Development and Validation of a Predictive Model for Postoperative Intracranial Infections in Neurosurgery with Risk Factor Analysis.

BACKGROUND: Currently, the diagnosis of post-neurosurgical intracranial infection is mainly dependent on cerebrospinal fluid (CSF) bacterial culture which has the disadvantages of time-consuming, low detection rate and easy to be affected by other factors. These disadvantages bring some difficulties to early diagnosis. Therefore, it is very important to construct a nomogram model to predict the risk of infection to provide a basis for early diagnosis and treatment. METHOD: This retrospective study analyzed post-neurosurgical patient data from the Fourth Affiliated Hospital of Harbin Medical University between January 2019 and September 2023. The patients were randomly assigned in an 8:2 ratio into the training cohort and the internal validation cohort. In the training cohort, initial screening of relevant indices was conducted via univariate analysis. Subsequently, the least absolute shrinkage and selection operator (Lasso) logistic regression identified significant potential risk factors for inclusion in the nomogram model. The model's discriminative ability was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), and its calibration was evaluated through calibration plots. The clinical utility of the model was determined using decision curve analysis (DCA) and further validated by the internal validation cohort. RESULTS: Multivariate logistic regression analysis of the training cohort identified seven independent risk factors for postoperative intracranial infection: duration of postoperative external drainage (odds ratio [OR] 1.19, P=0.005), continued fever (OR 2.11, P=0.036), CSF turbidity (OR 2.73, P=0.014), CSF pressure (OR 1.01, P=0.018), CSF total protein level (OR 1.26, P=0.026), CSF glucose concentration (OR 0.74, P=0.029), and postoperative serum albumin level (OR 0.84, P<0.001). Using these variables to construct the final model. The AUC value of the model was 0.868 in the training cohort and 0.900 in the internal validation cohort. Calibration and the DCA curve indicated high accuracy and clinical benefit of the nomogram, findings that were corroborated in the validation cohort. CONCLUSION: This study successfully developed a novel nomogram for predicting postoperative intracranial infection, demonstrating excellent predictive performance. It offers a pragmatic tool for early diagnosis of intracranial infection.

Causal Associations Between Gut Microbiota and Cerebrovascular Diseases.

BACKGROUND: Numerous studies suggest that the gut microbiota closely linked to cerebrovascular diseases, such as Intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (aSAH). Nevertheless, the confirmation of a definitive causal connection between gut microbiota, IA, and aSAH is still pending. The aim of our research is to explore the potential bidirectional causality among them. METHODS: This bidirectional Mendelian Randomization (MR) study used single nucleotide polymorphisms linked to gut microbiota, IA, and aSAH from Genome-Wide Association Studies. The Inverse Variance Weighted (IVW) method was used to explore causality. To assess the robustness of the result, sensitivity analyses were further performed, including weighted-median method, MR-Egger regression, Maximum-likelihood method, MR pleiotropy residual sum and outlier test and leave-one-out analysis. RESULTS: In the IVW method, the family Porphyromonadaceae (odds ratio [OR] 0.63; 95% CI 0.47-0.85; P: 0.002) and genus Bilophila (OR 0.66; 95% CI 0.50-0.86; P: 0.002) showed a significant negative association with the risk of IA. Similarly, the genus Bilophila (OR: 0.68; 95% CI: 0.50-0.93; P: 0.017) and genus Ruminococcus1 (OR: 0.48; 95% CI: 0.30-0.78; P: 0.003) were linked to reduced risk of aSAH. The sensitivity analysis yielded similar outcomes in the IVW approach. Through the adoption of reverse MR analysis, a potential correlation between IA and decreased abundance of genus Ruminococcus1 was identified (OR 0.94; 95% CI 0.90-0.99; P 0.024). CONCLUSIONS: This MR analysis investigated the causal associations between gut microbiota, IA, and aSAH risks. The findings expanded current knowledge of the microbiota-gut-brain axis and offered novel perspectives on preventing and managing these conditions.

Effects of wavelength on the treatment of contaminants of emerging concern by UV-assisted homogeneous advanced oxidation/reduction processes.

Pollutants of emerging concern in aqueous environments present a significant threat to both the aquatic ecosystem and human health due to their rapid transfer. Among the various treatment approaches to remove those pollutants, UV-assisted advanced oxidation/reduction processes are considered competent and cost-effective. The treatment effectiveness is highly dependent on the wavelength of the UV irradiation used. This article systematically discusses the wavelength dependency of direct photolysis, UV/peroxides, UV/chlor(am)ine, UV/ClO2, UV/natural organic matter, UV/nitrate, and UV/sulfite on the transformation of contaminants. Altering wavelengths affects the photolysis of target pollutants, photo-decay of the oxidant/reductant, and quantum yields of reactive species generated in the processes, which significantly impact the degradation rates and formation of disinfection byproducts. In general, the degradation of contaminants is most efficient when using wavelengths that closely match the highest molar absorption coefficients of the target pollutants or the oxidizing/reducing agents, and the contribution of pollutant absorption is generally more significant. By matching the wavelength with the peak absorbance of target compounds and oxidants/reductants, researchers and engineers have the potential to optimize the UV wavelengths used in UV-AO/RPs to effectively remove pollutants and control the formation of disinfection byproducts.