Functional MRI and Diffusion Tensor Imaging in Migraine: A Review of Migraine Functional and White Matter Microstructural Changes.

Migraine is a complex and heterogenous disorder whose disease mechanisms remain disputed. This narrative review summarizes functional MRI (fMRI) and diffusion tensor imaging (DTI) findings and interprets their association with migraine symptoms and subtype to support and expand our current understanding of migraine pathophysiology. Our PubMed search evaluated and included fMRI and DTI studies involving comparisons between migraineurs vs healthy controls, migraineurs with vs without aura, and episodic vs chronic migraineurs. Migraineurs demonstrate changes in functional connectivity (FC) and regional activation in numerous pain-related networks depending on migraine phase, presence of aura, and chronicity. Changes to diffusion indices are observed in major cortical white matter tracts extending to the brainstem and cerebellum, more prominent in chronic migraine and associated with FC changes. Reported changes in FC and regional activation likely relate to pain processing and sensory hypersensitivities. Diffuse white matter microstructural changes in dysfunctional cortical pain and sensory pathways complement these functional differences. Interpretations of reported fMRI and DTI measure trends have not achieved a clear consensus due to inconsistencies in the migraine neuroimaging literature. Future fMRI and DTI studies should establish and implement a uniform methodology that reproduces existing results and directly compares migraineurs with different subtypes. Combined fMRI and DTI imaging may provide better pathophysiological explanations for nonspecific FC and white matter microstructural differences.

Structural MRI in Migraine: A Review of Migraine Vascular and Structural Changes in Brain Parenchyma.

Migraine is a complex and common disorder that affects patients around the world. Despite recent advances in this field, the exact pathophysiology of migraine is still not completely understood. Structural MRI sequences have revealed a variety of changes to brain parenchyma associated with migraine, including white matter lesions, volume changes, and iron deposition. This Review highlights different structural imaging findings in various types of migraine and their relationship to migraine characteristics and subtypes in order to improve our understanding of migraine, its pathophysiologic mechanisms, and how to better diagnose and treat it.

Chronic Diclofenac Exposure Increases Mitochondrial Oxidative Stress, Inflammatory Mediators, and Cardiac Dysfunction.

PURPOSE: Nonsteroidal anti-inflammatory drugs (NSAIDs) are among one of the most commonly prescribed medications for pain and inflammation. Diclofenac (DIC) is a commonly prescribed NSAID that is known to increase the risk of cardiovascular diseases. However, the mechanisms underlying its cardiotoxic effects remain largely unknown. In this study, we tested the hypothesis that chronic exposure to DIC increases oxidative stress, which ultimately impairs cardiovascular function. METHODS AND RESULTS: Mice were treated with DIC for 4 weeks and subsequently subjected to in vivo and in vitro functional assessments. Chronic DIC exposure resulted in not only systolic but also diastolic dysfunction. DIC treatment, however, did not alter blood pressure or electrocardiographic recordings. Importantly, treatment with DIC significantly increased inflammatory cytokines and chemokines as well as cardiac fibroblast activation and proliferation. There was increased reactive oxygen species (ROS) production in cardiomyocytes from DIC-treated mice, which may contribute to the more depolarized mitochondrial membrane potential and reduced energy production, leading to a significant decrease in sarcoplasmic reticulum (SR) Ca2+ load, Ca2+ transients, and sarcomere shortening. Using unbiased metabolomic analyses, we demonstrated significant alterations in oxylipin profiles towards inflammatory features in chronic DIC treatment. CONCLUSIONS: Together, chronic treatment with DIC resulted in severe cardiotoxicity, which was mediated, in part, by an increase in mitochondrial oxidative stress.

Disruption of mitochondria-sarcoplasmic reticulum microdomain connectomics contributes to sinus node dysfunction in heart failure.

The sinoatrial node (SAN), the leading pacemaker region, generates electrical impulses that propagate throughout the heart. SAN dysfunction with bradyarrhythmia is well documented in heart failure (HF). However, the underlying mechanisms are not completely understood. Mitochondria are critical to cellular processes that determine the life or death of the cell. The release of Ca2+ from the ryanodine receptors 2 (RyR2) on the sarcoplasmic reticulum (SR) at mitochondria-SR microdomains serves as the critical communication to match energy production to meet metabolic demands. Therefore, we tested the hypothesis that alterations in the mitochondria-SR connectomics contribute to SAN dysfunction in HF. We took advantage of a mouse model of chronic pressure overload-induced HF by transverse aortic constriction (TAC) and a SAN-specific CRISPR-Cas9-mediated knockdown of mitofusin-2 (Mfn2), the mitochondria-SR tethering GTPase protein. TAC mice exhibited impaired cardiac function with HF, cardiac fibrosis, and profound SAN dysfunction. Ultrastructural imaging using electron microscope (EM) tomography revealed abnormal mitochondrial structure with increased mitochondria-SR distance. The expression of Mfn2 was significantly down-regulated and showed reduced colocalization with RyR2 in HF SAN cells. Indeed, SAN-specific Mfn2 knockdown led to alterations in the mitochondria-SR microdomains and SAN dysfunction. Finally, disruptions in the mitochondria-SR microdomains resulted in abnormal mitochondrial Ca2+ handling, alterations in localized protein kinase A (PKA) activity, and impaired mitochondrial function in HF SAN cells. The current study provides insights into the role of mitochondria-SR microdomains in SAN automaticity and possible therapeutic targets for SAN dysfunction in HF patients.

Continuous noninvasive blood gas estimation in critically ill pediatric patients with respiratory failure.

Patients supported by mechanical ventilation require frequent invasive blood gas samples to monitor and adjust the level of support. We developed a transparent and novel blood gas estimation model to provide continuous monitoring of blood pH and arterial CO2 in between gaps of blood draws, using only readily available noninvasive data sources in ventilated patients. The model was trained on a derivation dataset (1,883 patients, 12,344 samples) from a tertiary pediatric intensive care center, and tested on a validation dataset (286 patients, 4030 samples) from the same center obtained at a later time. The model uses pairwise non-linear interactions between predictors and provides point-estimates of blood gas pH and arterial CO2 along with a range of prediction uncertainty. The model predicted within Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptable blood gas machine equivalent in 74% of pH samples and 80% of PCO2 samples. Prediction uncertainty from the model improved estimation accuracy by 15% by identifying and abstaining on a minority of high-uncertainty samples. The proposed model estimates blood gas pH and CO2 accurately in a large percentage of samples. The model's abstention recommendation coupled with ranked display of top predictors for each estimation lends itself to real-time monitoring of gaps between blood draws, and the model may help users determine when a new blood draw is required and delay blood draws when not needed.

Disruption of protein quality control of the human ether-à-go-go related gene K+ channel results in profound long QT syndrome.

BACKGROUND: Long QT syndrome (LQTS) is a hereditary disease that predisposes patients to life-threatening cardiac arrhythmias and sudden cardiac death. Our previous study of the human ether-à-go-go related gene (hERG)-encoded K+ channel (Kv11.1) supports an association between hERG and RING finger protein 207 (RNF207) variants in aggravating the onset and severity of LQTS, specifically T613M hERG (hERGT613M) and RNF207 frameshift (RNF207G603fs) mutations. However, the underlying mechanistic underpinning remains unknown. OBJECTIVE: The purpose of the present study was to test the role of RNF207 in the function of hERG-encoded K+ channel subunits. METHODS: Whole-cell patch-clamp experiments were performed in human embryonic kidney (HEK 293) cells and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) together with immunofluorescent confocal and high resolution microscopy, auto-ubiquitinylation assays, and co-immunoprecipitation experiments to test the functional interactions between hERG and RNF207. RESULTS: Here, we demonstrated that RNF207 serves as an E3 ubiquitin ligase and targets misfolded hERGT613M proteins for degradation. RNF207G603fs exhibits decreased activity and hinders the normal degradation pathway; this increases the levels of hERGT613M subunits and their dominant-negative effect on the wild-type subunits, ultimately resulting in decreased current density. Similar findings are shown for hERGA614V, a known dominant-negative mutant subunit. Finally, the presence of RNF207G603fs with hERGT613M results in significantly prolonged action potential durations and reduced hERG current in human-induced pluripotent stem cell-derived cardiomyocytes. CONCLUSION: Our study establishes RNF207 as an interacting protein serving as a ubiquitin ligase for hERG-encoded K+ channel subunits. Normal function of RNF207 is critical for the quality control of hERG subunits and consequently cardiac repolarization. Moreover, our study provides evidence for protein quality control as a new paradigm in life-threatening cardiac arrhythmias in patients with LQTS.

Early prediction of hemodynamic interventions in the intensive care unit using machine learning.

BACKGROUND: Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions. METHODS: We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings. RESULTS: HSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold. CONCLUSIONS: The HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence.

Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome.

Heterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity. From the Philips eICU Research Institute (eRI) database, no less than 51,555 ARDS patients were extracted. We defined three subpopulations by outcome: (1) rapid death, (2) spontaneous recovery, and (3) long-stay patients. A retrospective univariate analysis identified highly predictive variables for each outcome. All 220 variables were used to determine the most accurate and generalizable model to predict long-stay patients. Multiclass gradient boosting was identified as the best-performing ML model. Whereas alterations in pH, bicarbonate or lactate proved to be strong predictors for rapid death in the univariate analysis, only the multivariate ML model was able to reliably differentiate the disease course of the long-stay outcome population (AUC of 0.77). We demonstrate the feasibility of prospective patient stratification using ML algorithms in the by far largest ARDS cohort reported to date. Our algorithm can identify patients with sufficiently long ARDS episodes to allow time for patients to respond to therapy, increasing statistical power. Further, early enrollment alerts may increase recruitment rate.

Lack of association of antihypertensive drugs with the risk and severity of COVID-19: A meta-analysis.

BACKGROUND: The association of antihypertensive drugs with the risk and severity of COVID-19 remains unknown. METHODS AND RESULTS: We systematically searched PubMed, MEDLINE, The Cochrane Library, Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, and medRxiv for publications before July 13, 2020. Cohort studies and case-control studies that contain information on the association of antihypertensive agents including angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), calcium-channel blockers (CCBs), ß-blockers, and diuretics with the risk and severity of COVID-19 were selected. The random or fixed-effects models were used to pool the odds ratio (OR) with 95% confidence interval (CI) for the outcomes. The literature search yielded 53 studies that satisfied our inclusion criteria, which comprised 39 cohort studies and 14 case-control studies. These studies included a total of 2,100,587 participants. We observed no association between prior usage of antihypertensive medications including ACEIs/ARBs, CCBs, ß-blockers, or diuretics and the risk and severity of COVID-19. Additionally, when only hypertensive patients were included, the severity and mortality were lower with prior usage of ACEIs/ARBs (overall OR of 0.81, 95% CI 0.66-0.99, p < 0.05 and overall OR of 0.77, 95% CI 0.66-0.91, p < 0.01). CONCLUSIONS: Taken together, usage of antihypertensive drugs is not associated with the risk and severity of COVID-19. Based on the current available literature, it is not recommended to abstain from the usage of these drugs in COVID-19 patients. REGISTRATION: The meta-analysis was registered on OSF (https://osf.io/ynd5g).

Assessment of Chloroquine and Hydroxychloroquine Safety Profiles: A Systematic Review and Meta-Analysis.

BACKGROUND: Chloroquine (CQ) and its derivative hydroxychloroquine (HCQ) have recently emerged as potential antiviral and immunomodulatory options for the treatment of 2019 coronavirus disease (COVID-19). To examine the safety profiles of these medications, we systematically evaluated the adverse events (AEs) of these medications from published randomized controlled trials (RCTs). METHODS: We systematically searched MEDLINE, the Cochrane library, the Cochrane Central Register of Controlled Trials (CENTRAL), and the ClinicalTrials.gov for all the RCTs comparing CQ or HCQ with placebo or other active agents, published before June 20, 2020. The random-effects or fixed-effects models were used to pool the risk estimates relative ratio (RR) with 95% confidence interval (CI) for the outcomes. RESULTS: The literature search yielded 23 and 19 studies for CQ and HCQ, respectively, that satisfied our inclusion criteria. Of these studies, we performed meta-analysis on 6 studies for CQ and 18 studies for HCQ. We did not limit our analysis to published records involving viral treatment alone; data also included the usage of either CQ or HCQ for the treatment of other diseases. The trials for the CQ consisted of a total of 2,137 participants (n = 1,077 CQ, n = 1,060 placebo), while the trials for HCQ involved 2,675 participants (n = 1,345 HCQ and n = 1,330 control). The overall mild and total AEs were significantly higher in CQ-treated non-COVID-19 patients, HCQ-treated non-COVID-19 patients, and HCQ-treated COVID-19 patients. The AEs were further categorized into four groups and analyses revealed that neurologic, gastrointestinal (GI), dermatologic, and sensory AEs were higher in participants taking CQ compared to placebo, while GI, dermatologic, sensory, and cardiovascular AEs were higher in HCQ-treated COVID-19 patients compared to control patients. Moreover, subgroup analysis suggested higher AEs with respect to dosage and duration in HCQ group. Data were acquired from studies with perceived low risk of bias, so plausible bias is unlikely to seriously affect the main findings of the current study. CONCLUSIONS: Taken together, we found that participants taking either CQ or HCQ exhibited more AEs than participants taking placebo or control. Precautionary measures should be taken when using these drugs to treat COVID-19. The meta-analysis was registered on OSF (https://osf.io/jm3d9). REGISTRATION: The meta-analysis was registered on OSF (https://osf.io/jm3d9).

Assessment of Hydroxychloroquine and Chloroquine Safety Profiles: A Systematic Review and Meta-Analysis.

BACKGROUND: Recently, chloroquine (CQ) and its derivative hydroxychloroquine (HCQ) have emerged as potential antiviral and immunomodulatory options for the treatment of 2019 coronavirus disease (COVID-19). To examine the safety profiles of these medications, we systematically evaluated the adverse events (AEs) of these medications from published randomized controlled trials (RCTs). METHODS: We systematically searched PubMed, MEDLINE, Cochrane, the Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, and the ClinicalTrials.gov for all the RCTs comparing CQ or HCQ with placebo or other active agents, published before March 31, 2020. The random-effects or fixed-effects models were used to pool the risk estimates relative ratio (RR) with 95% confidence interval (CI) for the outcomes. RESULTS: The literature search yielded 23 and 17 studies for CQ and HCQ, respectively, that satisfied our inclusion criteria. Of these studies, we performed meta-analysis on the ones that were placebo-controlled, which included 6 studies for CQ and 14 studies for HCQ. We did not limit our analysis to published reports involving viral treatment alone; data also included the usage of either CQ or HCQ for the treatment of other diseases. The trials for the CQ consisted of a total of 2,137 participants (n=1,077 CQ, n=1,060 placebo), while the trials for HCQ involved 1,096 participants (n=558 HCQ and n=538 placebo). The overall mild or total AEs were statistically higher comparing CQ or HCQ to placebo. The AEs were further categorized into four groups and analyses revealed that neurologic, gastrointestinal, dermatologic, and ophthalmic AEs were higher in participants taking CQ compared to placebo. Although this was not evident in HCQ treated groups, further analyses suggested that there were more AEs attributed to other organ system that were not included in the categorized meta-analyses. Additionally, meta-regression analyses revealed that total AEs was affected by dosage for the CQ group. CONCLUSIONS: Taken together, we found that participants taking either CQ or HCQ have more AEs than participants taking placebo. Precautionary measures should be taken when using these drugs to treat COVID-19.

Cardiac arrhythmia detection using deep learning: A review.

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operators/technicians. Therefore, several methods are proposed for either fully automatic arrhythmia detection or event selection for further verification by human experts. Traditional machine learning approaches have made significant progress in the past years. However, those methods rely on hand-crafted feature extraction, which requires in-depth domain knowledge and preprocessing of the signal (e.g., beat detection). This, plus the high variability in wave morphology among patients and the presence of noise, make it challenging for computerized interpretation to achieve high accuracy. Recent advances in deep learning make it possible to perform automatic high-level feature extraction and classification. Therefore, deep learning approaches have gained interest in arrhythmia detection. In this work, we reviewed the recent advancement of deep learning methods for automatic arrhythmia detection. We summarized existing literature from five aspects: utilized dataset, application, type of input data, model architecture, and performance evaluation. We also reported limitations of reviewed papers and potential future opportunities.

Prevalence and Temporal Distribution of Extrasystoles in Septic ICU Patients: The Feasibility of Predicting Fluid Responsiveness Using Extrasystoles.

BACKGROUND: Extrasystoles may be useful for predicting the response to fluid therapy in hemodynamically unstable patients but their prevalence is unknown. The aim of this study was to estimate the availability of extrasystoles in intensive care unit patients diagnosed with sepsis. The study aim was not to validate the fluid responsiveness prediction ability of extrasystoles. METHODS: Twenty-four-hour ECG recordings from a convenience sample of 50 patients diagnosed with sepsis were extracted from the MIMIC-II waveform database, and ECGs were visually examined for correct QRS complex detection. Custom-made algorithms identified potential extrasystoles based on RR intervals. Two raters visually confirmed or rejected the potential extrasystoles and then classified them as ventricular, supraventricular, or unknown origin. Extrasystole availability was calculated as extrasystolic coverage for each 24 h ECG recording, that is, the percentage of the 24 h recording where an extrasystole had occurred in the preceding 30 minutes. RESULTS: Mean extrasystolic coverage was 53.3% (confidence interval: [42.8; 63.6]%) and ventricular extrasystolic coverage was 21.4 [13.5; 29.8]%. Interrater reliability was strong for confirming/rejecting extrasystoles. CONCLUSIONS: Extrasystoles are available for fluid responsiveness prediction in septic patients in about half of the time. With this extrasystolic availability, we believe the method to be considered for clinical use, provided that future studies validate the method's fluid responsiveness prediction ability.

An ensemble boosting model for predicting transfer to the pediatric intensive care unit.

BACKGROUND: Early deterioration indicators have the potential to alert hospital care staff in advance of adverse events, such as patients requiring an increased level of care, or the need for rapid response teams to be called. Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. OBJECTIVES: The development of a data-driven pediatric early deterioration indicator for use by clinicians with the purpose of predicting encounters where transfer from the general ward to the PICU is likely. METHODS: Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop machine learning classifiers based on adaptive boosting and gradient tree boosting. We further combine these learned classifiers into an ensemble model and compare its performance to a modified pediatric early warning score (PEWS) baseline that relies on expert defined guidelines. To gauge model generalizability, we perform an inter-facility evaluation where we train our algorithm on data from one facility and perform evaluation on a hidden test dataset from a separate facility. RESULTS: We show that improvements are witnessed over the modified PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80 vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73). CONCLUSIONS: Data-driven, machine learning algorithms can improve PICU transfer prediction accuracy compared to expertly defined systems, such as a modified PEWS, but care must be taken in the training of such approaches to avoid inadvertently introducing bias into the outcomes of these systems.

A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit.

BACKGROUND: Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration. METHODS: The model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom. The PICU clinical information system database (Intellivue Clinical Information Portfolio, Philips, UK) was interrogated to collect physiological and laboratory data. The model was trained using a variant of AdaBoost, which learned a set of low-dimensional classifiers, each of which was age adjusted. RESULTS: A total of 7052 patients admitted to the US PICU was used for training the model, and a total of 970 patients admitted to the UK PICU was used for validation. On the training/validation datasets, the model showed better prediction of hemodynamic intervention (area under the receiver operating characteristic (AUROC) = 0.81/0.81) than systolic blood pressure-based (AUCROC = 0.58/0.67) or shock index-based (AUCROC = 0.63/0.65) models. Both of these models were age adjusted using the same classifier. CONCLUSIONS: The proposed model reliably predicted the need for hemodynamic intervention in PICU patients and provides better classification performance when compared to systolic blood pressure-based or shock index-based models alone. This model could readily be built into a clinical information system to identify patients at risk of hemodynamic instability.

Distinct subcellular mechanisms for the enhancement of the surface membrane expression of SK2 channel by its interacting proteins, α-actinin2 and filamin A.

KEY POINTS: Ion channels are transmembrane proteins that are synthesized within the cells but need to be trafficked to the cell membrane for the channels to function. Small-conductance, Ca2+ -activated K+ channels (SK, KCa 2) are unique subclasses of K+ channels that are regulated by Ca2+ inside the cells; they are expressed in human atrial myocytes and responsible for shaping atrial action potentials. We have previously shown that interacting proteins of SK2 channels are important for channel trafficking to the membrane. Using total internal reflection fluorescence (TIRF) and confocal microscopy, we studied the mechanisms by which the surface membrane localization of SK2 (KCa 2.2) channels is regulated by their interacting proteins. Understanding the mechanisms of SK channel trafficking may provide new insights into the regulation controlling the repolarization of atrial myocytes. ABSTRACT: The normal function of ion channels depends critically on the precise subcellular localization and the number of channel proteins on the cell surface membrane. Small-conductance, Ca2+ -activated K+ channels (SK, KCa 2) are expressed in human atrial myocytes and are responsible for shaping atrial action potentials. Understanding the mechanisms of SK channel trafficking may provide new insights into the regulation controlling the repolarization of atrial myocytes. We have previously demonstrated that the C- and N-termini of SK2 channels interact with the actin-binding proteins α-actinin2 and filamin A, respectively. However, the roles of the interacting proteins on SK2 channel trafficking remain incompletely understood. Using total internal reflection fluorescence (TIRF) microscopy, we studied the mechanisms of surface membrane localization of SK2 (KCa 2.2) channels. When SK2 channels were co-expressed with filamin A or α-actinin2, the membrane fluorescence intensity of SK2 channels increased significantly. We next tested the effects of primaquine and dynasore on SK2 channels expression. Treatment with primaquine significantly reduced the membrane expression of SK2 channels. In contrast, treatment with dynasore failed to alter the surface membrane expression of SK2 channels. Further investigations using constitutively active or dominant-negative forms of Rab GTPases provided additional insights into the distinct roles of the two cytoskeletal proteins on the recycling processes of SK2 channels from endosomes. α-Actinin2 facilitated recycling of SK2 channels from both early and recycling endosomes while filamin A probably aids the recycling of SK2 channels from recycling endosomes.

Ultrasound assisted combustion synthesis of TiC in Al-Ti-C system.

This research investigated the effects of high-intensity ultrasound on the combustion synthesis of TiC particles in Al-Ti-C system. The process involved that high-intensity ultrasound was applied on the surface of a compacted Al-Ti-C pellet directly through a Nb probe during the thermal explosion reaction. By comparing with the sample without ultrasonic treatment, it was found that the thermal explosion reaction for synthesizing TiC phase could take place thoroughly in the ultrasonically treated sample. During the process of synthesizing TiC phase, the dissolution of solid graphite particles into the Al-Ti melt, as well as the nucleation and growth of TiC particles could be promoted effectively due to the effects of ultrasound, leading to an enhancement of the formation of TiC particles. Ultrasound assisted combustion synthesis as a simple and effective approach was proposed for synthesizing materials in this research.

TMS perturbs saccade trajectories and unmasks an internal feedback controller for saccades.

When we applied a single pulse of transcranial magnetic stimulation (TMS) to any part of the human head during a saccadic eye movement, the ongoing eye velocity was reduced as early as 45 ms after the TMS, and lasted ∼32 ms. The perturbation to the saccade trajectory was not due to a mechanical effect of the lid on the eye (e.g., from blinks). When the saccade involved coordinated movements of both the eyes and the lids, e.g., in vertical saccades, TMS produced a synchronized inhibition of the motor commands to both eye and lid muscles. The TMS-induced perturbation of the eye trajectory did not show habituation with repetition, and was present in both pro-saccades and anti-saccades. Despite the perturbation, the eye trajectory was corrected within the same saccade with compensatory motor commands that guided the eyes to the target. This within-saccade correction did not rely on visual input, suggesting that the brain monitored the oculomotor commands as the saccade unfolded, maintained a real-time estimate of the position of the eyes, and corrected for the perturbation. TMS disrupted saccades regardless of the location of the coil on the head, suggesting that the coil discharge engages a nonhabituating startle-like reflex system. This system affects ongoing motor commands upstream of the oculomotor neurons, possibly at the level of the superior colliculus or omnipause neurons. Therefore, a TMS pulse centrally perturbs saccadic motor commands, which are monitored possibly via efference copy and are corrected via internal feedback.

'Staircase' square-wave jerks in early Parkinson's disease.

BACKGROUND/AIMS: Visually guided saccades and gaze-fixation ability were recorded in patients with early Parkinson's disease (PD). METHODS: Magnetic search coil system was used to measure horizontal and vertical eye positions. RESULTS: 'Staircase' visually guided saccades (multiple hypometric saccades separated by an intersaccadic interval) and 'staircase' square-wave jerks (SWJ) were present in all patients. The SWJ amplitude (total amplitude of the series of hypometric saccades comprising the SWJ) was abnormally large (mean 2°). SWJ frequency was also abnormally high (50-70 intrusions/min). CONCLUSION: Loss of dopaminergic neurons in the substantia nigra pars compacta leading to phasic excitation of the substantia nigra pars reticulata and, in turn, phasic inhibition of the superior colliculus (SC), may account for 'staircase' visually guided saccades in these patients. This mechanism, however, does not explain abnormally large SWJ, which in the traditional view results from decreased inhibition upon SC. The abnormally large SWJ could be due to a compensatory increase in frontal eye field activity secondary to the increased inhibition upon the SC. Abnormally large and frequent SWJ are often used clinically to distinguish multi-system atrophy from idiopathic PD. Our study, however, suggests that idiopathic PD cannot be excluded if the patient has large-amplitude SWJ.

Temporal discounting of reward and the cost of time in motor control.

Why do movements take a characteristic amount of time, and why do diseases that affect the reward system alter control of movements? Suppose that the purpose of any movement is to position our body in a more rewarding state. People and other animals discount future reward as a hyperbolic function of time. Here, we show that across populations of people and monkeys there is a correlation between discounting of reward and control of movements. We consider saccadic eye movements and hypothesize that duration of a movement is equivalent to a delay of reward. The hyperbolic cost of this delay not only accounts for kinematics of saccades in adults, it also accounts for the faster saccades of children, who temporally discount reward more steeply. Our theory explains why saccade velocities increase when reward is elevated, and why disorders in the encoding of reward, for example in Parkinson's disease and schizophrenia, produce changes in saccade. We show that delay of reward elevates the cost of saccades, reducing velocities. Finally, we consider coordinated movements that include motion of eyes and head and find that their kinematics is also consistent with a hyperbolic, reward-dependent cost of time. Therefore, each voluntary movement carries a cost because its duration delays acquisition of reward. The cost depends on the value that the brain assigns to stimuli, and the rate at which it discounts this value in time. The motor commands that move our eyes reflect this cost of time.