Deep Learning For Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS: In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS: The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS: Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

An integrative pan-cancer analysis of the molecular characteristics of dietary restriction in tumour microenvironment.

BACKGROUND: Dietary restriction (DR), a general term for dieting, has been demonstrated as an effective intervention in reducing the occurrence of cancers. Molecular activities associated with DR are crucial in mediating its anti-cancer effects, yet a comprehensive exploration of the landscape of these activities at the pan-cancer level is still lacking. METHODS: We proposed a computational approach for quantifying DR-related molecular activities and delineating the landscape of these activities across 33 cancer types and 30 normal tissues within 27,320 samples. We thoroughly examined the associations between DR-related molecular activities and various factors, including the tumour microenvironment, immunological phenotypes, genomic features, and clinical prognosis. Meanwhile, we identified two DR genes that show potential as prognostic predictors in hepatocellular carcinoma and verified them by immunohistochemical assays in 90 patients. FINDINGS: We found that DR-related molecular activities showed a close association with tumour immunity and hold potential for predicting immunotherapy responses in various cancers. Importantly, a higher level of DR-related molecular activities is associated with improved overall survival and cancer-specific survival. FZD1 and G6PD are two DR genes that serve as biomarkers for predicting the prognosis of patients with hepatocellular carcinoma. INTERPRETATION: This study presents a robust link between DR-related molecular activities and tumour immunity across multiple cancer types. Our research could open the path for further investigation of DR-related molecular processes in cancer treatment. FUNDING: National Natural Science Foundation of China (Grant No. 82000628) and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (Grant No. 2023LSYS001).

Development and validation of a deep learning signature for predicting lymphovascular invasion and survival outcomes in clinical stage IA lung adenocarcinoma: A multicenter retrospective cohort study.

PURPOSE: The presence of lymphovascular invasion (LVI) influences the management and outcomes of patients with clinical stage IA lung adenocarcinoma. The objective was the development of a deep learning (DL) signature for the prediction of LVI and stratification of prognosis. METHODS: A total of 2077 patients from three centers were retrospectively enrolled and divided into a training set (n = 1515), an internal validation set (n = 381), and an external set (n = 181). A -three-dimensional residual neural network was used to extract the DL signature and three models, namely, the clinical, DL, and combined models, were developed. Diagnostic efficiency was assessed by ROC curves and AUC values. Kaplan-Meier curves and Cox proportional hazards regression analyses were conducted to evaluate links between various factors and disease-free survival. RESULTS: The DL model could effectively predict LVI, shown by AUC values of 0.72 (95 %CI: 0.68-0.76) and 0.63 (0.54-0.73) in the internal and external validation sets, respectively. The incorporation of DL signature and clinical-radiological factors increased the AUC to 0.74 (0.71-0.78) and 0.77 (0.70-0.84) in comparison with the DL and clinical models (AUC of 0.71 [0.68-0.75], 0.71 [0.61-0.81]) in the internal and external validation sets, respectively. Pathologic LVI, LVI predicted by both DL and combined models were associated with unfavorable prognosis (all p < 0.05). CONCLUSION: The effectiveness of the DL signature in the diagnosis of LVI and prognosis prediction in patients with clinical stage IA lung adenocarcinoma was demonstrated. These findings suggest the potential of the model in clinical decision-making.

Proton-Gradient-Driven Porphyrin-Based Liposome Remote-Loaded with Imiquimod as In Situ Nanoadjuvants for Synergistically Augmented Tumor Photoimmunotherapy.

Cancer immunotherapy is expected to achieve tumor treatment mainly by stimulating the patient's own immune system to kill tumor cells. However, the low immunogenicity of the tumor and the poor efficiency of tumor antigen presentation result in a variety of solid tumors that do not respond to immunotherapy. Herein, we designed a proton-gradient-driven porphyrin-based liposome (PBL) with highly efficient Toll-like receptor 7 (TLR7) agonist (imiquimod, R837) encapsulation (R837@PBL). R837@PBL rapidly released R837 in the acid microenvironment to activate the TLR in the endosome inner membrane to promote bone-marrow-derived dendritic cell maturation and enhance antigen presentation. R837@PBL upon laser irradiation triggered immunogenic cell death of tumor cells and tumor-associated antigen release after subcutaneous injection, activated TLR7, formed in situ tumor nanoadjuvants, and enhanced the antigen presentation efficiency. Photoimmunotherapy promoted the infiltration of cytotoxic T lymphocytes into tumor tissues, inhibited the growth of the treated and abscopal tumors, and exerted highly effective photoimmunotherapeutic effects. Hence, our designed in situ tumor nanoadjuvants are expected to be an effective treatment for treated and abscopal tumors, providing a novel approach for synergistic photoimmunotherapy of tumors.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma.

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

BACKGROUND: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT). METHODS: Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking. RESULTS: A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC. CONCLUSIONS: Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.

Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task.

The black-box nature of machine learning models hinders the deployment of some high-accuracy medical diagnosis algorithms. It is risky to put one's life in the hands of models that medical researchers do not fully understand or trust. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai, China, to identify biomarkers indicative of infection severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, partial dependence plot, individual conditional expectation, accumulated local effects, local interpretable model-agnostic explanations, and Shapley additive explanation, we identify an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, a decrease in lymphocyte is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

Radiomics Nomogram with Added Nodal Features Improves Treatment Response Prediction in Locally Advanced Esophageal Squamous Cell Carcinoma: A Multicenter Study.

OBJECTIVE: We aimed to develop and validate a radiomics nomogram and determine the value of radiomic features from lymph nodes (LNs) for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: In this multicenter retrospective study, eligible participants who had undergone NCRT followed by radical esophagectomy were consecutively recruited. Three radiomics models (modelT, modelLN, and modelTLN) based on tumor and LN features, alone and combined, were developed in the training cohort. The radiomics nomogram was developed by incorporating the prediction value of the radiomics model and clinicoradiological risk factors using multivariate logistic regression, and was evaluated using the receiver operating characteristic curve, validated in two external validation cohorts. RESULTS: Between October 2011 and December 2018, 116 patients were included in the training cohort. Between June 2015 and October 2020, 51 and 27 patients from two independent hospitals were included in validation cohorts 1 and 2, respectively. The radiomics modelTLN performed better than the radiomics modelT for predicting pCR. The radiomics nomogram incorporating the predictive value of the radiomics modelTLN and heterogeneous after NCRT outperformed the clinicoradiological model, with an area under the curve (95% confidence interval) of 0.833 (0.765-0.894) versus 0.764 (0.686-0.833) [p = 0.088, DeLong test], 0.824 (0.718-0.909) versus 0.692 (0.554-0.809) [p = 0.012], and 0.902 (0.794-0.984) versus 0.696 (0.526-0.857) [p = 0.024] in all three cohorts. CONCLUSIONS: Radiomic features from LNs could provide additional value for predicting pCR in ESCC patients, and the radiomics nomogram provided an accurate prediction of pCR, which might aid treatment decision.

Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer.

Background: Colorectal cancer (CRC) is linked to distinct gut microbiome patterns. The efficacy of gut bacteria as diagnostic biomarkers for CRC has been confirmed. Despite the potential to influence microbiome physiology and evolution, the set of plasmids in the gut microbiome remains understudied. Methods: We investigated the essential features of gut plasmid using metagenomic data of 1,242 samples from eight distinct geographic cohorts. We identified 198 plasmid-related sequences that differed in abundance between CRC patients and controls and screened 21 markers for the CRC diagnosis model. We utilize these plasmid markers combined with bacteria to construct a random forest classifier model to diagnose CRC. Results: The plasmid markers were able to distinguish between the CRC patients and controls [mean area under the receiver operating characteristic curve (AUC = 0.70)] and maintained accuracy in two independent cohorts. In comparison to the bacteria-only model, the performance of the composite panel created by combining plasmid and bacteria features was significantly improved in all training cohorts (mean AUCcomposite = 0.804 and mean AUCbacteria = 0.787) and maintained high accuracy in all independent cohorts (mean AUCcomposite = 0.839 and mean AUCbacteria = 0.821). In comparison to controls, we found that the bacteria-plasmid correlation strength was weaker in CRC patients. Additionally, the KEGG orthology (KO) genes in plasmids that are independent of bacteria or plasmids significantly correlated with CRC. Conclusion: We identified plasmid features associated with CRC and showed how plasmid and bacterial markers could be combined to further enhance CRC diagnosis accuracy.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

PURPOSE: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). METHODS: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. RESULTS: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. CONCLUSIONS: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

Charge reversal yolk-shell liposome co-loaded JQ1 and doxorubicin with high drug loading and optimal ratio for synergistically enhanced tumor chemo-immunotherapy via blockade PD-L1 pathway.

Antitumor immunotherapy has become a powerful therapeutic modality to identify and kill various malignant tumors by harnessing the immune system. However, it is hampered by the immunosuppressive microenvironment and poor immunogenicity in malignant tumors. Herein, in order to achieve multi-loading of drugs with different pharmacokinetic properties and targets, a charge reversal yolk-shell liposome co-loaded with JQ1 and doxorubicin (DOX) into the poly (D,L-lactic-co-glycolic acid) (PLGA) yolk and the lumen of the liposome respectively was engineered to increase hydrophobic drug loading capacity and stability under physiological conditions and further enhance tumor chemotherapy via blockade programmed death ligand 1 (PD-L1) pathway. This nanoplatform could release less JQ1 compared to traditional liposomes to avoid drug leakage under physiological conditions due to the protection of liposomes on JQ1 loaded PLGA nanoparticles while the release of JQ1 increased in an acidic environment. In the tumor microenvironment, released DOX promoted immunogenic cell death (ICD), and JQ1 blocked the PD-L1 pathway to strengthen chemo-immunotherapy. The in vivo antitumor results demonstrated the collaborative treatment of DOX and JQ1 in B16-F10 tumor-bearing mice models with minimized systemic toxicity. Furthermore, the orchestrated yolk-shell nanoparticle system could enhance the ICD effect, caspase 3 activation, and cytotoxic T lymphocyte infiltration while inhibiting PD-L1 expression, provoking a strong antitumor effect, whereas yolk-shell liposomes encapsulating only JQ1 or DOX showed modest tumor therapeutic effects. Hence, the cooperative yolk-shell liposome strategy provides a potential candidate for enhancement of hydrophobic drug loading and stability, showing potential for clinic application and synergistic cancer chemo-immunotherapy.

Targeted Nanophotoimmunotherapy Potentiates Cancer Treatment by Enhancing Tumor Immunogenicity and Improving the Immunosuppressive Tumor Microenvironment.

Cancer immunotherapy, such as immune checkpoint blockade, chimeric antigen receptor, and cytokine therapy, has emerged as a robust therapeutic strategy activating the host immune system to inhibit primary and metastatic lesions. However, low tumor immunogenicity (LTI) and immunosuppressive tumor microenvironment (ITM) severely compromise the killing effect of immune cells on tumor cells, which fail to evoke a strong and effective immune response. As an exogenous stimulation therapy, phototherapy can induce immunogenic cell death (ICD), enhancing the therapeutic effect of tumor immunotherapy. However, the lack of tumor targeting and the occurrence of immune escape significantly reduce its efficacy in vivo, thus limiting its clinical application. Nanophotoimmunotherapy (nano-PIT) is a precision-targeted tumor treatment that co-loaded phototherapeutic agents and various immunotherapeutic agents by specifically targeted nanoparticles (NPs) to improve the effectiveness of phototherapy, reduce its phototoxicity, enhance tumor immunogenicity, and reverse the ITM. This review will focus on the theme of nano-PIT, introduce the current research status of nano-PIT on converting "cold" tumors to "hot" tumors to improve immune efficacy according to the classification of immunotherapy targets, and discuss the challenges, opportunities, and prospects.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning.

BACKGROUND: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

Puerarin: A review of its mechanisms of action and clinical studies in ophthalmology.

BACKGROUND: Pueraria is the common name of the dried root of either Pueraria montana var. lobata (Willd.) Maesen & S.M.Almeida ex Sanjappa & Predeep (syn. Pueraria lobata (Willd.) Ohwi) or Pueraria montana var. thomsonii (Benth.) M.R.Almeida (syn. Pueraria thomsonii Benth.). Puerarin is a C-glucoside of the isoflavone daidzein extracted from Pueraria. It has been widely investigated to explore its therapeutic role in eye diseases and the molecular mechanisms. PURPOSE: To collect the available literature from 2000 to 2022 on puerarin in the treatment of ocular diseases and suggest the future required directions to improve its medicinal value. METHOD: The content of this review was obtained from databases such as Web of Science, PubMed, Google Scholar, China National Knowledge Infrastructure (CNKI), and the Wanfang Database. RESULTS: The search yielded 428 articles, of which 159 articles were included after excluding duplicate articles and articles related to puerarin but less relevant to the topic of the review. In eleven articles, the bioavailability of puerarin was discussed. Despite puerarin possesses diverse biological activities, its bioavailability on its own is poor. There are 95 articles in which the therapeutic mechanisms of puerarin in ocular diseases was reported. Of these, 54 articles discussed the various signalling pathways related to occular diseases affected by puerarin. The other 41 articles discussed specific biological activities of puerarin. It plays a therapeutic role in ophthalmopathy via regulating nuclear factor kappa-B (NF-ĸB), mitogen-activated protein kinases (MAPKs), PI3K/AKT, JAK/STAT, protein kinase C (PKC) and other related pathways, affecting the expression of tumour necrosis factor α (TNF-α), interleukin-1ß (IL-1ß), intercellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), superoxide dismutase (SOD), B-cell lymphoma-2 (Bcl-2) and other cytokines resulting in anti-inflammatory, antioxidant and anti-apoptotic effects. The clinical applications of puerarin in ophthalmology were discussed in 25 articles. Eleven articles discussed the toxicity of puerarin. The literature suggests that puerarin has a good curative effect and can be used safely in clinical practice. CONCLUSION: This review has illustrated the diverse applications of puerarin acting on ocular diseases and suggested that puerarin can be used for treating diabetic retinopathy, retinal vascular occlusion, glaucoma and other ocular diseases in the clinic. Some ocular diseases are the result of the combined action of multiple factors, and the effect of puerarin on different factors needs to be further studied to improve a more complete mechanism of action of puerarin. In addition, it is necessary to increase the number of subjects in clinical trials and conduct clinical trials for other ocular diseases. The information presented here will guide future research studies.

Sophoridine: A review of its pharmacology, pharmacokinetics and toxicity.

BACKGROUND: Sophoridine is a bioactive alkaloid found in many Chinese herbs, such as Sophora alopecuroides l, Euchresta japonica Benth and Sophora moocrorftinan. Sophoridine hydrochloride injection has been approved as an anticancer drug in China. PURPOSE: This review aims to provide a comprehensive summary on the pharmacological, molecular mechanism, pharmacokinetic and toxicity studies of sophoridine. METHOD: PubMed, Web of Science and China National Knowledge Infrastructure were used for a systematic search with the keywords including "sophoridine", "pharmacology", "pharmacokinetics", and "toxicity". RESULTS: Emerging evidence suggests that sophoridine exhibits a broad spectrum of pharmacological activities including antitumor, anti-inflammatory, antiviral, myocardialprotective and hepatoprotective activities. These pharmacological properties lay foundation for using the plants containing sophoridine for the treatment of numerous diseases, such as cancer, colitis, injury of lungs, ischemia myocardial,etc. The mechanisms involved in the pharmacological actions of sophoridine are regulation of NF-κB, TLR4/IRF3, JNK/ERK, Akt/mTOR signaling pathways, down-regulating the expression of HMG3B, bcl-2, MMP-2, MMP-9, TNF-α, IL-1ß IL-6 and other cytokines or kinases. However, an increasing number of published reports indicated that sophoridine has serious adverse effects. The primary toxic effects are neurotoxicity and acute toxicity, which are of wide concern in worldwide. Moreover, sophoridine is reported to distribute in kidney, liver, uterus, lung and other organs. It undergoes glucuronidation and excreted in urine. CONCLUSION: Future studies should elucidate the detailed in vivo metabolism studies on sophoridine. The effect of substituent functional groups on sophoridine on metabolism, the enzymes involved in the metabolism and the chemistry of metabolites also should be studied. Either structural modification of sophoridine or its combined with other drugs may play a pivotal role to enhance its pharmacological activities and reduce its toxicity.

Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.

OBJECTIVES: To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS: This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV-3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS: A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV-3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS: A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]-3~+3) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.

Fructus arctii: an overview on its traditional uses, pharmacology and phytochemistry.

OBJECTIVES: Fructus arctii (F. arctii) is the dried ripe fruit of Arctium lappa Willd (Asteraceae). It is being used as a traditional medicine in China, Japan, Iran, Europe, Afghanistan, India, etc. for cough, inflammation, clearing the heat, detoxification, cancer and diabetes. This review summarized the botanical description, distribution, ethnopharmacology, bioactive constituents and pharmacological actions of F. arctii including methods to assess its quality. In addition, this review also provides insights into future research directions on F. arctii to further explore its bioactive constituents, mechanism involved in pharmacological activity, and clinical use including the development of new analytical methods for assessing the quality. KEY FINDINGS: The comprehensive analysis of the literature revealed that F. arctii contains lignans, volatile oil, flavonoids, sesquiterpenoids, triterpenes, phenolic acids, etc. Experimental studies on various extracts and drug formulations showed that it has antioxidant, antimicrobial, hypoglycaemic, lipid-lowering, anti-inflammatory, analgesic, antiviral, anti-tumour activity, etc. SUMMARY: The pharmacological activity of a few major constituents in F. arctii have been identified. However, there are still need more studies and more new technologies to prove the pharmacological activity and the effective mechanism of the other constituents that undergoing uncertain. Except for the animal experiments, clinical studies should be carried out to provide the evidence for clinical application.