Atezolizumab Plus Bevacizumab Combined with Transarterial Embolization Plus Hepatic Arterial Infusion Chemotherapy for Unresectable Hepatocellular Carcinoma with a Diameter >8 Cm: A Retrospective Study.

Purpose: Local in combination with systemic therapy might be an option for patients with advanced unresectable hepatocellular carcinoma (uHCC). This study examined the clinical benefits and adverse events (AEs) of first-line transarterial embolization (TAE) and hepatic arterial infusion chemotherapy (HAIC) combined with atezolizumab (Atezo) and bevacizumab (Bev) in patients with uHCC of a diameter larger than 8 cm. Patients and methods: This retrospective study included patients with uHCC of a diameter larger than 8 cm who were treated with first-line Atezo-Bev and TAE+HAIC at the First Affiliated Hospital of Sun Yat-Sen University between September 30, 2019, and September 30, 2022. Progression-free survival (PFS), overall survival (OS), tumor response according to mRECIST, and AEs were analyzed. Multivariable Cox analyses were performed to examine the factors associated with PFS. Results: Thirty patients were included. The objective response rate (ORR) was 74.4% (95% confidence interval [CI], 59.3%-89.5%), and the disease control rate (DCR) was 93.3% (95% CI, 85.4%-98.6%). The median follow-up was 11.4 (inter-quartile range [IQR], 5.5-17.9) months. The median PFS was 6.8 (95% CI, 2.6-11.1) months. The 3-, 6-, 9-, and 12-month survival rates were 86.2%, 82.5%, 68.6%, and 60%, respectively. The median OS was not estimated. Extrahepatic metastasis was independently associated with PFS (hazard ratio [HR]=3.468, 95% CI, 1.001-12.023). The most common AEs were fever (46.7%). Grade 4 AEs occurred one time as hematemesis but no 5 AEs were observed. Conclusion: Atezo-Bev combined with TAE and HAIC might benefit patients with uHCC of a diameter larger than 8 cm, with manageable AEs.

Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis.

The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance imaging (MRI) is challenging due to its complexity, multi-dimension, and noise with subtle alterations. In this paper, we propose a multi-modal deep learning framework based on transformers for precise post-menstrual age (PMA) estimation and brain development analysis using T2-weighted structural MRI (T2-sMRI) and diffusion MRI (dMRI) data. First, we build a two-stream dense network to learn modality-specific features from T2-sMRI and dMRI of brain individually. Then, a transformer module based on self-attention mechanism integrates these features for PMA prediction and preterm/term classification. Finally, saliency maps on brain templates are used to enhance the interpretability of results. Our method is evaluated on the multi-modal MRI dataset of the developing Human Connectome Project (dHCP), which contains 592 neonates, including 478 term-born and 114 preterm-born subjects. The results demonstrate that our method achieves a 0.5-week mean absolute error (MAE) in PMA estimation for term-born subjects. Notably, preterm-born subjects exhibit delayed brain development, worsening with increasing prematurity. Our method also achieves 95% accuracy in classification of term-born and preterm-born subjects, revealing significant group differences.

Application of 3D printing technology for green synthesis of Fe2O3 using ABS/TPU/chlorella skeletons for methyl orange removal.

Photocatalysis is widely acknowledged as an efficient and environmentally friendly method for treating dye-contaminated wastewater. However, the utilization of powdered photocatalysts presents significant challenges, including issues related to recyclability and the potential for secondary pollution. Herein, a novel technique based on 3D printing for the synthesizing of iron oxide (Fe2O3) involving chlorella was presented. Initially, chlorella powders were immobilized within acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) substrate plastics using melt extrusion technology. Subsequently, these composite materials were transformed into ABS/TPU/chlorella skeletons (ATCh40), through fused deposition molding (FDM) technology. The integration of Fe2O3 onto the ATCh40 (ATCh40-Fe2O3) skeletons was accomplished by subjecting them to controlled heating in an oil bath. A comprehensive characterization of the synthesized materials confirms the successful growth of Fe2O3 on the surface of 3D skeletons. This strategy effectively addresses the immobilization challenges associated with powdered photocatalysts. In photocatalytic degradation experiments targeting methyl orange (MO), the ATCh40-Fe2O3 skeletons exhibited a remarkable MO removal rate of 91% within 240 min. Under conditions where the pH of MO solution was maintained at 3, and the ATCh40-Fe2O3 skeletons were subjected to a heat treatment in a 150 °C blast drying oven for 2 hours, the degradation rate of MO remained substantial, achieving 90% removal after 6 cycles. In contrast, when the same synthetic procedure was applied to ABS/TPU (AT) skeletons, the resulting product was identified as α-FeOOH. The MO removal rate by the AT-α-FeOOH skeletons was considerably lower, reaching only 49% after 240 min. This research provided a practical approach for the construction of photocatalytic devices through the use of 3D printing technology.

Quantitative Parameters of Contrast-Enhanced Ultrasound Predicting the Response to Combined Immune Checkpoint Inhibitor and Anti-angiogenesis Therapies for Unresectable Hepatocellular Carcinoma.

OBJECTIVE: The aim of the work described here was to explore the value of contrast-enhanced ultrasound (CEUS) quantitative parameters in predicting the response of combined immune checkpoint inhibitor (ICI) and anti-angiogenesis therapies for unresectable hepatocellular carcinoma (HCC). METHODS: Sixty-six HCC patients who underwent combined ICI and anti-angiogenesis therapies were prospectively enrolled. A CEUS examination was performed at baseline, and tumor perfusion parameters were obtained with perfusion quantification software. The differences in CEUS quantitative parameters between the responder and non-responder groups were compared, and the correlations between CEUS parameters and progression-free survival (PFS) was evaluated. RESULTS: The objective response rate (ORR) was 21.2%. The values of rising time (RT) ratio, time to peak ratio, fall time ratio, peak enhancement ratio, wash-in rate ratio, wash-in perfusion index ratio and wash-out rate ratio differed significantly differed between the responder and non-responder groups (all p values < 0.05). Multivariable logistic regression analysis revealed that the RT ratio was the only independent factor associated with the ORR (odds ratio = 0.007, 95% confidence interval: 0.000-0.307, p = 0.010). The median RT ratios of the responder and non-responder groups were 36.9 and 58.9, respectively (p = 0.006). The appropriate cutoff point of the RT ratio was 80.1, determined with the X-tile program. Survival analysis indicated high PFS for the patients with a lower RT ratio (high RT ratio vs. low RT ratio = 4.4 mo vs. not reached, p = 0.001). CONCLUSION: CEUS quantitative parameters may predict the efficacy of ICI and anti-angiogenesis combined therapies for HCC.

Anlotinib combined with transarterial chemoembolization for unresectable hepatocellular carcinoma associated with hepatitis B virus: a retrospective controlled study.

Purpose: To investigate the efficacy and safety of combined treatment of anlotinib and transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (uHCC) associated with hepatitis B virus (HBV) infection. Methods: We retrospectively collected the data of 96 uHCC patients associated with HBV infection who received either TACE only (TO group; n = 64) or anlotinib combined with TACE (TA group; n = 32) from January 2017 to January 2021. The primary endpoint was overall survival (OS). The secondary outcomes included progression-free survival (PFS), tumor response according to modified Response Evaluation Criteria in Solid Tumors (mRECIST) and RECIST 1.1, and adverse events (AEs). Results: The median OS and median PFS were significantly longer in the TA group compared to the TO group (17.6 months vs. 9.4 months, p = 0.018; 6.7 months vs. 3.8 months, p = 0.003, respectively). In addition, the overall objective response rate (ORR) and disease control rate (DCR) numerically increased in the TA group (mRECIST, ORR 65.6% vs. 46.9%, p = 0.064, DCR 90.6% vs. 85.9%, p = 0.382; RECIST 1.1, ORR 46.9% vs. 15.6%, p = 0.001, DCR 90.6% vs. 85.9%, p = 0.382, respectively). It was worth noting that no treatment-related mortality occurred during the study. The most common AEs included elevated transaminases (56.3%), decreased appetite (46.9%), and abdominal pain (37.5%) in the TA group. Although the incidence rate of grade 3/4 AEs was higher in the TA group, all of them were controllable. Conclusions: The combination of anlotinib and TACE has shown promising results in improving outcomes for patients with HBV-related uHCC, as compared to TACE monotherapy. In addition, this combination therapy has demonstrated a controllable safety profile. However, further validation is urgently needed through randomized controlled trials with large sample sizes.

Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.

Background: Radiomics models could help assess the benign and malignant invasiveness and prognosis of pulmonary nodules. However, the lack of interpretability limits application of these models. We thus aimed to construct and validate an interpretable and generalized computed tomography (CT) radiomics model to evaluate the pathological invasiveness in patients with a solitary pulmonary nodule in order to improve the management of these patients. Methods: We retrospectively enrolled 248 patients with CT-diagnosed solitary pulmonary nodules. Radiomic features were extracted from nodular region and perinodular regions of 3 and 5 mm. After coarse-to-fine feature selection, the radiomics score (radscore) was calculated using the least absolute shrinkage and selection operator logistic method. Univariate and multivariate logistic regression analyses were performed to determine the invasiveness-related clinicoradiological factors. The clinical-radiomics model was then constructed using the logistic and extreme gradient boosting (XGBoost) algorithms. The Shapley additive explanations (SHAP) method was then used to explain the contributions of the features. After removing batch effects with the ComBat algorithm, we assessed the generalization of the explainable clinical-radiomics model in two independent external validation cohorts (n=147 and n=149). Results: The clinical-radiomic XGBoost model integrating the radscore, CT value, nodule length, and crescent sign demonstrated better predictive performance than did the clinical-radiomics logistic model in assessing pulmonary nodule invasiveness, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 [95% confidence interval (CI), 0.848-0.927] in the training cohort. The SHAP algorithm illustrates the contribution of each feature in the final model. The specific model decision process was visualized using a tree-based decision heatmap. Satisfactory generalization performance was shown with AUCs of 0.889 (95% CI, 0.823-0.942) and 0.915 (95% CI, 0.851-0.963) in the two external validation cohorts. Conclusions: An interpretable and generalized clinical-radiomics model for predicting pulmonary nodule invasibility was constructed to help clinicians determine the invasiveness of pulmonary nodules and devise assessment strategies in an easily understandable manner.

Comprehensive analysis of ZNF692 as a potential biomarker associated with immune infiltration in a pan cancer analysis and validation in hepatocellular carcinoma.

Currently, the roles of ZNF692 have been documented exclusively in lung, colon, and cervical cancers. However, its involvement in pan cancer remains unknown. In this study, we employed bioinformatics analysis and experimental validation to investigate the role of ZNF692 in pan cancer. Our findings revealed aberrant expression of ZNF692 across various types of cancer. High expression of ZNF692 was associated with poor overall survival (OS) in ACC, COAD, KIRC, LAML, and LIHC. ZNF692 exhibited promising diagnostic potential in certain tumor types. A significant correlation was observed between high ZNF692 expression and advanced stages of ACC, BLCA, KICH, KIRC, LIHC, and OV. The expression of ZNF692 exhibited a significant association with microsatellite instability (MSI) in eight types of cancer and tumor mutational burden (TMB) in ten types of cancer. A noteworthy correlation was observed between ZNF692 expression and immune infiltration as well as immune checkpoints. Amplification of ZNF692 emerged as the most frequent alteration in pan cancer. ZNF692 was implicated in various biological processes, cellular components, and molecular functions within the context of pan cancer. It is plausible that ZNF692 may contribute to chemotherapy and potentially be linked to chemoresistance. We constructed a competing endogenous RNA (ceRNA) network involving AC009403.11/miR-126-3p/ZNF692 in hepatocellular carcinoma (HCC). The expression of ZNF692 exhibited a notable upregulation in HCC cell lines. Aberrant expression of ZNF692 was observed across various types of cancer. ZNF692 holds potential as a valuable diagnostic, prognostic, and therapeutic target in the context of pan cancer.

Hepatic Arterial Infusion Chemotherapy Plus Lenvatinib and Tislelizumab with or Without Transhepatic Arterial Embolization for Unresectable Hepatocellular Carcinoma with Portal Vein Tumor Thrombus and High Tumor Burden: A Multicenter Retrospective Study.

Purpose: The current therapeutic strategies for high-risk, unresectable hepatocellular carcinoma (HCC) patients demonstrate suboptimal outcomes. This study aimed to assess the clinical efficacy of the combined approach of hepatic arterial infusion chemotherapy (HAIC), lenvatinib, and tislelizumab, either with or without transhepatic arterial embolization (TAE), in managing HCC patients with portal vein tumor thrombus (PVTT) and significant tumor load. Patients and Methods: In this multicenter retrospective study, we analyzed patients diagnosed with primary, unresectable HCC presenting with PVTT and substantial tumor load who had undergone treatment with HAIC, lenvatinib, and tislelizumab, with or without TAE (referred to as the THLP or HLP group), between January 2019 and February 2022 across four medical centers in China. The outcomes included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and progression-free survival (PFS). Results: The study cohort comprised 100 patients, 50 each in the THLP and HLP groups. The THLP group demonstrated a significantly superior ORR (72% vs 52%, P=0.039). However, both groups exhibited comparable DCR (88% vs 76%, P=0.118), as assessed by the modified response evaluation criteria in solid tumors. The median OS and PFS for the entire cohort were 12.5 months (95% CI, 10.9-14.8) and 5.0 months (95% CI, 4.2-5.4), respectively. The THLP group exhibited a significantly extended OS (median, 14.1 vs 11.3 months, P=0.041) and PFS (median, 5.6 vs 4.4 months, P=0.037) in comparison to the HLP group. The most frequently reported treatment-related adverse events included abdominal pain and nausea, both reported by 59% of patients. Conclusion: The combination of HAIC, lenvatinib, tislelizumab, and TAE was feasible in HCC patients with PVTT and high tumor burden, with tolerable safety.

A Hybrid Multi-Scale Attention Convolution and Aging Transformer Network for Alzheimer's Disease Diagnosis.

Deep neural networks have been successfully investigated in the computational analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, such as Alzheimer's disease (AD). The disease-related changes in sMRI may be different in local brain regions, which have variant structures but with some correlations. In addition, aging increases the risk of dementia. However, it is still challenging to capture the local variations and long-range correlations of different brain regions and make use of the age information for disease diagnosis. To address these problems, we propose a hybrid network with multi-scale attention convolution and aging transformer for AD diagnosis. First, to capture the local variations, a multi-scale attention convolution is proposed to learn the feature maps with multi-scale kernels, which are adaptively aggregated by an attention module. Then, to model the long-range correlations of brain regions, a pyramid non-local block is used on the high-level features to learn more powerful features. Finally, we propose an aging transformer subnetwork to embed the age information into image features and capture the dependencies between subjects at different ages. The proposed method can learn not only the subject-specific rich features but also the inter-subject age correlations in an end-to-end framework. Our method is evaluated with T1-weighted sMRI scans from a large cohort of subjects on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that our method has achieved promising performance for AD-related diagnosis.

Graph Transformer Geometric Learning of Brain Networks Using Multimodal MR Images for Brain Age Estimation.

Brain age is considered as an important biomarker for detecting aging-related diseases such as Alzheimer's Disease (AD). Magnetic resonance imaging (MRI) have been widely investigated with deep neural networks for brain age estimation. However, most existing methods cannot make full use of multimodal MRIs due to the difference in data structure. In this paper, we propose a graph transformer geometric learning framework to model the multimodal brain network constructed by structural MRI (sMRI) and diffusion tensor imaging (DTI) for brain age estimation. First, we build a two-stream convolutional autoencoder to learn the latent representations for each imaging modality. The brain template with prior knowledge is utilized to calculate the features from the regions of interest (ROIs). Then, a multi-level construction of the brain network is proposed to establish the hybrid ROI connections in space, feature and modality. Next, a graph transformer network is proposed to model the cross-modal interaction and fusion by geometric learning for brain age estimation. Finally, the difference between the estimated age and the chronological age is used as an important biomarker for AD diagnosis. Our method is evaluated with the sMRI and DTI data from UK Biobank and Alzheimer's Disease Neuroimaging Initiative database. Experimental results demonstrate that our method has achieved promising performances for brain age estimation and AD diagnosis.

Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images.

It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.

Clinical observation on therapeutic effect of filiform fire needle for vitiligo: A retrospective study.

Vitiligo is a common and refractory disease worldwide. The limited efficiency and side effects of the conventional treatment options create demands towards the development of strategies. Excellent repigmentation is demonstrated after several filiform fire needle sessions in the vitiligo lesions. In this observational study, we aimed to observe the response to filiform fire needle therapy in patients with vitiligo, and determine whether there was a difference of efficiency with respect to the type, affected site, and disease duration of vitiligo. Patients received filiform fire needle therapy once every 2 weeks for 12 consecutive weeks. The results of the 77 vitiligo patients were: 34 (44.15%) with an excellent repigmentation rate, 15 (19.48%) with a marked improvement, 15 (19.48%) with a moderate response, 6 (7.79%) with a slight improvement, and 7 (9.09%) with an absent response. Among the vitiligo patients with different affected sites, the most effective location of therapy was the face. Shorter course leads to better therapeutic effect. Two patients developed hypertrophic scars on the lesion site. In conclusion, this study shows filiform fire needle therapy is an effective and relatively safe therapeutic option for vitiligo.