Development and validation of radiology-clinical statistical and machine learning model for stroke-associated pneumonia after first intracerebral haemorrhage.

BACKGROUND: Society is burdened with stroke-associated pneumonia (SAP) after intracerebral haemorrhage (ICH). Cerebral small vessel disease (CSVD) complicates clinical manifestations of stroke. In this study, we redefined the CSVD burden score and incorporated it into a novel radiological-clinical prediction model for SAP. MATERIALS AND METHODS: A total of 1278 patients admitted to a tertiary hospital between 1 January 2010 and 31 December 2019 were included. The participants were divided into training and testing groups using fivefold cross-validation method. Four models, two traditional statistical models (logistic regression and ISAN) and two machine learning models (random forest and support vector machine), were established and evaluated. The outcomes and baseline characteristics were compared between the SAP and non-SAP groups. RESULTS: Among the of 1278 patients, 281(22.0%) developed SAP after their first ICH. Multivariate analysis revealed that the logistic regression (LR) model was superior in predicting SAP in both the training and testing groups. Independent predictors of SAP after ICH included total CSVD burden score (OR, 1.29; 95% CI, 1.03-1.54), haematoma extension into ventricle (OR, 2.28; 95% CI, 1.87-3.31), haematoma with multilobar involvement (OR, 2.14; 95% CI, 1.44-3.18), transpharyngeal intubation operation (OR, 3.89; 95% CI, 2.7-5.62), admission NIHSS score ≥ 10 (OR, 2.06; 95% CI, 1.42-3.01), male sex (OR, 1.69; 95% CI, 1.16-2.52), and age ≥ 67 (OR, 2.24; 95% CI, 1.56-3.22). The patients in the SAP group had worse outcomes than those in the non-SAP group. CONCLUSION: This study established a clinically combined imaging model for predicting stroke-associated pneumonia and demonstrated superior performance compared with the existing ISAN model. Given the poor outcomes observed in patients with SAP, the use of individualised predictive nomograms is vital in clinical practice.

Optimizing UVA and UVC synergy for effective control of harmful cyanobacterial blooms.

Harmful cyanobacterial blooms (HCBs) pose a global ecological threat. Ultraviolet C (UVC) irradiation at 254 nm is a promising method for controlling cyanobacterial proliferation, but the growth suppression is temporary. Resuscitation remains a challenge with UVC application, necessitating alternative strategies for lethal effects. Here, we show synergistic inhibition of Microcystis aeruginosa using ultraviolet A (UVA) pre-irradiation before UVC. We find that low-dosage UVA pre-irradiation (1.5 J cm-2) combined with UVC (0.085 J cm-2) reduces 85% more cell densities compared to UVC alone (0.085 J cm-2) and triggers mazEF-mediated regulated cell death (RCD), which led to cell lysis, while high-dosage UVA pre-irradiations (7.5 and 14.7 J cm-2) increase cell densities by 75-155%. Our oxygen evolution tests and transcriptomic analysis indicate that UVA pre-irradiation damages photosystem I (PSI) and, when combined with UVC-induced PSII damage, synergistically inhibits photosynthesis. However, higher UVA dosages activate the SOS response, facilitating the repair of UVC-induced DNA damage. This study highlights the impact of UVA pre-irradiation on UVC suppression of cyanobacteria and proposes a practical strategy for improved HCBs control.