Background and Objectives: Idiopathic anterior knee pain is a common condition in adolescents and is mostly of unknown cause. The aim of this study was to examine the influence of the Q-angle and muscle strength on idiopathic anterior knee pain. Materials and Methods: Seventy-one adolescents (41 females and 30 males) diagnosed with anterior knee pain were included in this prospective study. The extensor strength in the knee joint and the Q-angle were monitored. The healthy extremity was used as a control. The Student’s paired sample t-test was applied for testing the difference. Statistical significance was set at 0.05. Results: There was no statistically significant difference in the Q-angle value between the idiopathic AKP and the healthy extremity (p > 0.05) within the entire sample. A statistically significant higher Q-angle of the idiopathic AKP knee (p < 0.05) was obtained in the female subgroup. No statistically significant difference (p > 0.05) was found in the male subgroup. Within the male subgroup, the strength of the extensors within the knee joint of the healthy extremity had statistically significant higher values than the strength of these muscles in the affected extremity (p < 0.05). Conclusion: A greater Q-angle is a risk factor linked to anterior knee pain within the female population. Decreased muscle strength of knee joint extensors is a risk factor linked to anterior knee pain in both sex subgroups.

Fractal analysis (FA) is a contemporary computational technique that can assist in identifying and assessing nuanced structural alterations in cells and tissues after exposure to certain toxic chemical agents. Its application in toxicology may be particularly valuable for quantifying structural changes in cell nuclei during conventional microscopy assessments. In recent years, the fractal dimension and lacunarity of cell nuclei, considered among the most significant FA features, have been suggested as potentially important indicators of cell damage and death. In this study, we demonstrate the feasibility of developing a random forest machine learning model that employs fractal indicators as input data to identify yeast cells treated with oxidopamine (6-hydroxydopamine, 6-OHDA), a powerful toxin commonly applied in neuroscience research. The model achieves notable classification accuracy and discriminatory power, with an area under the receiver operating characteristics curve of more than 0.8. Moreover, it surpasses alternative decision tree models, such as the gradient-boosting classifier, in differentiating treated cells from their intact counterparts. Despite the methodological challenges associated with fractal analysis and random forest training, this approach offers a promising avenue for the continued exploration of machine learning applications in cellular physiology, pathology, and toxicology.

Renal cell carcinoma (RCC) is the deadliest urological neoplasm. Up to date, no validated biomarkers are included in clinical guidelines for the screening and follow up of patients suffering from RCC. Slug (Snail2) and Snail (Snail1) belong to the Snail superfamily of zinc finger transcriptional factors that take part in the epithelial–mesenchymal transition, a process important during embryogenesis but also involved in tumor progression. We examined Slug and Snail immunohistochemical expression in patients with different stages of renal cell carcinomas with the aim to investigate their potential role as staging and prognostic factors. A total of 166 samples of malignant renal cell neoplasms were analyzed using tissue microarray and immunohistochemistry. Slug and Snail expressions were evaluated qualitatively (presence or absence), in nuclear and cytoplasmic cell compartments and compared in relation to clinical parameters. The Kaplan–Meier survival analysis showed the impact of the sarcomatoid component and Slug expression on the survival longevity. Cox regression analysis separated Slug as the only independent prognostic factor (p = 0.046). The expression of Snail was associated with higher stages of the disease (p = 0.004), especially observing nuclear Snail expression (p < 0.001). All of the tumors that had metastasized showed nuclear immunoreactivity (p < 0.001). In clear cell RCC, we showed a significant relationship between a high nuclear grade and nuclear Snail expression (p = 0.039). Our results suggest that Slug and Snail could be useful immunohistochemical markers for staging and prognosis in patients suffering from various RCCs, representing potential targets for further therapy strategies of renal cancer.

Methods based on the evaluation of textural patterns in microscopy, such as the “gray-level co-occurrence matrix” (GLCM) analysis are modern and innovative computer and mathematical algorithms that can be used to quantify subtle structural changes in cells and their organelles. Potential application of GLCM method in the fields of psychophysiology and psychiatry to this date has not been systematically investigated. The main objective of our study was to test the existence and strength of the association between chromatin structural organization of peripheral blood neutrophils and levels of self-perceived mental stress. The research was done on a sample of 100 healthy student athletes, and the Depression, Anxiety, and Stress Scales (DASS-21) were used for the estimation of psychological distress. Chromatin textural homogeneity and uniformity were negatively correlated (p < 0.01) with mental distress and had relatively good discriminatory power in differentiating participants with normal and elevated stress levels. As an addition, we propose the creation of a machine learning model based on binomial logistic regression that uses these and other GLCM features to predict stress elevation. To the best of our knowledge, these results are one of the first to establish the link between neutrophil chromatin structural organization quantified by the GLCM method and indicators of normal psychological functioning.

Knee injuries, acute or chronic are one of the most often injuries in sport, both in adults and adolescents. They mostly occur in contact sports due to torsional and decelaration forces, causing around 80% painful knee conditions that disable sports performance.

Hemodialysis (HD) removes nitrogenous waste products from patients? blood through a semipermeable membrane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learning technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments.