https://repositorio.ufu.br/handle/123456789/5145 2026-06-16T06:44:42Z 2026-06-16T06:44:42Z https://repositorio.ufu.br/handle/123456789/48733 2026-06-04T06:19:20Z 2025-12-01T00:00:00Z

Title: Efeitos dos Canabinoides no Tremor Parkinsoniano e interações medicamentosas: análise por inteligência artificial explicável dos dados Fox Insight Abstract: Parkinson’s disease constitutes the second most prevalent neurodegenerative condition worldwide, affecting 1-2% of the population over 65 years of age, with tremor present in 70% of cases. Conventional pharmacological treatments present limitations in 30-40% of patients, establishing the need for therapeutic alternatives. This thesis aimed to develop an explainable artificial intelligence methodology for personalizing medical cannabis use in treating parkinsonian tremor, quantifying determinants of individual heterogeneity in therapeutic response. The methodology employed analysis based on SHAP (SHapley Additive exPlanations) values applied to 2,616 records from the Fox Insight platform, structured in three progressive datasets: SINGLE (n=973, isolated cannabis), FULL_NUMERICO (n=931, pharmacological groups), and FULL_GFEM (n=931, detailed individual characteristics). Machine learning models achieved elevated performance metrics: SINGLE (LightGBM) with accuracy 96.31%, precision 96.33%, recall 96.31%, F1-score 96.31%, and specificity 96.03%; FULL_NUMERICO (LightGBM) with accuracy 97.42%, precision 97.47%, recall 97.42%, F1-score 97.42%, and specificity 97.07%; FULL_GFEM (XGBoost) with accuracy 97.42%, precision 97.54%, recall 97.42%, F1-score 97.42%, and specificity 96.91%, demonstrating that interpretability does not compromise predictive performance when architecture is adequately designed. Results quantified eight main findings: (1) pronounced sexual dimorphism, with women presenting mean SHAP values of +1.52 ± 0.48 (improvement rate 94.1%) versus men -0.93 ± 0.37 (rate 38.5%), establishing a magnitude of 2.44x in the probability of favorable response; (2) identification of six distinct profiles through clustering, with 55% of patients presenting elevated improvement rates (89% to 100%) and SHAP values between +1.94 and +2.57; (3) non-linear inverted-U age pattern, with critical group 60-70 years (n=559, 60% of sample) presenting worse response (rate 38.8%) and partial recovery in 70-80 years (rate 76.2%, n=181); (4) hierarchy of administration routes quantified by MASV (Mean Absolute SHAP Value): oil (0.705) > sublingual (0.4959) > food (0.657 Class 1), demonstrating importance superior to dose; (5) bifasic optimal therapeutic windows for THC (1.5-2.5 mg/day, MASV = 0.2888) and CBD (2-4 mg/day, MASV = 0.2654), empirically validating that supra-therapeutic doses reduce efficacy; (6) quantified synergy with MAO-B inhibitors (MASV = 0.4485), superior to interactions with levodopa (MASV = 0.2506) and other dopaminergics (MASV = 0.2454); (7) moderate optimal frequency of 3-5 times/week, superior to continuous daily use; (8) therapeutic learning curve with optimization in 2-3 years and possible tolerance after >3 years. Findings were operationalized in a clinical nomogram that stratifies patients into five predictive categories, with 87.3% concordance with phenotypic clusters. Computational analysis through clustering identified difference in improvement rates between subgroups with elevated response (89-100%, n=513) and general population (66.4%, n=931), suggesting potential for therapeutic personalization based on identifiable clinical characteristics, hypothesis requiring confirmation in future clinical studies. This investigation establishes a new methodological standard for research in precision neurological medicine, providing computational tools and quantitative knowledge for prospective identification of subgroups with high probability of benefit, individualized therapeutic regimen optimization, and establishment of expectations based on measurable patient characteristics.

2025-12-01T00:00:00Z https://repositorio.ufu.br/handle/123456789/48707 2026-05-20T06:29:41Z 2026-05-04T00:00:00Z

Title: Avaliação da atividade eletromiográfica de músculos do core no exercício prancha ventral até a exaustão com e sem exergame plankpad Abstract: Core training plays an important role in quality of life, especially in reducing the prevalence of low back pain. Recently, exergames such as Plankpad have been developed to improve adherence to physical training and rehabilitation programs focused on core muscle strengthening. However, no studies have analyzed electromyographic (EMG) activity during the ventral plank (VP) exercise with and without exergame, including time-domain and frequency-domain analysis as well as muscle co-contraction. This study aimed to investigate the effects of incorporating the Plankpad exergame on core muscle EMG activity during the VP exercise until exhaustion. Fifteen young, healthy, physically active men with no history of low back pain participated. Participants performed the VP exercise until exhaustion under five conditions: stable ground (VP-GR), Plankpad platform without game (VP-PP), and Plankpad platform with the Fruit Splicer game at easy (VP-EA), medium (VP-ME), and hard (VP-HA) levels. EMG signals were collected from the rectus abdominis (RA), external oblique (EO), internal oblique (IO), erector spinae (ES), and multifidus (MU) muscles, along with time to exhaustion and rating of perceived exertion (RPE). Results showed progressive increases in RMS and decreases in median frequency (MF) over time in all exercises, indicating peripheral neuromuscular fatigue, with no significant differences among the five conditions for most parameters. Local co-contraction (IO/MU) was significantly higher in VP-PP compared to VP EA and decreased with fatigue across all exercises. Time to exhaustion and RPE were similar among conditions. Correlations between RPE and EMG parameters were weak to moderate. We conclude that the Plankpad exergame, at easy, medium, and hard levels, does not increase electromyographic activation, spectral fatigue, time to exhaustion, or RPE compared to traditional plank exercise in young healthy trained men.

2026-05-04T00:00:00Z https://repositorio.ufu.br/handle/123456789/48700 2026-05-19T06:34:38Z 2026-02-27T00:00:00Z

Title: Deep learning-based detection of carotid artery atheromas in panoramic radiographs Abstract: Stroke is among the leading global causes of mortality and long-term disability resulting from neurological sequelae, with carotid atherosclerosis being one of its main etiological mechanisms. This condition often progresses silently, which limits the use of standard diagnostic examinations such as Doppler ultrasound and computed tomography angiography (CTA) for population screening. However, calcifications located at the carotid bifurcation may be incidentally visualized on panoramic dental radiographs, making this exam a potential opportunistic screening tool due to its wide availability, low cost, and coverage of the C3–C4 region, where atheromas usually manifest. In this context, the present study aimed to develop and evaluate a deep-learning-based method using the MobileNetV2 architecture for the automatic detection of carotid atheromas in panoramic radiographs, aiming to optimize the clinical value of this routine exam as a screening tool. A total of 378 publicly available and fully anonymized Regions of Interest (ROIs) were used, cropped in the carotid region (640×320 px) and divided into training, validation, and test sets (264/57/57). The images underwent normalization, grayscale channel replication, and real-time data augmentation, and class imbalance was addressed using weighted loss. The model was implemented using MobileNetV2 with pretrained weights and a two-stage training scheme, consisting of initial backbone freezing followed by partial fine-tuning (~70%), with batch normalization and dropout (0.3). On the independent test set, the model achieved robust and well-balanced performance, with 94.7% accuracy, 95.7% sensitivity, 94.1% specificity, and AUC and AUPRC values of 0.963 and 0.968, respectively, using a threshold optimized by Youden’s J index. These results demonstrate strong discriminative capability and reinforce the potential of panoramic radiography combined with deep learning as an opportunistic screening tool for carotid atheromas. The proposed method shows promising performance and supports the use of routine panoramic radiographs for opportunistic screening, enabling the early referral of patients for specialized vascular evaluation and strengthening the interface between oral and systemic health.

2026-02-27T00:00:00Z https://repositorio.ufu.br/handle/123456789/48694 2026-05-13T06:23:43Z 2026-03-11T00:00:00Z

Title: Análise da eficiência logística no transporte de madeira com pentatrens em uma fábrica de celulose com Simulação de Eventos Discretos Abstract: This course conclusion work aims to study, analyze, and implement improvements in the logistics process of a dry wood pulp factory, using the concepts of Discrete Event Simulation. The purpose of this study is to understand each stage of the process and analyze it both individually and as a whole, in order to identify possible bottlenecks and optimization points that may be preventing the process from reaching its full performance capacity. To ensure better understanding, some logistics concepts, Discrete Event Modeling, and the tools that can be used to simulate systems will be explained. In order to measure essential parameters for the analysis, factors such as travel time, cargo capacity, average distance traveled, number of daily trips, and others will be monitored and measured. In this context, the study aims to demonstrate the applicability of Discrete Event Simulation in everyday scenarios, through the resolution of a real case from the job market, highlighting the importance of this field of study and its correlation with Engineering.

2026-03-11T00:00:00Z