Automated machine learning-based model for the prediction of delirium in patients after surgery for degenerative spinal disease.

OBJECTIVE: This study used machine learning algorithms to identify critical variables and predict postoperative delirium (POD) in patients with degenerative spinal disease. METHODS: We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The LASSO method was used to screen essential features associated with POD. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. The eXtreme Gradient Boosting algorithms (XGBOOST) model was used to predict POD. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Data of 49 patients were prospectively collected for model validation. RESULTS: The XGBOOST model outperformed the other classifier models in the training set (area under the curve [AUC]: 92.8%, 95% confidence interval [CI]: 90.7%-95.0%), validation set (AUC: 87.0%, 95% CI: 80.7%-93.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission-to-surgery time interval, C-reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular-cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission-intraoperative maximum blood pressure difference, were selected. The XGBOOST model performed well in the prospective cohort (accuracy: 85.71%). CONCLUSION: A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of POD risk during the perioperative period, which could guide appropriate preventive measures for high-risk patients.

Noncovalent Indocyanine Green Conjugate of C-Phycocyanin: Preparation and Tumor-Associated Macrophages-Targeted Photothermal Therapeutics.

Fabrication of a multifunctional near-infrared (NIR) theranostic nanoplatform has attracted increasing attention. Indocyanine green (ICG), a clinic-approved NIR fluorescence-imaging agent, is an excellent photothermal agent candidate. However, the stability and tumor targeting are still great obstacles for its wide application. In this work, C-phycocyanin (CPC) as a tumor-associated macrophages (TAMs) targeted vehicle was used to fabricate noncovalent ICG conjugate of CPC (ICG@CPC) via self-assembly in aqueous media. Compared to free ICG, ICG@CPC displays improved stabilities in aqueous solutions and under light irradiation and threefold increase in photothermal conversion efficiency. The in vitro results indicated that ICG@CPC could be selectively internalized into J774A.1 cells via SR-A-mediated endocytosis and lead to enhanced photocytotoxicity against J774A.1 cells. In vivo results showed that ICG@CPC had significantly improved drug accumulation in the tumor and photothermal therapeutic efficacy relative to ICG alone. This study for the first time utilizes CPC as a TAMs-targeted nanocarrier for ICG and may promote further rational design of ICG-based photothermal nanodrugs for precise and efficient cancer theranosis.

C-Phycocyanin as a tumour-associated macrophage-targeted photosensitiser and a vehicle of phthalocyanine for enhanced photodynamic therapy.

C-Phycocyanin (CPC) as a tumour-associated macrophage (TAM)-targeted photosensitiser has been first proved, and used as a vehicle of zinc phthalocyanine (ZnPc) to fabricate a ZnPc-CPC conjugate, which exhibits an efficient in vitro photodynamic activity, and selectively accumulates in tumour sites probably due to the affinity to TAM.

Mono- and tetra-substituted zinc(II) phthalocyanines containing morpholinyl moieties: Synthesis, antifungal photodynamic activities, and structure-activity relationships.

A series of zinc(II) phthalocyanines (ZnPcs) mono-substituted and tetra-substituted with morpholinyl moieties and their quaternized derivatives have been synthesized and evaluated for their antifungal photodynamic activities toward Candida albicans. The α-substituted, quaternized, and mono-substituted ZnPcs are found to have higher antifungal photoactivity than ß-substituted, neutral, and tetra-substituted counterparts. The cationic α-mono-substituted ZnPc (6a) exhibits the highest photocytotoxicity. Moreover, it is more potent than axially di-substituted analogue. The different photocytotoxicities of these compounds have also been rationalized by investigating their spectroscopic and photochemical properties, aggregation trend, partition coefficients, and cellular uptake. The IC90 value of 6a against C. albicans cells is as low as 3.3 µM with a light dose of 27 J cm(-2), meaning that 6a is a promising candidate as the antifungal photosensitizer for future investigations.