Optical method supported by machine learning for dynamics of C-reactive protein concentrations changes detection in biological matrix samples.

In this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern, such as hospitals, schools, and so on. The proposed spectroscopy method supported with machine learning for real-time detection of infectious agents will eliminate the need for time-consuming processes, which contribute to reducing costs. The spectra in range 220-750 nm were used for the study. We achieve accuracy of our prediction model up to 68% with using only absorption spectrophotometer and machine learning. The use of such a set makes the method universal, due to the possibility of using many different detectors.

C-reactive protein (CRP) evaluation in human urine using optical sensor supported by machine learning.

The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical diagnostics because, depending on that factor, it may indicate, e.g., the occurrence of inflammation of various origins, oncological, cardiovascular, bacterial or viral events. In this study, we describe an interferometric sensor able to detect the CRP level for distinguishing between no-inflammation and inflammation states. The measurement head was made of a single mode optical fiber with a microsphere structure created at the tip. Its surface has been biofunctionalized for specific CRP bonding. Standardized CRP solutions were measured in the range of 1.9 µg/L to 333 mg/L and classified in the initial phase of the study. The real samples obtained from hospitalized patients with diagnosed Urinary Tract Infection or Urosepsis were then investigated. 27 machine learning classifiers were tested for labeling the phantom samples as normal or high CRP levels. With the use of the ExtraTreesClassifier we obtained an accuracy of 95% for the validation dataset. The results of real samples classification showed up to 100% accuracy for the validation dataset using XGB classifier.

Clinical image of sepsis-associated encephalopathy midst E. coli urosepsis: Emergency department database study.

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, which, if untreated, leads to multi-organ failure. One of the severe possible complications is sepsis associated encephalopathy (SAE), a neurological dysfunction occurring secondary to a severe inflammatory response. It manifests as acute cognitive dysfunction and sudden-onset dysfunctions in mental state. Uropathogenic Escherichia coli is the most common pathogen causing bacteremia, responsible for 80% of uncomplicated outpatient urinary tract infections and 40% of nosocomial infections. The study aimed to assess the difference in the severity and the course of urosepsis caused by E. coli in patients with and without septic encephalopathy. Materials and methods: This study presents a retrospective analysis of the population of urosepsis patients admitted to the Emergency Department between September 2019 and June 2022. Inflammatory parameters, urinalysis and blood cultures were performed, along with a clinical evaluation of sepsis severity and encephalopathy. The patients were then stratified into SAE and non-SAE groups based on neurological manifestations and compared according to the collected data. Results: A total of 199 septic patients were included in the study. E. coli-induced urosepsis was diagnosed in 84 patients. In this group, SAE was diagnosed in 31 (36.9%) patients (33.3% in males, 40.5% females). Patients with SAE were found to be hypotensive (p < 0,005), with a higher respiratory rate (p < 0,017) resulting in a higher mortality rate (p = 0.002) compared to non-SAE septic patients. The APACHE II score was an independent risk factor associated with a higher mortality rate. Biochemical parameters between the groups did not show any statistical importance related to the severity of urosepsis. Conclusions: The severity of urosepsis and risk of SAE development increase according to the clinical condition and underlying comorbidities. Urosepsis patients with SAE are at a higher risk of death. Patients should undergo more careful screening for the presence of SAE on admission, and more intense monitoring and treatment should be provided for patients with SAE. This study indicates the need to develop projects aiming to further investigate neuroprotective interventions in sepsis.

Determination of urinary androgens in women with polycystic ovary syndrome using LC-QqQ/MS and the application of thin film solid-phase microextraction (TF-SPME).

Hyperandrogenism is one of the most pronounced symptoms of Polycystic Ovary Syndrome (PCOS) and seems to play a key role in the pathogenesis of this complex disorder. Nevertheless, there is still a lack of consistent results regarding common steroid predictors of PCOS. Therefore, a liquid chromatography tandem mass spectrometry (HPLC-QqQ/MS) method was developed and validated to determine the concentrations of four classic androgens: androstenedione (An-dione), testosterone (T), 5α-dihydrotestosterone (DHT) and androsterone (An) in urine samples obtained from women with PCOS and healthy controls. The limits of detection were between 0.04 and 0.09 ng/mL, while the limits of quantification ranged from 0.1 to 0.3 ng/mL respectively. As a pre-treatment procedure prior to analysis, hydrolysis using ß-glucuronidase and thin film solid-phase microextraction (TF-SPME) was applied. The methodology was employed to perform targeted metabolomics of urinary steroids in women with PCOS and healthy controls. All measured androgens: An-dione (p < 0.0001), T (p = 0.0001), DHT (p < 0.0001) and An (p = 0.0002) showed significantly higher concentrations in the urine of women with PCOS. The largest difference in the mean concentration was found for DHT, which was 2.8 times higher in the PCOS group (13.9 ± 14.1 ng/mg creatinine) in comparison to healthy controls (4.9 ± 3.4 ng/mg creatinine). The results of receiver operating characteristic curve indicated that determination of the panel of three urinary androgens: T+DHT+An-dione with, under the study assumptions, was the best predictor of PCOS diagnosis (AUC of ROC curve = 0.91 (95 % CI: 0.8212-0.9905). The application of an LC-MS/MS-based analysis, together with highly sensitive extraction techniques like TF-SPME, is a suitable approach to perform fast assays and obtain reliable results - crucial in the search for valuable and significant steroids predictors of PCOS.