Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation.

BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

Treatment alternatives to induce follicular wave emergence for timed-AI in lactating dairy Cows.

Two experiments evaluated the effect of different hormonal treatments to synchronize follicle wave emergence on follicle dynamics and pregnancies per AI (P/AI) in estradiol (E2)/progesterone (P4) timed-AI (TAI) protocols in lactating dairy cows. In Experiment 1, lactating, primiparous Holstein cows (n = 36) received a P4 releasing device (Day 0) and were allocated at random to one of the following three treatment groups: Group EB received 2 mg E2 benzoate (EB) intramuscularly (i.m.), Group EB + GnRH received 2 mg EB+20 µg buserelin (GnRH) i.m., or Group EB + P4 received 2 mg EB + 100 mg of injectable P4 (iP4) in oil i.m. All cows received 0.150 mg D-Cloprostenol on Days 7 and 8 followed by P4 device removal, 400 IU eCG and 1 mg ECP on Day 8. Daily ultrasound examinations revealed that although the interval from P4 device removal to ovulation was not affected by treatment, cows that received EB + GnRH had an earlier (P < 0.05) emergence of the new follicular wave (Day 2.6 ± 0.2) than the other two treatment groups (Days 3.5 ± 0.3 and 6.1 ± 0.3, for EB and EB + P4, respectively). In Experiment 2, 808 lactating cows were assigned randomly to the three treatments evaluated in Experiment 1, and all the cows were TAI to determine P/AI. Cows in the EB + GnRH group had greater P/AI (57.4 %, P < 0.01) than those in the EB (44.6 %) or EB + P4 (45.7 %) groups. In conclusion, the administration of GnRH, but not iP4, on the day of insertion of a P4 device improves P/AI in lactating dairy cows synchronized for TAI with an estradiol/P4-based protocol.

Low HDL-C/ApoA-I index is associated with cardiometabolic risk factors and coronary artery calcium: a sub-analysis of the genetics of atherosclerotic disease (GEA) study.

BACKGROUND: The high-density lipoprotein cholesterol to apolipoprotein A-I index (HDL-C/ApoA-I) may be practical and useful in clinical practice as a marker of atherosclerosis. This study aimed to investigate the association between the HDL-C/ApoA-I index with cardiometabolic risk factors and subclinical atherosclerosis. METHODS: In this cross-sectional sub-analysis of the GEA study, 1,363 individuals, women (51.3%) and men (48.7%) between 20 and 75 years old, without coronary heart disease or diabetes mellitus were included. We defined an adverse cardiometabolic profile as excess adipose tissue metrics, non-alcoholic liver fat measured by non-contrasted tomography, metabolic syndrome, dyslipidemias, and insulin resistance. The population was stratified by quartiles of the HDL-C/Apo-AI index, and its dose-relationship associations were analysed using Tobit regression, binomial, and multinomial logistic regression analysis. RESULTS: Body mass index, visceral and pericardial fat, metabolic syndrome, fatty liver, high blood pressure, and CAC were inversely associated with the HDL-C/ApoA-I index. The CAC > 0 prevalence was higher in quartile 1 (29.2%) than in the last quartile (22%) of HDL-C/ApoA-I index (p = 0.035). The probability of having CAC > 0 was higher when the HDL-C/ApoA-I index was less than 0.28 (p < 0.001). This association was independent of classical coronary risk factors, visceral and pericardial fat measurements. CONCLUSION: The HDL-C/ApoA-I index is inversely associated with an adverse cardiometabolic profile and CAC score, making it a potentially useful and practical biomarker of coronary atherosclerosis. Overall, these findings suggest that the HDL-C/ApoA-I index could be useful for evaluating the probability of having higher cardiometabolic risk factors and subclinical atherosclerosis in adults without CAD.

Evidence-Based Learning Strategies in Medicine Using AI.

Unlabelled: Large language models (LLMs), like ChatGPT, are transforming the landscape of medical education. They offer a vast range of applications, such as tutoring (personalized learning), patient simulation, generation of examination questions, and streamlined access to information. The rapid advancement of medical knowledge and the need for personalized learning underscore the relevance and timeliness of exploring innovative strategies for integrating artificial intelligence (AI) into medical education. In this paper, we propose coupling evidence-based learning strategies, such as active recall and memory cues, with AI to optimize learning. These strategies include the generation of tests, mnemonics, and visual cues.

A contemporary review of breast cancer risk factors and the role of artificial intelligence.

Background: Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics. Methods: A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses. Results: Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies. Conclusion: The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.

Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study.

Background: The deployment of OpenAI's ChatGPT-3.5 and its subsequent versions, ChatGPT-4 and ChatGPT-4 With Vision (4V; also known as "GPT-4 Turbo With Vision"), has notably influenced the medical field. Having demonstrated remarkable performance in medical examinations globally, these models show potential for educational applications. However, their effectiveness in non-English contexts, particularly in Chile's medical licensing examinations-a critical step for medical practitioners in Chile-is less explored. This gap highlights the need to evaluate ChatGPT's adaptability to diverse linguistic and cultural contexts. Objective: This study aims to evaluate the performance of ChatGPT versions 3.5, 4, and 4V in the EUNACOM (Examen Único Nacional de Conocimientos de Medicina), a major medical examination in Chile. Methods: Three official practice drills (540 questions) from the University of Chile, mirroring the EUNACOM's structure and difficulty, were used to test ChatGPT versions 3.5, 4, and 4V. The 3 ChatGPT versions were provided 3 attempts for each drill. Responses to questions during each attempt were systematically categorized and analyzed to assess their accuracy rate. Results: All versions of ChatGPT passed the EUNACOM drills. Specifically, versions 4 and 4V outperformed version 3.5, achieving average accuracy rates of 79.32% and 78.83%, respectively, compared to 57.53% for version 3.5 (P<.001). Version 4V, however, did not outperform version 4 (P=.73), despite the additional visual capabilities. We also evaluated ChatGPT's performance in different medical areas of the EUNACOM and found that versions 4 and 4V consistently outperformed version 3.5. Across the different medical areas, version 3.5 displayed the highest accuracy in psychiatry (69.84%), while versions 4 and 4V achieved the highest accuracy in surgery (90.00% and 86.11%, respectively). Versions 3.5 and 4 had the lowest performance in internal medicine (52.74% and 75.62%, respectively), while version 4V had the lowest performance in public health (74.07%). Conclusions: This study reveals ChatGPT's ability to pass the EUNACOM, with distinct proficiencies across versions 3.5, 4, and 4V. Notably, advancements in artificial intelligence (AI) have not significantly led to enhancements in performance on image-based questions. The variations in proficiency across medical fields suggest the need for more nuanced AI training. Additionally, the study underscores the importance of exploring innovative approaches to using AI to augment human cognition and enhance the learning process. Such advancements have the potential to significantly influence medical education, fostering not only knowledge acquisition but also the development of critical thinking and problem-solving skills among health care professionals.

Cuban Policosanol (Raydel®) Exerts Higher Antioxidant and Anti-Glycation Activities than Chinese Policosanol (BOC Sciences) in Reconstituted High-Density Lipoproteins: In Vivo Anti-Inflammatory Activities in Zebrafish and Its Embryos.

The present study compares sugarcane-wax purified policosanols sourced from Cuba (Raydel®) and China (BOC Sciences) and utilized following the synthesis of reconstituted high-density lipoproteins (rHDL). The two policosanols exhibited distinctly different ingredient ratios of long-chain aliphatic alcohols, particularly 1-octacosanol (C28) and 1-tetratriacotanol (C34). After synthesizing rHDL with apolipoprotein A-I (apoA-I), the two policosanols bound well with phospholipid and apoA-I to form the discoidal rHDL. Notably, rHDL-1, containing Cuban policosanol, displayed the largest particle diameter at approximately 78 ± 3 nm. In contrast, both control rHDL (rHDL-0) and rHDL containing Chinese policosanol (rHDL-2) exhibited smaller particles, with diameters of approximately 58 ± 3 nm and 61 ± 2 nm, respectively. Furthermore, rHDL-1 demonstrated enhanced anti-glycation activity, safeguarding apoA-I from degradation within HDL, and displayed the antioxidant ability to inhibit LDL oxidation. A microinjection of each rHDL into zebrafish embryos in the presence of carboxymethyllysine (CML) revealed rHDL-1 to have the strongest antioxidant activity with the highest embryo survivability and normal developmental morphology. Dermal application to recover the wound revealed rHDL-1 to have the highest wound-healing activity (75%) and survivability (92%) in the cutaneous wound area in the presence of CML. In adult zebrafish, injecting CML (250 µg) caused acute death and hyperinflammation, marked by heightened neutrophil infiltration and interleukin (IL)-6 production in liver. However, co-administering rHDL-1 notably increased survival (85%) and exhibited strong anti-inflammatory properties, reducing IL-6 production while improving the blood lipid profile. However, a co-injection of rHDL-2 resulted in the lowest survivability (47%) with more hepatic inflammation. In conclusion, Cuban policosanol (Raydel®) has more desirable properties for the in vitro synthesis of rHDL with stronger anti-glycation and antioxidant activities than those of Chinese policosanol (BOC Sciences). Moreover, Raydel-policosanol-integrated rHDL demonstrates a noteworthy effect on accelerated wound healing and robust anti-inflammatory properties, leading to increased survivability in zebrafish embryos and adults by effectively suppressing CML-induced hyperinflammation.

Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study.

Background: The use of artificial intelligence (AI) in medicine has been a trending subject in the past few years. Although not frequently used in daily practice yet, it brings along many expectations, doubts, and fears for physicians. Surveys can be used to help understand this situation. Objective: This study aimed to explore the degree of knowledge, expectations, and fears on possible AI use by physicians in daily practice, according to sex and time since graduation. Methods: An electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022. Results: A total of 164 physicians responded to our survey. Overall, 54.3% (89/164) of physicians considered themselves to have an intermediate knowledge of AI, and 78.5% (128/163) believed that AI should be regulated by a governmental agency. If AI solutions were reliable, fast, and available, 77.9% (127/163) intended to frequently or always use AI for diagnosis (143/164, 87.2%), management (140/164, 85.4%), or exams interpretation (150/164, 91.5%), but their approvals for AI when used by other health professionals (85/163, 52.1%) or directly by patients (82/162, 50.6%) were not as high. The main benefit would be increasing the speed for diagnosis and management (106/163, 61.3%), and the worst issue would be to over rely on AI and lose medical skills (118/163, 72.4%). Physicians believed that AI would be useful (106/163, 65%), facilitate their work (140/153, 91.5%), not alter the number of appointments (80/162, 49.4%), not interfere in their financial gain (94/162, 58%), and not replace their jobs but be an additional source of information (104/162, 64.2%). In case of disagreement between AI and physicians, most (108/159, 67.9%) answered that a third opinion should be requested. Physicians with ≤10 years since graduation would adopt AI solutions more frequently than those with >20 years since graduation (P=.04), and female physicians were more receptive to other hospital staff using AI than male physicians (P=.008). Conclusions: Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others. They also intend to use it, as long as it was approved by a regulatory agency. Although there was hope for a beneficial impact of AI on health care, it also brings specific concerns.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Effects of equine chorionic gonadotropin dosage and its splitting in different days on reproductive performance of Nellore cows synchronized for timed-artificial insemination.

This study evaluated the effects of dose of equine chorionic gonadotropin (eCG) and its splitting in different days of the synchronization protocol on reproductive performance of primiparous and multiparous Nellore cows. In the present study, 2,536 Nellore cows (1,634 primiparous and 902 multiparous) were assigned to receive in a 2 × 2 factorial design 1) an intravaginal progesterone (P4) device and 2.0 mg of estradiol benzoate (EB) on day -11, 12.5 mg (i.m.) of dinoprost tromethamine (PGF), 300 IU (i.m.) of eCG, 0.6 mg (i.m.) of estradiol cypionate (ECP), and P4 device withdrawal on day -2, followed by TAI on day 0 (n = 632 cows, being 409 primiparous and 223 multiparous; 300-2), 2) 300 IU (i.m) of eCG administered on days -4 and -2 (150 IU of eCG/day; n = 637 cows, being 412 primiparous and 225 multiparous; 300-4-2), 3) 400 IU (i.m.) of eCG administered on day -2 (n = 633 cows, being 406 primiparous and 227 multiparous; 400-2), and 4) 400 IU (i.m) of eCG administered on days -4 and -2 (200 IU of eCG/day; n = 634 cows, being 407 primiparous and 227 multiparous; 400-4-2). Individual cow BCS was assessed on days -11, 0 (timed-AI), and 31 of the study. Body condition score of the animals was classified into LOW or HIGH using the threshold of 2.75 (≤2.75 = LOW; >2.75 = HIGH). For primiparous cows, an eCG splitting effect was observed on follicle size, as cows receiving eCG on days -4 and -2 of the synchronization protocol had a larger follicle than cows administered eCG only on day -2. For day 31 P/AI, primiparous cows receiving 400-4-2, regardless of BCS, had a greater P/AI than cows from other treatments. Administering 400-4-2 to LOW BCS cows also resulted in greater P/AI than all other treatments assigned to LOW BCS cows. For multiparous cows, no treatment effect was observed for follicle size, estrus expression, and day 31 P/AI (P ≥ 0.21). In summary, increasing the dose and splitting the dose of eCG positively impacted the pregnancy rates of primiparous cows under a BCS ≤2.75, but no effects were detected on multiparous cows.

An Interpretable Approach with Explainable AI for Heart Stroke Prediction.

Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model's comprehensibility for healthcare practitioners. This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. Applying these techniques, including model interpretability measures such as permutation importance and explainability methods like LIME, has achieved impressive results. While permutation importance provides insights into feature importance globally, LIME complements this by offering local and instance-specific explanations. Together, they contribute to a comprehensive understanding of the Artificial Neural Network (ANN) model. The combination of these techniques not only aids in understanding the features that drive overall model performance but also helps in interpreting and validating individual predictions. The ANN model has achieved an outstanding accuracy rate of 95%.

Combination Therapy of Cuban Policosanol (Raydel®, 20 mg) and Intensive Exercise for 12 Weeks Resulted in Improvements in Obesity, Hypertension, and Dyslipidemia without a Decrease in Serum Coenzyme Q10: Enhancement of Lipoproteins Quality and Antioxidant Functionality in Obese Participants.

Obesity and overweight, frequently caused by a lack of exercise, are associated with many metabolic diseases, such as hypertension, diabetes, and dyslipidemia. Aerobic exercise effectively increases the high-density lipoproteins-cholesterol (HDL-C) levels and alleviates the triglyceride (TG) levels. The consumption of Cuban policosanol (Raydel®) is also effective in enhancing the HDL-C quantity and HDL functionality to treat dyslipidemia and hypertension. On the other hand, no study has examined the effects of a combination of high-intensity exercise and policosanol consumption in obese subjects to improve metabolic disorders. In the current study, 17 obese subjects (average BMI 30.1 ± 1.1 kg/m2, eight male and nine female) were recruited to participate in a program combining exercise and policosanol (20 mg) consumption for 12 weeks. After completion, their BMI, waist circumference, total fat mass, systolic blood pressure (SBP), and diastolic blood pressure (DBP) reduced significantly up to around -15%, -13%, -33%, -11%, and -13%, respectively. In the serum lipid profile, at Week 12, a significant reduction was observed in the total cholesterol (TC) and triglyceride (TG) levels, up to -17% and -54% from the baseline, respectively. The serum HDL-C was elevated by approximately +12% from the baseline, as well as the percentage of HDL-C in TC, and HDL-C/TC (%), was enhanced by up to +32% at Week 12. The serum coenzyme Q10 (CoQ10) level was increased 1.2-fold from the baseline in all participants at Week 12. In particular, the male participants exhibited a 1.4-fold increase from the baseline. The larger rise in serum CoQ10 was correlated with the larger increase in the serum HDL-C (r = 0.621, p = 0.018). The hepatic function parameters were improved; the serum γ-glutamyl transferase decreased at Week 12 by up to -55% (p < 0.007), while the aspartate aminotransferase and alanine transaminase levels diminished within the normal range. In the lipoprotein level, the extent of oxidation and glycation were reduced significantly with the reduction in TG content. The antioxidant abilities of HDL, such as paraoxonase (PON) and ferric ion reduction ability (FRA), were enhanced significantly by up to 1.8-fold and 1.6-fold at Week 12. The particle size and number of HDL were elevated up to +10% during the 12 weeks, with a remarkable decline in the TG content, glycation extent, and oxidation. The improvements in HDL quality and functionality were linked to the higher survivability of adult zebrafish and their embryos, under the co-presence of carboxymethyllysine (CML), a pro-inflammatory molecule known to cause acute death. In conclusion, 12 weeks of Cuban policosanol (Raydel®, 20 mg) consumption with high-intensity exercise displayed a significant improvement in blood pressure, body fat mass, blood lipid profile without liver damage, CoQ10 metabolism, and renal impairment.

Artificial Intelligence-Generated Scientific Literature: A Critical Appraisal.

BACKGROUND: Review articles play a critical role in informing medical decisions and identifying avenues for future research. With the introduction of artificial intelligence (AI), there has been a growing interest in the potential of this technology to transform the synthesis of medical literature. Open AI's Generative Pre-trained Transformer (GPT-4) (Open AI Inc, San Francisco, CA) tool provides access to advanced AI that is able to quickly produce medical literature following only simple prompts. The accuracy of the generated articles requires review, especially in subspecialty fields like Allergy/Immunology. OBJECTIVE: To critically appraise AI-synthesized allergy-focused minireviews. METHODS: We tasked the GPT-4 Chatbot with generating 2 1,000-word reviews on the topics of hereditary angioedema and eosinophilic esophagitis. Authors critically appraised these articles using the Joanna Briggs Institute (JBI) tool for text and opinion and additionally evaluated domains of interest such as language, reference quality, and accuracy of the content. RESULTS: The language of the AI-generated minireviews was carefully articulated and logically focused on the topic of interest; however, reviewers of the AI-generated articles indicated that the AI-generated content lacked depth, did not appear to be the result of an analytical process, missed critical information, and contained inaccurate information. Despite being provided instruction to utilize scientific references, the AI chatbot relied mainly on freely available resources, and the AI chatbot fabricated references. CONCLUSIONS: The AI holds the potential to change the landscape of synthesizing medical literature; however, apparent inaccurate and fabricated information calls for rigorous evaluation and validation of AI tools in generating medical literature, especially on subjects associated with limited resources.

Reabilitação protética bucomaxilofacial: um estudo retrospectivo de base universitária / Maxillofacial prosthetic rehabilitation: an university-based retrospective study

A Prótese bucomaxilofacial (PBMF) é a especialidade da Odontologia que reabilita proteticamente pacientes com perda de estrutura na região da face. Entende-se por PBMFs aquelas utilizadas na reabilitação de pacientes que apresentam deformidades por etiologia congênita, traumática ou patológica. Objetivo: Avaliar retrospectivamente o perfil dos pacientes bem como as características das reabilitações protéticas realizadas em um Projeto de Extensão em Prótese Bucomaxilofacial de uma Universidade no sul do Brasil.Materiais e métodos:Foram analisados 90 prontuários de pacientes atendidos no período de agosto de 2017 a dezembro de 2018, e coletados os seguintes dados: gênero, cor/etnia, idade, etiologia da deformidade, tipo de prótese reabilitadora realizada e referenciamento do paciente ao Projeto. Resultados:Observou-se que pacientes do gênero masculino e cor branca foram os mais frequentemente reabilitados com a maioria dos tipos de prótese, com exceção da prótese nasal. A idade dos pacientes variou de 5 a 81 anos. A prótese ocular foi a mais confeccionada. A etiologia patológica foi a que mais exigiu tratamento reabilitador. Médicos e equipes hospitalares foram os que mais referenciaram pacientes para o Projeto de Extensão.Discussão: A maior prevalência de atendidos foi de pacientes do gênero masculino, etiologia patológica, com idade 60 anos ou mais, o que reforça a sobrevida das pessoas que são diagnosticadas com câncer e necessitam reabilitação bucomaxilofacial. Conclusão: A grande procura por atendimento no Projeto de Extensão em PBMF mostra uma carência desse serviço e poucas pesquisas para esclarecer o perfil do paciente que mais procura atendimento PBMF.

Bucomaxillofacial Prosthesis (BMFP) is a specialty of Dentistry that rehabilitates patients with loss of structure in the face region. BMFP are known to be used in the rehabilitation of patients who present deformities due to congenital, traumatic or pathological etiology. Aim:In retrospect, to assess the profile of patients, as well as the features of clinical cases of rehabilitations performed at the Buccomaxillofacial Prosthesis Extension Project, at the Faculty of Dentistry of the Universidade Federal do Rio Grande do Sul, UFRGS. Materials and methods:from August 2017 to December 2018, 90 charts were cataloged with the following data: gender, ethnicity, age, etiology of the deformity, type of rehabilitation prosthesis, how the patient came to the Project. Results:It was concluded that white male patients were the predominant group to be benefited with prosthesis. The age gap was from 5 to 81 years. Ocular prosthesis was the most prevalent one. The pathological etiology was the one that most required rehabilitation treatment. Doctors and hospital staff were the ones who most referred patients to the Project.Discussion:The prevalence of patients attended was male, pathological etiology, aged 60 years or more, which reinforces the survival of people who are diagnosed with cancer and need oral and maxillofacial rehabilitation. Conclusion:The great demand for care in the BMFP Extension Project shows a lack of this service and little research to clarify the profile of the patient who most seeks BMFP care.

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models-without resorting to deep learning-is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.

Is Negative Affect that Bad? The Effect of Affective States on Conventional and Unconventional Creative Thinking in University Students.

Introduction: The role of affective states on the creative process has been receiving the attention of researchers and has led to contradictory results. Most research in creativity has emphasized the role of affective states, mainly positive ones, on creativity levels, namely those resulting from divergent thinking tasks that reveal the unconventional way of thinking in the creative process. However, there are no studies to date that focus on the impact of affective states on conventional and unconventional thinking, during the same creative process, which consider a single creative assessment task. The aim of this experimental study was to analyze the effect of induced affective states on both conventional and unconventional thinking of creativity in adults by using the TCT-DP (Test for Creative Thinking-Drawing Production). Method: Seventy-five university students, mostly female, with a mean age of 26.95 years, were randomly assigned into three affect elicitation conditions (pleasant vs. unpleasant vs. neutral). Results: Results indicated that the negative affective state led to higher levels of conventional thinking when compared to positive and neutral affective states. However, no significant differences were found on unconventional thinking across the three conditions. Conclusions: Our results do not support the assumption that the negative affect has a hindering effect on creativity nor the positive affect increases creativity. Negative affect seems to promote conventional thinking, perhaps due to its cognitive correlates, which can be manifested in focusing attention and analytic thinking. Practical and theoretical implications for future research on the role of affective states on creativity are discussed.

Introducción: El papel de los estados afectivos en el proceso creativo ha sido objeto de atención por parte de los investigadores y ha dado lugar a resultados contradictorios. La mayor parte de la investigación en creatividad ha hecho hincapié en el papel de los estados afectivos, principalmente los positivos, sobre los niveles de creatividad. A saber, los resultantes de las tareas de pensamiento divergente que revelan la forma no convencional de pensar en el proceso creativo. Este estudio tuvo como objetivo analizar el efecto de los estados afectivos inducidos en dos dimensiones distintas del pensamiento creativo en adultos. Método: Setenta y cinco voluntarios, en su mayoría mujeres, con una edad media de 26.95 años, fueron asignados aleatoriamente a tres condiciones de elicitación de estados afectivos (agradable vs desagradable vs neutral), justo antes de realizar una tarea de creatividad figurativa. Resultados: Los resultados indicaron que el estado afectivo negativo condujo a niveles más altos de pensamiento convencional. Conclusiones: Nuestros resultados no apoyan ni la hipótesis de que el afecto negativo tiene un efecto perjudicial sobre la creatividad ni la de que el afecto positivo aumenta la creatividad. El afecto negativo parece promover el pensamiento convencional, quizá debido a sus correlatos cognitivos, que pueden manifestarse en la atención focalizada y el pensamiento analítico. Se discuten las implicaciones prácticas y teóricas para futuras investigaciones sobre el papel de los estados afectivos en la creatividad.

Interactions of variants of human apolipoprotein A-I with biopolymeric model matrices. Effect of collagen and heparin.

BACKGROUND: The extracellular matrix (ECM) is a complex tridimensional scaffold that actively participates in physiological and pathological events. The objective of this study was to test whether structural proteins of the ECM and glycosaminoglycans (GAGs) may favor the retention of human apolipoprotein A-I (apoA-I) variants associated with amyloidosis and atherosclerosis. METHODS: Biopolymeric matrices containing collagen type I (Col, a main macromolecular component of the ECM) with or without heparin (Hep, a model of GAGs) were constructed and characterized, and used to compare the binding of apoA-I having the native sequence (Wt) or Arg173Pro, a natural variant inducing cardiac amyloidosis. Protein binding was observed by fluorescence microscopy and unbound proteins quantified by a colorimetric assay. RESULTS: Both, Wt and Arg173Pro bound to the scaffolds containing Col, but the presence of Hep diminished the binding efficiency. Col-Hep matrices retained Arg173Pro more than the Wt. The retained protein was only partially removed from the matrices with saline solutions, indicating that electrostatic interactions may occur but are not the main driving force. Using in addition thermodynamic molecular simulations and size exclusion chromatography approaches, we suggest that the binding of apoA-I variants to the biopolymeric matrices is driven by many low affinity interactions. CONCLUSIONS: Under this scenario Col-Hep scaffolds contribute to the binding of Arg173Pro, as a cooperative platform which could modify the native protein conformation affecting protein folding. GENERAL SIGNIFICANCE: We show that the composition of the ECM is key to the protein retention, and well characterized biosynthetic matrices offer an invaluable in vitro model to mimic the hallmark of pathologies with interstitial infiltration such as cardiac amyloidosis.

Melhores práticas de enfermagem para a pessoa idosa em Unidades de Estratégia Saúde da Família em um município do Sul do país

Compreender as melhores práticas de enfermagem desempenhadas na Atenção Primária à Saúde em unidades de Estratégia Saúde da família em um município da região Norte do Estado de Santa Catarina. Método: pesquisa qualitativa realizada no município de Joinville­SC por meio de entrevistas semiabertas e individuais. Resultados: Participaram da pesquisa 30 enfermeiros atuantes na atenção primária. Descrevem as melhores práticas de enfermagem voltadas à pessoa idosa, com destaque para a resolubilidade das práticas, a priorização do acesso da pessoa idosa, o cuidado ampliado para os familiares, a atuação do enfermeiro frente a situações de negligência e violência contra a pessoa idosa e, ainda, as barreiras para a efetivação das melhores práticas. Conclusão: a qualificação das ações oferecidas as pessoas idosas na atenção primária perpassa questões administrativas, sociais e resolutivas, e exigem que o enfermeiro esteja inserido no território, conhecendo potencialidades e limitações que interferem no planejamento do cuidado. Contudo, somente enfermeiros com autonomia e segurança no saber científico, aliado ao apoio institucional e ambiente adequado, desenvolvem melhores práticas.