Compartmental System of Reaction and Diffusion Mechanism of Carcinogenic Polycyclic Aromatic Hydrocarbons in Mammalian Cell

The effect of ubiquitous carcinogen pollutants in mammalian cells is the source of several problems. Carcinogenic compounds present in environment as persistent pollutants become the root of carcinoma, toxicity or cancer when they react with hereditary material. To study the cellular exposure of reaction and diffusion mechanism of these carcinogenic compounds in mammalian V79 cell earlier, mathematical modeling with the set of spatially distributed system (PDEs) was developed. In this paper, compartmental modeling approach have used with the inclusion of perinuclear space. The system reduced the spatially distributed (PDEs) system to the temporal (ODEs) system, thus reducing the complexity and computational cost. The compartmental system has been simulated computationally in Virtual Cell using homogenization technique. The quantitative consideration of the results of spatially distributed system and temporal system shows a nice agreement. We can extend the compartmental system adding more compartments, reaction and diffusion processes

Comparative Neurological and Behavioral Assessment of Central and Peripheral Stimulation Technologies for Induced Pain and Cognitive Tasks

Pain is a multifaceted, multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central stimulation (transcranial direct current stimulation—tDCS over F3/F4) and peripheral stimulation (transcutaneous electrical nerve stimulation—TENS over the median nerve) in pain inhibition during a cognitive task in healthy volunteers and to observe potential neuro-cognitive improvements. Eighty healthy participants underwent a comprehensive experimental protocol, including cognitive assessments, the Cold Pressor Test (CPT) for pain induction, and tDCS/TENS administration. EEG recordings were conducted pre- and post-intervention across all conditions. The protocol for this study was categorized into four groups: G1 (control), G2 (TENS), G3 (anodal-tDCS), and G4 (cathodal-tDCS). Paired t-tests (p < 0.05) were conducted to compare Pre-Stage, Post-Stage, and neuromodulation conditions, with t-values providing insights into effect magnitudes. The result showed a reduction in pain intensity with TENS (p = 0.002, t-value = −5.34) and cathodal-tDCS (p = 0.023, t-value = −5.08) and increased pain tolerance with TENS (p = 0.009, t-value = 4.98) and cathodal-tDCS (p = 0.001, t-value = 5.78). Anodal-tDCS (p = 0.041, t-value = 4.86) improved cognitive performance. The EEG analysis revealed distinct neural oscillatory patterns across the groups. Specifically, G2 and G4 showed delta-power reductions, while G3 observed an increase. Moreover, G2 exhibited increased theta-power in the occipital region during CPT and Post-Stages. In the alpha-band, G2, G3, and G4 had reductions Post-Stage, while G1 and G3 increased. Additionally, beta-power increased in the frontal region for G2 and G3, contrasting with a reduction in G4. Furthermore, gamma-power globally increased during CPT1, with G1, G2, and G3 showing reductions Post-Stage, while G4 displayed a global decrease. The findings confirm the efficacy of TENS and tDCS as possible non-drug therapeutic alternatives for cognition with alleviation from pain

Machine vision-based Statistical texture analysis techniques for characterization of liver tissues using CT images

Objective: To characterize human liver tissues by demonstrating the ability of machine vision, and to propose a new auto-generated report based on texture analysis that may work with co-occurrence matrix statistics. Method: The retrospective study was conducted at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, and comprised clinically verified computed tomography imaging data between October 2018 and September 2020. The image samples and related data were used to segregate classes 1-4. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues using supervised learning methods, principal component analysis, linear discriminant analysis, and non-linear discriminant analysis. Robust and reliable texture features were investigated by generating testing classes. Overall performance of the presented machine vision approach was analyzed using four parameters; precision, recall/sensitivity, F1-score, and accuracy. Statistical analysis was done using B11 software. Results: There were 312 image samples from 71 patients; 51(71.8%) males and 20(28.2%) females. Among the patients, 19(26.7%) had abscess, 15(21.1%) had metastatic disease, 23(32.4%) had tumour necrosis, 6(8.5%) had vascular disorder, and 8(11.3%) were normal. Principal component analysis, linear discriminant analysis, and non-linear discriminant analysis showed high >97.86% values, but the discrimination rate was 100% for class 4. Conclusion: Abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques using second-order statistics that may assist the radiologist and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases. Key Words: Liver abscess, Computed tomography imaging, Liver diseases, Image processing