SARS-CoV-2 infection is associated with an increase in new diagnoses of schizophrenia spectrum and psychotic disorder: A study using the US national COVID cohort collaborative (N3C).

Amid the ongoing global repercussions of SARS-CoV-2, it is crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab-negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-19 positive (n = 219,264), and COVID-19 negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-19 negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.

Racecadotril Versus Loperamide in Acute Radiation Enteritis: A Randomized, Double-Masked, Phase 3, Noninferiority Trial.

PURPOSE: There is currently no gold standard for the management of acute radiation enteritis. We compared the efficacy and safety of Racecadotril, an anti-hypersecretory drug, versus Loperamide, an anti-motility agent, in acute radiation enteritis. METHODS AND MATERIALS: We conducted a randomized, double-masked, non-inferiority trial at a single research institute. Patients receiving curative radiation for pelvic malignancies, who developed grade 2 or 3 diarrhea (as per Common Terminology Criteria for Adverse Events, v 4.0) were included in the study. Patients in the intervention arm received Racecadotril and placebo. Patients in the control arm received Loperamide and placebo. The primary outcome was the resolution of diarrhea, 48 hours after the start of treatment. RESULTS: 162 patients were randomized between 2019 and 2022. On intention-to-treat analysis, 68/81 patients, 84%, (95% CI, 74.1%-91.2%) in the Racecadotril arm and 70/81, 86.4%, (95% CI, 77.0%-93.0%) in the Loperamide arm improved from grade 2 or 3 diarrhea to grade 1 or 0, (P= .66, χ2 test). The difference in proportion was 2.4% (95% CI: -8.5% to 13.4%). Since the upper boundary of the 95% CI crossed our non-inferiority margin of 10% (13.4%) we could not prove the non-inferiority of Racecadotril over Loperamide. Rebound constipation was more in the Loperamide arm compared to Racecadotril (17.3% vs 6.2%; P = .028) CONCLUSIONS: The non-inferiority of Racecadotril to Loperamide in acute radiation enteritis could not be demonstrated. However, Racecadotril can be the preferred drug of choice in acute radiation enteritis because Racecadotril does not affect bowel motility, achieved a high clinical success rate similar to that of Loperamide, and was associated with lesser side effects.

SARS-CoV-2 Infection is Associated with an Increase in New Diagnoses of Schizophrenia Spectrum and Psychotic Disorder: A Study Using the US National COVID Cohort Collaborative (N3C).

Amid the ongoing global repercussions of SARS-CoV-2, it's crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-positive (n = 219,264), and COVID-negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared prediction model without moving the data outside their secure infrastructure. To do so, after having sent model updates to a central server, an update aggregation is performed, and the model is sent back to the sites for further training. Although widely applied on neural networks, the deployment of FL architectures is lacking scalability and support for machine learning techniques such as decision tree-based models. The latter, when embedded in FL, suffer from costly encryption techniques applied for sharing sensitive information such as the splitting decisions within the trees. In this work, we focus on predicting hemodynamic instability on ICU patients by enabling distributed gradient boosting in FL. We employ a clinical dataset from 25 hospitals generated based on the Philips eICU database and we design a FL pipeline that supports neural-based boosting models as well as conventional neural networks. This enhancement enables decision tree models in FL, which represent the state-of-the-art approach for classification tasks involving tabular clinical data. Comparable performances in terms of accuracy, precision, recall and F1 score have been reached when detecting hemodynamic instability in FL, and in a centralized setup. In summary, we demonstrate the feasibility of a scalable FL for detecting hemodynamic instability in ICU data, which preserves privacy and holds the deployment benefits of a neural-based architecture.

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.

Delivering multi-disease screening to migrants for latent TB and blood-borne viruses in an emergency department setting: A feasibility study.

BACKGROUND: Screening for latent tuberculosis infection (LTBI) in migrants is important for elimination of tuberculosis in low-incidence countries, alongside the need to detect blood-borne infections to align with new guidelines on migrant screening for multiple infections in European countries. However, feasibility needs to be better understood. METHODS: We did a feasibility study to test an innovative screening model offering combined testing for LTBI (QuantiFERON), HIV, hepatitis B/C in a UK emergency department, with two year follow-up. RESULTS: 96 economic migrants, asylum seekers and refugees from 43 countries were screened (46 [47.9%] women; mean age 35.2 years [SD 11.7; range 18-73]; mean time in the UK 4.8 years [SD 3.2; range 0-10]). 14 migrants (14.6%) tested positive for LTBI alongside HIV [1], hepatitis B [2], and hepatitis C [1] Of migrants with LTBI, 5 (35.7%) were successfully engaged in treatment. 74 (77.1%) migrants reported no previous screening since migrating to the UK. CONCLUSION: Multi-disease screening in this setting is feasible and merits being further tested in larger-scale studies. However, greater emphasis must be placed on ensuring successful treatment outcomes. We identified major gaps in current screening provision; most migrants had been offered no prior screening despite several years since migration, which holds relevance to policy and practice in the UK and other European countries.

Direct current stimulation boosts hebbian plasticity in vitro.

BACKGROUND: There is evidence that transcranial direct current stimulation (tDCS) can improve learning performance. Arguably, this effect is related to long term potentiation (LTP), but the precise biophysical mechanisms remain unknown. HYPOTHESIS: We propose that direct current stimulation (DCS) causes small changes in postsynaptic membrane potential during ongoing endogenous synaptic activity. The altered voltage dynamics in the postsynaptic neuron then modify synaptic strength via the machinery of endogenous voltage-dependent Hebbian plasticity. This hypothesis predicts that DCS should exhibit Hebbian properties, namely pathway specificity and associativity. METHODS: We studied the effects of DCS applied during the induction of LTP in the CA1 region of rat hippocampal slices and using a biophysical computational model. RESULTS: DCS enhanced LTP, but only at synapses that were undergoing plasticity, confirming that DCS respects Hebbian pathway specificity. When different synaptic pathways cooperated to produce LTP, DCS enhanced this cooperation, boosting Hebbian associativity. Further slice experiments and computer simulations support a model where polarization of postsynaptic pyramidal neurons drives these plasticity effects through endogenous Hebbian mechanisms. The model is able to reconcile several experimental results by capturing the complex interaction between the induced electric field, neuron morphology, and endogenous neural activity. CONCLUSIONS: These results suggest that tDCS can enhance associative learning. We propose that clinical tDCS should be applied during tasks that induce Hebbian plasticity to harness this phenomenon, and that the effects should be task specific through their interaction with endogenous plasticity mechanisms. Models that incorporate brain state and plasticity mechanisms may help to improve prediction of tDCS outcomes.

Randomized Controlled Trial of Simulation vs. Standard Training for Teaching Medical Students High-quality Cardiopulmonary Resuscitation.

INTRODUCTION: Most medical schools teach cardiopulmonary resuscitation (CPR) during the final year in course curriculum to prepare students to manage the first minutes of clinical emergencies. Little is known regarding the optimal method of instruction for this critical skill. Simulation has been shown in similar settings to enhance performance and knowledge. We evaluated the comparative effectiveness of high-fidelity simulation training vs. standard manikin training for teaching medical students the American Heart Association (AHA) guidelines for high-quality CPR. METHODS: This was a prospective, randomized, parallel-arm study of 70 fourth-year medical students to either simulation (SIM) or standard training (STD) over an eight-month period. SIM group learned the AHA guidelines for high-quality CPR via an hour session that included a PowerPoint lecture with training on a high-fidelity simulator. STD group learned identical content using a low-fidelity Resusci Anne® CPR manikin. All students managed a simulated cardiac arrest scenario with primary outcome based on the AHA guidelines definition of high-quality CPR (specifies metrics for compression rate, depth, recoil, and compression fraction). Secondary outcome was time to emergency medical services (EMS) activation. We analyzed data via Kruskal-Wallis rank sum test. Outcomes were performed on a simulated cardiac arrest case adapted from the AHA Advanced Cardiac Life Support (ACLS) SimMan® Scenario manual. RESULTS: Students in the SIM group performed CPR that more closely adhered to the AHA guidelines of compression depth and compression fraction. Mean compression depth was 4.57 centimeters (cm) (95% confidence interval [CI] [4.30-4.82]) for SIM and 3.89 cm (95% CI [3.50-4.27]) for STD, p=0.02. Mean compression fraction was 0.724 (95% CI [0.699-0.751]) for SIM group and 0.679 (95% CI [0.655-0.702]) for STD, p=0.01. There was no difference for compression rate or recoil between groups. Time to EMS activation was 24.7 seconds (s) (95% CI [15.7-40.8]) for SIM group and 79.5 s (95% CI [44.8-119.6]) for STD group, p=0.007. CONCLUSION: High-fidelity simulation training is superior to low-fidelity CPR manikin training for teaching fourth-year medical students implementation of high-quality CPR for chest compression depth and compression fraction.

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 s). For this purpose, we combined a signal quality index (SQI) algorithm, to assess noisy instances, and trained densely connected convolutional neural networks to classify ECG recordings. Two convolutional neural network (CNN) models (a main model that accepts 15 s ECG segments and a secondary model that processes shorter 9 s segments) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. The results achieved on the 2017 PhysioNet/Computing in Cardiology challenge test dataset were an overall F1 score of 0.82 (F1 for NSR, AF, and O were 0.91, 0.83, and 0.72, respectively). Compared with 80 challenge entries, this was the third best overall score achieved on the evaluation dataset.

Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is the most common sustained cardiac arrhythmia that is associated with an increased morbidity and mortality, its timely diagnosis is clinically desirable. The main aim of this study as our contribution to the PhysioNet/CinC Challenge 2017 was to develop an automatic algorithm for classification of normal sinus rhythm (NSR), AF, other rhythm (O), and noise using a short single-channel ECG. Furthermore, the impact of changing labels/annotations on performance of the proposed algorithm was studied in this article. APPROACH: The challenge training dataset (8528 ECG recordings) and a complementary dataset (6312 ECG recordings) from other sources were used for algorithm development. Version 3 (v3), which is an updated version of the annotations at the official phase of the challenge (v2), was used in this study. In the proposed algorithm, densely connected convolutional networks were combined with feature-based post-processing after initial signal quality analysis for the classification of ECG recordings. MAIN RESULTS: The F1 scores for classification of NSR, AF, and O were 0.91, 0.83, and 0.72, respectively, which led to a F1 of 0.82. There was a small or no performance difference between the top entries in the official phase of the challenge and our proposed method. An increase of 2.5% in F1 score was observed when the same annotations for training and test was used (using v3 annotations) compared to using different annotations (v2 annotations for training and v3 annotations for the test). SIGNIFICANCE: Our promising results suggest that the availability of more data with improved labeling along with improvement in signal quality analysis make our algorithm suitable for practical clinical applications.

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.

Food waste conversion to microbial polyhydroxyalkanoates.

Polyhydroxyalkanoates (PHAs) are biopolymers with desirable material properties similar to petrochemically derived plastics. PHAs are naturally produced by a wide range of microorganisms as a carbon storage mechanism and can accumulate to significantly high levels. PHAs are an environmentally friendly alternative to their petroleum counterparts because they can be easily degraded, potentially reducing the burden on municipal waste systems. Nevertheless, widespread use of PHAs is not currently realistic due to a variety of factors. One of the major constraints of large-scale PHA production is the cost of carbon substrate for PHA-producing microbes. The cost of production could potentially be reduced with the use of waste carbon from food-related processes. Food wastage is a global issue and therefore harbours immense potential to create valuable bioproducts. This article's main focus is to examine the state of the art of converting food-derived waste into carbon substrates for microbial metabolism and subsequent conversion into PHAs.

Direct current stimulation boosts synaptic gain and cooperativity in vitro.

KEY POINTS: Direct current stimulation (DCS) polarity specifically modulates synaptic efficacy during a continuous train of presynaptic inputs, despite synaptic depression. DCS polarizes afferent axons and postsynaptic neurons, boosting cooperativity between synaptic inputs. Polarization of afferent neurons in upstream brain regions may modulate activity in the target brain region during transcranial DCS (tDCS). A statistical theory of coincident activity predicts that the diffuse and weak profile of current flow can be advantageous in enhancing connectivity between co-active brain regions. ABSTRACT: Transcranial direct current stimulation (tDCS) produces sustained and diffuse current flow in the brain with effects that are state dependent and outlast stimulation. A mechanistic explanation for tDCS should capture these spatiotemporal features. It remains unclear how sustained DCS affects ongoing synaptic dynamics and how modulation of afferent inputs by diffuse stimulation changes synaptic activity at the target brain region. We tested the effect of acute DCS (10-20 V m-1 for 3-5 s) on synaptic dynamics with constant rate (5-40 Hz) and Poisson-distributed (4 Hz mean) trains of presynaptic inputs. Across tested frequencies, sustained synaptic activity was modulated by DCS with polarity-specific effects. Synaptic depression attenuates the sensitivity to DCS from 1.1% per V m-1 to 0.55%. DCS applied during synaptic activity facilitates cumulative neuromodulation, potentially reversing endogenous synaptic depression. We establish these effects are mediated by both postsynaptic membrane polarization and afferent axon fibre polarization, which boosts cooperativity between synaptic inputs. This potentially extends the locus of neuromodulation from the nominal target to afferent brain regions. Based on these results we hypothesized the polarization of afferent neurons in upstream brain regions may modulate activity in the target brain region during tDCS. A multiscale model of transcranial electrical stimulation including a finite element model of brain current flow, numerical simulations of neuronal activity, and a statistical theory of coincident activity predicts that the diffuse and weak profile of current flow can be advantageous. Thus, we propose that specifically because tDCS is diffuse, weak and sustained it can boost connectivity between co-active brain regions.

Direct Current Stimulation Alters Neuronal Input/Output Function.

BACKGROUND: Direct current stimulation (DCS) affects both neuronal firing rate and synaptic efficacy. The neuronal input/output (I/O) function determines the likelihood that a neuron elicits an action potential in response to synaptic input of a given strength. Changes of the neuronal I/O function by DCS may underlie previous observations in animal models and human testing, yet have not been directly assessed. OBJECTIVE: Test if the neuronal input/output function is affected by DCS METHODS: Using rat hippocampal brain slices and computational modeling, we provide evidence for how DCS modulates the neuronal I/O function. RESULTS: We show for the first time that DCS modulates the likelihood of neuronal firing for a given and fixed synaptic input. Opposing polarization of soma and dendrite may have a synergistic effect for anodal stimulation, increasing the driving force of synaptic activity while simultaneously increasing spiking probability at the soma. For cathodal stimulation, however, the opposing effects tend to cancel. This results in an asymmetry in the strength of the effects of stimulation for opposite polarities. CONCLUSIONS: Our results may explain the asymmetries observed in acute and long term effects of transcranial direct current stimulation.

Prospective Randomized Crossover Study of Telesimulation Versus Standard Simulation for Teaching Medical Students the Management of Critically Ill Patients.

OBJECTIVE: The objective was to evaluate the comparative effectiveness of telesimulation versus standard simulation in teaching medical students the management of critically ill patients. METHODS: Prospective, randomized crossover study of 32 fourth-year medical students at a university medical simulation center. Students were randomized to the standard simulation (SIM) or telesimulation (TeleSIM) group between September 2014 and February 2015. The SIM group experience included participating in a live, fully immersive simulation case followed by debriefing with their SIM cohort and a live TV Internet connection to the TeleSIM group. The TeleSIM group experience included remotely observing the live simulation case at an off-site location, followed by a shared group debriefing via live TV Internet connection. Subject assessment was performed with a written evaluation tool. During a second instructional session, the students crossed over and participated in a different simulation scenario and assessment. Mean evaluation scores were calculated along with 95% confidence intervals (CIs) and were analyzed via linear regression. Our secondary outcome was a survey evaluating the perceptions and attitudes held between the two simulation modalities. RESULTS: Of 33 eligible students, 32 participated in the study (97.0%). We found no significant difference in the mean evaluation scores of the two groups: SIM group mean = 96.6% (95% CI = 94.5%-98.6%) and TeleSIM group mean = 96.8% (95% CI = 94.8%-98.9%). We also found no significant difference in the favorability of teaching modality (TeleSIM vs. SIM) on the survey. CONCLUSION: In our prospective randomized crossover study evaluating telesimulation versus standard simulation, we found no significant difference in evaluation scores among the two groups. There was also no significant difference found in the favorability of one teaching modality on a posteducational session survey. Our data support and highlight the capability of telesimulation to provide educational benefit to learners who do not have direct access to simulation resources.

Animal models of transcranial direct current stimulation: Methods and mechanisms.

The objective of this review is to summarize the contribution of animal research using direct current stimulation (DCS) to our understanding of the physiological effects of transcranial direct current stimulation (tDCS). We comprehensively address experimental methodology in animal studies, broadly classified as: (1) transcranial stimulation; (2) direct cortical stimulation in vivo and (3) in vitro models. In each case advantages and disadvantages for translational research are discussed including dose translation and the overarching "quasi-uniform" assumption, which underpins translational relevance in all animal models of tDCS. Terminology such as anode, cathode, inward current, outward current, current density, electric field, and uniform are defined. Though we put key animal experiments spanning decades in perspective, our goal is not simply an exhaustive cataloging of relevant animal studies, but rather to put them in context of ongoing efforts to improve tDCS. Cellular targets, including excitatory neuronal somas, dendrites, axons, interneurons, glial cells, and endothelial cells are considered. We emphasize neurons are always depolarized and hyperpolarized such that effects of DCS on neuronal excitability can only be evaluated within subcellular regions of the neuron. Findings from animal studies on the effects of DCS on plasticity (LTP/LTD) and network oscillations are reviewed extensively. Any endogenous phenomena dependent on membrane potential changes are, in theory, susceptible to modulation by DCS. The relevance of morphological changes (galvanotropy) to tDCS is also considered, as we suggest microscopic migration of axon terminals or dendritic spines may be relevant during tDCS. A majority of clinical studies using tDCS employ a simplistic dose strategy where excitability is singularly increased or decreased under the anode and cathode, respectively. We discuss how this strategy, itself based on classic animal studies, cannot account for the complexity of normal and pathological brain function, and how recent studies have already indicated more sophisticated approaches are necessary. One tDCS theory regarding "functional targeting" suggests the specificity of tDCS effects are possible by modulating ongoing function (plasticity). Use of animal models of disease are summarized including pain, movement disorders, stroke, and epilepsy.

Microbubble assisted polyhydroxybutyrate production in Escherichia coli.

BACKGROUND: One of the potential limitations of large scale aerobic Escherichia coli fermentation is the need for increased dissolved oxygen for culture growth and bioproduct generation. As culture density increases the poor solubility of oxygen in water becomes one of the limiting factors for cell growth and product formation. A potential solution is to use a microbubble dispersion (MBD) generating device to reduce the diameter and increase the surface area of sparged bubbles in the fermentor. In this study, a recombinant E. coli strain was used to produce polyhydroxybutyrate (PHB) under conventional and MBD aerobic fermentation conditions. RESULTS: In conventional fermentation operating at 350 rpm and 0.8 vvm air flow rate, an OD600 of 6.21 and PHB yield of 23 % (dry cell basis) was achieved. MBD fermentation with similar bioreactor operating parameters produced an OD600 of 8.17 and PHB yield of 43 % PHB, which was nearly double that of the conventional fermentation. CONCLUSIONS: This study demonstrated that using a MBD generator can increase oxygen mass transfer into the aqueous phase, increasing E. coli growth and bioproduct generation.

Antimicrobial and toxicological studies on fruit pulp of Citrullus colocynthis L.

The methanolic extract of dried fruit pulp of Citrullus colocyn this (Cucurbitaceae) has been studied with respect to antimicrobial and toxicological properties. The antimicrobial profile was investigated against thirty bacterial isolates (10 Gram +ve and 20 Gram-ve) and five fungal species. None of the bacterial or fungal culture used in the study showed sensitivity against the extract. Acute toxicity studies carried out in Albino mice NMRI indicated the highly toxic nature of the colocynth. A very significant decrease in body weight of test animals was noted at P<0.05. The LD(50) was calculated as 1000mg/kg body weight. Within four days of experimentation mortality was 100%. Histopathological studies confirmed the toxic nature of extract. Gross changes in histology of Heart, Liver and Kidneys were noted. Section of spleen did not exhibit any abnormality.

Modeling sequence and quasi-uniform assumption in computational neurostimulation.

Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.