Consequences of Spiraea tomentosa invasion in Uropodina mite (Acari: Mesostigmata) communities in wet meadows.

Vegetation cover has been consistently reported to be a factor influencing soil biota. Massive spreading of invasive plants may transform native plant communities, changing the quality of habitats as a result of modification of soil properties, most often having a directional effect on soil microorganisms and soil fauna. One of the most numerous microarthropods in the litter and soil is Acari. It has been shown that invasive plants usually have a negative effect on mites. We hypothesized that invasive Spiraea tomentosa affects the structure of the Uropodina community and that the abundance and species richness of Uropodina are lower in stands monodominated by S. tomentosa than in wet meadows free of this alien species. The research was carried out in wet meadows, where permanent plots were established in an invaded and uninvaded area of each meadow, soil samples were collected, soil moisture was determined and the mites were extracted. We found that Uropodina mite communities differed in the abundance of individual species but that the abundance and richness of species in their communities were similar. S. tomentosa invasion led primarily to changes in the quality of Uropodina communities, due to an increase in the shares of species from forest and hygrophilous habitats. Our results suggest that alien plant invasion does not always induce directional changes in mite assemblages, and conclude that the impact of an alien species on Uropodina may cause significant changes in the abundance and richness of individual species without causing significant changes in the abundance and diversity of their community.

Pretreatment Circulating HPV16 DNA Viral Load Predicts Risk of Distant Metastasis in Patients with HPV16-Positive Oropharyngeal Cancer.

BACKGROUND: There are definite reasons to implement molecular diagnostics based on the measurement of human papillomavirus (HPV) DNA in liquid biopsy into clinical practice. It is a quick, repeatable, and health-safe test for cancer biomarkers in the blood. In this study, we investigated whether the circulating tumor-related HPV16 (ctHPV16) viral load (VL) in patients with oropharyngeal squamous cell carcinoma (OPSCC) was important for determining the risk of locoregional recurrence-free survival (LRFS), metastasis-free survival (MFS), and overall survival (OS). METHODS: This study included 91 patients with ctHPV16-positive OPSCC who had been treated with radical radiotherapy and chemotherapy. The VL was measured using quantitative PCR (qPCR) and a probe specific for HPV16. Based on 10 years of follow-up, the 2-, 3-, 5-, and 9-year LRFS, MFS, and OS were estimated. RESULTS: The 5-year actuarial LRFS, MFS, and OS rates of patients with ctHPV16-positive/OPSCC were 88%, 90%, and 81%, respectively. The VL was significantly higher in patients who subsequently developed distant metastases (DM) than in those who did not (VL 4.09 vs. 3.25; p = 0.009). In a Cox proportional hazards regression model for MFS, a higher ctHPV16 VL appeared to be a significant independent prognostic factor for the occurrence of DM (HR 2.22, p = 0.015). The ROC curve revealed a cutoff value of 3.556 for VL (p = 0.00001). CONCLUSIONS: A high VL before treatment indicates patients with a significant risk of DM, and should be used in OPSCC treatment stratification.

Dermoscopy of Chronic Radiation-Induced Dermatitis in Patients with Head and Neck Cancers Treated with Radiotherapy.

Radiotherapy (RT) is an integral part of many cancer treatment protocols. Chronic radiation-induced dermatitis (CRD) is a cutaneous toxicity that occurs in one-third of all patients treated with this method. CRD is usually observed several months after completion of treatment. Typical symptoms of CRD are telangiectasia, skin discoloration, atrophy, thickening, and cutaneous fibrosis. There are currently no data in the literature on the evaluation of the dermoscopic features of CRD. The aim of this prospective study was the identification of clinical and dermoscopic features in a group of 32 patients with head and neck cancer (HNC) in whom CRD developed after RT. CRD was assessed at 3, 6, and 12 months after RT in 16, 10, and 10 patients, respectively. CRD was assessed at one time point and two time points in 28 and 4 patients, respectively. The control included skin areas of the same patient not exposed to RT. The dataset consisted of 36 clinical and 216 dermoscopic photos. Clinical evaluation was performed according to the RTOG/EORTC radiation-induced dermatitis scale. The highest score was grade 2 observed in 21 patients. Clinical observations revealed the presence of slight and patchy atrophy, pigmentation change, moderate telangiectasias, and some and total hair loss. Dotted vessels, clustered vessel distribution, white patchy scale, perifollicular white color, white structureless areas, brown dots and globules, and white lines were the most frequently noted features in dermoscopy. Three independent risk factors for chronic toxicity, such as age, gender, and surgery before RT, were identified. The dermoscopic features that had been shown in our study reflect the biological reaction of the skin towards radiation and may be used for the parametrization of CRD regarding its intensity and any other clinical consequences in the future.

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.

Introduction: The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuroscience. Our ultimate goal is to utilize this approach to predict the early signs of mild cognitive impairment (MCI) in healthy elderly individuals, which could potentially lead to dementia. The advancements in health neuroscience research have revealed that affective reminiscence stimulation is an effective method for developing EEG-based neuro-biomarkers that can detect the signs of MCI. Methods: We use topological data analysis (TDA) on multivariate EEG data to extract features that can be used for unsupervised clustering, subsequent machine learning-based classification, and cognitive score regression. We perform EEG experiments to evaluate conscious awareness in affective reminiscent photography settings. Results: We use EEG and interior photography to distinguish between healthy cognitive aging and MCI. Our clustering UMAP and random forest application accurately predict MCI stage and MoCA scores. Discussion: Our team has successfully implemented TDA feature extraction, MCI classification, and an initial regression of MoCA scores. However, our study has certain limitations due to a small sample size of only 23 participants and an unbalanced class distribution. To enhance the accuracy and validity of our results, future research should focus on expanding the sample size, ensuring gender balance, and extending the study to a cross-cultural context.