Methicillin-resistant Staphylococcus aureus (MRSA) has become a rising clinical problem as its occurrence has increased due to the overuse and misuse of antibiotics. In this work, upconversion nanoparticles@AgBiS₂ core-shell were produced with enhanced photothermal transformation efficiency and ability to produce reactive oxygen species for synergistic photodynamic photothermal and photodynamic antibacterial performance. The nanoparticles exhibit good antibacterial effects in vitro and satisfactory therapeutic performance on healing MRSA-infected wounds in vivo experiments. RNA-sequencing technique has been used to investigate and reveal that photothermal-photodynamic therapy using the nanoparticles can interfere with metabolic processes such as galactose metabolism in MRSA bacteria, destroy the transport system on the surface of MRSA, and affect quorum sensing to hinder the formation of biofilms to achieve effective antibacterial efficacy. It was demonstrated that this work presents an alternative near-infrared photoactive multimodal nanostructure for antibacterial applications.

Background: A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients. Methods: As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA). Results: Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838–0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems. Conclusions: We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757–0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792–0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.

COVID-19-associated pulmonary fibrosis remains frequent. This study aimed to investigate pulmonary redox balance in COVID-19 ARDS patients and possible relationship with pulmonary fibrosis and long-term lung abnormalities. Baseline data, chest CT fibrosis scores, N-terminal peptide of alveolar collagen III (NT-PCP-III), transforming growth factor (TGF)-β1, superoxide dismutase (SOD), reduced glutathione (GSH), oxidized glutathione (GSSG) and malondialdehyde (MDA) in bronchoalveolar lavage fluid (BALF) were first collected and compared between SARS-CoV-2 RNA positive patients with moderate to severe ARDS (n = 65, COVID-19 ARDS) and SARS-CoV-2 RNA negative non-ARDS patients requiring mechanical ventilation (n = 63, non-ARDS). Then, correlations between fibroproliferative (NT-PCP-III and TGF-β1) and redox markers were analyzed within COVID-19 ARDS group, and comparisons between survivor and non-survivor subgroups were performed. Finally, follow-up of COVID-19 ARDS survivors was performed to analyze the relationship between pulmonary abnormalities, fibroproliferative and redox markers 3 months after discharge. Compared with non-ARDS group, COVID-19 ARDS group had significantly elevated chest CT fibrosis scores (p < 0.001) and NT-PCP-III (p < 0.001), TGF-β1 (p < 0.001), GSSG (p < 0.001), and MDA (p < 0.001) concentrations on admission, while decreased SOD (p < 0.001) and GSH (p < 0.001) levels were observed in BALF. Both NT-PCP-III and TGF-β1 in BALF from COVID-19 ARDS group were directly correlated with GSSG (p < 0.001) and MDA (p < 0.001) and were inversely correlated with SOD (p < 0.001) and GSH (p < 0.001). Within COVID-19 ARDS group, non-survivors (n = 28) showed significant pulmonary fibroproliferation (p < 0.001) with more severe redox imbalance (p < 0.001) than survivors (n = 37). Furthermore, according to data from COVID-19 ARDS survivor follow-up (n = 37), radiographic residual pulmonary fibrosis and lung function impairment improved 3 months after discharge compared with discharge (p < 0.001) and were associated with early pulmonary fibroproliferation and redox imbalance (p < 0.01). Pulmonary redox imbalance occurring early in COVID-19 ARDS patients drives fibroproliferative response and increases the risk of death. Long-term lung abnormalities post-COVID-19 are associated with early pulmonary fibroproliferation and redox imbalance.

Three polycarboxylate coordination polymers containing 2-(2-pyridyl)benzimidazole as co-ligand, [Cu(Bdc)(2-PyBIm)] _n (1), [Cu(HBtc)(2-PyBIm)] _n (2) and [Cd₂(HBtc)₂(2-PyBIm)₂] _n ·; nH₂O (3) (H₂Bdc = 1,4-benzenedicarboxylic acid; H₃Btc = 1,3,5-benzenetricarboxylic acid), have been synthesized and characterized by elemental analyses, IR spectra, TG-DSC and X-ray structural analyses. Complex 1 is a one-dimensional zigzag chain in which the Cu(II) is cis six-coordinated by two chelating carboxyl groups and a 2-PyBIm ligand. Complex 2 is a two-dimensional (4, 4) network in which H₃Btc is partially deprotonated. Complex 3 has a three-dimensional framework in which one Cd(II) is six-coordinate and the other is seven-coordinate. All 2-PyBIm groups are neutral, chelating, bidentate ligands in 1–3. These complexes are quite thermally stable. The luminescence of 3 has also been investigated.