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According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.
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Electroencefalografía , Convulsiones , Humanos , Convulsiones/diagnóstico , Convulsiones/fisiopatología , Electroencefalografía/métodos , Bases de Datos Factuales , Aprendizaje Automático , Femenino , Masculino , Redes Neurales de la Computación , AdultoRESUMEN
Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients' quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic concept drift adaptation methods in seizure prediction. Three patient-specific seizure prediction approaches with a 10-minute prediction horizon are compared: a seizure prediction algorithm incorporating a window adjustment method by optimising performance with Support Vector Machines (Backwards-Landmark Window), a seizure prediction algorithm incorporating a data-batch (seizures) selection method using a logistic regression (Seizure-batch Regression), and a seizure prediction algorithm with a dynamic integration of classifiers (Dynamic Weighted Ensemble). These methods incorporate a retraining process after each seizure and use a combination of univariate linear features and SVM classifiers. The Firing Power was used as a post-processing technique to generate alarms before seizures. These methodologies were compared with a control approach based on the typical machine learning pipeline, considering a group of 37 patients with Temporal Lobe Epilepsy from the EPILEPSIAE database. The best-performing approach (Backwards-Landmark Window) achieved results of 0.75 ± 0.33 for sensitivity and 1.03 ± 1.00 for false positive rate per hour. This new strategy performed above chance for 89% of patients with the surrogate predictor, whereas the control approach only validated 46%.
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Epilepsia , Calidad de Vida , Humanos , Convulsiones/diagnóstico , Epilepsia/diagnóstico , Electroencefalografía/métodos , Algoritmos , Aprendizaje Automático , Máquina de Vectores de SoporteRESUMEN
OBJECTIVE: Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% of patients with epilepsy. The present study aimed to ascertain the impact of incorporating sleep-wake information in seizure prediction. METHODS: We developed five patient-specific prediction approaches that use vigilance state information differently: i) using it as an input feature, ii) building a pool of two classifiers, each with different weights to sleep/wake training samples, iii) building a pool of two classifiers, each with only sleep/wake samples, iv) changing the alarm-threshold concerning each sleep/wake state, and v) adjusting the alarm-threshold after a sleep-wake transition. We compared these approaches with a control method that did not integrate sleep-wake information. Our models were tested with data (43 seizures and 482 hours) acquired during presurgical monitoring of 17 patients from the EPILEPSIAE database. As EPILEPSIAE does not contain vigilance state annotations, we developed a sleep-wake classifier using 33 patients diagnosed with nocturnal frontal lobe epilepsy from the CAP Sleep database. RESULTS: Although different patients may require different strategies, our best approach, the pool of weighted predictors, obtained 65% of patients performing above chance level with a surrogate analysis (against 41% in the control method). CONCLUSION: The inclusion of vigilance state information improves seizure prediction. Higher results and testing with long-term recordings from daily-life conditions are necessary to ensure clinical acceptance. SIGNIFICANCE: As automated sleep-wake detection is possible, it would be feasible to incorporate these algorithms into future devices for seizure prediction.
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Electroencefalografía , Convulsiones , Sueño , Vigilia , Humanos , Electroencefalografía/métodos , Convulsiones/fisiopatología , Convulsiones/diagnóstico , Sueño/fisiología , Vigilia/fisiología , Procesamiento de Señales Asistido por Computador , Masculino , Algoritmos , Adulto , FemeninoRESUMEN
Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient's epilepsy, seizure prediction plays a significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure prediction algorithms aim to identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this period is associated with substantial heterogeneity, varying among patients or even between seizures from the same patient. The present work proposes a patient-specific seizure prediction algorithm using post-processing techniques to explore the existence of a set of chronological events of brain activity that precedes epileptic seizures. The study was conducted with 37 patients with Temporal Lobe Epilepsy (TLE) from the EPILEPSIAE database. The designed methodology combines univariate linear features with a classifier based on Support Vector Machines (SVM) and two post-processing techniques to handle pre-seizure temporality in an easily explainable way, employing knowledge from network theory. In the Chronological Firing Power approach, we considered the preictal as a sequence of three brain activity events separated in time. In the Cumulative Firing Power approach, we assumed the preictal period as a sequence of three overlapping events. These methodologies were compared with a control approach based on the typical machine learning pipeline. We considered a Seizure Prediction horizon (SPH) of 5 mins and analyzed several values for the Seizure Occurrence Period (SOP) duration, between 10 and 55 mins. Our results showed that the Cumulative Firing Power approach may improve the seizure prediction performance. This new strategy performed above chance for 62% of patients, whereas the control approach only validated 49% of its models.
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Epilepsia , Convulsiones , Humanos , Convulsiones/diagnóstico , Epilepsia/diagnóstico , Electroencefalografía/métodos , Algoritmos , Aprendizaje AutomáticoRESUMEN
BACKGROUND: Liquid biopsy (LB) is a non-invasive tool to evaluate the heterogeneity of tumors. Since RAS mutations (RAS-mut) play a major role in resistance to antiepidermal growth factor receptor inhibitors (EGFR) monoclonal antibodies (Mabs), serial monitoring of RAS-mut with LB may be useful to guide treatment. The main aim of this study was to evaluate the prognostic value of the loss of RAS-mut (NeoRAS-wt) in LB, during the treatment of metastatic colorectal cancer (mCRC). METHODS: A retrospective study was conducted on patients with mCRC between January 2018 and December 2021. RAS-mut were examined in tissue biopsy, at mCRC diagnosis, and with LB, during treatment. RESULTS: Thirty-nine patients with RAS-mut mCRC were studied. LB was performed after a median of 3 lines (0-7) of systemic treatment including anti-vascular endothelial growth factor (anti-VEGF) Mabs. NeoRAS-wt was detected in 13 patients (33.3%); 9 (69.2%) of them received further treatment with anti-EGFR Mabs with a disease control rate of 44.4%. Median overall survival (OS), from the date of LB testing, was 20 months in the NeoRAS-wt group and 9 months in the persistent RAS-mut group (log-rank 2.985; Pâ =â .08), with a 12-month OS of 84.6% and 57.7%, respectively. NeoRAS-wt was identified as a predictor of survival (HRâ =â 0.29; Pâ =â .007), with an 11-month improvement in median OS and a 71% decrease in risk of death, in heavily pretreated patients. CONCLUSIONS: In conclusion, monitoring clonal evolution in mCRC by LB may provide an additional treatment line for patients with NeoRAS-wt in advanced disease.
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Antineoplásicos , Neoplasias del Colon , Neoplasias Colorrectales , Neoplasias del Recto , Humanos , Neoplasias Colorrectales/tratamiento farmacológico , Neoplasias Colorrectales/genética , Neoplasias Colorrectales/patología , Estudios Retrospectivos , Antineoplásicos/uso terapéutico , Anticuerpos Monoclonales/uso terapéutico , Biopsia Líquida , MutaciónRESUMEN
Resumo Introdução: a política de atenção ao uso e abuso de álcool e outras drogas dos últimos anos demonstra suas limitações para responder às necessidades de atenção à saúde do(a) trabalhador(a) que usa essas substâncias para aliviar os efeitos adversos dos mecanismos hostis no contexto do trabalho. Objetivo: articular abordagens que reúnem um conjunto teórico-conceitual suficiente para abordar temas complexos e multicausais, como a relação entre o sofrimento psíquico e o trabalho, tendo como elemento principal o uso e abuso de álcool e outras drogas. Resultados: há evidências que apontam a relação entre mudanças no mercado de trabalho no contexto neoliberal, violência moral e ética no ambiente profissional e efeitos negativos sobre a saúde física-psíquica de trabalhadores. Evidenciou-se a necessidade de se romper com a lógica do funcionalismo subjetivista sobre o tema do uso e abuso de álcool e outras drogas relacionados ao trabalho. Conclusão: as estratégias políticas de atenção ao sofrimento psíquico decorrente do uso de álcool e outras drogas devem ir além das práticas focadas somente na abstinência e se basear na interface entre aportes teóricos integrativos, redução de danos e que considere a relação entre sofrimento e defesas dos sujeitos, o trabalho e o meio.
Abstract Introduction: policies aimed at use and abuse of alcohol and other drugs in recent years have shown their limitations in responding to the healthcare needs of workers who use and/or abuse alcohol and other drugs to alleviate the adverse effects from hostile work-related mechanisms. Objective: to articulate approaches that gather a theoretical-conceptual set that adequately addresses complex and multi-causal topics, such as the relation between psychological distress and work, with the use and abuse of alcohol and other drugs as their main element. Results: scientific evidence points out to the relation between neoliberal labor market changes, moral and ethical violence in the professional environment and negative effects on workers' physical and mental health. Studies argue about the need to break with the logic of subjectivist functionalism on the topic of work-related use and abuse of alcohol and other drugs. Conclusion: political strategies aimed at psychological suffering resulting from the use/abuse of alcohol and other drugs must go beyond practices solely focused on abstinence and include the interface between integrative theoretical contributions and harm reduction while considering the interaction between suffering and subjects' defenses, work and environment.
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ABSTRACT Purpose: to assess the impact of electroacoustic verification on the satisfaction level and speech recognition in quiet and noise of hearing aid users. Methods: 24 individuals who had been using the device for at least 1 year and had not previously performed electroacoustic verification were assessed. In the first session, participants responded to the International Outcome Inventory for Hearing Aids validation questionnaire and indicated their satisfaction level on a visual analog scale. They were, then, subjected to speech perception tests in quiet and noise and electroacoustic verification, returning after 3 months and repeating the first stage assessments. The Wilcoxon test and paired t-test were used for data analysis, with significance set at p-value ≤ 0.05. Results: the visual analog scale assessment, the International Outcome Inventory for Hearing Aids score, the speech perception threshold in quiet and noise, and the signal/noise ratio improved after 3 months. Conclusion: individuals who had been using the device for at least 1 year had a substantial improvement in their satisfaction level and speech perception in quiet and noise after the electroacoustic verification.
RESUMO Objetivo: avaliar o impacto da verificação eletroacústica no grau de satisfação e no reconhecimento de fala no silêncio e no ruído em usuários de aparelho de amplificação sonora individual. Métodos: foram avaliados 24 usuários que utilizavam o dispositivo há pelo menos um ano e sem realização anterior de verificação eletroacústica. Em um primeiro encontro, os participantes responderam ao questionário de validação International Outcome Inventory for Hearing Aids e atribuíram uma nota de satisfação por meio da escala visual analógica. Em seguida, foram submetidos a testes de percepção de fala no silêncio e no ruído, finalizando com a realização da verificação eletroacústica. Retornaram após três meses e repetiram as avaliações da primeira etapa. Para a análise dos dados, foram utilizados os testes de Wilcoxon e Teste t pareado, valor de p ≤ 0,05. Resultados: observou-se melhora na avaliação da escala visual analógica, na pontuação do International Outcome Inventory for Hearing Aids, no limiar de percepção de fala no silêncio e ruído e na relação sinal/ruído no seguimento de três meses. Conclusão: após a verificação eletroacústica, os usuários que utilizavam o dispositivo há pelo menos um ano apresentaram melhora substancial no grau de satisfação e na percepção de fala no silêncio e no ruído.
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Recently, extreme wildfires have damaged important ecosystems worldwide and have affected urban areas miles away due to long-range transport of smoke plumes. We performed a comprehensive analysis to clarify how smoke plumes from Pantanal and Amazon forests wildfires and sugarcane harvest burning also from interior of the state of São Paulo (ISSP) were transported and injected into the atmosphere of the Metropolitan Area of São Paulo (MASP), where they worsened air quality and increased greenhouse gas (GHG) levels. To classify event days, multiple biomass burning fingerprints as carbon isotopes, Lidar ratio and specific compounds ratios were combined with back trajectories modeling. During smoke plume event days in the MASP fine particulate matter concentrations exceeded the WHO standard (>25 µg m-3), at 99 % of the air quality monitoring stations, and peak CO2 excess were 100 % to 1178 % higher than non-event days. We demonstrated how external pollution events such as wildfires pose an additional challenge for cities, regarding public health threats associated to air quality, and reinforces the importance of GHG monitoring networks to track local and remote GHG emissions and sources in urban areas.
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Contaminantes Atmosféricos , Contaminación del Aire , Incendios , Saccharum , Incendios Forestales , Contaminantes Atmosféricos/análisis , Brasil , Ecosistema , Serina Proteasas Asociadas a la Proteína de Unión a la Manosa/análisis , Contaminación del Aire/análisis , Material Particulado/análisis , Humo/análisis , Bosques , Monitoreo del AmbienteRESUMEN
BACKGROUND: Patellofemoral pain (PFP) is a common knee disorder that causes persistent pain, lower self-reported function and quality of life. People with PFP also present with altered psychological factors, which are associated with higher levels of pain and dysfunction. Mindfulness-based interventions (MBI) generally consist of meditative practices developed to provide a holistic approach to chronic conditions. However, the effects of MBI on clinical and psychological outcomes for people with PFP remains understudied. METHODS: This assessor-blinded, parallel, two-arm randomized clinical trial aims to investigate the effects of adding an 8-week online MBI program to exercise therapy and patient education on clinical and psychological factors for people with PFP. We also aim to investigate whether psychological factors mediate changes in pain and function. Sixty-two participants with PFP will be recruited and randomized into one of two treatment groups (Mindfulness or Control group). Both groups will receive an 8-week intervention involving exercise therapy and education delivered through an online platform. The Mindfulness group will additionally receive a MBI component including formal and informal practices. Outcomes will be assessed online at baseline, intervention endpoint (follow-up 1) and 12 months after intervention completion (follow-up 2). Comparisons between groups will be performed at all time points with linear mixed models. A mediation analysis will be performed using a 3-variable framework. DISCUSSION: Exercise therapy and patient education are considered the "best management" options for PFP. However, unsatisfactory long-term prognosis remains an issue. It is known that people with PFP present with altered psychological factors, which should be considered during the evaluation and treatment of people with PFP. Adding a MBI to the current best treatment for PFP may improve short and long-term effects by addressing the underlying psychological factors. TRIAL REGISTRATION: Registro Brasileiro de Ensaios Clínicos (ReBEC) RBR-4yhbqwk, registered in April 6, 2021.
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Terapia por Ejercicio , Atención Plena , Síndrome de Dolor Patelofemoral , Humanos , Terapia por Ejercicio/métodos , Atención Plena/métodos , Síndrome de Dolor Patelofemoral/diagnóstico , Síndrome de Dolor Patelofemoral/terapia , Educación del Paciente como Asunto , Calidad de Vida , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.
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Artefactos , Epilepsia del Lóbulo Temporal , Humanos , Convulsiones , Redes Neurales de la Computación , Electroencefalografía/métodosRESUMEN
Many state-of-the-art methods for seizure prediction, using the electroencephalogram, are based on machine learning models that are black boxes, weakening the trust of clinicians in them for high-risk decisions. Seizure prediction concerns a multidimensional time-series problem that performs continuous sliding window analysis and classification. In this work, we make a critical review of which explanations increase trust in models' decisions for predicting seizures. We developed three machine learning methodologies to explore their explainability potential. These contain different levels of model transparency: a logistic regression, an ensemble of 15 support vector machines, and an ensemble of three convolutional neural networks. For each methodology, we evaluated quasi-prospectively the performance in 40 patients (testing data comprised 2055 hours and 104 seizures). We selected patients with good and poor performance to explain the models' decisions. Then, with grounded theory, we evaluated how these explanations helped specialists (data scientists and clinicians working in epilepsy) to understand the obtained model dynamics. We obtained four lessons for better communication between data scientists and clinicians. We found that the goal of explainability is not to explain the system's decisions but to improve the system itself. Model transparency is not the most significant factor in explaining a model decision for seizure prediction. Even when using intuitive and state-of-the-art features, it is hard to understand brain dynamics and their relationship with the developed models. We achieve an increase in understanding by developing, in parallel, several systems that explicitly deal with signal dynamics changes that help develop a complete problem formulation.
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Epilepsia , Objetivos , Humanos , Convulsiones/diagnóstico , Encéfalo , Electroencefalografía/métodosRESUMEN
Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.
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Epilepsia Refractaria , Electroencefalografía , Humanos , Electroencefalografía/métodos , Convulsiones/diagnóstico , Epilepsia Refractaria/diagnóstico , Análisis por Conglomerados , Cuero CabelludoRESUMEN
The control of micturition depends on reflex mechanisms, however, it undergoes modulation from cortex, pons and medullary areas. This study investigated if the activation of 5-HT3 receptors in the medulla influences the urinary bladder (UB) regulation in rats. Isoflurane female Wistar rats were submitted to catheterization of the femoral artery and vein for mean arterial pressure (MAP) and heart rate (HR) recordings and injection of drugs, respectively. The UB was cannulated for intravesical pressure (IP) measurement. The Doppler flow probe was placed around the left renal artery for renal conductance (RC) recordings. Phenylbiguanide (PB) and granisetron (GN) were injected into the 4th brain ventricle in rats with guide cannulas implanted 5 days prior to the experiments; or PB and GN were randomly injected intravenously or applied topically (in situ) on the UB. PB injection into 4th V significantly increased IP (68.67 ± 11.70%) and decreased MAP (-29 ± 6 mmHg) compared to saline (0.34 ± 0.64% and -2 ± 2 mmHg), with no changes in the HR and RC. GN injection into the 4th V did not significantly change the IP and RC compared to saline, nevertheless, significantly increased MAP (25 ± 4 mmHg) and heart rate (36 ± 9 bpm) compared to saline. Intravenous PB and GN only produced cardiovascular effects, whilst PB but not GN in situ on the UB evoked increase in IP (111.60 ± 30.36%). Therefore, the activation of 5HT-3 receptors in medullary areas increases the intravesical pressure and these receptors are involved in the phasic control of UB. In contrast, 5-HT3 receptors in the medulla oblongata are involved in the pathways of the tonic control of the cardiovascular system. The activation of 5-HT3 receptors in the bladder cause increase in intravesical pressure and this regulation seem to be under phasic control as the blockade of such receptors elicits no changes in baseline intravesical pressure.
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Isoflurano , Receptores de Serotonina 5-HT3 , Ratas , Femenino , Animales , Vejiga Urinaria , Granisetrón , Ratas Wistar , Isoflurano/farmacología , Bulbo Raquídeo/fisiología , Presión SanguíneaRESUMEN
Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain's electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers.
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Artefactos , Epilepsia , Algoritmos , Electroencefalografía/métodos , Epilepsia/diagnóstico , Humanos , Procesamiento de Señales Asistido por ComputadorRESUMEN
Multicore magnetic nanoparticles of manganese ferrite were prepared using carboxymethyl dextran as an agglutinating compound or by an innovative method using melamine as a cross-coupling agent. The nanoparticles prepared using melamine exhibited a flower-shape structure, a saturation magnetization of 6.16 emu/g and good capabilities for magnetic hyperthermia, with a specific absorption rate (SAR) of 0.14 W/g. Magnetoliposome-like structures containing the multicore nanoparticles were prepared, and their bilayer structure was confirmed by FRET (Förster Resonance Energy Transfer) assays. The nanosystems exhibited sizes in the range of 250-400 nm and a low polydispersity index. A new antitumor thienopyridine derivative, 7-[4-(pyridin-2-yl)-1H-1,2,3-triazol-1-yl]thieno[3,2-b]pyridine, active against HeLa (cervical carcinoma), MCF-7 (breast adenocarcinoma), NCI-H460 (non-small-cell lung carcinoma) and HepG2 (hepatocellular carcinoma) cell lines, was loaded in these nanocarriers, obtaining a high encapsulation efficiency of 98 ± 2.6%. The results indicate that the new magnetoliposomes can be suitable for dual cancer therapy (combined magnetic hyperthermia and chemotherapy).
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INTRODUCTION: Guidelines recommend regional lymphadenectomy with a lymph node yield (LNY) of at least 12 lymph nodes (LN) for adequate colon cancer (CC) staging. LNY ≥22LN may improve survival, especially in right-sided CC [Lee et al., Surg Oncol, 27(3), 2018]. This multicentric retrospective cohort study evaluated the impact of LNY and tumor laterality on CC staging and survival. MATERIALS AND METHODS: Patients with stage I-III CC that underwent surgery from 2012 to 2018 were grouped according to LNY: <22 and ≥ 22. Primary outcomes were LN positivity (N+ rate) and disease-free survival (DFS). Overall survival (OS) was the secondary outcome. Exploratory analyses were performed for laterality and stage. RESULTS: We included 795 patients (417 < 22LN, 378 ≥ 22LN); 53% had left-sided CC and 29%/37%/38% had stage I/II/III tumors. There was no association between LNY ≥22LN and N+ rate after adjustment for grade, T stage, lymphovascular invasion (LVI) and perineural invasion; a trend for a higher N+ rate in left-sided CC was identified (interaction p = 0.033). With a median follow-up of 63.6 months for DFS and 73.2 months for OS, 254 patients (31.9%) relapsed and 207 (26.0%) died. In multivariate analysis adjusted for age, ASA score, laparoscopic approach, T/N stage, mucinous histology, LVI and adjuvant chemotherapy, LNY ≥22LN was significantly associated with both DFS (HR 0.75, p = 0.031) and OS (HR 0.71, p = 0.025). Restricted cubic spline analysis showed a more significant benefit for right-sided CC. CONCLUSION: LNY ≥22LN was associated with longer DFS and OS in patients with operable CC, especially for right-sided CC.
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Neoplasias del Colon , Ganglios Linfáticos , Neoplasias del Colon/patología , Humanos , Escisión del Ganglio Linfático , Ganglios Linfáticos/patología , Ganglios Linfáticos/cirugía , Estadificación de Neoplasias , Pronóstico , Estudios RetrospectivosRESUMEN
Seizure prediction may be the solution for epileptic patients whose drugs and surgery do not control seizures. Despite 46 years of research, few devices/systems underwent clinical trials and/or are commercialized, where the most recent state-of-the-art approaches, as neural networks models, are not used to their full potential. The latter demonstrates the existence of social barriers to new methodologies due to data bias, patient safety, and legislation compliance. In the form of literature review, we performed a qualitative study to analyze the seizure prediction ecosystem to find these social barriers. With the Grounded Theory, we draw hypotheses from data, while with the Actor-Network Theory we considered that technology shapes social configurations and interests, being fundamental in healthcare. We obtained a social network that describes the ecosystem and propose research guidelines aiming at clinical acceptance. Our most relevant conclusion is the need for model explainability, but not necessarily intrinsically interpretable models, for the case of seizure prediction. Accordingly, we argue that it is possible to develop robust prediction models, including black-box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, if they can deliver human- comprehensible explanations. Due to skepticism and patient safety reasons, many authors advocate the use of transparent models which may limit their performance and potential. Our study highlights a possible path, by using model explainability, on how to overcome these barriers while allowing the use of more computationally robust models.
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Electroencefalografía , Epilepsia , Ecosistema , Electroencefalografía/métodos , Humanos , Redes Neurales de la Computación , Convulsiones/diagnósticoRESUMEN
Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm's decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 ([Formula: see text]38%) were solely validated by our methodology, while 24 ([Formula: see text]44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.
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Epilepsia Refractaria , Epilepsia , Algoritmos , Epilepsia Refractaria/diagnóstico , Electroencefalografía/métodos , Humanos , Convulsiones/diagnósticoRESUMEN
OBJECTIVE: Independent component analysis (ICA) is commonly used to remove noisy artifacts from multi-channel scalp electroencephalogram (EEG) signals. ICA decomposes EEG into different independent components (ICs) and then, experts remove the noisy ones. This process is highly time-consuming and experts are not always available. To surpass this drawback, research is going on to develop models to automatically conduct IC classification. Current state-of-the-art models use power spectrum densities (PSDs) and topoplots to classify ICs. The performance of these methods may be limited by disregarding the IC time-series that would contribute to fully simulate the visual inspection performed by experts. METHODS: We present a novel ensemble deep neural network that combines time-series, PSDs, and topoplots to classify ICs. Moreover, we study the ability to use our model in transfer learning approaches. RESULTS: Experimental results showed that using time-series improves IC classification. Results also indicated that transfer learning obtained higher performance than simply training a new model from scratch. CONCLUSION: Researchers should develop IC classifiers using the three sources of information. Moreover, transfer learning approaches should be considered when producing new deep learning models. SIGNIFICANCE: This work improves IC classification, enhancing the automatic removal of EEG artifacts. Additionally, since labelled ICs are generally not publicly available, the possibility of using our model in transfer learning studies may motivate other researchers to develop their own classifiers.
Asunto(s)
Artefactos , Procesamiento de Señales Asistido por Computador , Algoritmos , Encéfalo , Electroencefalografía/métodos , Humanos , Redes Neurales de la ComputaciónRESUMEN
Resumo O uso de conceitos e práticas de Redução de Danos (RD) na atenção às pessoas que usam drogas foi iniciado no Brasil nos anos 1990, como resposta preventiva à propagação do HIV/aids. Ao longo dos anos, ganhou espaço em outros campos de ação, como a saúde mental e a proteção social. Em 2019, com as mudanças no governo federal, esta prática foi retirada da definição da política sobre drogas. Com foco nos reflexos dessas mudanças, este estudo visou caracterizar quem atua e quais ações são desenvolvidas junto às pessoas que usam drogas, sob a perspectiva da RD, em municípios da região Sul do Brasil (RS, SC e PR). Trata-se de um estudo exploratório descritivo e transversal, que usou um questionário virtual composto por questões abertas, fechadas e métodos mistos de análise de dados. Os participantes foram recrutados por meio do método Snowball Sampling, de forma virtual, alcançando 72 questionários validados. Foi desenvolvida uma análise exploratória dos dados quantitativos. Para as respostas qualitativas foi usada a Análise Textual Discursiva e o software NVivo (versão 12). Os resultados demonstraram o perfil dos profissionais; mudanças ao longo dos anos e a necessidade de ampliar investimentos em pesquisas sobre RD.
Abstract Harm Reduction (HR) concepts and practices in the care of people who use drugs stared being used in Brazil in the 1990s, as a preventive response to the spread of HIV/AIDS. Over the years, it gained space in other fields, such as mental health and social protection. In 2019, with changes in the federal government, this practice was removed from the definitions of the drug policy. Focusing on the consequences of these changes, this study aimed to characterize who works and what actions are developed with the people who use drugs, in the HR perspective, in the southern region of Brazil. This is a descriptive and cross-sectional exploratory study, which used an online questionnaire with open and closed questions, and mixed methods of data analysis. Participants were recruited using the Snowball Sampling method, by virtual means, reaching 72 validated questionnaires. An exploratory analysis of the quantitative data was developed. For the qualitative data, the Discursive Textual Analysis and NVivo software (version 12) were used. The results showed the profile of professionals; the changes over the years; and the need to expand investments in HR research.