Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76-89], diagnostic odds ratio (DOR) of 496 [198-1,245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94-98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96-0.98] and 0.92 [0.91-0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study.

BACKGROUND: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. METHODS: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. RESULTS: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835-0.910]). CONCLUSIONS: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

Rigorous performance assessment of the algorithms for resolving motor unit action potential superpositions.

It is necessary to decompose the intra-muscular EMG signal to extract motor unit action potential (MUAP) waveforms and firing times. Some algorithms were proposed in the literature to resolve superimposed MUAPs, including Peel-Off (PO), branch and bound (BB), genetic algorithm (GA), and particle swarm optimization (PSO). This study aimed to compare these algorithms in terms of overall accuracy and running time. Two sets of two-to-five MUAP templates (set1: a wide range of energies, and set2: a high degree of similarity) were used. Such templates were time-shifted, and white Gaussian noise was added. A total of 1000 superpositions were simulated for each template and were resolved using PO (also, POI: interpolated PO), BB, GA, and PSO algorithms. The generalized estimating equation was used to identify which method significantly outperformed, while the overall rank product was used for overall ranking. The rankings were PSO, BB, GA, PO, and POI in the first, and BB, PSO, GA, PO, POI in the second set. The overall ranking was BB, PSO, GA, PO, and POI in the entire dataset. Although the BB algorithm is generally fast, there are cases where the BB algorithm is too slow and it is thus not suitable for real-time applications.

A one-year hospital-based prospective COVID-19 open-cohort in the Eastern Mediterranean region: The Khorshid COVID Cohort (KCC) study.

The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising. Thus, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The patient recruitment phase finished at the end of August 2020, and the follow-up continues by the end of August 2021. The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered primary, and impaired pulmonary function and psychotic disorders were considered main secondary outcomes. Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, 490 patients with complete information (39% female; the average age of 57±15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The Cox Proportional-Hazards Model for PCEP indicated the following significant risk factors: Oxygen saturation < 80% (HR = 6.3; [CI 95%: 2.5,15.5]), lymphopenia (HR = 3.5; [CI 95%: 2.2,5.5]), Oxygen saturation 80%-90% (HR = 2.5; [CI 95%: 1.1,5.8]), and thrombocytopenia (HR = 1.6; [CI 95%: 1.1,2.5]). This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points.

Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

Correction: PARS risk charts: A 10-year study of risk assessment for cardiovascular diseases in Eastern Mediterranean Region.

[This corrects the article DOI: 10.1371/journal.pone.0189389.].

PARS risk charts: A 10-year study of risk assessment for cardiovascular diseases in Eastern Mediterranean Region.

This study was designed to develop a risk assessment chart for the clinical management and prevention of the risk of cardiovascular disease (CVD) in Iranian population, which is vital for developing national prevention programs. The Isfahan Cohort Study (ICS) is a population-based prospective study of 6504 Iranian adults ≥35 years old, followed-up for ten years, from 2001 to 2010. Behavioral and cardiometabolic risk factors were examined every five years, while biennial follow-ups for the occurrence of the events was performed by phone calls or by verbal autopsy. Among these participants, 5432 (2784 women, 51.3%) were CVD free at baseline examination and had at least one follow-up. Cox proportional hazard regression was used to predict the risk of ischemic CVD events, including sudden cardiac death due to unstable angina, myocardial infarction, and stroke. The model fit statistics such as area under the receiver-operating characteristic (AUROC), calibration chi-square and the overall bias were used to assess the model performance. We also tested the Framingham model for comparison. Seven hundred and five CVD events occurred during 49452.8 person-years of follow-up. The event probabilities were calculated and presented color-coded on each gender-specific PARS chart. The AUROC and Harrell's C indices were 0.74 (95% CI, 0.72-0.76) and 0.73, respectively. In the calibration, the Nam-D'Agostino χ2 was 10.82 (p = 0.29). The overall bias of the proposed model was 95.60%. PARS model was also internally validated using cross-validation. The Android app and the Web-based risk assessment tool were also developed as to have an impact on public health. In comparison, the refitted and recalibrated Framingham models, estimated the CVD incidence with the overall bias of 149.60% and 128.23% for men, and 222.70% and 176.07% for women, respectively. In conclusion, the PARS risk assessment chart is a simple, accurate, and well-calibrated tool for predicting a 10-year risk of CVD occurrence in Iranian population and can be used in an attempt to develop national guidelines for the CVD management.

Neurophysiological evidence of impaired self-monitoring in schizotypal personality disorder and its reversal by dopaminergic antagonism.

BACKGROUND: Schizotypal personality disorder (SPD) is a schizophrenia-spectrum disorder characterized by odd or bizarre behavior, strange speech, magical thinking, unusual perceptual experiences, and social anhedonia. Schizophrenia proper has been associated with anomalies in dopaminergic neurotransmission and deficits in neurophysiological markers of self-monitoring, such as low amplitude in cognitive event-related brain potentials (ERPs) like the error-related negativity (ERN), and the error positivity (Pe). These components occur after performance errors, rely on adequate fronto-striatal function, and are sensitive to dopaminergic modulation. Here we postulated that analogous to observations in schizophrenia, SPD individuals would show deficits in self-monitoring, as measured by the ERN and the Pe. We also assessed the capacity of dopaminergic antagonists to reverse these postulated deficits. METHODS: We recorded the electroencephalogram (EEG) from 9 SPD individuals and 12 healthy controls in two separate experimental sessions while they performed the Eriksen Flanker Task, a classical task recruiting behavioral monitoring. Participants received a placebo or 1 mg risperidone according to a double-blind randomized design. RESULTS: After placebo, SPD individuals showed slower reaction times to hits, longer correction times following errors and reduced ERN and Pe amplitudes. While risperidone impaired performance and decreased ERN and Pe in the control group, it led to behavioral improvements and ERN amplitude increases in the SPD individuals. CONCLUSIONS: These results indicate that SPD individuals show deficits in self-monitoring analogous to those in schizophrenia. These deficits can be evidenced by neurophysiological measures, suggest a dopaminergic imbalance, and can be reverted by dopaminergic antagonists.

Inhibition of alpha oscillations through serotonin-2A receptor activation underlies the visual effects of ayahuasca in humans.

Ayahuasca is an Amazonian psychotropic plant tea typically obtained from two plants, Banisteriopsis caapi and Psychotria viridis. It contains the psychedelic 5-HT2A and sigma-1 agonist N,N-dimethyltryptamine (DMT) plus ß-carboline alkaloids with monoamine-oxidase (MAO)-inhibiting properties. Although the psychoactive effects of ayahuasca have commonly been attributed solely to agonism at the 5-HT2A receptor, the molecular target of classical psychedelics, this has not been tested experimentally. Here we wished to study the contribution of the 5-HT2A receptor to the neurophysiological and psychological effects of ayahuasca in humans. We measured drug-induced changes in spontaneous brain oscillations and subjective effects in a double-blind randomized placebo-controlled study involving the oral administration of ayahuasca (0.75mg DMT/kg body weight) and the 5-HT2A antagonist ketanserin (40mg). Twelve healthy, experienced psychedelic users (5 females) participated in four experimental sessions in which they received the following drug combinations: placebo+placebo, placebo+ayahuasca, ketanserin+placebo and ketanserin+ayahuasca. Ayahuasca induced EEG power decreases in the delta, theta and alpha frequency bands. Current density in alpha-band oscillations in parietal and occipital cortex was inversely correlated with the intensity of visual imagery induced by ayahuasca. Pretreatment with ketanserin inhibited neurophysiological modifications, reduced the correlation between alpha and visual effects, and attenuated the intensity of the subjective experience. These findings suggest that despite the chemical complexity of ayahuasca, 5-HT2A activation plays a key role in the neurophysiological and visual effects of ayahuasca in humans.

Neurophysiological Evidence of Compensatory Brain Mechanisms in Early-Stage Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic central nervous system disorder characterized by white matter inflammation, demyelination and neurodegeneration. Although cognitive dysfunction is a common manifestation, it may go unnoticed in recently-diagnosed patients. Prior studies suggest MS patients develop compensatory mechanisms potentially involving enhanced performance monitoring. Here we assessed the performance monitoring system in early-stage MS patients using the error-related negativity (ERN), an event-related brain potential (ERP) observed following behavioral errors. Twenty-seven early-stage MS patients and 31 controls were neuropsychologically assessed. Electroencephalography recordings were obtained while participants performed: a) a stop task and b) an auditory oddball task. Behavior and ERP measures were assessed. No differences in performance were found between groups in most neuropsychological tests or in behavior or ERP components in the auditory oddball task. However, the amplitude of the ERN associated with stop errors in the stop task was significantly higher in patients. ERN amplitude correlated positively with scores on the Expanded Disability Status Scale and the Multiple Sclerosis Severity Score, and negatively with the time since last relapse. Patients showed higher neuronal recruitment in tasks involving performance monitoring. Results suggest the development of compensatory brain mechanisms in early-stage MS and reflect the sensitivity of the ERN to detect these changes.

Serotonergic psychedelics temporarily modify information transfer in humans.

BACKGROUND: Psychedelics induce intense modifications in the sensorium, the sense of "self," and the experience of reality. Despite advances in our understanding of the molecular and cellular level mechanisms of these drugs, knowledge of their actions on global brain dynamics is still incomplete. Recent imaging studies have found changes in functional coupling between frontal and parietal brain structures, suggesting a modification in information flow between brain regions during acute effects. METHODS: Here we assessed the psychedelic-induced changes in directionality of information flow during the acute effects of a psychedelic in humans. We measured modifications in connectivity of brain oscillations using transfer entropy, a nonlinear measure of directed functional connectivity based on information theory. Ten healthy male volunteers with prior experience with psychedelics participated in 2 experimental sessions. They received a placebo or a dose of ayahuasca, a psychedelic preparation containing the serotonergic 5-HT2A agonist N,N-dimethyltryptamine. RESULTS: The analysis showed significant changes in the coupling of brain oscillations between anterior and posterior recording sites. Transfer entropy analysis showed that frontal sources decreased their influence over central, parietal, and occipital sites. Conversely, sources in posterior locations increased their influence over signals measured at anterior locations. Exploratory correlations found that anterior-to-posterior transfer entropy decreases were correlated with the intensity of subjective effects, while the imbalance between anterior-to-posterior and posterior-to-anterior transfer entropy correlated with the degree of incapacitation experienced. CONCLUSIONS: These results suggest that psychedelics induce a temporary disruption of neural hierarchies by reducing top-down control and increasing bottom-up information transfer in the human brain.

Apathy in Parkinson's disease: neurophysiological evidence of impaired incentive processing.

Apathy is one of the most common and debilitating nonmotor manifestations of Parkinson's disease (PD) and is characterized by diminished motivation, decreased goal-directed behavior, and flattened affect. Despite its high prevalence, its underlying mechanisms are still poorly understood, having been associated with executive dysfunction, and impaired emotional processing and decision making. Apathy, as a syndrome, has recently been associated with reduced activation in the ventral striatum, suggesting that early- to middle-stage Parkinson's disease patients with this manifestation may have a compromised mesocorticolimbic dopaminergic pathway and impaired incentive processing. To test this hypothesis, we measured the amplitude of the feedback-related negativity, an event-related brain potential associated with performance outcome valence, following monetary gains and losses in human PD patients (12 women) and healthy controls (6 women) performing a gambling task. Early- to middle-stage PD patients presenting clinically meaningful symptoms of apathy were compared with nonapathetic PD patients and healthy controls. Patients with cognitive impairment, depression, and other psychiatric disturbances were excluded. Results showed that the amplitude of the feedback-related negativity, measured as the difference wave in the event-related brain potential between gains and losses, was significantly reduced in PD patients with apathy compared with nonapathetic patients and healthy controls. These findings indicate impaired incentive processing and suggest a compromised mesocorticolimbic pathway in cognitively intact PD patients with apathy.