RESUMEN

Paracoccidioidomycosis is a systemic mycosis endemic in Latin America. The most frequent form involves a chronic compromise of the lungs, skin, and mucosa. The patient started with a single oral lesion that lasted for several years. The absence of other symptoms pointed out a possible malignant neoplasm, specifically a squamous cell carcinoma. Differentiation between both diagnoses ­fungal infection and carcinoma­ depends on the results of the direct examination, the histopathological study, and the initial and subsequent cultures. However, in this case, those findings were not conclusive. The coexistence of both diagnoses is frequent and increases the diagnostic challenge. After several consultations and tests, direct examination, immunodiffusion and real-time PCR findings the multifocal chronic paracoccidioidomycosis diagnosis was confirmed. This case warns about a systematical absence of clinical suspicion of endemic mycoses before the appereance of mucocutaneous lesions, which can be produced by fungi like Paracoccidioides spp, and the importance of considering those mycoses among the differential diagnoses.

La paracoccidioidomicosis es una micosis sistémica endémica en Latinoamérica. La presentación más frecuente compromete crónicamente los pulmones, la piel y las mucosas. Al inicio, este paciente presentó, por varios años, una lesión única en la mucosa oral que, en ausencia de otros síntomas, se relacionó con una neoplasia maligna, específicamente con un carcinoma escamocelular. La diferenciación entre los dos diagnósticos se hace mediante un examen directo, un estudio histopatológico y cultivos iniciales y subsecuentes. Sin embargo, tales estudios no fueron concluyentes. Después de varias consultas y pruebas, con los resultados del examen directo, la inmunodifusión y la PCR en tiempo real se confirmó el diagnóstico de paracoccidioidomicosis crónica multifocal. Este caso alerta sobre la ausencia de sospecha clínica de micosis endémicas, dada la presencia de lesiones mucocutáneas que pueden ser producidas por hongos como Paracoccidioides spp, y la importancia de considerarlas entre los diagnósticos diferenciales.

Asunto(s)

RESUMEN

INTRODUCTION: For over a century, Sporothrix schenckii was considered the sole species responsible for sporotrichosis. In 2007, scientific community confirmed the disease could be caused by various Sporothrix species. These species differed in their virulence factors and their antifungal sensitivity. OBJECTIVE: This study aims to characterize 42 Colombian clinical isolates of Sporothrix spp. phenotypically and genotypically. MATERIAL AND METHODS: Forty-two clinical isolates were characterized using phenotypic methods. It involved various culture media to determine their growth range at different temperatures and to assess the type and distribution of pigment and colony texture. Microscopic morphology was evaluated through microcultures, as well as the conidia diameter, type of sporulation, and morphology. Additionally, the assimilation of carbohydrates was selected as a physiological trait for species identification. Genotyping of 40 isolates was performed through partial amplification of the calmodulin gene, followed by sequence analysis. RESULTS: Molecular studies enabled the identification of 32 isolates of S. schenckii and 8 isolates of S. globosa. The combination of phenotypic and genotypic methods eased these species characterizations and the recognition keys development based on parameters such as growth diameter at 25 and 30 ºC, colony texture (membranous or velvety) on potato dextrose agar, and microscopic morphology with predominance of pigmented triangular, elongated oval globose, or subglobose conidia. CONCLUSIONS: Confirmation of the phenotypic characteristics and molecular analysis is crucial for identifying Sporothrix species and determining adequate treatment. This study represents the first phenotypical and genotypical characterization of clinical isolates of Sporothrix spp. reported in Colombia.

Introducción: Por más de un siglo se creyó que Sporothrix schenckii era la única especie responsable de la esporotricosis. Sin embargo, en el 2007, se consideró que podría ser causada por diferentes especies de Sporothrix, que difieren en sus factores de virulencia y su sensibilidad a los antifúngicos. Objetivo: Caracterizar fenotípica y genotípicamente 42 aislamientos clínicos colombianos de Sporothrix spp. Materiales y métodos: Se caracterizaron 42 aislamientos clínicos mediante métodos fenotípicos. Se usaron varios medios de cultivo para determinar el rango de crecimiento a diferentes temperaturas, el tipo y la distribución del pigmento, y la textura de las colonias. Se evaluó la morfología microscópica por microcultivos mediante la determinación del diámetro, el tipo de esporulación y la morfología de las conidias. La asimilación de carbohidratos se usó como una característica fisiológica para identificar las especies. La genotipificación de los 40 aislamientos se llevó a cabo mediante la amplificación parcial del gen que codifica para la calmodulina y se confirmó por secuenciación. Resultados: Mediante estudios moleculares, se identificaron 32 aislamientos de S. schenckii y ocho de S. globosa. La combinación de métodos fenotípicos y genotípicos permitió caracterizar las especies y construir claves para su reconocimiento, con base en parámetros como el diámetro de crecimiento a 25 y 30 ºC, la textura de las colonias (membranosa, aterciopelada) en agar papa dextrosa y la morfología microscópica con predominio de conidias (triangulares pigmentadas, ovales globosas elongadas, subglobosas). Conclusiones: La caracterización fenotípica y los análisis moleculares son necesarios para identificar las especies de Sporothrix y, de esta forma, elegir el tratamiento indicado. Esta es la primera caracterización fenotípica y genotípica reportada de aislamientos clínicos colombianos de Sporothrix spp.

Asunto(s)

RESUMEN

PROBLEM: Systems theory applied to biology and medicine assumes that the complexity of a system can be described by quasi-generic models to predict the behavior of many other similar systems. To this end, the aim of various research works in systems theory is to develop inductive modeling (based on data-intensive analysis) or deductive modeling (based on the deduction of mechanistic principles) to discover patterns and identify plausible correlations between past and present events, or to connect different causal relationships of interacting elements at different scales and compute mathematical predictions. Mathematical principles assume that there are constant and observable universal causal principles that apply to all biological systems. Nowadays, there are no suitable tools to assess the soundness of these universal causal principles, especially considering that organisms not only respond to environmental stimuli (and inherent processes) across multiple scales but also integrate information about and within these scales. This implies an uncontrollable degree of uncertainty. METHODOLOGY: A method has been developed to detect the stability of causal processes by evaluating the information contained in the trajectories identified in a phase space. Time series patterns are analyzed using concepts from geometric information theory and persistent homology. In essence, recognizing these patterns in different time periods and evaluating their geometrically integrated information leads to the assessment of causal relationships. With this method, and together with the evaluation of persistent entropy in trajectories in relation to different individual systems, we have developed a method called Φ-S diagram as a complexity measure to recognize when organisms follow causal pathways leading to mechanistic responses. RESULTS: We calculated the Φ-S diagram of a deterministic dataset available in the ICU repository to test the method's interpretability. We also calculated the Φ-S diagram of time series from health data available in the same repository. This includes patients' physiological response to sport measured with wearables outside laboratory conditions. We confirmed the mechanistic nature of both datasets in both calculations. In addition, there is evidence that some individuals show a high degree of autonomous response and variability. Therefore, persistent individual variability may limit the ability to observe the cardiac response. In this study, we present the first demonstration of the concept of developing a more robust framework for representing complex biological systems.

Asunto(s)

RESUMEN

Background: Medicine is characterized by its inherent uncertainty, i.e., the difficulty of identifying and obtaining exact outcomes from available data. Electronic Health Records aim to improve the exactitude of health management, for instance using automatic data recording techniques or the integration of structured as well as unstructured data. However, this data is far from perfect and is usually noisy, implying that epistemic uncertainty is almost always present in all biomedical research fields. This impairs the correct use and interpretation of the data not only by health professionals but also in modeling techniques and AI models incorporated in professional recommender systems. Method: In this work, we report a novel modeling methodology combining structural explainable models, defined on Logic Neural Networks which replace conventional deep-learning methods with logical gates embedded in neural networks, and Bayesian Networks to model data uncertainties. This means, we do not account for the variability of the input data, but we train single models according to the data and deliver different Logic-Operator neural network models that could adapt to the input data, for instance, medical procedures (Therapy Keys depending on the inherent uncertainty of the observed data. Result: Thus, our model does not only aim to assist physicians in their decisions by providing accurate recommendations; it is above all a user-centered solution that informs the physician when a given recommendation, in this case, a therapy, is uncertain and must be carefully evaluated. As a result, the physician must be a professional who does not solely rely on automatic recommendations. This novel methodology was tested on a database for patients with heart insufficiency and can be the basis for future applications of recommender systems in medicine.

RESUMEN

BACKGROUND: Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic health records (EHRs) offer the potential to uncover relevant patterns relating to diseases and therapies, which in turn could help identify empirical medical guidelines to reflect best practices in a healthcare system. Based on this pattern of identification model, it is thus possible to implement recommender systems with the notion that a higher volume of procedures is often associated with better high-quality models. METHODS: Although there are several different applications that uses machine learning methods to identify such patterns, such identification is still a challenge, due in part because these methods often ignore the basic structure of the population, or even considering the similarity of diagnoses and patient typology. To this end, we have developed a method based on graph-data representation aimed to cluster 'similar' patients. Using such a model, patients will be linked when there is a same and/or similar patterns are being observed amongst them, a concept that will enable the construction of a network-like structure which is called a patient graph.1 This structure can be then analyzed by Graph Neural Networks (GNN) to identify relevant labels, and in this case the appropriate medical procedures that will be recommended. RESULTS: We were able to construct a patient graph structure based on the patient's basic information like age and gender as well as the diagnosis and the trained GNNs models to identify the corresponding patient's therapies using a synthetic patient database. We have even compared our GNN models against different baseline models (using the SCIKIT-learn library of python) and also against the performance of these different model-methods. We have found that the GNNs models are superior, with an average improvement of the f1 score of 6.48 % in respect to the baseline models. In addition, the GNNs models are useful in performing additional clustering analysis which allow a distinctive identification of specific therapeutic/treatment clusters relating to a particular combination of diagnoses. CONCLUSIONS: We found that the GNNs models offer a promising lead to model the distribution of diagnoses in patient population, and is thus a better model in identifying patients with similar phenotype based on the combination of morbidities and/or comorbidities. Nevertheless, network/graph building is still challenging and prone to biases as it is highly dependent on how the ICD distribution affects the patient network embedding space. This graph setup not only requires a high quality of the underlying diagnostic ecosystem, but it also requires a good understanding on how patients at hand are identified by disease respectively. For this reason, additional work is still needed to better improve patient embedding in graph structures for future investigations and the applications of this service-based technology. Therefore, there has not been any interventional study yet.

Asunto(s)

RESUMEN

BACKGROUND: Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. However, physicians can reduce their workload by using recommender systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters. METHODS: We test our methodology in a patient population with diabetes and heart insufficiency that becomes a therapy (beta-blockers, ACE or Aspirin). The original database (Pakistan database) is publicly available and accessible via the internet. However, to explore methods to protect the patient's identity and guarantee data privacy we implemented a methodology on a variable-by-variable basis by fitting a sequence of regression models and drawing synthetic values from the corresponding predictive distributions using linear regressions and norm rank. Furthermore, we implemented a deep-learning model based on logical gates modeled by perceptrons with fixed weights and biases. While a first trainable layer automatically recognizes a meaningful parameter hierarchy, the implemented Logic-Operator Neuronal Network (LONN) simulates cognitive processes like a rational, logical thinking process, considering that this logic is joined by fuzziness, i.e., logical operations are not exact but essentially fuzzy due to the implemented continuous-valued operators. The predicted outcomes of the model (kind of therapy-ACE, Aspirin or beta-blocker- and expected therapy time of the patient) are then implemented in a recommender system that compares two different models: model 1 trained on a population excluding negative outcomes (patient group 1, with no patient dead and long therapy times) and a model 2 trained on the whole patient population (patient group 2). In this way, we provide a recommendation of the best possible therapy based on the outcome of the model and the confidence of this recommendation when the outcome of model 1 is compared with the outcome of model 2. RESULTS: With the applied method for data synthetization, we obtained an error of about 1% for all the relevant parameters. Furthermore, we demonstrate that the LONN models reach an accuracy of about 75%. After comparing the LONN models against conventional deep-learning models we observe that our implemented models are less accurate (accuracy loss of about 8%). However, the loss of accuracy is compensated by the fact that LONN models are transparent and safe because the freezing of training parameters makes them less prone to adversarial attacks. Finally, we predict the best therapy as well as the expected therapy time. We were able to predict individualized therapies, which were classified as optimal (binary value) when the prediction fully matched predictions made with models 1 and 2. The results provided by the recommender system are displayed using a graphical interface. The current is a proof of concept to improve the quality of the disease management, while the methods are continuously visualized to preserve transparency for the customers. CONCLUSIONS: This work contributes to simplify administrative functions and boost the quality of management of patients improving the quality of healthcare with models that are both transparent and safe. Our methodology can be extended to different clinical scenarios where recommender systems can be applied. The acceptance and further development of the app is one of the next important steps and still requires further development depending on specific requirements of the health management, the physicians or health professionals, and the patent population.

Asunto(s)

RESUMEN

PURPOSE: Super-refractory status epilepticus (SRSE) presents management challenges due to the absence of randomized controlled trials and a plethora of potential medical therapies. The literature on treatment options for SRSE reports variable success and quality of evidence. This review is a sequel to the 2020 American Epilepsy Society (AES) comprehensive review of the treatment of convulsive refractory status epilepticus (RSE). METHODS: We sought to determine the effectiveness of treatment options for SRSE. We performed a structured literature search (MEDLINE, Embase, CENTRAL, CINAHL) for studies on reported treatments of SRSE. We excluded antiseizure medications (ASMs) covered in the 2016 AES guideline on the treatment of established SE and the convulsive RSE comprehensive review of the 2020 AES. Literature was reviewed on the effectiveness of vagus nerve stimulation, ketogenic diet (KD), lidocaine, inhalation anesthetics, brain surgery, therapeutic hypothermia, perampanel, pregabalin (PGB), and topiramate in the treatment of SRSE. Two authors reviewed each therapeutic intervention. We graded the level of the evidence according to the 2017 classification scheme of the American Academy of Neurology. RESULTS: For SRSE (level U; 39 class IV studies total), insufficient evidence exists to support that perampanel, PGB, lidocaine, or acute vagus nerve stimulation (VNS) is effective. For children and adults with SRSE, insufficient evidence exists to support that the KD is effective (level U; 5 class IV studies). For adults with SRSE, insufficient evidence exists that brain surgery is effective (level U, 7 class IV studies). For adults with SRSE insufficient, evidence exists that therapeutic hypothermia is effective (level C, 1 class II and 4 class IV studies). For neonates with hypoxic-ischemic encephalopathy, insufficient evidence exists that therapeutic hypothermia reduces seizure burden (level U; 1 class IV study). For adults with SRSE, insufficient evidence exists that inhalation anesthetics are effective (level U, 1 class IV study) and that there is a potential risk of neurotoxicity. CONCLUSION: For patients with SRSE insufficient, evidence exists that any of the ASMs reviewed, inhalational anesthetics, ketogenic diet, acute VNS, brain surgery, and therapeutic hypothermia are effective treatments. Data supporting the use of these treatments for SRSE are scarce and limited mainly to small case series and case reports and are confounded by differences in patients' population, and comedications, among other factors.

RESUMEN

PURPOSE: EEG is a common diagnostic tool to localize epileptic activity with excellent temporal resolution and, however, with relatively low spatial resolution. High-density EEG recording is limited in clinical practice, mainly because of electrode placement difficulties, need of high technical skills, and advanced equipment requirement. METHODS: We described the technique of long-term EEG recording using a 128-channel neoprene cap placed with a dielectric paste in 7 patients with refractory epilepsy. We captured electrographic seizures in six of seven patients. The 128-channel EEG cap was well tolerated except for a mild headache. Daily impedance checks and reapplication of the high impedance leads maintained the recording with impedances below 10 kΩ. RESULTS: Successful long-term recording of high-density EEG was able to capture seizures in six of seven patients. The time needed to apply the electrodes was approximately 1 hour and approximately 30 minutes daily for maintenance. The EEG source localization was obtained in six of seven patients, concordant within the sublobar region for both standard and high-density EEG recordings. Three patients reported a mild headache not leading to discontinuation of the recording. CONCLUSIONS: In general, long-term high-density scalp EEG recording with a dielectric paste is well tolerated and allows capturing both interictal and ictal data for localization. This small sample does not show a significant advantage in terms of sublobar localization when high-density EEG source is compared with standard 10 to 20 placement as long as the subtemporal areas are recorded.

Asunto(s)

RESUMEN

Explanations based on low-level interacting elements are valuable and powerful since they contribute to identify the key mechanisms of biological functions. However, many dynamic systems based on low-level interacting elements with unambiguous, finite, and complete information of initial states generate future states that cannot be predicted, implying an increase of complexity and open-ended evolution. Such systems are like Turing machines, that overlap with dynamical systems that cannot halt. We argue that organisms find halting conditions by distorting these mechanisms, creating conditions for a constant creativity that drives evolution. We introduce a modulus of elasticity to measure the changes in these mechanisms in response to changes in the computed environment. We test this concept in a population of predators and predated cells with chemotactic mechanisms and demonstrate how the selection of a given mechanism depends on the entire population. We finally explore this concept in different frameworks and postulate that the identification of predictive mechanisms is only successful with small elasticity modulus.

Asunto(s)

RESUMEN

This was a prospective observational study to correlate the clinical symptoms, electrophysiology, imaging, and surgical pathology of patients with temporal lobe epilepsy (TLE) without hippocampal sclerosis. We selected consecutive patients with TLE and normal MRI undergoing temporal lobe resection between April and September 2015. Clinical features, imaging, and functional data were reviewed. Intracranial monitoring and language mapping were performed when it was required according to our team recommendation. Prior to hippocampal resection, intraoperative electrocorticography was performed using depth electrodes in the amygdala and the hippocampus. The resected hippocampus was sent for pathological analysis. RESULTS: Five patients with diagnosis with non-lesional TLE were included. We did not find distinctive clinical features that could be a characteristic of non-lesional TLE. The mean follow-up was 13.2months (11-15months); 80% of patients achieved Engel Class I outcome. There was no distinctive electrographic findings in these patients. Histopathologic analysis was negative for mesial temporal sclerosis. A second blinded independent neuropathologist with expertise in epilepsy found ILAE type I focal cortical dysplasia in the parahippocampal gyrus in all patients. A third independent neuropathologist reported changes in layer 2 with larger pyramidal neurons in 4 cases but concluded that none of these cases met the diagnostic criteria of FCD. Subtle pathological changes could be associated with a parahippocampal epileptic zone and should be investigated in patients with MRI-negative TLE. This study also highlights the lack of interobserver reliability for the diagnosis of mild cortical dysplasia. Finally, selective amygdalo-hippocampectomy or laser ablation of the hippocampus may not control intractable epilepsy in this specific population.

Asunto(s)

RESUMEN

The European Union's ban on animal testing for cosmetic ingredients and products has generated a strong momentum for the development of in silico and in vitro alternative methods. One of the focus of the COSMOS project was ab initio prediction of kinetics and toxic effects through multiscale pharmacokinetic modeling and in vitro data integration. In our experience, mathematical or computer modeling and in vitro experiments are complementary. We present here a summary of the main models and results obtained within the framework of the project on these topics. A first section presents our work at the organelle and cellular level. We then go toward modeling cell levels effects (monitored continuously), multiscale physiologically based pharmacokinetic and effect models, and route to route extrapolation. We follow with a short presentation of the automated KNIME workflows developed for dissemination and easy use of the models. We end with a discussion of two challenges to the field: our limited ability to deal with massive data and complex computations.

Asunto(s)

RESUMEN

Objective. Review presurgical use of ictal HFO mapping to detect ictal activation areas with dual seizure focus in both the temporal and extratemporal cortex. Methods. Review of consecutive patients admitted to the University of South Alabama Epilepsy Monitoring Unit (SouthCEP) between January 2014 and October 2015, with suspected temporal lobe epilepsy and intracranial electrode recording. Ictal HFO localization was displayed in 3D reconstructed brain images using the patient's own coregistered magnetic resonance imaging (MRI) and computed tomography (CT) with the implanted electrodes. Results. Four of fifteen patients showed evidence of extratemporal involvement at the onset of the clinical seizures. Ictal HFO mapping involving both frontal and temporal lobe changed the surgical resection areas in three patients where the initial surgical plan included only the temporal lobe. Resection of the ictal HFO at the onset of the seizure and the initial propagation region was associated with seizure freedom in all patients; follow-up period ranged from 12 to 25 months. Significance. Extratemporal ictal involvement may not have clinical manifestations and may account for surgical failure in temporal lobe epilepsy. Ictal HFO mapping is useful to define the ictal cortical network and may help detect an extratemporal focus.

RESUMEN

This EEG Guideline incorporates the practice of structuring a report of results obtained during routine adult electroencephalography. It is intended to reflect one of the current practices in reporting an EEG and serves as a revision of the previous guideline entitled "Writing an EEG Report." The goal of this guideline is not only to convey clinically relevant information, but also to improve interrater reliability for clinical and research use by standardizing the format of EEG reports. With this in mind, there is expanded documentation of the patient history to include more relevant clinical information that can affect the EEG recording and interpretation. Recommendations for the technical conditions of the recording are also enhanced to include post hoc review parameters and type of EEG recording. Sleep feature documentation is also expanded upon. More descriptive terms are included for background features and interictal discharges that are concordant with efforts to standardize terminology. In the clinical correlation section, examples of common clinical scenarios are now provided that encourages uniformity in reporting. Including digital samples of abnormal waveforms is now readily available with current EEG recording systems and may be beneficial in augmenting reports when controversial waveforms or important features are encountered.

Asunto(s)

RESUMEN

OPINION STATEMENT: Benzodiazepines are commonly prescribed as anxiolytics, sedatives, and anticonvulsants. They act on the GABAA receptor by increasing the conductance chloride through ionic channels, promoting a state of central nervous system depression. The clinical properties of benzodiazepines are dependent upon the composition of the different subunits of the GABAA receptor. Each subunit, in turn, has multiple subtypes that are present throughout the central nervous system, all of which impart different clinical responses. Benzodiazepines are the first-line treatment of status epilepticus. Time to treatment is crucial, and clinical response to benzodiazepines is lost with prolonged status epilepticus. Non-intravenous routes of midazolam should be considered as an equally efficacious alternative to intravenous lorazepam, which is the most commonly administered benzodiazepine for status epilepticus when intravenous access is available. Outpatient therapy with benzodiazepines for the acute treatment of seizures is currently limited to rectal diazepam, but alternative routes of administration are under development. Clobazam and clonazepam are good options for seizure prophylaxis in patients with epilepsy refractory to multiple antiepileptic drugs. Clobazam is preferred due to its affinity for the α2 subunit of the GABAA receptor, which leads to less potential for sedation. Adverse effects of chronic benzodiazepine use are sedation, tolerance, and potential for addiction and misuse in some patients.

RESUMEN

This EEG Guideline incorporates the practice of structuring a report of results obtained during routine adult electroencephalography. It is intended to reflect one of the current practices in reporting an EEG and serves as a revision of the previous guideline entitled "Writing an EEG Report." The goal of this guideline is not only to convey clinically relevant information, but also to improve interrater reliability for clinical and research use by standardizing the format of EEG reports. With this in mind, there is expanded documentation of the patient history to include more relevant clinical information that can affect the EEG recording and interpretation. Recommendations for the technical conditions of the recording are also enhanced to include post hoc review parameters and type of EEG recording. Sleep feature documentation is also expanded upon. More descriptive terms are included for background features and interictal discharges that are concordant with efforts to standardize terminology. In the clinical correlation section, examples of common clinical scenarios are now provided that encourages uniformity in reporting. Including digital samples of abnormal waveforms is now readily available with current EEG recording systems and may be beneficial in augmenting reports when controversial waveforms or important features are encountered.

Asunto(s)

RESUMEN

Paracoccidioidomycosis (PCM) is a systemic granulomatous human mycosis caused by fungi of the genus Paracoccidioides, which is geographically restricted to Latin America. Inhalation of spores, the infectious particles of the fungus, is a common route of infection. The PCM treatment of choice is azoles such as itraconazole, but sulfonamides and amphotericin B are used in some cases despite their toxicity to mammalian cells. The current availability of treatments highlights the need to identify and characterize novel targets for antifungal treatment of PCM as well as the need to search for new antifungal compounds obtained from natural sources or by chemical synthesis. To this end, we evaluated the antifungal activity of a camphene thiosemicarbazide derivative (TSC-C) compound on Paracoccidioides yeast. To determine the response of Paracoccidioides spp. to TSC-C, we analyzed the transcriptional profile of the fungus after 8 h of contact with the compound. The results demonstrate that Paracoccidioides lutzii induced the expression of genes related to metabolism; cell cycle and DNA processing; biogenesis of cellular components; cell transduction/signal; cell rescue, defense and virulence; cellular transport, transport facilities and transport routes; energy; protein synthesis; protein fate; transcription; and other proteins without classification. Additionally, we observed intensely inhibited genes related to protein synthesis. Analysis by fluorescence microscopy and flow cytometry revealed that the compound induced the production of reactive oxygen species. Using an isolate with down-regulated SOD1 gene expression (SOD1-aRNA), we sought to determine the function of this gene in the defense of Paracoccidioides yeast cells against the compound. Mutant cells were more susceptible to TSC-C, demonstrating the importance of this gene in response to the compound. The results presented herein suggest that TSC-C is a promising candidate for PCM treatment.

Asunto(s)

RESUMEN

Many patients with Lewy body dementia develop visual hallucinations and other psychiatric symptoms. These patients are hypersensitive to antipsychotic drugs. Although patients tolerate atypical better than typical antipsychotics, both types can cause major extrapyramidal side effects. The anticonvulsant mood stabilizer topiramate, which does not cause parkinsonism, has been used as adjuvant therapy for both the positive and negative symptoms of schizophrenia; these symptoms can resemble those of Lewy body dementia. This report documents a 65-year-old woman with a 3-year history of progressive dementia that over the past 2 years had become complicated by severe extrapyramidal symptoms and agitated hallucinations. Her hallucinations became daily and were disrupting to her family. She was given a clinical diagnosis of Lewy body dementia after imaging and laboratory studies ruled out other etiologies. Treatment with olanzapine relieved her psychotic symptoms but caused severe dystonias, daily myoclonic jerks, and tremors. Stopping the olanzapine and starting topiramate 25 mg daily eliminated the hallucinations and agitation without worsening her extrapyramidal side effects. However, the topiramate was stopped because the patient reportedly developed anorexia and significant weight loss. Her hallucinations returned. When topiramate was reinstated at 12.5 mg a day, her agitation resolved, although her hallucinations continued. After 6 months on this dose, her agitation was still fairly well controlled without serious side effects or worsening of her parkinsonian symptoms.

Asunto(s)

RESUMEN

Integrating in vitro and in silico approaches has great potential for reducing experimental effort and delivering know-how and intellectual property in drug development. Here, we focus on a possible framework for multiscale modeling in pharmaceutical drug development. Looking at the modeling frameworks at different scales, it is obvious that choosing the proper level of complexity and abstraction is not a trivial task. At cellular level, we consider that the application of validated kinetic models of cellular toxicity mechanisms of drugs is particularly important for deriving valid predictions. These kinetic models can be applied for integrating inter-individual differences, e.g. obtained from data measured in surgical liver samples, into predictions of drug effects. Challenges identified include (i) the development of sufficiently detailed, structured organ models, (ii) definition of multiscale models that can be efficiently handled by available super-computing facilities, and (iii) availability of validated cell-type and organ-specific kinetic metabolic models. Multiscale models can streamline drug development by facilitating the design of experiments and trials, by providing and testing hypotheses, and by reducing time and costs due to less experiments and improved decision-making. In this review, we discuss the required pieces, possibilities, and challenges in multiscale modeling for the prediction of drug effects.

RESUMEN

Although antiepileptic drugs are often effective in the control of seizures, some patients show little or no improvement. As alternative treatments, different dietary modifications were shown to be beneficial for patients with poor tolerance for AEDS. Previous reports have shown that rice-based oral electrolyte hydration therapy is effective in seizure control in patients with refractory absence seizures. In the present study, using an animal model of absence epilepsy, we showed that the occurrence of spike-and-wave discharges significantly decreases upon switching to electrolyte therapy. We also showed that consumption of solution with the same osmolarity as rice-based oral electrolyte solution leads to a decrease in the number of spike-and-wave discharges per hour. We suggest that the antiepileptic effect of rice-based oral electrolyte hydration therapy can be at least in part due to hyperosmolarity of the ingested solution.

Asunto(s)