Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.

Background: While the TNM staging system provides valuable insights into the extent of disease, predicting postoperative progression in early-stage non-small cell lung cancer (NSCLC) remains a significant challenge. An effective bioimaging prognostic marker for early-stage NSCLC, powered by artificial intelligence, could greatly assist clinicians in making informed treatment decisions.

Methods: A total of 926 patients from four centers (A, B, C, and D) with histologically confirmed stage I or II solid non-small cell lung cancer (NSCLC) who underwent surgical resection were retrospectively reviewed.

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

Rationale And Objectives: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic information from whole-slide images (WSIs) to predict the epidermal growth factor receptor (EGFR) mutations of primary lung adenocarcinoma in patients with advanced-stage disease.

Materials And Methods: Data from 396 patients with lung adenocarcinoma across two medical institutions were analyzed. The data from 243 cases were divided into a training set (n=145) and an internal validation set (n=98) in a 6:4 ratio, and data from an additional 153 cases from another medical institution were included as an external validation set.

Cell membrane derived biomimetic nanomedicine for precision delivery of traditional Chinese medicine in cancer therapy.

The rapidly developing modern nanotechnology has brought new vitality to the application of traditional Chinese medicine (TCM), improving the pharmacokinetics and bioavailability of unmodified natural drugs. However, synthetic materials inevitably introduce incompatibilities. This has led to focusing on biomimetic drug delivery systems (DDS) based on biologically derived cell membranes.

Advances in copper-containing biomaterials for managing bone-related diseases.

Bone-related diseases pose a major challenge in contemporary society, with significant implications for both health and economy. Copper, a vital trace metal in the human body, facilitates a wide range of physiological processes by being crucial for the function of proteins and enzymes. Numerous studies have validated copper's role in bone regeneration and protection, particularly in the development and expansion of bone collagen.

Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer.

Purpose: To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).

Materials And Methods: A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria.

Clerodendranthus Spicatus: A review of its active compounds, mechanisms of action, and clinical studies in urinary diseases.

Clerodendranthus spicatus (Thunb.) C.Y.

Maximizing arsenic trioxide's anticancer potential: Targeted nanocarriers for solid tumor therapy.

Arsenic trioxide (ATO) has gained significant attention due to its promising therapeutic effects in treating different diseases, particularly acute promyelocytic leukemia (APL). Its potent anticancer mechanisms have been extensively studied. Despite the great efficacy ATO shows in fighting cancers, drawbacks in the clinical use are obvious, especially for solid tumors, which include rapid renal clearance and short half-life, severe adverse effects, and high toxicity to normal cells.

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.

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).

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

Objective: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs).

Materials And Methods: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network.