The multimodality cell segmentation challenge: toward universal solutions.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

A randomised trial of anti-GM-CSF otilimab in severe COVID-19 pneumonia (OSCAR).

BACKGROUND: Granulocyte-macrophage colony-stimulating factor (GM-CSF) and dysregulated myeloid cell responses are implicated in the pathophysiology and severity of COVID-19. METHODS: In this randomised, sequential, multicentre, placebo-controlled, double-blind study, adults aged 18-79 years (Part 1) or ≥70 years (Part 2) with severe COVID-19, respiratory failure and systemic inflammation (elevated C-reactive protein/ferritin) received a single intravenous infusion of otilimab 90 mg (human anti-GM-CSF monoclonal antibody) plus standard care (NCT04376684). The primary outcome was the proportion of patients alive and free of respiratory failure at Day 28. RESULTS: In Part 1 (n=806 randomised 1:1 otilimab:placebo), 71% of otilimab-treated patients were alive and free of respiratory failure at Day 28 versus 67% who received placebo; the model-adjusted difference of 5.3% was not statistically significant (95% CI -0.8-11.4%, p=0.09). A nominally significant model-adjusted difference of 19.1% (95% CI 5.2-33.1%, p=0.009) was observed in the predefined 70-79 years subgroup, but this was not confirmed in Part 2 (n=350 randomised) where the model-adjusted difference was 0.9% (95% CI -9.3-11.2%, p=0.86). Compared with placebo, otilimab resulted in lower serum concentrations of key inflammatory markers, including the putative pharmacodynamic biomarker CC chemokine ligand 17, indicative of GM-CSF pathway blockade. Adverse events were comparable between groups and consistent with severe COVID-19. CONCLUSIONS: There was no significant difference in the proportion of patients alive and free of respiratory failure at Day 28. However, despite the lack of clinical benefit, a reduction in inflammatory markers was observed with otilimab, in addition to an acceptable safety profile.

Anti-GM-CSF otilimab versus sarilumab or placebo in patients with rheumatoid arthritis and inadequate response to targeted therapies: a phase III randomised trial (contRAst 3).

OBJECTIVES: To investigate the efficacy and safety of otilimab, an anti-granulocyte-macrophage colony-stimulating factor antibody, in patients with active rheumatoid arthritis and an inadequate response to conventional synthetic (cs) and biologic disease-modifying antirheumatic drugs (DMARDs) and/or Janus kinase inhibitors. METHODS: ContRAst 3 was a 24-week, phase III, multicentre, randomised controlled trial. Patients received subcutaneous otilimab (90/150 mg once weekly), subcutaneous sarilumab (200 mg every 2 weeks) or placebo for 12 weeks, in addition to csDMARDs. Patients receiving placebo were switched to active interventions at week 12 and treatment continued to week 24. The primary end point was the proportion of patients achieving an American College of Rheumatology ≥20% response (ACR20) at week 12. RESULTS: Overall, 549 patients received treatment. At week 12, there was no significant difference in the proportion of ACR20 responders with otilimab 90 mg and 150 mg versus placebo (45% (p=0.2868) and 51% (p=0.0596) vs 38%, respectively). There were no significant differences in Clinical Disease Activity Index, Health Assessment Questionnaire-Disability Index, pain Visual Analogue Scale or Functional Assessment of Chronic Illness Therapy-Fatigue scores with otilimab versus placebo at week 12. Sarilumab demonstrated superiority to otilimab in ACR20 response and secondary end points. The incidence of adverse or serious adverse events was similar across treatment groups. CONCLUSIONS: Otilimab demonstrated an acceptable safety profile but failed to achieve the primary end point of ACR20 and improve secondary end points versus placebo or demonstrate non-inferiority to sarilumab in this patient population. TRIAL REGISTRATION NUMBER: NCT04134728.

Anti-GM-CSF otilimab versus tofacitinib or placebo in patients with active rheumatoid arthritis and an inadequate response to conventional or biologic DMARDs: two phase 3 randomised trials (contRAst 1 and contRAst 2).

OBJECTIVES: To investigate the efficacy and safety of otilimab, an antigranulocyte-macrophage colony-stimulating factor antibody, in patients with active rheumatoid arthritis. METHODS: Two phase 3, double-blind randomised controlled trials including patients with inadequate responses to methotrexate (contRAst 1) or conventional synthetic/biologic disease-modifying antirheumatic drugs (cs/bDMARDs; contRAst 2). Patients received background csDMARDs. Through a testing hierarchy, subcutaneous otilimab (90/150 mg once weekly) was compared with placebo for week 12 endpoints (after which, patients receiving placebo switched to active interventions) or oral tofacitinib (5 mg two times per day) for week 24 endpoints. PRIMARY ENDPOINT: proportion of patients achieving an American College of Rheumatology response ≥20% (ACR20) at week 12. RESULTS: The intention-to-treat populations comprised 1537 (contRAst 1) and 1625 (contRAst 2) patients. PRIMARY ENDPOINT: proportions of ACR20 responders were statistically significantly greater with otilimab 90 mg and 150 mg vs placebo in contRAst 1 (54.7% (p=0.0023) and 50.9% (p=0.0362) vs 41.7%) and contRAst 2 (54.9% (p<0.0001) and 54.5% (p<0.0001) vs 32.5%). Secondary endpoints: in both trials, compared with placebo, otilimab increased the proportion of Clinical Disease Activity Index (CDAI) low disease activity (LDA) responders (not significant for otilimab 150 mg in contRAst 1), and reduced Health Assessment Questionnaire-Disability Index (HAQ-DI) scores. Benefits with tofacitinib were consistently greater than with otilimab across multiple endpoints. Safety outcomes were similar across treatment groups. CONCLUSIONS: Although otilimab demonstrated superiority to placebo in ACR20, CDAI LDA and HAQ-DI, improved symptoms, and had an acceptable safety profile, it was inferior to tofacitinib. TRIAL REGISTRATION NUMBERS: NCT03980483, NCT03970837.

ARCANE-ROG: Algorithm for reconstruction of cancer evolution from single-cell data using robust graph learning.

Tumor heterogeneity, marked by the presence of divergent clonal subpopulations of tumor cells, impedes the treatment response in cancer patients. Single-cell sequencing technology provides substantial prospects to gain an in-depth understanding of the cellular phenotypic variability driving tumor progression. A comprehensive insight into the intra-tumor heterogeneity may further assist in dealing with the treatment-resistant clones in cancer patients, thereby improving their overall survival. However, this task is hampered due to the challenges associated with the single-cell data, such as false positives, false negatives and missing bases, and the increase in their size. As a result, the computational cost of the existing methods increases, thereby limiting their usage. In this work, we propose a robust graph learning-based method, ARCANE-ROG (Algorithm for Reconstruction of CANcer Evolution via RObust Graph learning), for inferring clonal evolution from single-cell datasets. The first step of the proposed method is a joint framework of denoising with data imputation for the noisy and incomplete matrix while simultaneously learning an adjacency graph. Both the operations in the joint framework boost each other such that the overall performance of the denoising algorithm is improved. In the second step, an optimal number of clusters are identified via the Leiden method. In the last step, clonal evolution trees are inferred via a minimum spanning tree algorithm. The method has been benchmarked against a state-of-the-art method, RobustClone, using simulated datasets of varying sizes and five real datasets. The performance of our proposed method is found to be significantly superior (p-value < 0.05) in terms of reconstruction error, False Positive to False Negative (FPFN) ratio, tree distance error and V-measure compared to the other method. Overall, the proposed method is an improvement over the existing methods as it enhances cluster assignment and inference on clonal hierarchies.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

Heart rate variability as a marker of cardiovascular dysautonomia in post-COVID-19 syndrome using artificial intelligence.

INTRODUCTION: Cardiovascular dysautonomia comprising postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH) is one of the presentations in COVID-19 recovered subjects. We aim to determine the prevalence of cardiovascular dysautonomia in post COVID-19 patients and to evaluate an Artificial Intelligence (AI) model to identify time domain heart rate variability (HRV) measures most suitable for short term ECG in these subjects. METHODS: This observational study enrolled 92 recently COVID-19 recovered subjects who underwent measurement of heart rate and blood pressure response to standing up from supine position and a 12-lead ECG recording for 60 s period during supine paced breathing. Using feature extraction, ECG features including those of HRV (RMSSD and SDNN) were obtained. An AI model was constructed with ShAP AI interpretability to determine time domain HRV features representing post COVID-19 recovered state. In addition, 120 healthy volunteers were enrolled as controls. RESULTS: Cardiovascular dysautonomia was present in 15.21% (OH:13.04%; POTS:2.17%). Patients with OH had significantly lower HRV and higher inflammatory markers. HRV (RMSSD) was significantly lower in post COVID-19 patients compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms; P = 0.01) with inverse correlation between HRV and inflammatory markers. Multiple perceptron was best performing AI model with HRV(RMSSD) being the top time domain HRV feature distinguishing between COVID-19 recovered patients and healthy controls. CONCLUSION: Present study showed that cardiovascular dysautonomia is common in COVID-19 recovered subjects with a significantly lower HRV compared to healthy controls. The AI model was able to distinguish between COVID-19 recovered patients and healthy controls.

Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations.

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

Left ventricular global longitudinal strain in COVID-19 recovered patients.

OBJECTIVES: Myocardial injury during active coronavirus disease-2019 (COVID-19) infection is well described; however, its persistence during recovery is unclear. We assessed left ventricle (LV) global longitudinal strain (GLS) using speckle tracking echocardiography (STE) in COVID-19 recovered patients and its correlation with various parameters. METHODS: A total of 134 subjects within 30-45 days post recovery from COVID-19 infection and normal LV ejection fraction were enrolled. Routine blood investigations, inflammatory markers (on admission) and comprehensive echocardiography including STE were done for all. RESULTS: Of the 134 subjects, 121 (90.3%) were symptomatic during COVID-19 illness and were categorized as mild: 61 (45.5%), moderate: 50 (37.3%) and severe: 10 (7.5%) COVID-19 illness. Asymptomatic COVID-19 infection was reported in 13 (9.7%) patients. Subclinical LV and right ventricle (RV) dysfunction were seen in 40 (29.9%) and 14 (10.5%) patients, respectively. Impaired LVGLS was reported in 1 (7.7%), 8 (13.1%), 22 (44%) and 9 (90%) subjects with asymptomatic, mild, moderate and severe disease, respectively. LVGLS was significantly lower in patients recovered from severe illness(mild: -21 ± 3.4%; moderate: -18.1 ± 6.9%; severe: -15.5 ± 3.1%; p < 0.0001). Subjects with reduced LVGLS had significantly higher interleukin-6 (p < 0.0001), C-reactive protein (p = 0.001), lactate dehydrogenase (p = 0.009), serum ferritin (p = 0.03), and troponin (p = 0.01) levels during index admission. CONCLUSIONS: Subclinical LV dysfunction was seen in nearly a third of recovered COVID-19 patients while 10.5% had RV dysfunction. Our study suggests a need for closer follow-up among COVID-19 recovered subjects to elucidate long-term cardiovascular outcomes.

Shallow water channel estimation with energy efficient transmitted signal design.

This paper proposes channel estimation using energy efficient transmission of signal dictionaries for shallow water acoustic communications. Specifically, the multi-columned structure of the channel delay spread is exploited to design partially sampled dictionary in a two-dimensional (2-D) frequency representation of the channel. The key contribution of this work is to achieve considerable energy saving in the transmission of complex exponential signals, designed specifically for real-time shallow water channel estimation at the receiver. This is accomplished by harnessing 2-D frequency localization with compressive transmission and modified-compressive sensing with prior information to exploit the sparse structure of the rapidly fluctuating shallow water acoustic channel in real time. The proposed technique reduces demands on transmitted signal energy by harnessing the reconstruction ability of sparse sensing while retaining key non-sparse channel elements that represent important multipath phenomena. Numerical evidence based on experimental channel estimates demonstrates the efficacy of the proposed work.

Suspended Carbon Nanotubes for Humidity Sensing.

A room temperature microfabrication technique using SU8, an epoxy-based highly functional photoresist as a sacrificial layer, is developed to obtain suspended aligned carbon nanotube beams. The humidity-sensing characteristics of aligned suspended single-walled carbon nanotube films are studied. A comparative study between suspended and non-suspended architectures is done by recording the resistance change in the nanotubes under humidity. For the tests, the humidity was varied from 15% to 98% RH. A comparative study between suspended and non-suspended devices shows that the response and recovery times of the suspended devices was found to be almost 3 times shorter than the non-suspended devices. The suspended devices also showed minimal hysteresis even after 10 humidity cycles, and also exhibit enhanced sensitivity. Repeatability tests were performed by subjecting the sensors to continuous humidification cycles. All tests reported here have been performed using pristine non-functionalized nanotubes.

Population pharmacokinetic analysis of lenvatinib in healthy subjects and patients with cancer.

AIMS: Lenvatinib was recently approved for the treatment of radioiodine-refractory differentiated thyroid cancer (RR-DTC). Here, we characterized the pharmacokinetic (PK) profile of lenvatinib and identified intrinsic and extrinsic factors that explain interindividual PK variability in humans. METHODS: This population PK analysis used pooled data from 15 clinical studies, including eight phase 1 studies in healthy subjects, four phase 1 studies in patients with solid tumours, two phase 2 studies in patients with thyroid cancer and one phase 3 study in patients with RR-DTC. RESULTS: The final pooled dataset included data from 779 subjects receiving 3.2-32 mg oral lenvatinib, mainly once daily as tablets or capsules. Lenvatinib PK was best described by a three-compartment model with linear elimination. Lenvatinib absorption was best described by simultaneous first- and zero-order absorption. The population mean value for lenvatinib apparent clearance (CL/F) was 6.56 l h(-1) [percent coefficient of variation (%CV) 25.5], and was independent of dose and time. The relative bioavailability of lenvatinib in capsule form was 90% vs. tablets (%CV 30.2). The final PK model included significant but marginal effects of body weight (2.8% of CL/F variation), liver-function markers [alkaline phosphatase (-11.7%) and albumin (-6.3%)] and concomitant cytochrome P450 3A4 inducers (+30%) and inhibitors (-7.8%) on lenvatinib CL/F. Lenvatinib PK was unaffected by pH-elevating agents, dose, age, sex, race, alanine aminotransferase, aspartate aminotransferase or bilirubin levels, or renal function. CONCLUSIONS: The significant effects of several covariates on lenvatinib PK variability were small in magnitude, and therefore were not considered clinically relevant, or to warrant any dose adjustment.

Shallow water acoustic channel estimation using two-dimensional frequency characterization.

Shallow water acoustic channel estimation techniques are presented at the intersection of time, frequency, and sparsity. Specifically, a mathematical framework is introduced that translates the problem of channel estimation to non-uniform sparse channel recovery in two-dimensional frequency domain. This representation facilitates disambiguation of slowly varying channel components against high-energy transients, which occupy different frequency ranges and also exhibit significantly different sparsity along their local distribution. This useful feature is exploited to perform non-uniform sampling across different frequency ranges, with compressive sampling across higher Doppler frequencies and close to full-rate sampling at lower Doppler frequencies, to recover both slowly varying and rapidly fluctuating channel components at high precision. Extensive numerical experiments are performed to measure relative performance of the proposed channel estimation technique using non-uniform compressive sampling against traditional compressive sampling techniques as well as sparsity-constrained least squares across a range of observation window lengths, ambient noise levels, and sampling ratios. Numerical experiments are based on channel estimates from the SPACE08 experiment as well as on a recently developed channel simulator tested against several field trials.

Subgroup analysis of Japanese patients in a phase 3 study of lenvatinib in radioiodine-refractory differentiated thyroid cancer.

Lenvatinib significantly prolonged progression-free survival (PFS) versus placebo in patients with radioiodine-refractory differentiated thyroid cancer (RR-DTC) in the phase 3 Study of (E7080) Lenvatinib in Differentiated Cancer of the Thyroid (SELECT) trial. This subanalysis evaluated the efficacy and safety of lenvatinib in Japanese patients who participated in SELECT. Outcomes for Japanese patients (lenvatinib, n = 30; placebo, n = 10) were assessed in relationship to the SELECT population (lenvatinib, n = 261; placebo, n = 131). The primary endpoint was PFS; secondary endpoints included overall survival, overall response rate, and safety. Lenvatinib PFS benefit was shown in Japanese patients (median PFS: lenvatinib, 16.5 months; placebo, 3.7 months), although significance was not reached, presumably due to sample size (hazard ratio, 0.39; 95% confidence interval, 0.10-1.57; P = 0.067). Overall response rates were 63.3% and 0% for lenvatinib and placebo, respectively. No significant difference was found in overall survival. The lenvatinib safety profile was similar between the Japanese and overall SELECT population, except for higher incidences of hypertension (any grade: Japanese, 87%; overall, 68%; grade ≥3: Japanese, 80%; overall, 42%), palmar-plantar erythrodysesthesia syndrome (any grade: Japanese, 70%; overall, 32%; grade ≥3: Japanese, 3%; overall, 3%), and proteinuria (any grade: Japanese, 63%; overall, 31%; grade ≥3: Japanese, 20%; overall, 10%). Japanese patients had more dose reductions (Japanese, 90%; overall, 67.8%), but fewer discontinuations due to adverse events (Japanese, 3.3%; overall, 14.2%). There was no difference in lenvatinib exposure between the Japanese and overall SELECT populations after adjusting for body weight. In Japanese patients with radioiodine-refractory differentiated thyroid cancer, lenvatinib showed similar clinical outcomes to the overall SELECT population. Some differences in adverse event frequencies and dose modifications were observed. Clinical trial registration no.: NCT01321554.

Pharmacokinetics and excretion of (14)C-lenvatinib in patients with advanced solid tumors or lymphomas.

Lenvatinib is an orally available multi-targeted tyrosine kinase inhibitor with anti-angiogenic and antitumor activity. To get more insight into the disposition of lenvatinib, a mass balance study was performed in patients with advanced solid tumors. A single oral 24 mg (100 µCi) dose of (14)C-lenvatinib was administered to six patients, followed by collection of blood, plasma, urine and feces for 7 to 10 days. The collected material was analyzed for total radioactivity, unchanged lenvatinib and selected metabolites. The safety and antitumor effect of a daily oral dose of 24 mg non-labeled lenvatinib were assessed in the extension phase of the study. Peak plasma concentrations of lenvatinib and total radioactivity were reached 1.6 and 1.4 h after administration, respectively, and their terminal phase half-lifes were 34.5 and 17.8 h, respectively. Unchanged lenvatinib systemic exposure accounted for 60 % of the total radioactivity in plasma. Peak concentrations of the analyzed metabolite were over 700-fold lower than the peak plasma concentration of lenvatinib. Ten days after the initial dose, the geometric mean (± CV) recovery of administered dose was 89 % ±10 %, with 64 % ±11 % recovered in feces and 25 % ±18 % in urine. Unchanged lenvatinib in urine and feces accounted for 2.5 % ±68 % of the administered dose, indicating a major role of metabolism in the elimination of lenvatinib. In conclusion, lenvatinib is rapidly absorbed and extensively metabolized, with subsequent excretion in urine and, more predominantly, in feces. Additionally, lenvatinib showed acceptable safety and preliminary antitumor activity.

On the hypercomplex numbers and normed division algebras in all dimensions: A unified multiplication.

Mathematics is the foundational discipline for all sciences, engineering, and technology, and the pursuit of normed division algebras in all finite dimensions represents a paramount mathematical objective. In the quest for a real three-dimensional, normed, associative division algebra, Hamilton discovered quaternions, constituting a non-commutative division algebra of quadruples. Subsequent investigations revealed the existence of only four division algebras over reals, each with dimensions 1, 2, 4, and 8. This study transcends such limitations by introducing generalized hypercomplex numbers extending across all dimensions, serving as extensions of traditional complex numbers. The space formed by these numbers constitutes a non-distributive normed division algebra extendable to all finite dimensions. The derivation of these extensions involves the definitions of two new π-periodic functions and a unified multiplication operation, designated as spherical multiplication, that is fully compatible with the existing multiplication structures. Importantly, these new hypercomplex numbers and their associated algebras are compatible with the existing real and complex number systems, ensuring continuity across dimensionalities. Most importantly, like the addition operation, the proposed multiplication in all dimensions forms an Abelian group while simultaneously preserving the norm. In summary, this study presents a comprehensive generalization of complex numbers and the Euler identity in higher dimensions, shedding light on the geometric properties of vectors within these extended spaces. Finally, we elucidate the practical applications of the proposed methodology as a viable alternative for expressing a quantum state through the multiplication of specified quantum states, thereby offering a potential complement to the established superposition paradigm. Additionally, we explore its utility in point cloud image processing.

Machine learning based classification of presence utilizing psychophysiological signals in immersive virtual environments.

In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, including electroencephalography and electrodermal activity. An experiment has been undertaken whereby participants (N = 22) were each exposed to three different levels of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodiment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector Machine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Regression, and Multiple Layer Perceptron. Results demonstrated that the Multiple Layer Perceptron model obtained the highest macro average accuracy of 93 ± 0.03 % . Posthoc analysis revealed that relative band power, which is expressed as the ratio of power in a specific frequency band to the total baseline power, in both the frontal and parietal regions, including beta over theta and alpha ratio, and differential entropy were most significant in detecting different levels of presence.

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization.

The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.

Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia.

AIMS: Eribulin mesilate is an inhibitor of microtubule dynamics that is approved for the treatment of late-stage metastatic breast cancer. Neutropenia is one of the major dose-limiting adverse effects of eribulin. The objective of this analysis was to develop a population pharmacokinetic-pharmacodynamic model for eribulin-associated neutropenia. METHODS: A combined data set of 12 phase I, II and III studies for eribulin mesilate was analysed. The population pharmacokinetics of eribulin was described using a previously developed model. The relationship between eribulin pharmacokinetic and neutropenia was described using a semi-physiological lifespan model for haematological toxicity. Patient characteristics predictive of increased sensitivity to develop neutropenia were evaluated using a simulation framework. RESULTS: Absolute neutrophil counts were available from 1579 patients. In the final covariate model, the baseline neutrophil count (ANC0) was estimated to be 4.03 × 10(9) neutrophils l(-1) [relative standard error (RSE) 1.2%], with interindividual variability (IIV, 37.3 coefficient of variation % [CV%]). The mean transition time was estimated to be 109 h (RSE 1.8%, IIV 13.9CV%), the feedback constant (γ) was estimated to be 0.216 (RSE 1.4%, IIV 12.2CV%), and the linear drug effect coefficient (SLOPE) was estimated to be 0.0451 µg l(-1) (RSE 3.2%, IIV 54CV%). Albumin, aspartate transaminase and receival of granulocyte colony-stimulating factor (G-CSF) were identified as significant covariates on SLOPE, and albumin, bilirubin, G-CSF, alkaline phosphatase and lactate dehydrogenase were identified as significant covariates on mean transition time. CONCLUSIONS: The developed model can be applied to investigate optimal treatment strategies quantitatively across different patient groups with respect to neutropenia. Albumin was identified as the most clinically important covariate predictive of interindividual variability in the neutropenia time course.

BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.

Identification of the genomic features responsible for the progression of Multiple Myeloma (MM) cancer from its precancerous stage MGUS can improve the understanding of the disease pathogenesis and, in devising suitable preventive and treatment measures. We have designed an innovative AI-based model, namely, the Bio-inspired Deep Learning architecture for the identification of altered Signaling Pathways (BDL-SP) to discover pivotal genomic biomarkers that can potentially distinguish MM from MGUS. The proposed BDL-SP model comprehends gene-gene interactions using the PPI network and analyzes genomic features using a deep learning (DL) architecture to identify significantly altered genes and signaling pathways in MM and MGUS. For this, whole exome sequencing data of 1174 MM and 61 MGUS patients were analyzed. In the quantitative benchmarking with the other popular machine learning models, BDL-SP performed almost similar to the two other best performing predictive ML models of Random Forest and CatBoost. However, an extensive post-hoc explainability analysis, capturing the application specific nuances, clearly established the significance of the BDL-SP model. This analysis revealed that BDL-SP identified a maximum number of previously reported oncogenes, tumor-suppressor genes, and actionable genes of high relevance in MM as the top significantly altered genes. Further, the post-hoc analysis revealed a significant contribution of the total number of single nucleotide variants (SNVs) and genomic features associated with synonymous SNVs in disease stage classification. Finally, the pathway enrichment analysis of the top significantly altered genes showed that many cancer pathways are selectively and significantly dysregulated in MM compared to its precursor stage of MGUS, while a few that lost their significance with disease progression from MGUS to MM were actually related to the other disease types. These observations may pave the way for appropriate therapeutic interventions to halt the progression to overt MM in the future.