Quantitative Assessment of Fungal Biomarkers in Clinical Samples via an Interface-Modulated Optical Fiber Biosensor.

Invasive fungal infections pose a significant public health threat. The lack of precise and timely diagnosis is a primary factor contributing to the significant increase in patient mortality rates. Here, an interface-modulated biosensor utilizing an optical fiber for quantitative analysis of fungal biomarkers at the early stage of point-of-care testing (POCT), is reported. By integrating surface refractive index (RI) modulation and plasmon enhancement, the sensor to achieve high sensitivity in a directional response to the target analytes, is successfully optimized. As a result, a compact fiber-optic sensor with rapid response time, cost-effectiveness, exceptional sensitivity, stability, and specificity, is developed. This sensor can successfully identify the biomarkers of specific pathogens from blood or other tissue specimens in animal models. It quantifies clinical blood samples with precision and effectively discriminates between negative and positive cases, thereby providing timely alerts to potential patients. It significantly reduces the detection time of fungal infection to only 30 min. Additionally, this approach exhibits remarkable stability and achieves a limit of detection (LOD) three orders of magnitude lower than existing methods. It overcomes the limitations of existing detection methods, including a high rate of misdiagnosis, prolonged detection time, elevated costs, and the requirement for stringent laboratory conditions.

Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.

PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. METHODS: 202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. RESULTS: The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. CONCLUSIONS: The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

Assessment of dynamic hepatic and renal imaging changes in COVID-19 survivors using T1 mapping and IVIM-DWI.

PURPOSE: To explore the imaging changes of the liver and kidneys in COVID-19 survivors using variable flip angle (VFA) T1 mapping and intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI). METHODS: This prospective study included 37 discharged COVID-19 participants and 24 age-matched non-COVID-19 volunteers who underwent abdominal MRI with VFA T1 mapping and IVIM-DWI sequencing as a COVID-19 group and control group, respectively. Among those discharged COVID-19 participants, 23 patients underwent two follow-up MRI scans, and were enrolled as the 3-month follow-up group and 1-year follow-up group, respectively. The demographics, clinical characteristics, and laboratory tests were collected. Imaging parameters of the liver and kidneys were measured. All collected values were compared among different groups. RESULTS: The 3-month follow-up group had the lowest hepatic T1 value, which was significantly lower than the value in the control group (P < 0.001). Additionally, the 3-month follow-up group had the highest hepatic ADC and D values, cortical ADC and f values, which were significantly higher than those in the control group (for all, P < 0.05). The hepatic D value in the 1-year follow-up group decreased significantly in comparison with that in the 3-month follow-up group (P = 0.001). Compared to non-severe patients, severe cases had significantly higher hepatic D* and f*D* values (P = 0.031, P = 0.015, respectively). CONCLUSION: The dynamic alterations of hepatic and renal imaging parameters detected with T1 mapping and IVIM-DWI suggested that COVID-19 survivors might develop mild, non-symptomatic liver and kidney impairments, of which liver impairment could probably relieve over time and kidney impairment might be long-existing.