The prognosis of esophageal squamous cell carcinoma is poor. In order to find out appropriate treatment for each group of patients, we aim to examine the prognostic factors influencing survival for esophageal cancer patients in Taiwan. Data were obtained from the Taiwan Society of Cancer Registry. There were 14,394 esophageal cancer patients analyzed between 2008 and 2014 in this retrospective review. The impact of the clinicopathologic factors on overall survival was assessed. The following clinic‐pathologic factors were included to analyses: age, sex, tumor location, tumor length, histologic grade, clinical T, clinical N, clinical M, clinical stage, and all therapeutic methods within 3 months after diagnosis. The 5‐year survival rate was 16.8%, with a median survival of 343 days. The distribution of patients by their clinical stage is as follows: stage 0 (n = 162; 1.1%); stage I (n = 964; 6.7%); stage II (n = 2392; 16.6%); stage III (n = 6636; 46.1%); and stage IV (n = 3661; 25.4%). In the multivariate analysis, age, sex, tumor location, tumor length, clinical T, clinical N, clinical M, and treatment remained independent prognostic factors. Our data indicated that age, sex, tumor location, tumor length, clinical T, clinical N, clinical M, and treatment remained independent prognostic factors. Patients who could receive surgery had significantly better outcomes.

Lung cancer is one of the leading causes of cancer related deaths worldwide. Marine microalgae are a source of biologically active compounds and are widely consumed as a nutritional supplement in East Asian countries. It has been reported that Chlorella or Chlorella extracts have various beneficial pharmacological compounds that modulate immune responses; however, no studies have investigated the anti-cancer effects of Chlorella sorokiniana (CS) on non-small cell lung cancer (NSCLC). In this study, we evaluated the anti-cancer effects of CS in two human NSCLC cell lines (A549 and CL1-5 human lung adenocarcinoma cells), and its effects on tumor growth in a subcutaneous xenograft tumor model. We also investigated the possible molecular mechanisms governing the pharmacological function of CS. Our results showed that exposure of the two cell lines to CS resulted in a concentration-dependent reduction in cell viability. In addition, the percentage of apoptotic cells increased in a dose-dependent manner, suggesting that CS might induce apoptosis in human NSCLC cells. Western blot analysis revealed that exposure to CS resulted in increased protein expression of the cleaved/activated forms of caspase-3, caspase-9, and PARP, except caspase-8. ZDEVD (caspase-3 inhibitor) and Z-LEHD (caspase-9 inhibitor) were sufficient at preventing apoptosis in both A549 and CL1-5 cells, proving that CS induced cell death via the mitochondria-mediated apoptotic pathway. Exposure of A549 and CL1-5 cells to CS for 24 h resulted in decreased expression of Bcl-2 protein and increased expression of Bax protein as well as decreased expression of two IAP family proteins, survivin and XIAP. We demonstrated that CS induces mitochondrial-mediated apoptosis in NSCLC cells via downregulation of Bcl-2, XIAP and survivin. In addition, we also found that the tumors growth of subcutaneous xenograft in vivo was markedly inhibited after oral intake of CS.

Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process.

Water-soluble upconversion nanoparticles (UCNPs) that exhibit significant ultraviolet, blue, and red emissions under 980-nm laser excitation were successfully synthesized for performing near infrared (NIR)-triggered photodynamic therapy (PDT). The lanthanide-doped UCNPs bearing oleate ligands were first exchanged by citrates to generate polyanionic surfaces and then sequentially encapsulated with NH₂-terminated poly(amido amine) (PAMAM) dendrimers (G4) and chlorine6 (Ce6) using a layer-by-layer (LBL) absorption strategy. Transmission electron microscopy and X-ray diffraction analysis confirm that the hybrid UCNPs possess a polygonal morphology with an average dimension of 16.0 ± 2.1 nm and α-phase crystallinity. A simple calculation derived through thermogravimetric analysis revealed that one polycationic G4 dendrimer could be firmly accommodated by approximately 150 polyanionic citrates through multivalent interactions. Moreover, zeta potential measurements indicated that the LBL fabrication results in the hybrid nanoparticles with positively charged surfaces originated from these dendrimers, which assist the cellular uptake in biological specimens. The cytotoxic singlet oxygen based on the photosensitization of the adsorbed Ce6 through the upconversion emissions can be readily accumulated by increasing the irradiation time of the incident lasers. Compared with that of 660-nm lasers, NIR-laser excitation exhibits optimized in vitro PDT effects toward human breast cancer MCF-7 cells cultured in the tumorspheres, and less than 40% of cells survived under a low Ce6 dosage of 2.5 × 10⁻⁷ M. Fluorescence microscopy analysis indicated that the NIR-driven PDT causes more effective destruction of the cells located inside spheres that exhibit significant cancer stem cell or progenitor cell properties. Moreover, an in vivo assessment based on immunohistochemical analysis for a 4T1 tumor-bearing mouse model confirmed the effective inhibition of cancer cell proliferation through cellular DNA damage by the expression of Ki67 and γH2AX^ser139 protein markers. Thus, the hybrid UCNPs are a promising NIR-triggered PDT module for cancer treatment.