Enantioselective Copper-Catalyzed Sequential Hydrosilylation of Arylmethylenecyclopropanes.

Despite impressive advances in the construction of enantioenriched silacarbocycles featuring silicon-stereogenic centers via a selection of well-defined sila-synthons, the development of a more convenient and economic method with readily available starting materials is significantly less explored and remains a considerable challenge. Herein, we report the first example of copper-catalyzed sequential hydrosilylation of readily accessible methylenecyclopropanes (MCPs) and primary silanes, affording an efficient and convenient route to a wide range of chiral silacyclopentanes bearing consecutive silicon- and carbon-stereogenic centers with excellent enantio- and diastereoselectivities (generally ≥98% ee, > 25:1 dr). Mechanistic studies reveal that these reactions combine copper-catalyzed intermolecular ring-opening hydrosilylation of aryl MCPs and intramolecular asymmetric hydrosilylation of the resultant Z/E mixture of homoallylic silanes.

Research on the osteogenic properties of 3D-printed porous titanium alloy scaffolds loaded with Gelma/PAAM-ZOL composite hydrogels.

Although titanium alloy is the most widely used endoplant material in orthopedics, the material is bioinert and good bone integration is difficult to achieve. Zoledronic acid (ZOL) has been shown to locally inhibit osteoclast formation and prevent osteoporosis, but excessive concentrations of ZOL exert an inhibitory effect on osteoblasts; therefore, stable and controlled local release of ZOL may reshape bone balance and promote bone regeneration. To promote the adhesion of osteoblasts to many polar groups, researchers have applied gelatine methacryloyl (Gelma) combined with polyacrylamide hydrogel (PAAM), which significantly increased the hydrogen bonding force between the samples and improved the stability of the coating and drug release. A series of experiments demonstrated that the Gelma/PAAM-ZOL bioactive coating on the surface of the titanium alloy was successfully prepared. The coating can induce osteoclast apoptosis, promote osteoblast proliferation and differentiation, achieve dual regulation of bone regeneration, successfully disrupt the balance of bone remodelling and promote bone tissue regeneration. Additionally, the coating improves the metal biological inertness on the surface of titanium alloys and improves the bone integration of the scaffold, offering a new strategy for bone tissue engineering to promote bone technology.

Fracturing Construction Curves and Fracture Geometries of Coals in the Southern Qinshui Basin, China: Implication for Coalbed Methane Productivity.

Hydraulic fracturing technology has become a common practice to enhance the permeability of coal seams, and its effectiveness significantly impacts the productivity of coalbed methane (CBM) wells. In this study, multiple data sets, including fracturing reports, productivity data, and microseismic monitoring, were utilized to analyze the factors influencing fracturing effectiveness and gas well production at the southern margin of the Qinshui Basin, China, especially the Zhengzhuang and Fanzhuang blocks. Statistics revealed that the fracturing displacement, liquid consumption, and sand consumption were 8 m3/min, 17.69-1386.52 m3 (averaging 728.42 m3), and 28.5-46.1 m3 (averaging 40 m3), respectively. There were differences in the types of fracturing curves among blocks or well groups, and the natural fracture system was identified as the key factor affecting their characteristics. The coal seams with high density of microfractures in Zhengzhuang reduce the fracture threshold of reservoirs during hydraulic fracturing, resulting in a lower fracture response ratio than that of Fanzhuang (71.0 vs 80.3%). Well groups with higher microfracture width in coal seams (Group-ZC and DS) experienced a further reduction in fracture response (averaging 67.2 vs 80.3%), and the connection of hydraulic fractures (HFs) with these high-permeability microfractures lead to an increase in the proportion of fluctuating fracturing curves (averaging 52.2 vs 33.6%). Regional structural features and fracturing effectiveness jointly affected the production of CBM wells. The productivity in Zhengzhuang was lower than that in Fanzhuang due to the highly developed faults and deeply buried coal seams. Shallow coal seams with a high width of microfractures and a low-stress environment were easily supported by a proppant, forming a complex HF network and yielding a high productivity (Group-ZC and DS). For other deeper well groups, proppant migration became unfavorable once high-angle and complex branch HF clusters formed in coal seams, leading to local low-efficiency wells and fluctuating fracturing curves.

Tungsten sulfide highly boosted Fe(III)/peroxymonosulfate system for rapid degradation of cyclohexanecarboxylic acid: Performance, mechanisms, and applications.

In this study, the Fe(III)/WS2/peroxymonosulfate (PMS) system was found to remove up to 97% of cyclohexanecarboxylic acid (CHA) within 10 min. CHA is a model compound for naphthenic acids (NAs), which are prevalent in petroleum industrial wastewater. The addition of WS2 effectively activated the Fe(III)/PMS system, significantly enhancing its ability to produce reactive oxidative species (ROS) for the oxidation of CHA. Further experimental results and characterization analyses demonstrated that the metallic element W(IV) in WS2 could provide electrons for the direct reduction of Fe(III) to Fe(II), thus rapidly activating PMS and initiating a chain redox process to produce ROS (SO4•-, •OH, and 1O2). Repeated tests and practical exploratory experiments indicated that WS2 exhibited excellent catalytic performance, reusability and anti-interference capacity, achieving efficient degradation of commercial NAs mixtures. Therefore, applying WS2 to catalyze the Fe(III)/PMS system can overcome speed limitations and facilitate simple, economical engineering applications.

Copper-Catalyzed Regiodivergent Asymmetric Difunctionalization of Terminal Alkynes.

We herein describe the first example of ligand-controlled, copper-catalyzed regiodivergent asymmetric difunctionalization of terminal alkynes through a cascade hydroboration and hydroallylation process. The catalytic system, consisting of (R)-DTBM-Segphos and CuBr, could efficiently achieve asymmetric 1,1-difunctionalization of aryl terminal alkynes, while ligand switching to (S,S)-Ph-BPE could result in asymmetric 1,2-difunctionalization exclusively. In addition, alkyl substituted terminal alkynes, especially industrially relevant acetylene and propyne, were also valid feedstocks for asymmetric 1,1-difunctionalization. This protocol is characterized by good functional group tolerance, a broad scope of substrates (> 150 examples), and mild reaction conditions. We also showcase the value of this method in the late-stage functionalization of complicated bioactive molecules and simplifying the synthetic routes toward the key intermediacy of natural product (bruguierol A). Mechanistic studies combined with DFT calculations provide insight into the mechanism and origins of this ligand-controlled regio- and stereoselectivity.

Deep learning promoted target volumes delineation of total marrow and total lymphoid irradiation for accelerated radiotherapy: A multi-institutional study.

BACKGROUND AND PURPOSE: One of the current roadblocks to the widespread use of Total Marrow Irradiation (TMI) and Total Marrow and Lymphoid Irradiation (TMLI) is the challenging difficulties in tumor target contouring workflow. This study aims to develop a hybrid neural network model that promotes accurate, automatic, and rapid segmentation of multi-class clinical target volumes. MATERIALS AND METHODS: Patients who underwent TMI and TMLI from January 2018 to May 2022 were included. Two independent oncologists manually contoured eight target volumes for patients on CT images. A novel Dual-Encoder Alignment Network (DEA-Net) was developed and trained using 46 patients from one internal institution and independently evaluated on a total of 39 internal and external patients. Performance was evaluated on accuracy metrics and delineation time. RESULTS: The DEA-Net achieved a mean dice similarity coefficient of 90.1 % ± 1.8 % for internal testing dataset (23 patients) and 91.1 % ± 2.5 % for external testing dataset (16 patients). The 95 % Hausdorff distance and average symmetric surface distance were 2.04 ± 0.62 mm and 0.57 ± 0.11 mm for internal testing dataset, and 2.17 ± 0.68 mm, and 0.57 ± 0.20 mm for external testing dataset, respectively, outperforming most of existing state-of-the-art methods. In addition, the automatic segmentation workflow reduced delineation time by 98 % compared to the conventional manual contouring process (mean 173 ± 29 s vs. 12168 ± 1690 s; P < 0.001). Ablation study validate the effectiveness of hybrid structures. CONCLUSION: The proposed deep learning framework achieved comparable or superior target volume delineation accuracy, significantly accelerating the radiotherapy planning process.

PD-1 Inhibitors Combined with Tyrosine Kinase Inhibitors with or without Hepatic Artery Infusion Chemotherapy for the First-Line Treatment of HBV-Related Advanced Hepatocellular Carcinoma: A Retrospective Study.

Purpose: Comparing the efficacy and safety of programmed cell death protein-1 (PD-1) inhibitors combined with tyrosine kinase inhibitors (TKIs) with or without hepatic artery infusion chemotherapy (HAIC) in HBV-related advanced HCC and exploring prognostic predictors of the combined regimen. Patients and Methods: A total of 194 patients diagnosed with HBV-related advanced HCC between 2020 and 2022 were included in the study, including 99 in the HAIC combined with PD-1 inhibitors plus TKIs (HPT group) and 95 in the PD-1 inhibitors plus TKIs (PT group). The efficacy was evaluated according to the tumor response rate and survival, and the safety was evaluated according to the adverse events. Results: The HPT group showed higher overall response rate and disease control rate than the PT group. The median overall survival (OS) of the HPT group and the PT group were 18.10 months and 12.57 months, respectively, and the difference was statistically significant (hazard ratio (HR) = 0.519, 95% confidence interval (CI): 0.374-0.722, P < 0.001). The median progression-free survival (PFS) was 9.20 months in the HPT group and 6.33 months in the PT group (HR = 0.632, 95% CI: 0.470-0.851, P = 0.002). In addition, albumin bilirubin (ALBI) and systemic inflammatory response index (SIRI) are independent prognostic factors affecting HAIC combined with targeted immunotherapy and can be used as prognostic predictors. Almost all patients included in the study experienced treatment-related adverse events (TRAEs) of varying degrees of severity, with grade 1-2 adverse events predominating. Conclusion: The HPT group had better OS and PFS than the PT group in patients with HBV-related advanced HCC. In addition, high ALBI and high SIRI were associated with poor prognosis in the HAIC combined group.

Evaluation of recoverable potential of deep coalbed methane in the Linxing Block, Eastern Margin of the Ordos Basin.

The deep coalbed methane (CBM) resources are widely developed in the Linxing Block. However, the evaluation of CBM geological areas suitable for CBM exploitation remains unexplored, hindering further development. This research optimizes the key geological parameters that influence the development of deep CBM from the perspectives of resource and development conditions. The evaluation system for deep CBM recoverability has been established, and the multi-fuzzy evaluation method has been used to perform the quantitative evaluation of recoverability. The results indicate that the resource conditions of No.8 + 9 coal seam are superior to those of No.4 + 5 coal seam. Favorable resource conditions are predominantly concentrated in the northeast and specific southern portions of the research area. Favorable development conditions for both coal seams are mostly concentrated in the northeastern area. Based on the classification standard of recoverable favorable areas, the Level II area is crucial for the development of No.4 + 5 coal seam. This area is primarily distributed in the northeast of the research area., Both Level I and Level II areas for the No. 8 + 9 coal seam are situated in the northeast. The Level III area is earmarked for deep CBM production and shows potential for exploration. Further analysis reveals that the resource conditions in the favorable area are generally superior to the development conditions. These areas are classified as Class A, including categories such as I-A, II-A, and III-A, indicating relatively complex reservoir transformation.

SNIL: Generating Sports News From Insights With Large Language Models.

To enhance the appeal and informativeness of data news, there is an increasing reliance on data analysis techniques and visualizations, which poses a high demand for journalists' abilities. While numerous visual analytics systems have been developed for deriving insights, few tools specifically support and disseminate viewpoints for journalism. Thus, this work aims to facilitate the automatic creation of sports news from natural language insights. To achieve this, we conducted an extensive preliminary study on the published sports articles. Based on our findings, we propose a workflow - 1) exploring the data space behind insights, 2) generating narrative structures, 3) progressively generating each episode, and 4) mapping data spaces into communicative visualizations. We have implemented a human-AI interaction system called SNIL, which incorporates user input in conjunction with large language models (LLMs). It supports the modification of textual and graphical content within the episode-based structure by adjusting the description. We conduct user studies to demonstrate the usability of SNIL and the benefit of bridging the gap between analysis tasks and communicative tasks through expert and fan feedback.

Sulfonamides as N-Centered Radical Precursors for C-N Coupling Reactions To Generate Amidines.

Nitrogen-centered radicals (NCRs) are valuable intermediates for the construction of C-N bonds. Traditional methods for the generation of NCRs employ toxic radical initiators, transition metal catalysts, photocatalysts, or organometallic reagents. Herein, we report a novel strategy for the generation of NCRs toward the construction of C-N bonds under transition-metal-free conditions. Thus, super-electron-donor (SED) 2-azaallyl anions undergo single-electron transfer (SET) with sulfonamides, forming aminyl radicals (R2N•, R = alkyl) and culminating in the generation of amidines bearing various functional groups (33 examples, up to 96% yield). Broad substrate scope and gram-scale telescoped preparation demonstrate the practicality of this method. Radical clock and electron paramagnetic resonance (EPR) experiments support the proposed radical coupling pathway between the generated N-centered radical and the C-centered 2-azaallyl radical.

Mutual learning with reliable pseudo label for semi-supervised medical image segmentation.

Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels is crucial in this regard. Unreliable pseudo-labeling can result in the introduction of noise, leading the model to converge to suboptimal solutions. To address this issue, we propose learning from reliable pseudo-labels. In this paper, we tackle two critical questions in learning from reliable pseudo-labels: which pseudo-labels are reliable and how reliable are they? Specifically, we conduct a comparative analysis of two subnetworks to address both challenges. Initially, we compare the prediction confidence of the two subnetworks. A higher confidence score indicates a more reliable pseudo-label. Subsequently, we utilize intra-class similarity to assess the reliability of the pseudo-labels to address the second challenge. The greater the intra-class similarity of the predicted classes, the more reliable the pseudo-label. The subnetwork selectively incorporates knowledge imparted by the other subnetwork model, contingent on the reliability of the pseudo labels. By reducing the introduction of noise from unreliable pseudo-labels, we are able to improve the performance of segmentation. To demonstrate the superiority of our approach, we conducted an extensive set of experiments on three datasets: Left Atrium, Pancreas-CT and Brats-2019. The experimental results demonstrate that our approach achieves state-of-the-art performance. Code is available at: https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels.

Zr-Based MOF-Stabilized CO2-Responsive Pickering Emulsions for Efficient Reduction of Nitroarenes.

A Pickering emulsion is a natural microreactor for interfacial catalysis in which an emulsifier is critical. Recently, a metal-organic framework (MOF) has attracted attention to emulsify water-organic mixtures for constructing a Pickering emulsion. However, a few stimuli-responsive Pickering emulsions based on MOFs have been reported, and the MOF emulsifiers cannot be regenerated at room temperature. Herein, the Zr-MOF with a rodlike morphology is synthesized using ionic liquid as a modulator and then modified with n-(trimethoxysilylpropyl)imidazole (C3im) to prepare a series of functionalized Zr-MOFs (MOF-C3im). It is found that MOF-C3im is an excellent emulsifier to construct stable and CO2-responsive Pickering emulsions even at low content (>0.20 wt %). Notably, the emulsification and demulsification of the emulsions can be easily and reversibly switched by bubbling of CO2 and N2 alternatively at room temperature because CO2 and imidazole molecules anchored on the Zr-MOF underwent a reversible acid-base reaction, resulting in an obvious change in the wettability of the emulsifier. As a proof of concept, the reduction reactions of nitrobenzene have been successfully carried out in these Pickering emulsions, demonstrating the efficient integration as a microreactor for chemical reaction, product separation, and emulsifier recycling under ambient conditions. This strategy provides an innovative option to develop stimulus-responsive Pickering emulsions for sustainable chemical processes.

ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language.

The use of natural language interfaces (NLIs) to create charts is becoming increasingly popular due to the intuitiveness of natural language interactions. One key challenge in this approach is to accurately capture user intents and transform them to proper chart specifications. This obstructs the wide use of NLI in chart generation, as users' natural language inputs are generally abstract (i.e., ambiguous or under-specified), without a clear specification of visual encodings. Recently, pre-trained large language models (LLMs) have exhibited superior performance in understanding and generating natural language, demonstrating great potential for downstream tasks. Inspired by this major trend, we propose ChartGPT, generating charts from abstract natural language inputs. However, LLMs are struggling to address complex logic problems. To enable the model to accurately specify the complex parameters and perform operations in chart generation, we decompose the generation process into a step-by-step reasoning pipeline, so that the model only needs to reason a single and specific sub-task during each run. Moreover, LLMs are pre-trained on general datasets, which might be biased for the task of chart generation. To provide adequate visualization knowledge, we create a dataset consisting of abstract utterances and charts and improve model performance through fine-tuning. We further design an interactive interface for ChartGPT that allows users to check and modify the intermediate outputs of each step. The effectiveness of the proposed system is evaluated through quantitative evaluations and a user study.

Value of multi-center 18F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma.

BACKGROUND: Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F-FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi-center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR-related research involved model visualization and interpretation, and multi-center trial. PURPOSE: To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi-center 18F-FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. METHODS: Baseline 18F-FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross-combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis. RESULTS: STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort). CONCLUSIONS: Explainable machine learning models combined with radiomics features extracted from multi-center/scanner 18F-FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.

Genesis and Accumulation Mechanism of Low-Rank CBM: A Case Study in the Jiergalangtu Block of Erlian Basin, Northern China.

In order to elucidate the origin of coalbed methane (CBM) in the Jiergalangtu block of Erlian Basin, Inner Mongolia of China, gas components, stable isotope tests of 22 gas samples, radioisotope dating measurements, and water quality analysis of 15 coproduced water samples were evaluated. On account of the geochemical data and genetic indicators, including C1/C1-n, C1/(C2 + C3), and CO2/(CO2 + CH4) (CDMI) values, δ13C(CO2), Δδ13C(CO2-CH4), δ15N, and 3He/4He combined with vitrinite reflectance (Ro) (0.29-0.48%, avg. 0.35%) of Saihantala formation, the results indicate that methane in the Jiergalangtu block is mostly dominated by primary and secondary biological gas, 40.91% of the gas samples are secondary biogas and primary biogas accounts for 59.19%. Among them, methyl-type fermentation accounts for 31.82%, and carbon dioxide (CO2) reduction makes up 68.18%. CO2 reduction generally occurs region-wide but is mainly associated with the central part of the block, where CO2 depletion and 13C enrichment take place correspondingly. Methane and CO2 δ13C almost tend to isotopically light along the margin of the block, indicating that gas generation is significantly affected by the methyl-type fermentation pathway. Meanwhile, the genesis analysis of other gas components in CBM is also investigated, CO2 is mainly the associated product of microbial methanogenesis, and nitrogen (N2) is primarily from the atmosphere with a little amount from the earth's crust. Furthermore, the formation time of coalbed water has been dissected based on the hydrogeochemical properties of the coproduced water samples. The coalbed water exhibit a Na-HCO3 and Na-HCO3-Cl type and have a total dissolved solid (TDS) value ranging from 2458.58 to 5579.1 mg/L, with an average of 3440.55 mg/L. Moreover, comprehensive analysis of δD(H2O), δ18O(H2O), δ13CDIC, and the radioisotope dating index [3H, 14C(Fm) and 14C(BP)] indicates that the coalbed water was formed in the Quaternary Pleistocene and rarely replenished by the present surface water. The mechanism of CBM accumulation is basically sorted out by synthesizing the history of burial, heat, and hydrocarbon generation. The CBM formation can be divided into four stages. That is, microbial gas production approximately began at the beginning of the Early Cretaceous and reached the peak of thermogenic gas production in the middle and late Early Cretaceous. At the end of the Early Cretaceous, strata possibly began to uplift, and denudation led to gas escape. From Neogene to Pleistocene, glacial meltwater tended to penetrate into coalbed on a large scale, and N2 and CO2 also entered the coal seams, stimulating abundant secondary biological gas generation. Since Holocene, geological conditions including temperature and TDS have become hostile to biogas generation, and biogas generation tends to stop. Therefore, the Jiergalangtu block mainly represents sealed primary biological gas and secondary biological gas in CBM reservoirs.

Visualizing Large-Scale Spatial Time Series with GeoChron.

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

A Bimetallic Polymerization Network for Effective Increase in Labile Iron Pool and Robust Activation of cGAS/STING Induces Ferroptosis-Based Tumor Immunotherapy.

Due to the inherent low immunogenicity and immunosuppressive tumor microenvironment (TME) of malignant cancers, the clinical efficacy and application of tumor immunotherapy have been limited. Herein, a bimetallic drug-gene co-loading network (Cu/ZIF-8@U-104@siNFS1-HA) is developed that increased the intracellular labile iron pool (LIP) and enhanced the weakly acidic TME by co-suppressing the dual enzymatic activities of carbonic anhydrase IX (CA IX) and cysteine desulfurylase (NFS1), inducing a safe and efficient initial tumor immunogenic ferroptosis. During this process, Cu2+ is responsively released to deplete glutathione (GSH) and reduce the enzyme activity of glutathione peroxidase 4 (GPX4), achieving the co-inhibition of the three enzymes and further inducing lipid peroxidation (LPO). Additionally, the reactive oxygen species (ROS) storm in target cells promoted the generation of large numbers of double-stranded DNA breaks. The presence of Zn2+ substantially increased the expression of cGAS/STING, which cooperated with ferroptosis to strengthen the immunogenic cell death (ICD) response and remodel the immunosuppressive TME. In brief, Cu/ZIF-8@U-104@siNFS1-HA linked ferroptosis with immunotherapy through multiple pathways, including the increase in LIP, regulation of pH, depletion of GSH/GPX4, and activation of STING, effectively inhibiting cancer growth and metastasis.

Effect of Rapid Hollow Cathode Plasma Nitriding Treatment on Corrosion Resistance and Friction Performance of AISI 304 Stainless Steel.

Low-temperature plasma nitriding of austenitic stainless steel can ensure that its corrosion resistance does not deteriorate, improving surface hardness and wear performance. Nevertheless, it requires a longer processing time. The hollow cathode discharge effect helps increase the plasma density quickly while radiatively heating the workpiece. This work is based on the hollow cathode discharge effect to perform a rapid nitriding strengthening treatment on AISI 304 stainless steels. The experiments were conducted at three different temperatures (450, 475, and 500 °C) for 1 h in an ammonia atmosphere. The samples were characterized using various techniques, including SEM, AFM, XPS, XRD, and micro-hardness measurement. Potentiodynamic polarization and electrochemical impedance spectroscopy methods were employed to assess the electrochemical behavior of the different samples in a 3.5% NaCl solution. The finding suggests that rapid hollow cathode plasma nitriding can enhance the hardness, wear resistance, and corrosion properties of AISI 304 stainless steel.

Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study.

Background: Accurate tumour response prediction to targeted therapy allows for personalised conversion therapy for patients with unresectable colorectal cancer liver metastases (CRLM). In this study, we aimed to develop and validate a multi-modal deep learning model to predict the efficacy of bevacizumab in patients with initially unresectable CRLM using baseline PET/CT, clinical data, and colonoscopy biopsy specimens. Methods: In this multicentre cohort study, we retrospectively collected data of 307 patients with CRLM from the BECOME study (NCT01972490) (Zhongshan Hospital of Fudan University, Shanghai) and two independent Chinese cohorts (internal validation cohort from January 1, 2018 to December 31, 2018 at Zhongshan Hospital of Fudan University; external validation cohort from January 1, 2020 to December 31, 2020 at Zhongshan Hospital-Xiamen, Shanghai, and the First Hospital of Wenzhou Medical University, Wenzhou). The main inclusion criteria were that patients with CRLM had pre-treatment PET/CT images as well as colonoscopy specimens. After extracting PET/CT features with deep neural networks (DNN) and selecting related clinical factors using LASSO analysis, a random forest classifier was built as the Deep Radiomics Bevacizumab efficacy predicting model (DERBY). Furthermore, by combining histopathological biomarkers into DERBY, we established DERBY+. The performance of model was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Findings: DERBY achieved promising performance in predicting bevacizumab sensitivity with an AUC of 0.77 and 95% confidence interval (CI) [0.67-0.87]. After combining histopathological features, we developed DERBY+, which had more robust accuracy for predicting tumour response in external validation cohort (AUC 0.83 and 95% CI [0.75-0.92], sensitivity 80.4%, specificity 76.8%). DERBY+ also had prognostic value: the responders had longer progression-free survival (median progression-free survival: 9.6 vs 6.3 months, p = 0.002) and overall survival (median overall survival: 27.6 vs 18.5 months, p = 0.010) than non-responders. Interpretation: This multi-modal deep radiomics model, using PET/CT, clinical data and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favourable approach for precise patient treatment. To further validate and explore the clinical impact of this work, future prospective studies with larger patient cohorts are warranted. Funding: The National Natural Science Foundation of China; Fujian Provincial Health Commission Project; Xiamen Science and Technology Agency Program; Clinical Research Plan of SHDC; Shanghai Science and Technology Committee Project; Clinical Research Plan of SHDC; Zhejiang Provincial Natural Science Foundation of China; and National Science Foundation of Xiamen.

Total marrow lymphoid irradiation IMRT treatment using a novel CT-linac.

BACKGROUND: A novel CT-linac (kilovolt fan-beam CT-linac) has been introduced into total marrow and lymphoid irradiation (TMLI) treatment. Its integrated kilovolt fan-beam CT (kV FBCT) can be used not only for image guidance (IGRT) but also to re-calculate the dose. PURPOSE: This study reported our clinical routine on performing TMIL treatment on the CT-linac, as well as dose distribution comparison between planned and re-calculated based on IGRT FBCT image sets. METHODS: 11 sets of data from 5 male and 6 female patients who had underwent the TMLI treatment with uRT-linac 506c were selected for this study. The planning target volumes consist of all skeletal bones exclusion of the mandible and lymphatic sanctuary sites. A planned dose of 10 Gy was prescribed to all skeletal bones exclusion of the mandible in two fractions and 12 Gy in two fractions was prescribed to lymphatic sanctuary sites. Each TMLI plan contained two sub-plans, one dynamic IMRT for the upper body and the other VMAT for the lower extremity. Two attempts were made to obtain homogeneous dose in the overlapping region, i.e., applying two plans with different isocenters for the treatment of two fractions, and using a dose gradient matching scheme. The CT scans, including planning CT and IGRT FBCT, were stitched to a whole body CT scan for dose distribution evaluation. RESULTS: The average beam-on time of Planupper is 30.6 min, ranging from 24.9 to 37.5 min, and the average beam-on time of Planlower is 6.3 min, ranging from 5.7 to 8.2 min. For the planned dose distribution, the 94.79% of the PTVbone is covered by the prescription dose of 10 Gy (V10), and the 94.68% of the PTVlymph is covered by the prescription dose of 12 Gy (V12). For the re-calculated dose distribution, the 92.17% of the PTVbone is covered by the prescription dose of 10 Gy (V10), and the 90.07% of the PTVlymph is covered by the prescription dose of 12 Gy (V12). The results showed that there is a significant difference (p < 0.05) between planning V10, V12 and delivery V10, V12. There is no significant difference (p > 0.05) between planned dose and re-calculated dose on selected organs, except for right lens (p < 0.05, Dmax). The actual delivered maximum dose of right lens is apparently larger than the planned dose of it. CONCLUSION: TMLI treatment can be performed on the CT-linac with clinical acceptable quality and high efficiency. Evaluation of the recalculated dose on IGRT FBCT suggests the treatment was delivered with adequate target coverage.