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Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) continues its significant health and economic impact globally. Despite the success of spike-protein vaccines in preventing severe disease, long-lasting protection against emerging variants and the prevention of breakthrough infections and transmission remain elusive. We generate an intranasal live-attenuated SARS-CoV-2 vaccine, CDO-7N-1, using codon deoptimization. CDO-7N-1 shows highly attenuated replication and minimal or no lung pathology in vivo over multiple passages. It induces robust mucosal and systemic neutralizing antibody and T-cell subset responses, in mice (female K18-hACE2 and male HFH4-hACE2 mice), hamsters, and macaques triggered by a single immunization. Mice and hamsters vaccinated with CDO-7N-1 are protected from challenge with wild-type (WT) SARS-CoV-2 and other variants of concern. Serum from vaccinated animals neutralizes WT SARS-CoV-2, variants of concern (beta and delta), variants of interest (omicron XBB.1.5) and SARS-CoV-1. Antibody responses are sustained and enhanced by repeated immunization or infection with WT SARS-CoV-2. Immunity against all SARS-CoV-2 proteins by CDO-7N-1 should improve efficacy against future SARS-CoV-2 variants.

Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain.Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome.Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%.Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.

Abstract Humans’ ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.

Background: While single pulse electrical stimulation (SPES) is increasingly used to study effective connectivity, the effects of varying stimulation parameters on the resulting cortico-cortical evoked potentials (CCEPs) have not been systematically explored. Objective: We sought to understand the interacting effects of stimulation pulse width, current intensity, and charge on CCEPs through an extensive testing of this parameter space and analysis of several response metrics. Methods: We conducted SPES in 11 patients undergoing intracranial EEG monitoring using five combinations of current intensity (1.5, 2.0, 3.0, 5.0, and 7.5 mA) and pulse width at each of three charges (0.750, 1.125, and 1.500 μC/phase) to study how CCEP amplitude, distribution, latency, morphology, and stimulus artifact amplitude vary with each parameter. Results: Stimulations with a greater charge or a greater current intensity and shorter pulse width at a given charge generally resulted in greater CCEP amplitudes and spatial distributions, shorter latencies, and increased waveform correlation. These effects interacted such that stimulations with the lowest charge and highest current intensities resulted in greater response amplitudes and spatial distributions than stimulations with the highest charge and lowest current intensities. Stimulus artifact amplitude increased with charge, but this could be mitigated by using shorter pulse widths. Conclusions: Our results indicate that individual combinations of current intensity and pulse width, in addition to charge, are important determinants of CCEP magnitude, morphology, and spatial extent. Together, these findings suggest that high current intensity, short pulse width stimulations are optimal SPES settings for eliciting strong and consistent responses while minimizing charge.

This study reports the biosorption of nickel using powdered fallen coffee leaves. BET, SEM, FTIR, and XRD characterize the biosorbents. We go over the findings from thermodynamic, kinetic, and equilibrium biosorption experiments. Here are some of the factors that were studied: agitation duration (t), biosorbent size, pH of the aqueous solution (ranging from 1 to 8), initial nickel concentration (C0), pH (ranging from 5 to 150), temperature (ranging from 283 to 323), and so on. Coffee leaf powder is best when the pH is 4. This biosorbent pair works best when dosed at 18 g/L for nickel biosorption. Nitrogen absorption is 4.219 mg/g when coffee leaf powder is used. For nickel biosorption, CCD has optimized four parameters. The data on nickel biosorption are tightly matched to the biosorbent model of pseudo-second-order. The negative sign of the free energy change (ΔG) for coffee leaf powder (-1546 J/mol) reflects both the practicality and spontaneous nature of the biosorbent-material. Powdered coffee leaves are predicted to undergo enthalpy changes of 62.99 J/mol K and entropy changes of 51.08 J/mol K. When the value of ΔS is positive, it indicates that the randomness is increasing, and when the value of enthalpy change is positive, it indicates that the process is endothermic.

Abstract Priya Iyer Background Breast cancer in young adults is rare and accounts for 5 to 6% of all cancers in this age group. We conducted the present study to look at the demographic features, clinical presentation, and outcomes in this group of patients treated at our center. Patients and Methods The study included breast cancer patients between the age of 15 and 30 years treated at our institute from January 2009 to December 2016. Data were analyzed retrospectively from case records. Event-free survival (EFS) and overall survival (OS) were calculated using the Kaplan–Meier method. Results Young adult breast cancers were reported in 145 out of 6,000 patients (2.41%) diagnosed with breast cancer in the study period. The median age of the patients was 29 years (range: 21–30 years). Stage I, II, III, and IV was observed in 3.4, 33.7, 46.2, and 16.5% of patients, respectively. The median follow-up was 45 months (range: 1.7–128.1 months). The 5-year EFS and OS for stage I, II, III, and IV was 100, 74.5, 47.9, and 0% and 100, 90.8, 55.1, and 0%, respectively. On univariate analysis, stage of the disease and pregnancy-associated breast cancers were found to have a significant association with decreased EFS and OS (p

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Objective: The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients.Design: The design of the study is a retrospective observational cohort study.Setting: The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD.Patients: The patients included in the study were

For epileptic patients requiring resective surgery, a modality called stereo-electroencephalography (SEEG) may be used to monitor the patient's brain signals to help identify epileptogenic regions that generate and propagate seizures. SEEG involves the insertion of multiple depth electrodes into the patient's brain, each with 10 or more recording contacts along its length. However, a significant fraction (≈ 30% or more) of the contacts typically reside in white matter or other areas of the brain which can not be epileptogenic themselves. Thus, an important step in the analysis of SEEG recordings is distinguishing between electrode contacts which reside in gray matter vs. those that do not. MRI images overlaid with CT scans are currently used for this task, but they take significant amounts of time to manually annotate, and even then it may be difficult to determine the status of some contacts. In this paper we present a fast, automated method for classifying contacts in gray vs. white matter based only on the recorded signal and relative contact depth. We observe that bipolar referenced contacts in white matter have less power in all frequencies below 150 Hz than contacts in gray matter, which we use in a Bayesian classifier to attain an average area under the receiver operating characteristic curve of 0.85 ± 0.079 (SD) across 29 patients. Because our method gives a probability for each contact rather than a hard labeling, and uses a feature of the recorded signal that has direct clinical relevance, it can be useful to supplement decision-making on difficult to classify contacts or as a rapid, first-pass filter when choosing subsets of contacts from which to save recordings.

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during passive patient monitoring. Cortical stimulation is currently performed on patients undergoing invasive EEG monitoring for the main purpose of mapping functional brain networks such as language and motor networks. We hypothesized that evoked responses from single pulse electrical stimulation (SPES) can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we constructed patient specific transfer function models from the evoked responses recorded from 22 epilepsy patients that underwent SPES evaluation and iEEG monitoring. We then computed the frequency and connectivity dependent “peak gain” of the system as measured by the H∞ norm from systems theory. We found that in cases for which clinicians had high confidence in localizing the SOZ, the highest peak gain transfer functions with the smallest “floor gain” (gain at which the dipped H∞ 3dB below DC gain) corresponded to when the clinically annotated SOZ and early spread regions were stimulated. In more complex cases, there was a large spread of the peak-to-floor (PF) ratios when the clinically annotated SOZ was stimulated. Interestingly for patients who had successful surgeries, our ratio of gains, agreed with clinical localization, no matter the complexity of the case. For patients with failed surgeries, the PF ratio did not match clinical annotations. Our findings suggest that transfer function gains and their corresponding frequency responses computed from SPES evoked responses may improve SOZ localization and thus surgical outcomes.

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ. Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often require observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during, and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

Globus pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through specific modulations in their oscillatory patterns. We analyzed neuronal spiking activity of 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We found that, in directionally tuned neurons, the power in the gamma band is significantly (p < 0.05) greater than that in the beta band (a “cross-over” effect), during the planning stages of movements in their preferred direction. This cross-over effect is not observed in the non-directionally tuned neurons. These data suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates.

Biosorption of lead onto Fallen Coffee Plant leaves (FCPL) powder from an aqueous solution was studied in the present study. The characterization of FCPL powder was done by FTIR, XRD, SEM and BET. The equilibrium agitation time for lead biosorption is 60 min. The optimum pH and dosage values are 5.2 and 20 g/L respectively. In the range of variables studied, percentage biosorption is increased from 75.1 to 95.5 %. The maximum uptake capacity of 3.664 mg/g is obtained at 303 K. In the present investigation the equilibrium data was well explained by Langmuir, Redlich-Peterson with a correlation coefficient of 0.99, and followed by Temkin and Freundlich isotherms. The kinetic studies reveal that the biosorption system obeyed the pseudo second order kinetic model by considering the correlation coefficient value as 0.99. From the values of ∆S, ∆H and ∆G it is observed that the biosorption of lead onto Fallen coffee plant leaves(FCPL) powder was irreversible, endothermic and spontaneous.

ABSTRACT Background: Thyroid function disorders lead to changes in the lipoprotein metabolism. Objectives: To study the lipid and the glycaemic abnormalities in the subclinical hypothyroidism cases and to compare the same with the euthyroid, overt hypothyroid and the hyperthyroid subjects. Methodology: Four groups, euthyroid (Group-I), hypothyroid (Group-II), subclinical hypothyroid (Group-III) and hyperthyroid (Group-IV), which consisted of 30 subjects each, of either sex, who were aged 25-55 years, underwent Fasting Plasma Glucose (FPG), fructosamine, lipid profile and total T3, T4 and TSH estimations. The subjects who were on lipid lowering or thyroid disorder drugs and known diabetics were excluded from the study. Results: In Group-III, all the lipid fractions were comparable to those of Group-II and they were significantly deranged, as compared to those of Group-I. The fructosamine levels were significantly higher in Group-II and Group-III (p

It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. Closed-loop therapy could therefore entail detecting when the network goes unstable from spike train measurements, and then stimulating with an exogenous current to stabilize the network. In his study, a nonlinear stochastic model of a neuronal network was used to simulate both seizure and non-seizure activity. In particular, synaptic weights between neurons were chosen such that the network's fixed point is stable during non-seizure periods, and a subset of these connections (the most fragile) were perturbed to make the same fixed point unstable to model seizure events; and, the model randomly transitions between these two modes. The goal of this study is to measure spike train observations from this epileptic network and then apply a feedback controller that will (i) detect when the network goes unstable, and then (ii) apply a state-feedback gain control input to the network to re-stabilize the network. The stability detector is based on a 2-state (stable, unstable) hidden Markov model (HMM) of the network, and detects the transition from the stable mode to the unstable mode from the firing rate of the most fragile node in the network (which is the output of the HMM). When the unstable mode is detected, a state-feedback gain is applied to generate a control input to the fragile node to bring the network back to the stable mode. Finally, when the network is detected as stable again, the feedback control input is switched off. High performance was achieved for the stability detector, and feedback controlled suppressed seizures within 2 seconds after onset.

Abstract Hereditary cancers are thought to account for around 5% of cancers, particularly breast/ovarian and colorectal cancers. In India there is a paucity of data on hereditary cancers and the mutations in some of the common genes linked to hereditary cancers, such as BRCA1, BRCA2, hMSH2 and hMLH1. The country's first comprehensive hereditary cancer clinic was established in February 2002. The article describes the organization and running of the Clinic. It also discusses some of the social issues relevant to the given population in running the Hereditary Cancer Clinic.

Visual cues open a unique window to the understanding of Parkinson’s disease (PD). These cues can temporarily but dramatically improve PD motor symptoms. Although details are unclear, cues are believed to suppress pathological basal ganglia (BG) activity through activation of corticostriatal pathways. In this study, we investigated human BG neurophysiology under different cued conditions. We evaluated bursting, 10-30Hz oscillations (OSCs), and directional tuning (DT) dynamics in the subthalamic nucleus activity while 7 patients executed a two-step motor task. In the first step (predicted +cue), the patient moved to a target when prompted by a visual go cue that appeared 100% of the time. Here, the timing of the cue is predictable and the cue serves an external trigger to execute a motor plan. In the second step, the cue appeared randomly 50% of the time, and the patient had to move to the same target as in the first step. When it appeared (unpredicted +cue), the motor plan was to be triggered by the cue, but its timing was not predictable. When the cue failed to appear (unpredicted -cue), the motor plan was triggered by the absence of the visual cue. We found that during predicted +cue and unpredicted -cue trials, OSCs significantly decreased and DT significantly increased above baseline, though these modulations occurred an average of 640 milliseconds later in unpredicted -cue trials. Movement and reaction times were comparable in these trials. During unpredicted +cue trials, OSCs and DT failed to modulate though bursting significantly decreased after movement. Correspondingly, movement performance deteriorated. These findings suggest that during motor planning either a predictably timed external cue or an internally generated cue (generated by the absence of a cue) trigger the execution of a motor plan in premotor cortex, whose increased activation then suppresses pathological activity in STN through direct pathways, leading to motor facilitation in PD.

Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) directly modulates the basal ganglia, but how such stimulation impacts the cortex upstream is largely unknown. There is evidence of cortical activation in 6-hydroxydopamine-lesioned rats and facilitation of motor evoked potentials in Parkinson’s disease (PD) patients, but the impact of the DBS settings on the cortical activity in normal vs. Parkinsonian conditions is still debated.In recent studies, we used point process models to analyze non-stationary activation patterns and inter-neuronal dependencies in the motor and sensory cortices of awake non-human primates during STN DBS. We reported that these features are enhanced after treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which causes a consistent PD-like motor impairment, and that high-frequency DBS (i.e., >100 pulses-per-second [pps]) strongly reduces the short-term patterns (3-7ms period) both before and after MPTP treatment, while it elicits a consistent short-latency post-stimulus activation. Low-frequency DBS (≤50pps), instead, had negligible effects on the non-stationary features while decreased the burstiness of the spike trains.We evaluate here the impact of the DBS settings on the cortical discharge patterns by using tools from the information theory (receiver operating characteristic curve, information rate, etc.) and report that the probability of spiking of the cortical neurons is significantly conditioned on the DBS settings, with such dependency being significantly larger for high- vs. low-frequency DBS. Overall, the selective suppression of non-stationary features and the increased modulation of the spike probability suggest that high-frequency STN DBS enhances the neuronal activation in motor and sensory cortices, presumably because of reinforcement mechanisms, which perhaps involve the overlap between feedback antidromic and feed-forward orthodromic responses along the basal ganglia-thalamo-cortical loop.

Relay cells are prevalent throughout sensory systems and receive two types of inputs: driving and modulating. The driving input contains receptive field properties that must be transmitted while the modulating input alters the specifics of transmission. For example, the visual thalamus contains relay neurons that receive driving inputs from the retina that encode a visual image, and modulating inputs from reticular activating system and layer 6 of visual cortex that control what aspects of the image will be relayed back to visual cortex for perception. What gets relayed depends on several factors such as attentional demands and a subject's goals. In this paper, we analyze a biophysical based model of a relay cell and use systems theoretic tools to construct analytic bounds on how well the cell transmits a driving input as a function of the neuron's electrophysiological properties, the modulating input, and the driving signal parameters. We assume that the modulating input belongs to a class of sinusoidal signals and that the driving input is an irregular train of pulses with inter-pulse intervals obeying an exponential distribution. Our analysis applies to any [Formula: see text] order model as long as the neuron does not spike without a driving input pulse and exhibits a refractory period. Our bounds on relay reliability contain performance obtained through simulation of a second and third order model, and suggest, for instance, that if the frequency of the modulating input increases or the DC offset decreases, then relay increases. Our analysis also shows, for the first time, how the biophysical properties of the neuron (e.g. ion channel dynamics) define the oscillatory patterns needed in the modulating input for appropriately timed relay of sensory information. In our discussion, we describe how our bounds predict experimentally observed neural activity in the basal ganglia in (i) health, (ii) in Parkinson's disease (PD), and (iii) in PD during therapeutic deep brain stimulation. Our bounds also predict different rhythms that emerge in the lateral geniculate nucleus in the thalamus during different attentional states.

Abstract Ichthyosis uteri is a rare condition in which the entire surface of the endometrium is replaced by stratified squamous epithelium. Though the condition often is considered as benign, anaplastic and dysplastic changes have been reported. We describe herein a rare case of low-grade squamous cell carcinoma of endometrium associated with extensive ichthyosis uteri with dysplasia. The cervix showed moderate to severe dysplastic changes while the right fallopian tube showed extensive squamous metaplasia with dysplastic changes. We conclude that squamous cell carcinoma could develop into pre-existing ichthyosis uteri.