSL as an imbalanced regression problem which offers empirical evidences to interpret how semantic labels lead to imbalanced semantic predictions. We then propose a re-weighted loss termed Re-balanced Mean-Squared Error (ReMSE), which tracks the mean and variance of error distributions, thus ensuring rebalanced learning across classes. As a major contribution, we conduct a series of analyses showing that ReMSE is theoretically well established. Extensive experiments demonstrate that the proposed method effectively alleviates the imbalance in semantic prediction and outperforms many state-of-the-art ZSL methods.

Driving Pressure-Guided Ventilation in Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy: A Randomized Controlled Trial.

Purpose: This study aims to compare the conventional lung protective ventilation strategy (LPVS) with driving pressure-guided ventilation in obese patients undergoing laparoscopic sleeve gastrectomy (LSG). Methods: Forty-five patients undergoing elective LSG under general anesthesia were randomly assigned to the conventional LPVS group (group L) or the driving pressure-guided ventilation group (group D) using random numbers generated by Excel. The primary outcome was the driving pressure of both groups 90 min after pneumoperitoneum. Results: After 30 min of pneumoperitoneum, 90 min of pneumoperitoneum, 10 min of closing the pneumoperitoneum, and restoring the supine position, the driving pressure of group L and group D were 20.0 ± 2.9 cm H2O vs 16.6 ± 3.0 cm H2O (P < 0.001), 20.7 ± 3.2 cm H2O vs 17.3 ± 2.8 cm H2O (P < 0.001), and 16.3 ± 3.1 cm H2O vs 13.3 ± 2.5 cm H2O (P = 0.001), respectively; the respiratory compliance of groups L and D were 23.4 ± 3.7 mL/cm H2O vs 27.6 ± 5.1 mL/cm H2O (P = 0.003), 22.7 ± 3.8 mL/cm H2O vs 26.4 ± 3.5 mL/cm H2O (P = 0.005), and 29.6 ± 6.8 mL/cm H2O vs 34.7 ± 5.3 mL/cm H2O (P = 0.007), respectively. The intraoperative PEEP in groups L and group D was 5 (5-5) cm H2O vs 10 (9-11) cm H2O (P < 0.001). Conclusion: An individualized peep-based driving pressure-guided ventilation strategy can reduce intraoperative driving pressure and increase respiratory compliance in obese patients undergoing LSG.

Virtual Pain Unit Is Associated with Improvement of Postoperative Analgesia Quality: A Retrospective Single-Center Clinical Study.

INTRODUCTION: Acute postoperative pain is a major concern among surgical patients. Thus, this study established a new acute pain management model and compared the effects of the acute pain service (APS) model in 2020 and the virtual pain unit (VPU) model in 2021 on postoperative analgesia quality. METHODS: This retrospective, single-center clinical study involved 21,281 patients from 2020 to 2021. First, the patients were grouped on the basis of their pain management model (APS and VPU). The incidence of moderate to severe postoperative pain (MSPP) [numeric rating scale (NRS) score ≥ 5], postoperative nausea and vomiting (PONV), and postoperative dizziness were recorded. RESULTS: The VPU group recorded significantly lower MSPP incidence (1-12 months), PONV, and postoperative dizziness (1-10 months and 12 months) compared with the APS group. In addition, the annual average incidence of MSPP, PONV, and postoperative dizziness in the VPU group was significantly lower than in the APS group. CONCLUSIONS: The VPU model reduces the incidence of moderate to severe postoperative pain, nausea, vomiting, and dizziness; hence, it is a promising acute pain management model.

Multi-Task Learning for Pulmonary Arterial Hypertension Prognosis Prediction Via Memory Drift and Prior Prompt Learning on 3D Chest CT.

Pulmonary arterial hypertension (PAH) prognosis prediction on 3D non-contrast CT images is one of the most important tasks for PAH treatment. It will help clinicians stratify patients into different groups for early diagnosis and timely intervention via automatically extracting the potential biomarkers of PAH to predict mortality. However, it is still a task of great challenges due to the large volume and low-contrast regions of interest in 3D chest CT images. In this paper, we propose the first multi-task learning-based PAH prognosis prediction framework, P 2-Net, which effectively optimizes the model and powerfully represents task-dependent features via our Memory Drift (MD) and Prior Prompt Learning (PPL) strategies. 1) Our MD maintains a large memory bank to provide a dense sampling of the deep biomarkers' distribution. Therefore, although the batch size is very small caused by our large volume, a reliable (negative log partial) likelihood loss is still able to be calculated on a representative probability distribution for robust optimization. 2) Our PPL simultaneously learns an additional manual biomarkers prediction task to embed clinical prior knowledge into our deep prognosis prediction task in hidden and explicit ways. Therefore, it will prompt the prediction of deep biomarkers and improve the perception of task-dependent features in our low-contrast regions. Our P 2-Net achieves a high prognostic correlation of the prediction and great generalization with the highest 70.19% C-index and 2.14 HR. Extensive experiments with promising results on our PAH prognosis prediction reveal powerful prognosis performance and great clinical significance in PAH treatment. All of our code will be made publicly available online Opened source: https://github.com/YutingHe-list/P2-Net.

Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction: a nationwide, multicohort, prospective study.

BACKGROUND: Oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction have a dismal prognosis, and early detection is key to reduce mortality. However, early detection depends on upper gastrointestinal endoscopy, which is not feasible to implement at a population level. We aimed to develop and validate a fully automated machine learning-based prediction tool integrating a minimally invasive sponge cytology test and epidemiological risk factors for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction before endoscopy. METHODS: For this multicohort prospective study, we enrolled participants aged 40-75 years undergoing upper gastrointestinal endoscopy screening at 39 tertiary or secondary hospitals in China for model training and testing, and included community-based screening participants for further validation. All participants underwent questionnaire surveys, sponge cytology testing, and endoscopy in a sequential manner. We trained machine learning models to predict a composite outcome of high-grade lesions, defined as histology-confirmed high-grade intraepithelial neoplasia and carcinoma of the oesophagus and oesophagogastric junction. The predictive features included 105 cytological and 15 epidemiological features. Model performance was primarily measured with the area under the receiver operating characteristic curve (AUROC) and average precision. The performance measures for cytologists with AI assistance was also assessed. FINDINGS: Between Jan 1, 2021, and June 30, 2022, 17 498 eligible participants were involved in model training and validation. In the testing set, the AUROC of the final model was 0·960 (95% CI 0·937 to 0·977) and the average precision was 0·482 (0·470 to 0·494). The model achieved similar performance to consensus of cytologists with AI assistance (AUROC 0·955 [95% CI 0·933 to 0·975]; p=0·749; difference 0·005, 95% CI, -0·011 to 0·020). If the model-defined moderate-risk and high-risk groups were referred for endoscopy, the sensitivity was 94·5% (95% CI 88·8 to 97·5), specificity was 91·9% (91·2 to 92·5), and the predictive positive value was 18·4% (15·6 to 21·6), and 90·3% of endoscopies could be avoided. Further validation in community-based screening showed that the AUROC of the model was 0·964 (95% CI 0·920 to 0·990), and 92·8% of endoscopies could be avoided after risk stratification. INTERPRETATION: We developed a prediction tool with favourable performance for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction. This approach could prevent the need for endoscopy screening in many low-risk individuals and ensure resource optimisation by prioritising high-risk individuals. FUNDING: Science and Technology Commission of Shanghai Municipality.

Facile and scalable synthesis of baphicacanthin A by a two-pot procedure.

Facile two-pot total synthesis of baphicacanthin A, a natural phenoxazinone alkaloid isolated from the roots of Baphicacanthus cusia which has been utilized as a traditional chinese medicine to effectively treat disease caused by coronavirus, has been developed from simple and commercially available starting materials. Catalytic aerobic oxidative cross-cyclocondensation of equimolar 2-aminophenol and 3-methoxy-2-hydroxylphenol in water was used to construct the key molecular skeleton 2-hydroxy-3H-phenoxazin-3-one. Gram scale synthesis was realized in 80% overall yield with practical convenience.

Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy.

BACKGROUND: Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT-based ART. PURPOSE: To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high-quality sCT images from CBCT and achieve an accurate dose calculation. METHODS: The proposed SARN was trained in a self-supervised manner. Artifact-CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact-CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U-Net, cycleGAN, attention-gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. RESULTS: The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIUm ) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U-Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively. CONCLUSIONS: The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high-quality sCT images with fewer artifacts, high-accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.