Exploring pathogenic pathways in carpal tunnel syndrome: sterile inflammation and oxidative stress.

OBJECTIVES: The main objective of the current study was to find the association between oxidative stress, inflammatory markers, and electrophysiological profile with symptom severity in patients of carpal tunnel syndrome (CTS). METHODS: Thirty-two carpal tunnel syndrome patients and 32 controls were included in the study. Boston CTS questionnaire along with plasma oxidative stress markers including superoxide dismutase, malondialdehyde, and nitric oxide and inflammatory markers including IL-6 and TNF-α were compared with the electrophysiological parameters derived from nerve conduction studies. Statistical significance of the levels between groups was calculated using unpaired-t test after checking for normality with D'Agostino & Pearson omnibus normality test. RESULTS: We found that the median nerve conduction velocity was prolonged, amplitude was decreased, while the levels of oxidative stress markers like malondialdehyde (MDA), superoxidase dismutase (SOD), and nitric oxide (NO) were increased in CTS patients compared to controls. Inflammatory markers like interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) were also increased in CTS patients. We found that plasma SOD and TNF-α correlated well with the median motor amplitude. There was no other significant correlation between oxidative stress markers and inflammatory markers with nerve conduction studies or disease severity. Patients with mild disease also showed lesser levels of SOD, NO, IL-6, and TNF-α markers than patients with severe disease. CONCLUSIONS: CTS is probably a disease of sterile inflammation and disbalance of oxidative stress, with higher inflammatory and oxidative stress markers pointing to a more severe disease.

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.