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Background: Clinically important medication errors are common after hospital discharge. They include preventable or ameliorable adverse drug events (ADEs), as well as medication discrepancies or nonadherence with high potential for future harm (potential ADEs).Objective: To determine the effect of a tailored intervention on the occurrence of clinically important medication errors after hospital discharge.Design: Randomized, controlled trial with concealed allocation and blinded outcome assessors. (ClinicalTrials.gov registration number: NCT00632021)Setting: Two tertiary care academic hospitals.Patients: Adults hospitalized with acute coronary syndromes or acute decompensated heart failure.Intervention: Pharmacist-assisted medication reconciliation, inpatient pharmacist counseling, low-literacy adherence aids, and individualized telephone follow-up after discharge.Measurements: The primary outcome was the number of clinically important medication errors per patient during the first 30 days after hospital discharge. Secondary outcomes included preventable or ameliorable ADEs, as well as potential ADEs.Results: Among 851 participants, 432 (50.8%) had 1 or more clinically important medication errors; 22.9% of such errors were judged to be serious and 1.8% life-threatening. Adverse drug events occurred in 258 patients (30.3%) and potential ADEs in 253 patients (29.7%). The intervention did not significantly alter the per-patient number of clinically important medication errors (unadjusted incidence rate ratio, 0.92 [95% CI, 0.77 to 1.10]) or ADEs (unadjusted incidence rate ratio, 1.09 [CI, 0.86 to 1.39]). Patients in the intervention group tended to have fewer potential ADEs (unadjusted incidence rate ratio, 0.80 [CI, 0.61 to 1.04]).Limitation: The characteristics of the study hospitals and participants may limit generalizability.Conclusion: Clinically important medication errors were present among one half of patients after hospital discharge and were not significantly reduced by a health-literacy-sensitive, pharmacist-delivered intervention.Primary Funding Source: National Heart, Lung, and Blood Institute. [ABSTRACT FROM AUTHOR]

Objective: Interruptive alerts within electronic applications can cause "alert fatigue" if they fire too frequently or are clinically reasonable only some of the time. We assessed the impact of non-interruptive, real-time medication laboratory alerts on provider lab test ordering.Design: We enrolled 22 outpatient practices into a prospective, randomized, controlled trial. Clinics either used the existing system or received on-screen recommendations for baseline laboratory tests when prescribing new medications. Since the warnings were non-interruptive, providers did not have to act upon or acknowledge the notification to complete a medication request.Measurements: Data were collected each time providers performed suggested laboratory testing within 14 days of a new prescription order. Findings were adjusted for patient and provider characteristics as well as patient clustering within clinics.Results: Among 12 clinics with 191 providers in the control group and 10 clinics with 175 providers in the intervention group, there were 3673 total events where baseline lab tests would have been advised: 1988 events in the control group and 1685 in the intervention group. In the control group, baseline labs were requested for 771 (39%) of the medications. In the intervention group, baseline labs were ordered by clinicians in 689 (41%) of the cases. Overall, no significant association existed between the intervention and the rate of ordering appropriate baseline laboratory tests.Conclusion: We found that non-interruptive medication laboratory monitoring alerts were not effective in improving receipt of recommended baseline laboratory test monitoring for medications. Further work is necessary to optimize compliance with non-critical recommendations. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Few data exist measuring the effect of differentiating drug-drug interaction (DDI) alerts in computerized provider order entry systems (CPOE) by level of severity ('tiering'). We sought to determine if rates of provider compliance with DDI alerts in the inpatient setting differed when a tiered presentation was implemented. DESIGN: We performed a retrospective analysis of alert log data on hospitalized patients at two academic medical centers during the period from 2/1/2004 through 2/1/2005. Both inpatient CPOE systems used the same DDI checking service, but one displayed alerts differentially by severity level (tiered presentation, including hard stops for the most severe alerts) while the other did not. Participants were adult inpatients who generated a DDI alert, and providers who wrote the orders. Alerts were presented during the order entry process, providing the clinician with the opportunity to change the patient's medication orders to avoid the interaction. MEASUREMENTS: Rate of compliance to alerts at a tiered site compared to a non-tiered site. RESULTS: We reviewed 71,350 alerts, of which 39,474 occurred at the non-tiered site and 31,876 at the tiered site. Compliance with DDI alerts was significantly higher at the site with tiered DDI alerts compared to the non-tiered site (29% vs. 10%, p < 0.001). At the tiered site, 100% of the most severe alerts were accepted, vs. only 34% at the non-tiered site; moderately severe alerts were also more likely to be accepted at the tiered site (29% vs. 10%). CONCLUSION: Tiered alerting by severity was associated with higher compliance rates of DDI alerts in the inpatient setting, and lack of tiering was associated with a high override rate of more severe alerts. [ABSTRACT FROM AUTHOR]

We designed the Pre-Admission Medication List (PAML) Builder medication reconciliation application and implemented it at two academic hospitals. We asked 1,714 users to complete a survey of their satisfaction with the application and analyzed factors associated with user efficiency. The survey was completed by 626 (36.5%) users. Most (64%) responders agreed that medication reconciliation improves patient care. Improvement requests included better medication information sources and propagation of medication information to order entry. Sixty-nine percent of admitting clinicians reported a typical time to build a PAML of <10 min. Decreased reported time to build a PAML was associated with reported experience with the application and ease of use but not the average number of medications on the PAML. Most users agreed that medication reconciliation improves patient care but requested tighter integration of the different stages of the medication reconciliation process. Further training may be helpful in improving user efficiency. [ABSTRACT FROM AUTHOR]

While medications can improve patients' health, the process of prescribing them is complex and error prone, and medication errors cause many preventable injuries. Computer provider order entry (CPOE) with clinical decision support (CDS), can improve patient safety and lower medication-related costs. To realize the medication-related benefits of CDS within CPOE, one must overcome significant challenges. Healthcare organizations implementing CPOE must understand what classes of CDS their CPOE systems can support, assure that clinical knowledge underlying their CDS systems is reasonable, and appropriately represent electronic patient data. These issues often influence to what extent an institution will succeed with its CPOE implementation and achieve its desired goals. Medication-related decision support is probably best introduced into healthcare organizations in two stages, basic and advanced. Basic decision support includes drug-allergy checking, basic dosing guidance, formulary decision support, duplicate therapy checking, and drug-drug interaction checking. Advanced decision support includes dosing support for renal insufficiency and geriatric patients, guidance for medication-related laboratory testing, drug-pregnancy checking, and drug-disease contraindication checking. In this paper, the authors outline some of the challenges associated with both basic and advanced decision support and discuss how those challenges might be addressed. The authors conclude with summary recommendations for delivering effective medication-related clinical decision support addressed to healthcare organizations, application and knowledge base vendors, policy makers, and researchers. [ABSTRACT FROM AUTHOR]

Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workflow disruptions by designating only critical to high-severity alerts to be interruptive to clinician workflow. The alerts were presented to clinicians using computerized prescribing within an electronic medical record in 31 Boston-area practices. There were 18,115 drug alerts generated during our six-month study period. Of these, 12,933 (71%) were noninterruptive and 5,182 (29%) interruptive. Of the 5,182 interruptive alerts, 67% were accepted. Reasons for overrides varied for each drug alert category and provided potentially useful information for future alert improvement. These data suggest that it is possible to design computerized prescribing decision support with high rates of alert recommendation acceptance by clinicians. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate the impact of an integrated patient-specific electronic clinical reminder system on diabetes and coronary artery disease (CAD) care and to assess physician attitudes toward this reminder system.Design: We enrolled 194 primary care physicians caring for 4549 patients with diabetes and 2199 patients with CAD at 20 ambulatory clinics. Clinics were randomized so that physicians received either evidence-based electronic reminders within their patients' electronic medical record or usual care. There were five reminders for diabetes care and four reminders for CAD care.Measurements: The primary outcome was receipt of recommended care for diabetes and CAD. We created a summary outcome to assess the odds of increased compliance with overall diabetes care (based on five measures) and overall CAD care (based on four measures). We surveyed physicians to assess attitudes toward the reminder system.Results: Baseline adherence rates to all quality measures were low. While electronic reminders increased the odds of recommended diabetes care (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.01-1.67) and CAD (OR 1.25, 95% CI 1.01-1.55), the impact of individual reminders was variable. A total of three of nine reminders effectively increased rates of recommended care for diabetes or CAD. The majority of physicians (76%) thought that reminders improved quality of care.Conclusion: An integrated electronic reminder system resulted in variable improvement in care for diabetes and CAD. These improvements were often limited and quality gaps persist. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine characteristics of drug allergy alert overrides, assess how often they lead to preventable adverse drug events (ADEs), and suggest methods for improving the allergy-alerting system.Design: Chart review was performed on a stratified random subset of all allergy alerts occurring during a 3-month period (August through October 2002) at a large academic hospital.Measurements: Factors that were measured were drug/allergy combinations that triggered alerts, frequency of specific override reasons, characteristics of ADEs, and completeness of allergy documentation.Results: A total of 6,182 (80%) of 7,761 alerts were overridden in 1,150 patients. In this sample, only 10% of alerts were triggered by an exact match between the drug ordered and allergy listed. Physicians' most common reasons for overriding alerts were "Aware/Will monitor" (55%), "Patient does not have this allergy/tolerates" (33%), and "Patient taking already" (10%). In a stratified random subset of 320 patients (28% of 1,150) on chart review, 19 (6%) experienced ADEs attributed to the overridden drug; of these, 9 (47%) were serious. None of the ADEs was considered preventable, because the overrides were deemed clinically justifiable. The degree of completeness of patients' allergy lists was highly variable and generally low in both paper charts and the CPOE system.Conclusion: Overrides of drug-allergy alerts were common and about 1 in 20 resulted in ADEs, but all of the overrides resulting in ADEs appeared clinically justifiable. The high rate of alert overrides was attributable to frequent nonexact match alerts and infrequent updating of allergy lists. Based on these findings, we have made specific recommendations for increasing the specificity of alerting and thereby improving the clinical utility of the drug allergy alerting system. [ABSTRACT FROM AUTHOR]

Abstract Background Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available repositories for human PPIs are currently available. Each of these databases has their own unique features with a large variation in the type and depth of their annotations. Results We analyzed the major publicly available primary databases that contain literature curated PPI information for human proteins. This included BIND, DIP, HPRD, IntAct, MINT, MIPS, PDZBase and Reactome databases. The number of binary non-redundant human PPIs ranged from 101 in PDZBase and 346 in MIPS to 11,367 in MINT and 36,617 in HPRD. The number of genes annotated with at least one interactor was 9,427 in HPRD, 4,975 in MINT, 4,614 in IntAct, 3,887 in BIND and Conclusion Given the importance of PPIs, we suggest that submission of PPIs to repositories be made mandatory by scientific journals at the time of manuscript submission as this will minimize annotation errors, promote standardization and help keep the information up to date. We hope that our analysis will help guide biomedical scientists in selecting the most appropriate database for their needs especially in light of the dramatic differences in their content.

Few data exist about the impact of computerized prescribing systems on outpatient medication errors (MEs) and adverse drug events (ADEs). We compared the rates of MEs and ADEs in handwritten sites versus sites with basic computerized prescribing. These systems reduced ME rates but did not significantly reduce ADE rates. Failure to monitor accounted for a large percentage of preventable ADEs. More advanced computerized prescribing systems with decision support and monitoring functions may be necessary to reduce outpatient ADE rates. [ABSTRACT FROM AUTHOR]

This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

Human visual recognition is remarkably robust to chromatic changes. In this work, we provide a potential account of the roots of this resilience based on observations with 10 congenitally blind children who gained sight late in life. Several months or years following their sight-restoring surgeries, the removal of color cues markedly reduced their recognition performance, whereas age-matched normally sighted children showed no such decrement. This finding may be explained by the greater-than-neonatal maturity of the late-sighted children's color system at sight onset, inducing overly strong reliance on chromatic cues. Simulations with deep neural networks corroborate this hypothesis. These findings highlight the adaptive significance of typical developmental trajectories and provide guidelines for enhancing machine vision systems.

Delirium, memory loss, attention deficit and fatigue are frequently reported by COVID survivors, yet the neurological pathways underlying these symptoms are not well understood. To study the possible mechanisms for these long-term sequelae after COVID-19 recovery, we investigated the microstructural properties of white matter in Indian cohorts of COVID-recovered patients and healthy controls. For the cross-sectional study presented here, we recruited 44 COVID-recovered patients and 29 healthy controls in New Delhi, India. Using deterministic whole-brain tractography on the acquired diffusion MRI scans, we traced 20 white matter tracts and compared fractional anisotropy, axial, mean and radial diffusivity between the cohorts. Our results revealed statistically significant differences ( P FWE < 0.01) in the uncinate fasciculus, cingulum cingulate, cingulum hippocampus and arcuate fasciculus in COVID survivors, suggesting the presence of microstructural abnormalities. Additionally, in a subsequent subgroup analysis based on infection severity (healthy control, non-hospitalized patients and hospitalized patients), we observed a correlation between tract diffusion measures and COVID-19 infection severity. Although there were significant differences between healthy controls and infected groups, we found no significant differences between hospitalized and non-hospitalized COVID patients. Notably, the identified tracts are part of the limbic system and orbitofrontal cortex, indicating microstructural differences in neural circuits associated with memory and emotion. The observed white matter alterations in the limbic system resonate strongly with the functional deficits reported in Long COVID. Overall, our study provides additional evidence that damage to the limbic system could be a neuroimaging signature of Long COVID. The findings identify targets for follow-up studies investigating the long-term physiological and psychological impact of COVID-19., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

While cortical regions involved in processing binocular disparities have been studied extensively, little is known on how the human visual system adapts to changing disparity magnitudes. In this paper, we investigate causal mechanisms of coarse and fine binocular disparity processing using fMRI with a clinically validated, custom anaglyph-based stimulus. We make use of Granger causality and graph measures to reveal the existence of distinct rich and diverse clubs across different disparity magnitudes. We demonstrate that Middle Temporal area (MT) plays a specialized role with overlapping rich and diverse characteristics. Next, we show that subtle interhemispheric differences exist across various brain regions, despite an overall right hemisphere dominance. Finally, we pass the graph measures through the decision tree and found that the diverse clubs outperform rich clubs in decoding disparity magnitudes. Our study sets the stage for conducting further investigations on binocular disparity processing, particularly in the context of neuro-ophthalmic disorders with binocular impairments., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

This study aimed to develop an automatic diagnostic scheme for bruxism, a sleep-related disorder characterized by teeth grinding and clenching. The aim was to improve on existing methods, which have been proven to be inefficient and challenging. We utilized a novel hybrid machine learning classifier, facilitated by the Weka tool, to diagnose bruxism from biological signals. The study processed and examined these biological signals by calculating the power spectral density. Data were categorized into normal or bruxism categories based on the EEG channel (C4-A1), and the sleeping phases were classified into wake (w) and rapid eye movement (REM) stages using the ECG channel (ECG1-ECG2). The classification resulted in a maximum specificity of 93% and an accuracy of 95% for the EEG-based diagnosis. The ECG-based classification yielded a supreme specificity of 87% and an accuracy of 96%. Furthermore, combining these phases using the EMG channel (EMG1-EMG2) achieved the highest specificity of 95% and accuracy of 98%. The ensemble Weka tool combined all three physiological signals EMG, ECG, and EEG, to classify the sleep stages and subjects. This integration increased the specificity and accuracy to 97% and 99%, respectively. This indicates that a more precise bruxism diagnosis can be obtained by including all three biological signals. The proposed method significantly improves bruxism diagnosis accuracy, potentially enhancing automatic home monitoring systems for this disorder. Future studies may expand this work by applying it to patients for practical use., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Introduction: Unraveling the network pathobiology in neurodegenerative disorders is a popular and promising field in research. We use a relatively newer network measure of assortativity in metabolic connectivity to understand network differences in patients with Alzheimer's Disease (AD), compared with those with mild cognitive impairment (MCI). Methods: Eighty-three demographically matched patients with dementia (56 AD and 27 MCI) who underwent positron emission tomography-magnetic resonance imaging (PET-MRI) study were recruited for this exploratory study. Global and nodal network measures obtained using the BRain Analysis using graPH theory toolbox were used to derive group-level differences (corrected p  < 0.05). The methods were validated in age, and gender-matched 23 cognitively normal, 25 MCI, and 53 AD patients from the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Regions that revealed significant differences were correlated with the Addenbrooke's Cognitive Examination-III (ACE-III) scores. Results: Patients with AD revealed significantly increased global assortativity compared with the MCI group. In addition, they also revealed increased modularity and decreased participation coefficient. These findings were validated in the ADNI data. We also found that the regional standard uptake values of the right superior parietal and left superior temporal lobes were proportional to the ACE-III memory subdomain scores. Conclusion: Global errors associated with network assortativity are found in patients with AD, making the networks more regular and less resilient. Since the regional measures of these network errors were proportional to memory deficits, these measures could be useful in understanding the network pathobiology in AD.

Objective: We present a novel framework for the detection and continuous evaluation of 3D motion perception by deploying a virtual reality environment with built-in eye tracking., Methods: We created a biologically-motivated virtual scene that involved a ball moving in a restricted Gaussian random walk against a background of 1/f noise. Sixteen visually healthy participants were asked to follow the moving ball while their eye movements were monitored binocularly using the eye tracker. We calculated the convergence positions of their gaze in 3D using their fronto-parallel coordinates and linear least-squares optimization. Subsequently, to quantify 3D pursuit performance, we employed a first-order linear kernel analysis known as the Eye Movement Correlogram technique to separately analyze the horizontal, vertical and depth components of the eye movements. Finally, we checked the robustness of our method by adding systematic and variable noise to the gaze directions and re-evaluating 3D pursuit performance., Results: We found that the pursuit performance in the motion-through depth component was reduced significantly compared to that for fronto-parallel motion components. We found that our technique was robust in evaluating 3D motion perception, even when systematic and variable noise was added to the gaze directions., Conclusion: The proposed framework enables the assessment of 3D Motion perception by evaluating continuous pursuit performance through eye-tracking., Significance: Our framework paves the way for a rapid, standardized and intuitive assessment of 3D motion perception in patients with various eye disorders.

Artificial intelligence (AI) has tremendous potential to improve the cognitive and work burden of clinicians across a range of clinical activities, which could lead to reduced burnout and better clinical care. The recent explosion of generative AI nicely illustrates this potential. Developers and organizations deploying AI have a responsibility to ensure AI is designed and implemented with end-user input, has mechanisms to identify and potentially reduce bias, and that the impact on cognitive and work burden is measured, monitored, and improved. This article focuses specifically on the role AI can play in reducing cognitive and work burden, outlines the critical issues associated with the use of AI, and serves as a call to action for vendors and users to work together to develop functionality that addresses these challenges., Competing Interests: T.K.G. is an employee of Press Ganey Associates LLC and is on the medical advisory board of Curai. D.C. is a part-time employee of Pascal Metrics. C.A.S. has no competing interests. D.C.R. is an employee of Microsoft. N.V.G. is an employee of Oracle Health. A.R. is an employee of Oracle Health. F.F. has no competing interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Continuous monitoring of breathing activity is vital in detecting respiratory-based diseases such as obstructive sleep apnea (OSA) and hypopnea. Sleep apnea (SA) is a potentially dangerous sleep problem that occurs when a person's breathing stops and begins periodically while they sleep. In addition, SA interrupts sleep, causing significant daytime sleepiness, weirdness, and irritability. This study aims to design a single inertial measurement unit (IMU) sensor-based system to analyze the respiratory rate of humans. The results of the developed system is validated with the Equivital Wireless Physiological Systems for different activities. Further, the experiment has been designed to identify the optimal sensor placement location for efficient respiration rate estimation during different activities. The performance of the developed model has been quantified using breathing rate estimation accuracy (% BREA) and mean absolute error (MAE). Among all sensor placement locations and activities combinations, a window size of 30sec resulted in the worst performance, whereas a window size ≥ 60sec resulted in a better performance (p-value<0.05). In addition, the performance of the model has been found consistent for window size ≥ 60sec (p-value>0.05). For activity 3 (lying straight), comparably similar performance, 0.52±0.24 and 0.52±0.12 (p-value>0.05) have been depicted by the sensor placement position 3 (Abdomen) and position 1 (chest), respectively. Further, for the other two activities, activity 1 (sitting and working) and activity 2 (sitting straight), the best performance has been depicted as 0.32±0.18, 0.49±0.21 respectively (p-value<0.05), by the sensor placement position 2 (left ribs). This research presents a reliable, cost-effective, portable respiration monitoring system that could detect SA during sleep.

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children and is characterised by inattention, impulsiveness and hyperactivity. While several studies have analysed the static functional connectivity in the resting-state functional MRI (rs-fMRI) of ADHD patients, detailed investigations are required to characterize the connectivity dynamics in the brain. In an attempt to establish a link between attention instability and the dynamic properties of Functional Connectivity (FC), we investigated the differences in temporal variability of FC between 40 children with ADHD and 40 Typically Developing (TD) children. Using a sliding-window method to segment the rs-fMRI scans in time, we employed seed-to-voxel correlation analysis for each window to obtain time-evolving seed connectivity maps for seeds placed in the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC). For each subject, the standard deviation of the voxel connectivity time series was used as a measure of the temporal variability of FC. Results showed that ADHD patients exhibited significantly higher variability in dFC than TD children in the cingulo-temporal, cingulo-parietal, fronto-temporal, and fronto-parietal networks ( pFW E < 0.05). Atypical temporal variability was observed in the left and right temporal gyri, the anterior cingulate cortex, and lateral regions of the right parietal cortex. The observations are consistent with visual attention issues, executive control deficit, and rightward parietal dysfunction reported in ADHD, respectively. These results help in understanding the disorder with a fresh perspective linking behavioural inattention with instability in FC in the brain.

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to medium age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of 89.4% and 88.4% are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.

Even after recovery from the COVID-19 infection, there have been a multitude of cases reporting post-COVID neurological symptoms including memory loss, brain fog, and attention deficit. Many studies have observed localized microstructural damages in the white matter regions of COVID survivors, indicating potential damage to the axonal pathways in the brain. Therefore, in this study, we have investigated the global impact of localized damage to white matter tracts using graph theoretical analysis of the structural connectome of 45 COVID-recovered subjects and 30 Healthy Controls (HCs). We have implemented Diffusion Tensor Imaging based reconstruction followed by deterministic tractography to extract structural connections among different regions of the brain. Interpreting this structural connectivity as weighted undirected graphs, we have used graph theoretical measures like global efficiency, characteristic path length (CPL), clustering coefficient (CC), modularity, Fiedler value, and assortativity coefficient to quantify the global integration, segregation, and robustness of the brain networks. We statistically compare the cohorts based on these graph measures by employing permutation testing for 100,000 permutations. Post multiple comparisons error correction, we find that the COVID-recovered cohort shows a reduction in global efficiency and CC while they exhibit higher modularity and CPL. This disruption of the balance between global integration and segregation indicates the loss of small-world property in COVID survivors' connectomes which has been linked with other disorders such as cognitive impairment and Alzheimer's. Overall, our study sheds light on the alterations in structural connectivity and its role in post-COVID symptoms.

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.Clinical relevance- We demonstrate the utilization of Natural Language Processing (NLP) for developing assistive technology for Aphasia patients. While disorders like Broca's aphasia offer a small sample size of patients and data, synthetic linguistic models like ours offer extensive scope for developing assistive technology and rehabilitation monitoring.

How humans recognise faces and objects effortlessly, has become a great point of interest. To understand the underlying process, one of the approaches is to study the facial features, in particular ordinal contrast relations around the eye region, which plays a crucial role in face recognition and perception. Recently the graph-theoretic approaches to electroencephalogram (EEG) analysis are found to be effective in understating the underlying process of human brain while performing various tasks. We have explored this approach in face recognition and perception to know the importance of contrast features around the eye region. We studied functional brain networks, formed using EEG responses, corresponding to four types of visual stimuli with varying contrast relationships: Positive faces, chimeric faces (photo-negated faces, preserving the polarity of contrast relationships around eyes), photo-negated faces and only eyes. We observed the variations in brain networks of each type of stimuli by finding the distribution of graph distances across brain networks of all subjects. Moreover, our statistical analysis shows that positive and chimeric faces are equally easy to recognise in contrast to difficult recognition of negative faces and only eyes.

The visual system develops abnormally when visual input is absent or degraded during a critical period early in life. Restoration of the visual input later in life is generally thought to have limited benefit because the visual system will lack sufficient plasticity to adapt to and utilize the information from the eyes. Recent evidence, however, shows that congenitally blind adolescents can recover both low-level and higher-level visual function following surgery. In this study, we assessed behavioral performance in both a visual acuity and a face perception task alongside longitudinal structural white matter changes in terms of fractional anisotropy (FA) and mean diffusivity (MD). We studied congenitally blind patients with dense bilateral cataracts, who received cataract surgery at different stages of adolescence. Our goal was to differentiate between age- and surgery-related changes in both behavioral performance and structural measures to identify neural correlates which might contribute to recovery of visual function. We observed surgery-related long-term increases of structural integrity of late-visual pathways connecting the occipital regions with ipsilateral fronto-parieto-temporal regions or homotopic contralateral areas. Comparison to a group of age-matched healthy participants indicated that these improvements went beyond the expected changes in FA and MD based on maturation alone. Finally, we found that the extent of behavioral improvement in face perception was mediated by changes in structural integrity in late visual pathways. Our results suggest that sufficient plasticity remains in adolescence to partially overcome abnormal visual development and help localize the sites of neural change underlying sight recovery.

We estimated the proportion of children with stereopsis following surgery in congenital and developmental cataracts by systematic review and meta-analysis and also considered the factors influencing stereopsis, such as intervention age and presence of strabismus. Stereopsis is directly related to quality of life, and investigating its levels following cataract surgery in children may help decide the right time to intervene, particularly in the context of brain plasticity. We conducted a systematic literature search using Scopus, PubMed, and Web of Science and found 25 case series, 3 cohorts, and 3 clinical trial studies from 1/1/1995 to 31/12/2020. Study-specific proportions of stereopsis from 923 children were pooled using a random-effects model, and stratified analyses were conducted based on intervention age and pre-existing strabismus as a confounder. We appraised the risk of bias using tools published by National Institutes of Health and evaluated publication bias with funnel plots and the Egger test. The pooled proportions of stereopsis based on 8 unilateral and 6 bilateral congenital cataract studies were 0.37 (95% CIs: [0.24, 0.53]) and 0.45 (95% CIs: [0.24,0.68]) when patients with preexisting strabismus were excluded as a confounder. When the intervention age was ≤6 months, proportions in unilateral congenital cataract group significantly increased to 0.52 (95% CIs: [0.37, 0.66]; P = 0.49) compared to 0.26 (95% CIs: [0.14, 0.44]; P = 0.16) otherwise. A similar increase in proportions was found when intervention age ≤4 months. In both unilateral and bilateral congenital cataract groups, proportions increased significantly when the confounder was excluded. Overall, proportions in bilateral congenital cataracts were significantly greater than unilateral cases (irrespective of confounder). Eight unilateral and 5 bilateral developmental cataract studies resulted in pooled proportions of 0.62 (95% CIs: [0.27, 0.88] and 0.82 (95% CIs: [0.4, 0.97]), respectively. Although proportions for bilateral developmental cataracts were greater than unilateral cataracts (irrespective of confounder), results were not statistically significant. Finally, proportions in unilateral developmental cataracts were significantly greater than unilateral congenital cataracts (Z = 7.413, P = 6.173694e-14). We conclude that surgical intervention within first 4-6 months can significantly affect postoperative outcomes in unilateral congenital cataracts. Analysis of existing data does not show a significant effect of intervention age on stereopsis outcomes for developmental cataracts., Competing Interests: Conflicts of Interest No conflicting relationship exists for any author., (Copyright © 2022. Published by Elsevier Inc.)

Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections. The proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier B.V. All rights reserved.)

Objectives: Delayed emergency department (ED) and hospital patient throughput is recognized as a critical threat to patient safety. Increasingly, hospitals are investing significantly in deploying command centers, long used in airlines and the military, to proactively manage hospital-wide patient flow. This scoping review characterizes the evidence related to hospital capacity command centers (CCCs) and synthesizes current data regarding their implementation., Methods: As no consensus definition exists for CCCs, we characterized them as units (i) involving interdisciplinary, permanently colocated teams, (ii) using real-time data, and (iii) managing 2 or more patient flow functions (e.g., bed management, transfers, discharge planning, etc.), to distinguish CCCs from transfer centers. We undertook a scoping review of the medical and gray literature published through April 2019 related to CCCs meeting these criteria., Results: We identified 8 eligible articles (including 4 peer-reviewed studies) describing 7 CCCs of varying designs. The most common CCC outcome measures related to transfer volume (n = 5) and ED boarding (n = 4). Several CCCs also monitored patient-level clinical parameters. Although all articles reported performance improvements, heterogeneity in CCC design and evidence quality currently restricts generalizability of findings., Conclusions: Numerous anecdotal accounts suggest that CCCs are being widely deployed in an effort to improve hospital patient flow and safety, yet peer-reviewed evidence regarding their design and effectiveness is in its earliest stages. The costs, objectives, and growing deployment of CCCs merit an investment in rigorous research to better measure their processes and outcomes. We propose a standard definition, conceptual framework, research priorities, and reporting standards to guide future investigation of CCCs., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Brain Source Localization (BSL) using Electroencephalogram (EEG) has been a useful noninvasive modality for the diagnosis of epileptogenic zones, study of evoked related potentials, and brain disorders. The inverse solution of BSL is limited by high computational cost and localization error. The performance is additionally limited by head shape assumption and the corresponding harmonics basis function. In this work, an anatomical harmonics basis (Spherical Harmonics (SH), and more particularly Head Harmonics (H 2 )) based BSL is presented. The spatio-temporal four shell head model is formulated in SH and H 2 domain. The anatomical harmonics domain formulation leads to dimensionality reduction and increased contribution of source eigenvalues, resulting in decreased computation and increased accuracy respectively. The performance of spatial subspace based Multiple Signal Classification (MUSIC) and Recursively Applied and Projected (RAP)-MUSIC method is compared with the proposed SH and H 2 counterparts on simulated data. SH and H 2 domain processing effectively resolves the problem of high computational cost without sacrificing the inverse source localization accuracy. The proposed H 2 MUSIC was additionally validated for epileptogenic zone localization on clinical EEG data. The proposed framework offers an effective solution to clinicians in automated and time efficient seizure localization., (© 2022. The Author(s).)