Addressing data limitations in seizure prediction through transfer learning.

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.

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.

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.

Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance.

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.

EEG epilepsy seizure prediction: the post-processing stage as a chronology.

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.

Loss of RAS Mutations in Liquid Biopsies of Patients With Multi-Treated Metastatic Colorectal Cancer.

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).

Impact of extreme wildfires from the Brazilian Forests and sugarcane burning on the air quality of the biggest megacity on South America.

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.

Effects of an online program including mindfulness, exercise therapy and patient education compared to online exercise therapy and patient education for people with Patellofemoral Pain: protocol for a randomized clinical trial.

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.

Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models.

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.

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.

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.

Activation of 5-HT3 receptors in the medulla oblongata is involved in the phasic control of urinary bladder.

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.

EPIC: Annotated epileptic EEG independent components for artifact reduction.

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.

Magnetoliposomes Containing Multicore Nanoparticles and a New Antitumor Thienopyridine Compound with Potential Application in Chemo/Thermotherapy.

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.

Lymph node yield in the pathological staging of resected nonmetastatic colon cancer: The more the better?

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].

On the clinical acceptance of black-box systems for EEG seizure prediction.

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.

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm.

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.

Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components.

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.

Cultural and technology elucidation of the Tupi-Guarani tradition through analysis of potsherds from Travessão do Rio Vermelho site (Santa Catarina - Brazil) by spectroscopy, SEM-EDS and chemometrics.

Ceramic fragments can provide an insight into the ancient culture and practices of groups of humans and their way of life (technology, cultural identity, social organization, habitation and economy). Scientific analysis can be used to obtain information on the ceramic production process, as well as the specificities of the material employed. In this research, all samples of archaeological potsherd from the Tupi-Guarani tradition were analyzed in order to identify and to characterize the structures, morphologies and the elemental composition by using by scanning electron microscopy (SEM-EDS) and multivariate statistical methods (PCA and HCA).

Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy.

Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient.

A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction.

Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity.

Appendectomy: prognostic factors in the brazilian unified health system.

Introduction: Acute appendicitis (AA) is the most common cause of surgical acute abdomen. Postoperative complications in emergency care are reflections of the surgical procedure and pre- and postoperative factors.

Objective: Define prognostic factors for patients who underwent appendectomy, comparing them with the literature.

Febrile Neutropenia in Patients with Solid Tumors Undergoing Intravenous Chemotherapy.

Introduction: Febrile neutropenia (FN) is a potentially life-threatening complication of systemic chemotherapy (CT) that often requires hospital admission. Delay in diagnosis and treatment are associated with higher morbidity and mortality.

Objective: We aimed to determine the factors that influence FN episodes outcomes in the emergency room (ER).

Fatal febrile neutropenia during adjuvant chemotherapy for early breast cancer.

We report the case of a 76-year-old female patient with early breast cancer (hormone receptor-positive erbb2 amplified) that had started adjuvant chemotherapy with docetaxel, carboplatin and trastuzumab (TCH). Eight days after the first cycle of TCH chemotherapy, the patient was diagnosed with grade 1 oral mucositis, treated conservatively. The next day she started with nausea, vomiting, chills and fever, followed by a generalised tonicoclonic seizure.

Comparing Different Methods for Named Entity Recognition in Portuguese Neurology Text.

Electronic Medical Records (EMRs) are written in an unstructured way, often using natural language. Information Extraction (IE) may be used for acquiring knowledge from such texts, including the automatic recognition of meaningful entities, through models for Named Entity Recognition (NER). However, while most work on the previous was made for English, this experience aimed at testing different methods in Portuguese text, more precisely, on the domain of Neurology, and take some conclusions.