Search Results (92)

Background: Androgen deprivation therapy (ADT) is a primary treatment for prostate cancer (PCa) that effectively reduces androgen levels to suppress tumor progression. However, growing evidence suggests potential cognitive side effects, raising concerns about the long-term neurological consequences of this treatment. Objective: This scoping review aims to synthesize the existing evidence linking ADT to cognitive changes in men with PCa, identifying the key cognitive domains affected and outlining gaps in the existing literature. Methods: A systematic literature search was conducted according to the PRISMA-ScR guidelines in CINAHL, PubMed, Scopus, and Web of Science. Studies investigating cognitive function in ADT-treated PCa patients were included, covering randomized controlled trials (RCTs) and cohort, case–control, and cross-sectional studies. The extracted data included the study design, evaluated cognitive characteristics, measurement tools, and overall findings. Results: A total of 22 studies met the inclusion and exclusion criteria. Cognitive assessments varied across studies. While some studies reported cognitive impairments in ADT-treated patients—particularly in working, verbal, and visual memory and executive function—others found no significant effects. The variability in prostate cancer staging, epidemiological study designs, and treatment regimens; the exclusion of comorbid conditions; and the differences in assessment tools, sample sizes, and study durations hinder definitive conclusions about the cognitive effects of ADT. Conclusions: This scoping review highlights the heterogeneous and often contradictory evidence regarding ADT-associated cognitive dysfunction. While certain cognitive domains may be affected, methodological inconsistencies limit robust conclusions. Standardized cognitive assessments and longer longitudinal studies are required to clarify ADT’s role in cognitive decline. As the PCa survival rate increases with extended ADT use, integrating routine cognitive monitoring into clinical practice should be considered for PCa patients. Full article

Celtis iguanaea, widely used in Brazilian folk medicine, is known for its analgesic and anti-inflammatory properties. This study evaluated the in vitro antioxidant capacity and the in vivo antinociceptive and anti-inflammatory mechanisms of the standardized spray-dried Celtis iguanaea hydroethanolic leaf extract (SDCi). Phytochemical analysis showed that SDCi contains 21.78 ± 0.82 mg/g polyphenols, 49.69 ± 0.57 mg/g flavonoids, and 518.81 ± 18.02 mg/g phytosterols. UFLC-DAD-MS identified iridoid glycosides, p-coumaric acid glycosides, flavones, and unsaturated fatty acids. Antioxidant assays revealed an IC₅₀ of 301.6 ± 38.8 µg/mL for DPPH scavenging and an electrochemical index of 6.1 μA/V. In vivo, SDCi (100–1000 mg/kg, p.o) did not impair locomotor function (rotarod test) but significantly reduced acetic acid-induced abdominal writhing and both phases of the formalin test at higher doses (300 and 1000 mg/kg). The antinociceptive effects were independent of α-2 adrenergic receptors. SDCi also increased latency in the hot-plate test and reduced paw edema in the carrageenan model, accompanied by decreased IL-1β and increased IL-10 levels. Histological analysis showed a 50% reduction in inflammatory cell infiltration. These findings support SDCi as an effective anti-inflammatory and antinociceptive phytopharmaceutical intermediate, with potential applications in managing pain and inflammation. Full article

Background/Objectives: COVID-19 has been associated with a wide range of immune responses, including the production of autoantibodies, particularly in severe cases. This study investigates the IgG autoantibody responses in patients with varying severities of COVID-19 infection and compares these responses with vaccinated individuals. Methods: We utilized proteomic profiling to analyze autoantibody reactivity against a broad spectrum of proteins expressed in lymphoid and myeloid cell subsets in serum samples from severe and moderate COVID-19 patients, as well as vaccinated individuals who received the inactivated CoronaVac (Sinovac) vaccine. Results: Our findings indicate a marked increase in the diversity and number of IgG autoantibodies targeting intracellular and membrane-associated proteins in severe COVID-19 cases, compared to those with moderate cases of the disease. The autoantibody response in severe cases was found to primarily target proteins involved in immune cell activation, signaling, and differentiation, suggesting potential pathways of immune dysregulation and autoimmunity. In contrast, vaccinated individuals did not exhibit similar autoantibody reactivity, pointing to a more controlled immune response post-vaccination. Notably, no significant autoimmune responses were detected in the vaccinated cohort, suggesting that the inactivated vaccine does not induce autoreactive IgG. These findings align with the established safety profile of COVID-19 vaccines, especially in comparison to the heightened immune dysregulation observed in severe COVID-19 patients. The absence of a significant autoantibody response in vaccinated individuals supports the notion that vaccines, while inducing robust immune activation, do not typically trigger autoimmunity in healthy individuals. Conclusions: Our study underscores the importance of distinguishing between the immune responses triggered by infection and vaccination and highlights the need for the continued monitoring of autoimmune responses in severe COVID-19 cases. Future research should focus on the long-term persistence and clinical relevance of these autoantibodies, particularly in individuals with pre-existing autoimmune conditions or genetic predispositions. Full article

Fractal dimension has proven to be a valuable tool in the analysis of geological data. For instance, it can be used for assessing the distribution and connectivity of fractures in rocks, which is important for evaluating hydrocarbon storage potential. However, while calculating a single fractal dimension for an entire geological profile provides a general overview, it can obscure local variations. These localized fluctuations, if analyzed, can offer a more detailed and nuanced understanding of the profile’s characteristics. Hence, this study proposes a fractal characterization procedure using a new strategy based on moving windows applied to the analysis domain, enabling the evaluation of data multifractality through the Dynamical Approach Method. Validations for the proposed methodology were performed using controlled artificial data generated from Weierstrass–Mandelbrot functions. Then, the methodology was applied to real geological profile data measuring permeability and porosity in oil wells, revealing the fractal dimensions of these data along the depth of each analyzed case. The results demonstrate that the proposed methodology effectively captures a wide range of fractal dimensions, from high to low, in artificially generated data. Moreover, when applied to geological datasets, it successfully identifies regions exhibiting distinct fractal characteristics, which may contribute to a deeper understanding of reservoir properties and fluid flow dynamics. Full article

Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Effective screening strategies for early diagnosis that could improve disease prognosis are lacking. Non-invasive breath analysis of volatile organic compounds (VOC) is a potential method for earlier LC detection. This study explores the association of VOC profiles with artificial intelligence (AI) to achieve a sensitive, specific, and fast method for LC detection. Patients and methods: Exhaled breath air samples were collected from 123 healthy individuals and 73 LC patients at two clinical sites. The enrolled patients had LC diagnosed with different stages. Breath samples were collected before undergoing any treatment, including surgery, and analyzed using gas chromatography coupled to ion-mobility spectrometry (GC-IMS). AI methods classified the overall chromatographic profiles. Results: GC-IMS is highly sensitive, yielding detailed chromatographic profiles. AI methods ranked the sets of exhaled breath profiles across both groups through training and validation steps, while qualitative information was deliberately not taking part nor influencing the results. The K-nearest neighbor (KNN) algorithm classified the groups with an accuracy of 90% (sensitivity = 87%, specificity = 92%). Narrowing the LC group to those only in early-stage IA, the accuracy was 90% (sensitivity = 90%, specificity = 93%). Conclusions: Evaluation of the global exhaled breath profiles using AI algorithms enabled LC detection and demonstrated that qualitative information may not be required, thus easing the frustration that many studies have experienced so far. The results show that this approach coupled with screening protocols may improve earlier detection of LC and hence its prognosis. Full article

Corn (Zea mays L.) productivity is often compromised by phytosanitary challenges, with fungal disease like Curvularia leaf spot being particularly significant. While synthetic fungicides are commonly used, there is growing interest in exploring alternative compounds that are effective against pathogens, ensure food safety, and have low toxicity to non-target organisms. In this study, we examined the biochemical changes in corn plants treated with Lippia sidoides essential oil and its major compound, thymol. Both treatments serve as preventive measures for inoculated plants and induced resistance. We tested five concentrations of each product in in vivo experiments. After evaluating the area under the disease progress curve, we analyzed leaf samples for enzymatic activities, including superoxide dismutase, catalase, ascorbate peroxidase, and chitinase. Phytoalexin induction was assessed using soybean cotyledons and sorghum mesocotyls. Cytotoxicity tests revealed lower toxicity at concentrations below 50 µL/mL. Both essential oil and thymol stimulated the production of reactive oxygen species, with thymol primarily activating catalase and L. sidoides oil increasing ascorbate peroxidase levels. Both thymol and L. sidoides were also key activators of chitinase. These findings suggest that L. sidoides essential oil and thymol are promising candidates for developing biological control products to enhance plant defense against pathogens. Full article

Background: Machine learning (ML) techniques have been recently proposed as a solution for aiding in the prevention and diagnosis of microbiome-related diseases. Here, we applied auto-ML approaches on real-case metagenomic datasets from saliva and subgingival peri-implant biofilm microbiomes to explore a wide range of ML algorithms to benchmark best-performing algorithms for predicting peri-implantitis (PI). Methods: A total of 100 metagenomes from the NCBI SRA database (PRJNA1163384) were used in this study to construct biofilm and saliva metagenomes datasets. Two AI-driven auto-ML approaches were used on constructed datasets to generate 100 ML-based models for the prediction of PI. These were compared with statistically significant single-microorganism-based models. Results: Several ML algorithms were pinpointed as suitable bespoke predictive approaches to apply to metagenomic data, outperforming the single-microorganism-based classification. Auto-ML approaches rendered high-performing models with Receiver Operating Characteristic–Area Under the Curve, sensitivities and specificities between 80% and 100%. Among these, classifiers based on ML-driven scoring of combinations of 2–4 microorganisms presented top-ranked performances and can be suitable for clinical application. Moreover, models generated based on the saliva microbiome showed higher predictive performance than those from the biofilm microbiome. Conclusions: This feasibility study bridges complex AI research with practical dental applications by benchmarking ML algorithms and exploring oral microbiomes as foundations for developing intuitive, cost-effective, and clinically relevant diagnostic platforms. Full article

Background: The human oral microbiome is a critical reservoir for antibiotic resistance; however, subgingival peri-implant biofilms remain underexplored in this context. We aimed to explore the prevalence and distribution of antibiotic resistance genes (ARGs) in metagenomes derived from saliva and subgingival peri-implant biofilms. Methods: A total of 100 metagenome datasets from 40 individuals were retrieved from the Sequence Read Archive (SRA) database. Of these, 20 individuals had exclusively healthy implants and 20 had both healthy and affected implants with peri-implantitis. ARGs and their taxonomic assignments were identified using the ABRicate tool, and plasmid detection was performed with PlasmidFinder. Results: Four plasmid replicons were identified in 72 metagenomes, and 55 distinct ARGs from 13 antibiotic classes were detected in 89 metagenomes. ARGs conferring resistance to macrolides–lincosamides–streptogramins, tetracyclines, beta-lactams, and fluoroquinolones were the most prevalent. The msr(D) and mef(A) genes showed the highest prevalence, except in saliva samples from individuals with healthy implants, where mef(A) ranked fourth. A pairwise PERMANOVA of principal coordinate analysis based on Jaccard distances revealed that saliva samples exhibited significantly greater ARG diversity than subgingival biofilm samples (p < 0.05). However, no significant differences were observed between healthy and peri-implantitis-affected subgingival biofilm groups (p > 0.05). The taxonomic origins of ARGs were also analyzed to understand their distribution and potential impact on oral microbial communities. Conclusions: Resistome profiles associated with both peri-implant health and disease showed no significant differences and higher salivary abundance of ARGs compared to subgingival biofilm samples. Full article

This paper presents an original and comprehensive comparative analysis of eight fractal analysis methods, including Box Counting, Compass, Detrended Fluctuation Analysis, Dynamical Fractal Approach, Hurst, Mass, Modified Mass, and Persistence. These methods are applied to artificially generated fractal data, such as Weierstrass–Mandelbrot functions and fractal Brownian motion, as well as natural datasets related to environmental and geophysical domains. The objectives of this research are to evaluate the methods’ capabilities in capturing fractal properties, their computational efficiency, and their sensitivity to data fluctuations. Main findings indicate that the Dynamical Fractal Approach consistently demonstrated the highest accuracy across different datasets, particularly for artificial data. Conversely, methods like Mass and Modified Mass showed limitations in complex fractal structures. For natural datasets, including meteorological and geological data, the fractal dimensions varied significantly across methods, reflecting their differing sensitivities to structural complexities. Computational efficiency analysis revealed that methods with linear or logarithmic complexity, such as Persistence and Compass, are most suited for larger datasets, while methods like DFA and Dynamic Fractal Approaches required higher computational resources. This study provides an original comparative study for researchers to select appropriate fractal analysis techniques based on dataset characteristics and computational limitations. Full article

Objective: This review examined the influence of anthropometric characteristics, such as body height (BH) and body mass (BM), on the impact of punches in striking-combat sports. Despite their perceived importance for combat strategy, the relationship between these characteristics and punch impact remains unclear. Methods: We included experimental, quasi-experimental and cross-sectional studies. The search was conducted on 30 August 2024, in three databases. The review analyzed 23 studies involving 381 participants (304 men, 30 women, 47 participants of unknown gender). Various instruments were used in the included studies, including ten instruments used to measure impact force and two instruments used to measure impact power. Results: Impact force ranged from 989 ± 116.76 to 5008.6 ± 76.3 N, with rear-hand straight punches and rear-hand hooks producing the greatest force. The PowerKube, a device specifically designed to measure punch impact power, revealed that the rear-hand straight punch generated the highest power, ranging from 15,183.27 ± 4368.90 to 22,014 ± 1336 W. While higher BM categories were associated with stronger punches, BM alone was not the only predictor. Other factors, such as technique, gender, and sport type, also played roles. The relationship between BH and punch impact showed mixed results. Conclusions: The data suggest that while higher BM categories are associated with greater punch impact, BM is not the only determining factor. The relationship between BH and impact also showed mixed results, with no clear association found. The review highlights the lack of a “gold standard” instrument for evaluating punch impact. Full article

Background: Breast cancer is a heterogeneous malignant disease with a varying prognosis and is classified into four molecular subtypes. It remains one of the most prevalent cancers globally, with the tumor microenvironment playing a critical role in disease progression and patient outcomes. Methods: This study evaluated tumor samples from 40 female patients with luminal A and B breast cancer, utilizing flow cytometry to phenotypically characterize the immune cells and tumor cells present within the tumor tissue. Results: The luminal B-like tumor samples exhibited increased infiltration of CD4⁺ cells, regulatory T cells (Tregs), and Th17 cells and decreased levels of NK cells, γδ T cells, Th1 cells, and follicular T cells, which is indicative of a more immunosuppressive tumor microenvironment. Conclusions: These findings suggest that luminal B-like tumors have a microenvironment that is less supportive of effective anti-tumor immune responses compared to luminal A tumors. This study enhances the understanding of the immunological differences between luminal subtypes of breast cancer and identifies potential new therapeutic targets and biomarkers that could drive advancements in precision medicine for breast cancer management. Full article

Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health. Full article

Cellulose obtained from oat hulls by bleaching with peracetic acid was modified, employing an ultrasound method that resulted in an esterification reaction with different vegetable oils (soybean, sunflower, and coconut) to produce modified cellulose (MC) with increased hydrophobicity. MC samples were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction, scanning electron microscopy, and their wettability and oil and water absorption capacities. FTIR indicated that the reaction occurred with all oils, which was observed by forming a new band associated with ester carbonyl groups at 1747 cm⁻¹. The modification did not affect the crystalline structure or surface morphology of the cellulose. MC samples modified with all oil sources showed a 6 to 9-fold decrease in water absorption capacity, a 3-fold increase in oil absorption capacity, and a higher affinity for nonpolar solvents. The modified samples adsorbed lower amounts of water at a slower rate. Different oil sources did not affect the main properties of MC. The ultrasonication-assisted process was not only effective in modifying cellulose by esterification with vegetable oils but was also an eco-friendly and simple strategy that does not require toxic reagents, providing reassurance of its sustainability. Full article

The study aimed to evaluate the precision of different Pentacam indices in diagnosing keratoconus (KC) in pediatric patients with and without Down Syndrome (DS) and determine suitable cutoff values. This prospective multicenter cross-sectional study evaluated 216 eyes of 131 patients aged 6–18 years (mean age 12.5 ± 3.2 years) using Pentacam. Patients were categorized into four groups: KC, forme fruste keratoconus (FK), DS, and control, excluding DS patients with topographic KC. Receiver operating characteristic curves were generated to determine the optimal cutoff points and compare the accuracy in identifying KC and FK in patients with and without DS. In DS patients, corneal morphology resembled KC features. The most effective indices for distinguishing KC in DS patients were the average pachymetric progression index (AUC = 0.961), higher-order aberration of the anterior cornea (AUC = 0.953), anterior elevation (AUC = 0.946), posterior elevation (AUC = 0.947), index of vertical asymmetry (AUC = 0.943), and Belin/Ambrosio enhanced ectasia total derivation value (AUC = 0.941). None of the indices showed good accuracy for distinguishing FK in DS patients. The thresholds of these indices differed significantly from non-DS patients. The results highlighted the need for DS-specific cutoff values to avoid false-positive or false-negative diagnoses in this population. Full article

Brazil is one of the countries most affected by COVID-19, with the highest number of deaths recorded. Brazilian Health Institutions have reported four main peaks of positive COVID-19 cases. The last two waves were characterized by the emergence of the VOC Omicron and its sublineages. This study aimed to conduct a retrospective surveillance study illustrating the emergence, dissemination, and diversification of the VOC Omicron in 15 regional health units (RHUs) in MG, the second most populous state in Brazil, by combining epidemiological and genomic data. A total of 5643 confirmed positive COVID-19 samples were genotyped using the panels TaqMan SARS-CoV-2 Mutation and 4Plex SC2/VOC Bio-Manguinhos to define mutations classifying the BA.1, BA.2, BA.4, and BA.5 sublineages. While sublineages BA.1 and BA.2 were more prevalent during the third wave, BA.4 and BA.5 dominated the fourth wave in the state. Epidemiological and viral genome data suggest that age and vaccination with booster doses were the main factors related to clinical outcomes, reducing the number of deaths, irrespective of the Omicron sublineages. Complete genome sequencing of 253 positive samples confirmed the circulation of the BA.1, BA.2, BA.4, and BA.5 subvariants, and phylogenomic analysis demonstrated that the VOC Omicron was introduced through multiple international events, followed by transmission within the state of MG. In addition to the four subvariants, other lineages have been identified at low frequency, including BQ.1.1 and XAG. This integrative study reinforces that the evolution of Omicron sublineages was the most significant factor driving the highest peaks of positive COVID-19 cases without an increase in more severe cases, prevented by vaccination boosters. Full article