Logistic regression modeling of cytokines for cerebrospinal fluid evaluation in primary central nervous system lymphoma.

BACKGROUND: The diagnostic utility of cerebrospinal fluid (CSF) cytology encounters impediments stemming from variability in cell collection techniques and pathologists' morphological acumen, resulting in wide-ranging CSF positivity rates for primary central nervous system lymphomas (PCNSL). Such disparity impacts patient evaluation, treatment stratagem, and prognostication. Thus, this study endeavors to explore liquid biomarkers complementary to CSF cytology or immunophenotype analysis in the diagnosis of CSF involvement. METHODS: 398 newly diagnosed PCNSL patients were categorized into CSF involvement and non-involvement groups based on CSF cytology and immunophenotype analysis. Binary logistic regression analysis was performed on 338 patients to investigate factors predicting CSF involvement and to develop a joint prediction model. An additional cohort of 60 PCNSL patients was recruited for model validation. Statistical analyses included the Mann-Whitney U test for comparing various CSF parameters between two groups. ROC curve analyses were performed for each biomarker to identify PCNSL CSF involvement. RESULTS: The cytokine IL-10 level in CSF has emerged as the most promising biomarker for CSF evaluation, boasting an ROC AUC of 0.922. C-TNFα and soluble C-IL2R demonstrate efficacy in quantifying tumor burden within the CSF. Logistic regression identified C-IL10lg (OR = 30.103, P < 0.001), C-TNC (OR = 1.126, P < 0.001), C-IL2Rlg (OR = 3.743, P = 0.029) as independent predictors for CSF involvement, contributing to a joint predictive model with an AUC of 0.935, sensitivity of 74.1 %, and specificity of 93.0 %. Validation of the model in an independent cohort confirmed its effectiveness, achieving an AUC of 0.9713. CONCLUSIONS: The identification of these feasible biomarkers and the development of an accurate prediction model may facilitate the precise evaluation of CSF status in PCNSL, offering significant advancements in patient management.

[Nitrogen Removal Performance of ANAMMOX with Different Organic Carbon Sources].

Anaerobic ammonium oxidation (ANAMMOX) has been regarded as an efficient process to treat high-strength wastewater without organic carbon source. To investigate the nitrogen removal performance of ANAMMOX in the presence of organic carbon source can broaden its application in organic wastewater treatment. In this work, an anaerobic sequencing batch reactor (ASBR) was used to study the effect of organic carbon source on ANAMMOX process. The experimental results indicated that the activity of anaerobic ammonia oxidizing bacteria (AAOB) decreased by 84.2% when 200 mg·L-1 COD of glucose was added. When sodium acetate was added, the activity of AAOB was affected little. Besides, it even promoted the activity with COD less than 120 mg·L-1. The effect of sucrose on ANAMMOX process was similar to that of sodium acetate and the maximum specific ANAMMOX activity (SAA) increased by 25.0% with 80 mg·L-1 COD. When citric acid was added, the maximum SAA peaked with 80 mg·L-1 COD. The order of ANAMMOX promotion resulted from organic carbon source was sucrose, sodium acetate, citric acid and glucose. With addition of organic carbon source, nitrate could also be removed through the synergy of ANAMMOX and denitrification, and the total nitrogen removal efficiency increased.

[Recovery Performance of ANAMMOX Process after Inhibition Resulting from Seawater].

An anaerobic sequencing batch reactor (ASBR) was operated to investigate the recovery performance of ANAMMOX reactor after the inhibition of 100% seawater concentration. The results showed that the nitrogen removal performance of ANAMMOX reactor suffered inhibition of high salinity concentration. However, it could enter a period of stable nitrogen removal efficiency again after an interim stable period and a recovery period. The nitrogen removal rate (NRR) could reach 0.52 kg·(m3·d)-1, which was similar to the control group, containing 10% seawater and having a NRR of 0.462 kg·(m3·d)-1. The modified Logistic model and modified Gompertz model were revised and their application field was broadened. The re-modified Logistic model was suggested to be used to simulate the NRR recovery process of ANAMMOX reactor that suffered inhibition of 100% seawater concentration. The doubling time of NRR was calculated to be 11.359 d using the prediction formula established for the recovery time of NRR.