Trastuzumab deruxtecan in human epidermal growth factor receptor 2-positive breast cancer brain metastases: a systematic review and meta-analysis.

BACKGROUND: Trastuzumab deruxtecan (T-DXd) has shown promising results in patients with breast cancer brain metastases (BCBMs). We conducted a systematic review and meta-analysis to evaluate the effectiveness and safety of T-DXd in the human epidermal growth factor receptor 2 (HER2)-positive BCBM population. PATIENTS AND METHODS: We searched PubMed, Embase, and Cochrane Library databases as well as American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), and San Antonio Breast Cancer Symposium (SABCS) websites for clinical trials (CTs) and observational studies evaluating T-DXd in patients with HER2-positive BCBM. Heterogeneity was assessed with I2 statistics. Random effects models were used for all statistical analyses, which were carried out using R software (version 4.2.2). RESULTS: Ten studies were included, six CTs (n = 189) and four observational studies (n = 130), with a total of 319 patients. The median progression-free survival was 15 months [95% confidence interval (CI) 13.9-16.1 months]. The objective response rate (ORR) was 61% (95% CI 52% to 70%), and the intracranial (IC)-ORR was 61% (95% CI 54% to 69%). No significant differences in ORR and IC-ORR were observed between CTs and observational studies (P = 0.31 and 0.58, respectively). The clinical benefit rate (CBR) was 80% (95% CI 52% to 94%), and the IC-CBR was 70% (95% CI 54% to 82%). The ORR was 68% (95% CI 57% to 77%) in the subgroup of patients with stable BMs and 60% (95% CI 48%-72%) in patients with active BM, with no significant difference between groups (P = 0.35). CONCLUSIONS: Our systematic review and meta-analysis supports the IC activity of T-DXd in patients with stable BM and active BM. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) under the protocol number CRD42023422589.

Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures.

OBJECTIVE: Patients with rheumatoid arthritis (RA) have different presentations and prognoses. Cluster analysis based on proteomic signatures creates independent phenogroups of patients with different pathophysiological backgrounds. We aimed to identify distinct pathophysiological clusters of RA patients based on circulating proteomic biomarkers. METHOD: This was a cohort study including 399 RA patients. Clustering was performed on 94 circulating proteins (92 CVDII Olink®, high-sensitivity troponin T, and C-reactive protein). Unsupervised clustering was performed using a partitioning cluster algorithm. RESULTS: The clustering algorithm identified two distinct clusters: cluster 1 (n = 223) and cluster 2 (n = 176). Compared with cluster 1, cluster 2 included older patients with a higher burden of comorbidities (cardiovascular and RA related), more erosive and longer RA duration, more dyspnoea and fatigue, walking a shorter distance in the Six-Minute Walk Test, with more severe diastolic dysfunction, and a 4.5-fold higher risk of death or hospitalization for cardiovascular reasons. Tumour necrosis factor (TNF) receptor superfamily-related pathways were mainly responsible for the model's discriminative ability. CONCLUSION: Using unsupervised cluster analysis based on proteomic phenotypes, we identified two clusters of RA patients with distinct biomarkers profiles, clinical characteristics, and different outcomes that could reflect different pathophysiological backgrounds. TNF receptor superfamily-related proteins may be used to distinguish subgroups.

The role of additive and diffusive coupling on the dynamics of neural populations.

Dynamical models consisting of networks of neural masses commonly assume that the interactions between neural populations are via additive or diffusive coupling. When using the additive coupling, a population's activity is affected by the sum of the activities of neighbouring populations. In contrast, when using the diffusive coupling a neural population is affected by the sum of the differences between its activity and the activity of its neighbours. These two coupling functions have been used interchangeably for similar applications. In this study, we show that the choice of coupling can lead to strikingly different brain network dynamics. We focus on a phenomenological model of seizure transitions that has been used both with additive and diffusive coupling in the literature. We consider small networks with two and three nodes, as well as large random and scale-free networks with 64 nodes. We further assess resting-state functional networks inferred from magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME) and healthy controls. To characterize the seizure dynamics on these networks, we use the escape time, the brain network ictogenicity (BNI) and the node ictogenicity (NI), which are measures of the network's global and local ability to generate seizure activity. Our main result is that the level of ictogenicity of a network is strongly dependent on the coupling function. Overall, we show that networks with additive coupling have a higher propensity to generate seizures than those with diffusive coupling. We find that people with JME have higher additive BNI than controls, which is the hypothesized BNI deviation between groups, while the diffusive BNI provides opposite results. Moreover, we find that the nodes that are more likely to drive seizures in the additive coupling case are more likely to prevent seizures in the diffusive coupling case, and that these features correlate to the node's number of connections. Consequently, previous results in the literature involving such models to interrogate functional or structural brain networks could be highly dependent on the choice of coupling. Our results on the MEG functional networks and evidence from the literature suggest that the additive coupling may be a better modeling choice than the diffusive coupling, at least for BNI and NI studies. Thus, we highlight the need to motivate and validate the choice of coupling in future studies involving network models of brain activity.

A Computational Biomarker of Photosensitive Epilepsy from Interictal EEG.

People with photosensitive epilepsy (PSE) are prone to seizures elicited by visual stimuli. The possibility of inducing epileptiform activity in a reliable way makes PSE a useful model to understand epilepsy, with potential applications for the development of new diagnostic methods and new treatments for epilepsy. A relationship has been demonstrated between PSE and both occipital and more widespread cortical hyperexcitability using various types of stimulation. Here we aimed to test whether hyperexcitability could be inferred from resting interictal electroencephalographic (EEG) data without stimulation. We considered a cohort of 46 individuals with idiopathic generalized epilepsy who underwent EEG during intermittent photic stimulation: 26 had a photoparoxysmal response (PPR), the PPR group, and 20 did not, the non-PPR group. For each individual, we computed functional networks from the resting EEG data before stimulation. We then placed a computer model of ictogenicity into the networks and simulated the propensity of the network to generate seizures in silico [the brain network ictogenicity (BNI)]. Furthermore, we computed the node ictogenicity (NI), a measure of how much each brain region contributes to the overall ictogenic propensity. We used the BNI and NI as proxies for testing widespread and occipital hyperexcitability, respectively. We found that the BNI was not higher in the PPR group relative to the non-PPR group. However, we observed that the (right) occipital NI was significantly higher in the PPR group relative to the non-PPR group. Other regions did not have significant differences in NI values between groups.

A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease.

People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.

OBJECTIVE: For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). METHODS: The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. RESULTS: We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. CONCLUSIONS: The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. SIGNIFICANCE: The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.

Recurrence quantification analysis of dynamic brain networks.

Evidence suggests that brain network dynamics are a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than in healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy.

Targeted nanostructured lipid carriers for doxorubicin oral delivery.

The treatment with anticancer drugs remains a challenge, as available drugs still entail the risk of deleterious off-target effects. The present study describes folic acid conjugated nanostructured lipid carriers (NLCs) as an effective doxorubicin delivery approach targeted to breast cancer cells. Two distinct NLCs formulations were designed and optimized leading to an encapsulation efficiency over than 65%. Cytotoxic and targeting potential of NLCs were studied in vitro, using MDA-MB-231 cell line. Results showed an enhanced cellular uptake of conjugated NLCs. In vitro release studies, mimicking the path in the body after oral administration, show that all formulations would reach the tumor microenvironment bearing 50% of the encapsulated doxorubicin. Moreover, NLCs demonstrated storage stability at 25 °C for at least 42 days. Overall, results revealed that the developed NLCs enable the possibility of oral administration and are a promising approach for the targeted delivery of doxorubicin to breast cancer cells.

Enrichment diets of pigs with oil blends and its effects on performance, carcass characteristics and fatty acid profile / Dietas enriquecidas com blends de óleos para suínos em terminação e seus efeitos sobre o desempenho, as características de carcaça e o perfil de ácidos graxos

The addition of different oil blends in the feed of finishing pigs was evaluated. Twenty-four castrated male finishing pigs were used in a randomized block design containing four treatments and six replicates. The treatments consisted of: Reference ration (RR) - 100% soybean oil feed; and the combination of the different oils: Blend1 - 50.0% soybean oil (SO), 25.0% flaxseed oil (FO), 12.5% olive oil (OO) and 12.5% canola oil (CO); Blend2 - 25.0% SO, 50.0% FO, 12.5% OO and 12.5% CO; and Blend3 - 25.0% SO, 12.5% FO, 12.5% OO and 50.0% CO. The performance, quantitative and qualitative carcass parameters, fatty acids profile and economic feasibility of the diets were evaluated. The use of blends in the diets did not influence the performance or carcass quality, but increased marbling and carcass yield. The fatty acid profile of the loin presented greater amounts of stearic acid in Blend3 and higher percentage of unsaturated fatty acids in animals fed with Blend1. The fatty tissue presented greater amounts of myristic acid in Blend1 and oleic acid in Blend3. The reference ration was the most economic. The Blends did not affect performance or carcass characteristics and improved the fatty acid profile.(AU)

Foi avaliada a utilização de diferentes blends de óleo em dietas de suínos em terminação. Foram utilizados 24 suínos, machos, castrados, distribuídos em delineamento de blocos ao acaso, com quatro tratamentos e seis repetições. Os tratamentos consistiram de: ração referência (RR) - 100% de ração com utilização de óleo de soja; e a combinação de diferentes óleos: Blend1 - 50,0% de óleo de soja (OS), 25,0% de óleo de linhaça (OL), 12,5% de óleo de oliva (OO) e 12,5% de óleo de canola (OC); Blend2 - 25,0% OS; 50,0% OL; 12,5% OO e 12,5% OC; e Blend3 - 25,0% OS; 12,5% OL; 12,5% OO e 50,0% OC. Foram avaliados os parâmetros de desempenho, a qualidade de carcaça, o perfil de ácidos graxos e a viabilidade econômica. O uso de blends nas dietas não influenciou o desempenho ou a qualidade da carcaça, mas aumentou o marmoreio e o rendimento de carcaça. O perfil de ácidos graxos do lombo apresentou maiores quantidades de ácido esteárico com a utilização do Blend3 e maior porcentagem de ácidos graxos insaturados nos animais alimentados com o Blend1. O tecido adiposo apresentou maiores quantidades de ácido mirístico quando se forneceu o Blend1 e de ácido oleico com o Blend3. A ração testemunha foi a mais econômica. As misturas não afetaram o desempenho e as características de carcaça e melhoraram o perfil de ácidos graxos da carne.(AU)

The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures.

Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.

Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy.

OBJECTIVE: The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. METHODS: We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network's ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. RESULTS: The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). CONCLUSIONS: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. SIGNIFICANCE: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.

Quantification and Selection of Ictogenic Zones in Epilepsy Surgery.

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected.

Bothrops alternatus Snake Venom Induces Cytokine Expression and Oxidative Stress on Renal Function.

BACKGROUND: Envenomation caused by Bothrops alternatus is common in Southern Brazil. Acute Kidney Injury occurs after Bothrops snakebite and more information is necessaryrequired to understand its mechanism. OBJECTIVE: The objective was to evaluate the effect of Bothrops alternatus venom (BaV) on renal cells and rat isolated kidney function. METHODS: Wistar rats (n = 6, weighing 260-320 g) were perfused with a Krebs-Henseleit solution containing 6 g 100 mL-1 of bovine serum albumin. After 30 minutes, the kidneys were perfused with BaV to a final concentration of 1 and 3 µgmL-1; and subsequently were evaluated for Perfusion Pressure (PP), Renal Vascular Resistance (RVR), Urinary Flow (UF), Glomerular Filtration Rate (GFR), and percentage of electrolyte tubular transport. Renal histological analysis, cytokine release, oxidative stress and cytotoxicity in renal proximal tubular cells were assessed. RESULTS: BaV reduced PP, RVR, GFR, UF, total and proximal sodium transport (%TNa+), and chloride (%TCl-) in the isolated kidney perfusion model. Histological analysis of perfused kidneys disclosed the presence of proteinaceous material in the glomeruli and renal tubules, vacuolar tubular epithelial cell degeneration, Bowman's capsule degeneration, swelling of glomerular epithelial cells, glomerular atrophy and degeneration, and the presence of intratubular protein. Cytokine release (TNF-α, IL-1ß, IL-10) and oxidative stress were increased in the kidneys. The viability of LLC-MK2 cells (IC50: 221.3 µg/mL) was decreased by BaV and necrosis was involved in cell death. CONCLUSION: These findings indicate that BaV modifies functional parameters in an isolated perfused kidney model and has cytotoxic effects on renal lineage cells.

Revealing epilepsy type using a computational analysis of interictal EEG.

Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.

The role that choice of model plays in predictions for epilepsy surgery.

Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.

A Model-Based Assessment of the Seizure Onset Zone Predictive Power to Inform the Epileptogenic Zone.

Epilepsy surgery is a clinical procedure that aims to remove the brain tissue responsible for the emergence of seizures, the epileptogenic zone (EZ). It is preceded by an evaluation to determine the brain tissue that must be resected. The identification of the seizure onset zone (SOZ) from intracranial EEG recordings stands as one of the key proxies for the EZ. In this study we used computational models of epilepsy to assess to what extent the SOZ may or may not represent the EZ. We considered a set of different synthetic networks (e.g., regular, small-world, random, and scale-free networks) to represent large-scale brain networks and a phenomenological network model of seizure generation. In the model, the SOZ was inferred from the seizure likelihood (SL), a measure of the propensity of single nodes to produce epileptiform dynamics, whilst a surgery corresponded to the removal of nodes and connections from the network. We used the concept of node ictogenicity (NI) to quantify the effectiveness of each node removal on reducing the network's propensity to generate seizures. This framework enabled us to systematically compare the SOZ and the seizure control achieved by each considered surgery. Specifically, we compared the distributions of SL and NI across different networks. We found that SL and NI were concordant when all nodes were similarly ictogenic, whereas when there was a small fraction of nodes with high NI, the SL was not specific at identifying these nodes. We further considered networks with heterogeneous node excitabilities, i.e., nodes with different susceptibilities of being engaged in seizure activity, to understand how such heterogeneity may affect the relationship between SL and NI. We found that while SL and NI are concordant when there is a small fraction of hyper-excitable nodes in a network that is otherwise homogeneous, they do diverge if the network is heterogeneous, such as in scale-free networks. We observe that SL is highly dependent on node excitabilities, whilst the effect of surgical resections as revealed by NI is mostly determined by network structure. Together our results suggest that the SOZ is not always a good marker of the EZ.

Rotavirus analyses by SYBR Green real-time PCR and microbiological contamination in bivalves cultivated in coastal water of Amazonian Brazil.

The contamination of mussels and oysters by viruses and bacteria is often associated with water contamination and gastroenteritis in humans. The present study evaluated viral and bacterial contamination in 380 samples, from nine mollusk-producing regions in coastal water north of the Brazilian Amazon. Rotavirus contamination was studied for groups A to H, using a two-step SYBR Green RT-qPCR (quantitative reverse transcription polymerase chain reaction), and bacterial families Enterobacteriaceae, Vibrionaceae, and Aeromonadaceae by classical and molecular methods. From the 19 pools analyzed, 26.3% (5/19) were positive for group A Rotavirus, I2 genotype for VP6 region, without amplifications for groups B-H. Bacteriological analysis identified Escherichia coli isolates in 89.5% (17/19) with identification of atypical enteropathogenic E. coli aEPEC in 10.5% (2/19), Salmonella (Groups C1 and G) (10.5%, 2/19), Vibrio alginolyticus (57.9%, 11/19) V. parahaemolyticus (63.2%, 12/19), V. fluvialis (42.1%, 8/19), V. vulnificus (10.5%, 2/19), V. cholerae non-O1, non O139(10.5%, 2/19) and Aeromonas salmonicida (52.6%, 10/19). All the samples investigated presented some level of contamination by enterobacteria, rotavirus, or both, and these results may reflect the level of contamination in the Northern Amazon Region, due to the natural maintenance of some of these agents or by the proximity with human populations and their sewer.

Detection and function of lipopolysaccharide and its purified lipid A after treatment with auxiliary chemical substances and calcium hydroxide dressings used in root canal treatment.

AIM: To investigate the influence of auxiliary chemical substances (ACSs) and calcium hydroxide [Ca(OH)2 ] dressings on lipopolysaccharides (LPS)/lipid A detection and its functional ability in activating Toll-like receptor 4 (TLR4). METHODOLOGY: Fusobacterium nucleatum pellets were exposed to antimicrobial agents as following: (i) ACS: 5.25%, 2.5% and 1% sodium hypochlorite solutions (NaOCl), 2% chlorhexidine (CHX) (gel and solution) and 17% ethylenediaminetetraacetic acid (EDTA); (ii) intracanal medicament: Ca(OH)2 paste for various periods (1 h, 24 h, 7 days, 14 days and 30 days); (iii) combination of substances: (a) 2.5% NaOCl (1 h), followed by 17% EDTA (3 min) and Ca(OH)2 (7 days); (b) 2% CHX (1 h), afterwards, 17% EDTA (3 min) followed by Ca(OH)2 (7 days). Saline solution was the control. Samples were submitted to LPS isolation and lipid A purification. Lipid A peaks were assessed by matrix-assisted laser desorption ionization time-of-flight mass spectrom (MALDI-TOF MS) whilst LPS bands by SDS-PAGE separation and silver staining. TLR4 activation determined LPS function activities. Statistical comparisons were carried out using one-way anova with Tukey-Kramer post-hoc tests at the 5% significance level. RESULTS: Matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis of control lipid A demonstrated the ion cluster at mass/charge (m/z) 1882 and an intense band in SDS-PAGE followed by silver staining of control LPS. In parallel, LPS control induced a robust TLR4 activation when compared to ACS (P ≤ .001). 5.25% NaOCl treatment led to the absence of lipid A peaks and LPS bands, whilst no changes occurred to lipid A/LPS after treatment with others ACS. Concomitantly, 5.25% NaOCl-treated LPS did not activate TLR4 (P < .0001). As for Ca(OH)2 , lipid A was not detected by MALDI-TOF nor by gel electrophoresis within 24 h. LPS treated with Ca(OH)2 was a weak TLR4 activator (P < .0001). From 24 h onwards, no significant differences were found amongst the time periods tested (P > 0.05). The addition of Ca(OH)2 for 7 days to cells treated either with 2.5% NaOCl or 2% CHX led to the absence of lipid A peaks and LPS bands, leading to a lower activation of TLR4. CONCLUSION: 5.25% NaOCl and Ca(OH)2 dressings from 24 h onwards were able to induce both, loss of lipid A peaks and no detection of LPS bands, rendering a diminished immunostimulatory activity through TLR4.

Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control.

Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.