Sonodynamic therapy (SDT) is a noninvasive ultrasound-triggered therapeutic strategy for site-specific treatment of tumors with great depth penetration. The design of nano-sonosensitizers suitable for SDT treatment of bladder cancer (BCa) post-intravesical instillation has not yet been reported. Herein, a transmucosal oxygen-self-production SDT nanoplatform is developed to achieve highly efficient SDT against BCa. In this system, fluorinated chitosan (FCS) is synthesized as a highly effective nontoxic transmucosal delivery carrier to assemble with meso-tetra(4-carboxyphenyl)porphineconjugated catalase (CAT-TCPP). The formed CAT-TCPP/ FCS nanoparticles after intravesical instillation into the bladder cavity exhibit excellent transmucosal and intratumoral penetration capacities and could efficiently relieve hypoxia in tumor tissues by the catalase-catalyzed O 2 generation from tumor endogenous H 2 O 2 to further improve the therapeutic efficacy of SDT to ablate orthotopic bladder tumors under ultrasound. Our work presents a nano-sonosensitizer formulation with FCS to enhance transmucosal delivery and intratumoral diffusion and CAT to improve tumor oxygenation, promising for instillation-based SDT to treat bladder tumors without the concern of systemic toxicity.

Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. Results All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. Conclusions This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.

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BackgroundEarly detection of the Acute Respiratory Distress Syndrome (ARDS) has the potential to improvethe prognosis of critically ill patients admitted to the intensive care unit (ICU). However, no reliable biomarkers are currently available for accurate early detection of ARDS in patients with predisposing conditions.ObjectivesThis study examined risk factors and biomarkers for ARDS development and mortality in two prospective cohort studies.MethodsWe examined clinical risk factors for ARDS in a cohort of 178 patients in Beijing, China who were admitted to the ICU and were at high risk for ARDS. Identified biomarkers were then replicated in a second cohort of1,878 patients in Boston, USA.ResultsOf 178 patients recruited from participating hospitals in Beijing, 75 developed ARDS. After multivariate adjustment, sepsis (odds ratio [OR]:5.58, 95% CI: 1.70–18.3), pulmonary injury (OR: 3.22; 95% CI: 1.60–6.47), and thrombocytopenia, defined as platelet count <80×103/µL, (OR: 2.67; 95% CI: 1.27–5.62)were significantly associated with increased risk of developing ARDS. Thrombocytopenia was also associated with increased mortality in patients who developed ARDS (adjusted hazard ratio [AHR]: 1.38, 95% CI: 1.07–1.57) but not in those who did not develop ARDS(AHR: 1.25, 95% CI: 0.96–1.62). The presence of both thrombocytopenia and ARDS substantially increased 60-daymortality. Sensitivity analyses showed that a platelet count of <100×103/µLin combination with ARDS provide the highest prognostic value for mortality. These associations were replicated in the cohort of US patients.ConclusionsThis study of ICU patients in both China and US showed that thrombocytopenia is associated with an increased risk of ARDS and platelet count in combination with ARDS had a high predictive value for patient mortality.