Clustering of clinical and echocardiographic phenotypes of covid-19 patients.

We sought to divide COVID-19 patients into distinct phenotypical subgroups using echocardiography and clinical markers to elucidate the pathogenesis of the disease and its heterogeneous cardiac involvement. A total of 506 consecutive patients hospitalized with COVID-19 infection underwent complete evaluation, including echocardiography, at admission. A k-prototypes algorithm applied to patients' clinical and imaging data at admission partitioned the patients into four phenotypical clusters: Clusters 0 and 1 were younger and healthier, 2 and 3 were older with worse cardiac indexes, and clusters 1 and 3 had a stronger inflammatory response. The clusters manifested very distinct survival patterns (C-index for the Cox proportional hazard model 0.77), with survival best for cluster 0, intermediate for 1-2 and worst for 3. Interestingly, cluster 1 showed a harsher disease course than cluster 2 but with similar survival. Clusters obtained with echocardiography were more predictive of mortality than clusters obtained without echocardiography. Additionally, several echocardiography variables (E' lat, E' sept, E/e average) showed high discriminative power among the clusters. The results suggested that older infected males have a higher chance to deteriorate than older infected females. In conclusion, COVID-19 manifests differently for distinctive clusters of patients. These clusters reflect different disease manifestations and prognoses. Although including echocardiography improved the predictive power, its marginal contribution over clustering using clinical parameters only does not justify the burden of echocardiography data collection.

Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Reliability of online dental final exams in the pre and post COVID-19 era: A comparative study.

Amidst the fourth COVID-19 wave in Viet Nam, national lockdowns necessitated the closure of numerous dental schools. To assess DDS (Doctor of Dental Surgery) graduation exams, this study analyzed their 2021 implementation in comparison to onsite exams conducted in 2020 and 2022 at the Faculty of Odonto-Stomatology, University of Medicine and Pharmacy at Ho Chi Minh City, Viet Nam (FOS-UMPH). The final online examination comprises two main sessions: a synchronous online examination using FOS-UMPH e-Learning for theories (consisting of 200 MCQs and 3 written tests with 3 clinical situations needed be solved) and a synchronous online examination using Microsoft Teams for practicum (comprising of 12 online OSCE stations). The final grades were evaluated using the same metrics in face-to-face final examinations in 2022 and 2020. A total of 114, 112 and 95 students were recruited for the first-time exams in 2020, 2021 and 2022, respectively. In order to analyze the reliability, histogram and k-mean clustering were employed. The histograms from 2020, 2021 and 2022 showed a striking similarity. However, fewer students failed in 2021 and 2022 (13% and 12.6%, respectively) compared to 2020 (28%), with clinical problem-solving part grades (belonging to theory session) being notably higher in 2021 and 2022. Intriguingly, the MCQ Score results showed the identical patterns. The courses of orthodontics, dental public health, and pediatrics subjects (in the group of prevention and development dentistry) stood out for their exceptional accuracy across both sessions. After examining data gathered over three years, we identified three distinct clusters: the first comprised of scattered average and low scores, the second characterized by high scores but unstable and scattered and the third cluster boasting consistently high and centered scores. According to our study, online and onsite traditional graduation exam results are relatively equivalent, but additional measures are necessary to standardize the final examination and adapt to the new normal trend in dental education.

Competition for popularity and interventions on a Chinese microblogging site.

Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this study, we investigate the public attention dynamics through the Hot Search List (HSL) of the Chinese microblog Sina Weibo, where trending hashtags are ranked based on a multi-dimensional search volume index. We characterize the rank dynamics by the time spent by hashtags on the list, the time of the day they appear there, the rank diversity, and by the ranking trajectories. We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning clustering algorithm. By analyzing patterns of ranking dynamics using various measures, we identify anomalies that are likely to result from the platform provider's intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. We propose a simple model of ranking that explains the mechanism of this anchoring effect. We found an over-representation of hashtags related to international politics at 3 out of 4 anchoring ranks on the HSL, indicating possible manipulations of public opinion.

Severity outcomes associated with SARS-CoV-2 XBB variants, an observational analysis.

The rapidity with which SARS-CoV-2 XBB variants rose to predominance has been alarming. We used a large cohort of patients diagnosed with Omicron infections between September 2022 and mid-February 2023 to evaluate the likelihood of admission or need for supplemental oxygen in patients infected with XBB variants. Our data showed no significant association between XBB or XBB.1.5 infections and admissions. Older age groups, lack of vaccination, immunosuppression and underlying heart, kidney, and lung disease showed significant associations with hospitalization.

An investigation into epidemiological situations of COVID-19 with fuzzy K-means and K-prototype clustering methods.

The ten countries with the highest population during the pandemic were analyzed for clustering based on the quantitative numbers of COVID-19 and policy plans. The Fuzzy K-Means (FKM) and K-prototype algorithms were used for clustering, and various performance indices such as Partition Coefficient (PC), Partition Entropy (PE), Xie-Beni (XB), and Silhouette Fuzzy (SIL.F) were used for evaluating the clusters. The analysis included variables such as confirmed cases, tests, vaccines, school and workplace closures, event cancellations, gathering restrictions, transport closures, stay-at-home restrictions, international movement restrictions, testing policies, facial coverings, and vaccination policy statuses. PC, PE, XB, and SIL.F indices were used to analyze the performance indices of the clusters. The Elbow method was used to analyze the performance evaluations for the K-prototype. The K-prototype algorithm's performance evaluations were analyzed using the Elbow method, and the optimum number of clusters for both methods was found to be two. The first cluster included Brazil, Mexico, Nigeria, Bangladesh, US, Indonesia, Russia, and Pakistan, while the second cluster comprised India and China. The analysis also examined the relationship between population and confirmed tests and vaccines, and standardization was made for the country with the largest population for significant correlations. The results showed that the FKM method was superior to the K-prototype method in terms of clustering. In conclusion, it is crucial to accurately evaluate COVID-19 data for countries and develop appropriate policies. The clustering analysis using the FKM and K-prototype algorithms provides valuable insights into identifying groups of countries with similar COVID-19 data and policy plans.

Predicting the negative conversion time of nonsevere COVID-19 patients using machine learning methods.

Based on the patient's clinical characteristics and laboratory indicators, different machine-learning methods were used to develop models for predicting the negative conversion time of nonsevere coronavirus disease 2019 (COVID-19) patients. A retrospective analysis was performed on 376 nonsevere COVID-19 patients admitted to Wuxi Fifth People's Hospital from May 2, 2022, to May 14, 2022. The patients were divided into training set (n = 309) and test set (n = 67). The clinical features and laboratory parameters of the patients were collected. In the training set, the least absolute shrinkage and selection operator (LASSO) was used to select predictive features and train six machine learning models: multiple linear regression (MLR), K-Nearest Neighbors Regression (KNNR), random forest regression (RFR), support vector machine regression (SVR), XGBoost regression (XGBR), and multilayer perceptron regression (MLPR). Seven best predictive features selected by LASSO included: age, gender, vaccination status, IgG, lymphocyte ratio, monocyte ratio, and lymphocyte count. The predictive performance of the models in the test set was MLPR > SVR > MLR > KNNR > XGBR > RFR, and MLPR had the strongest generalization performance, which is significantly better than SVR and MLR. In the MLPR model, vaccination status, IgG, lymphocyte count, and lymphocyte ratio were protective factors for negative conversion time; male gender, age, and monocyte ratio were risk factors. The top three features with the highest weights were vaccination status, gender, and IgG. Machine learning methods (especially MLPR) can effectively predict the negative conversion time of non-severe COVID-19 patients. It can help to rationally allocate limited medical resources and prevent disease transmission, especially during the Omicron pandemic.

Population Segmentation for COVID-19 Vaccine Outreach: A Clustering Analysis and Implementation in Missouri.

OBJECTIVES: The purpose of this work was to segment the Missouri population into unique groups related to COVID-19 vaccine acceptance using data science and behavioral science methods to develop tailored vaccine outreach strategies. METHODS: Cluster analysis techniques were applied to a large data set that aggregated vaccination data with behavioral and demographic data from the American Community Survey and Deloitte's HealthPrism™ data set. Outreach recommendations were developed for each cluster, specific to each group's practical and motivational barriers to vaccination. RESULTS: Following selection procedures, 10 clusters-or segments-of census tracts across Missouri were identified on the basis of k -means clustering analysis of 18 different variables. Each cluster exhibited unique geographic, demographic, socioeconomic, and behavioral patterns, and outreach strategies were developed on the basis of each cluster's practical and motivational barriers. DISCUSSION: The segmentation analysis served as the foundation for "working groups" comprising the 115 local public health agencies (LPHAs) across the state. LPHAs with similar community segments in their service area were grouped together to discuss their communities' specific challenges, share lessons learned, and brainstorm new approaches. The working groups provided a novel way for public health to organize and collaborate across the state. Widening the aperture beyond Missouri, population segmentation via cluster analysis is a promising approach for public health practitioners interested in developing a richer understanding of the types of populations they serve. By pairing segmentation with behavioral science, practitioners can develop outreach programs and communications campaigns that are personalized to the specific behavioral barriers and needs of the population in focus. While our work focused on COVID-19, this approach has broad applicability to enhance the way public health practitioners understand the populations they serve to deliver more tailored services.

MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks.

MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package's phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering.

Social mixing and risk exposures for SARS-CoV-2 infections in elderly persons.

AIMS OF THE STUDY: During the transitional phase between the two pandemic waves of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infection rates were temporarily rising among younger persons only. However, following a temporal delay infections started to expand to older age groups. A comprehensive understanding of such transmission dynamics will be key for managing the pandemic in the time to come and to anticipate future developments. The present study thus extends the scope of previous SARS-CoV-2-related research in Switzerland by contributing to deeper insight into the potential impact of “social mixing” of different age groups on the spread of SARS-CoV-2 infections. METHODS: The present study examined persons aged 65 years and older with respect to possible SARS-CoV-2 exposure risks using longitudinal panel data from the Swiss COVID-19 Social Monitor. The study used data from two assessments (survey “May” and survey “August”). Survey “May” took place shortly after the release of the lockdown in Switzerland. Survey “August” was conducted in mid-August. To identify at-risk elderly persons, we conducted a combined factor/k-means clustering analysis of the survey data assessed in August in order to examine different patterns of adherence to recommended preventive measures. RESULTS: In summary, 270 (survey “May”) and 256 (survey “August”) persons aged 65 years and older were analysed for the present study. Adherence to established preventive measures was similar across the two surveys, whereas adherence pertaining to social contacts decreased substantially from survey “May” to survey “August”. The combined factor/k-means clustering analysis to identify at-risk elderly individuals yielded four distinct groups with regard to different patterns of adherence to recommended preventive measures: a larger group of individuals with many social contacts but high self-reported adherence to preventive measures (n = 86); a small group with many social contacts and overall lower adherence (n = 26); a group with comparatively few contacts and few social activities (n = 66); and a group which differed from the latter through fewer contacts but more social activities (n = 78). Sociodemographic characteristics and risk perception with regard to SARS-CoV-2 infections among the four groups did not differ in a relevant way across the four groups. CONCLUSIONS: Although many elderly persons continued to follow the recommended preventive measures during the transitional phase between the two pandemic waves, social mixing with younger persons constitutes a way for transmission of infections across age groups. Pandemic containment among all age groups thus remains essential to protect vulnerable populations, including the elderly.

Internet severity and activities addiction questionnaire (ISAAQ): Psychometrics of item response theory and clustering of online activities.

BACKGROUND: Problematic usage of the internet (PUI) is an umbrella term, referring to a variety of maladaptive online behaviors linked to functional impairment. There is ongoing need for the development of instruments capturing not only PUI severity, but also the online activity types. The Internet Severity and Activities Questionnaire (ISAAQ), previously developed to address this need, required further refinement and validation. METHODS: Cross-sectional data was gathered in two separate samples (South Africa n = 3275, USA-UK n = 943) using the Internet Severity and Activities Addiction Questionnaire (ISAAQ). Item Response Theory (IRT) was used to examine the properties of the scale (Part A of the ISAAQ) and differential item functioning against demographic parameters. The severity scale of the ISAAQ was optimized by eliminating the poorest performing items using an iterative approach and examining validity metrics. Cluster analyses was used to examine internet activities and commonalities across samples (Part B of the ISAAQ). RESULTS: Optimization of ISAAQ using IRT yielded a refined 10-item version (ISAAQ-10), with less differential item functioning and a robust unidimensional factor structure. The ISAAQ-10 severity score correlated strongly with established measures of internet addiction (Compulsive Internet Use Scale [Person's r = 0.86] and the Internet Addiction Test-10 [r = 0.75]). Combined with gaming activity score it correlated moderately strongly with the established Internet Gaming Disorder Test (r = 0.65). Exploratory cluster analyses in both samples identified two groups, one of "low-PUI" [98.1-98.5%], and one of "high-PUI" [1.5-1.9%]. Multiple facets of internet activity appeared elevated in the high-PUI cluster. DISCUSSION: The ISAAQ-10 supersedes the earlier longer version of the ISAAQ, and provides a useful, psychometrically robust measure of PUI severity (Part A), and captures the extent of engagement in a wide gamut of online specific internet activities (Part B). ISAAQ-10 constitutes a valuable objective measurement tool for future studies.

Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020.

OBJECTIVES: To identify the proportion of coronavirus disease 2019 (COVID-19) cases that occurred within households or buildings in New York City (NYC) beginning in March 2020 during the first stay-at-home order to determine transmission attributable to these settings and inform targeted prevention strategies. DESIGN: The residential addresses of cases were geocoded (converting descriptive addresses to latitude and longitude coordinates) and used to identify clusters of cases residing in unique buildings based on building identification number (BIN), a unique building identifier. Household clusters were defined as 2 or more cases within 2 weeks of onset or diagnosis date in the same BIN with the same unit number, last name, or in a single-family home. Building clusters were defined as 3 or more cases with onset date or diagnosis date within 2 weeks in the same BIN who do not reside in the same household. SETTING: NYC from March to December 2020. PARTICIPANTS: NYC residents with a positive SARS-CoV-2 nucleic acid amplification or antigen test result with a specimen collected during March 1, 2020, to December 31, 2020. MAIN OUTCOME MEASURE: The proportion of NYC COVID-19 cases in a household or building cluster. RESULTS: The BIN analysis identified 65 343 building and household clusters: 17 139 (26%) building clusters and 48 204 (74%) household clusters. A substantial proportion of NYC COVID-19 cases (43%) were potentially attributable to household transmission in the first 9 months of the pandemic. CONCLUSIONS: Geocoded address matching assisted in identifying COVID-19 household clusters. Close contact transmission within a household or building cluster was found in 43% of noncongregate cases with a valid residential NYC address. The BIN analysis should be utilized to identify disease clustering for improved surveillance.

K-medoids clustering of hospital admission characteristics to classify severity of influenza virus infection.

Background: Patients are admitted to the hospital for respiratory illness at different stages of their disease course. It is important to appropriately analyse this heterogeneity in surveillance data to accurately measure disease severity among those hospitalized. The purpose of this study was to determine if unique baseline clusters of influenza patients exist and to examine the association between cluster membership and in-hospital outcomes. Methods: Patients hospitalized with influenza at two hospitals in Southeast Michigan during the 2017/2018 (n = 242) and 2018/2019 (n = 115) influenza seasons were included. Physiologic and laboratory variables were collected for the first 24 h of the hospital stay. K-medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth's logistic regression were used to examine the association between cluster membership and clinical outcomes. Results: Three clusters were selected for 2017/2018, mainly differentiated by blood glucose level. After adjustment, those in C171 had 5.6 times the odds of mechanical ventilator use than those in C172 (95% CI: 1.49, 21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, 95% CI: 0.2, 2.7) and C173 (mean 1.4 days longer, 95% CI: 0.3, 2.5). Similar results were seen between the two clusters selected for 2018/2019. Conclusion: In this study of hospitalized influenza patients, we show that distinct clusters with higher disease acuity can be identified and could be targeted for evaluations of vaccine and influenza antiviral effectiveness against disease attenuation. The association of higher disease acuity with glucose level merits evaluation.

COVID-19 vaccine hesitancy in Spain and associated factors.

Introduction: The present study explores the reasons of those who have not been vaccinated in the later stage of the vaccine rollout in Spain and its associated determinants. Methods: Cluster and logistic regression analyses were used to assess differences in claimed reasons for vaccine hesitancy in Spain using two samples of unvaccinated people (18-40 years old) gathered by an online cross-sectional survey from social networks (n = 910) and from a representative panel (n = 963) in October-November 2021. Results: The main reasons for not being vaccinated were believing that the COVID-19 vaccines had been developed too fast, they were experimental, and they were not safe, endorsed by 68.7% participants in the social network sample and 55.4% in the panel sample. The cluster analysis classified the participants into two groups. Logistic regression showed that Cluster 2 (individuals who reported structural constraints and health-related reasons such as pregnancy or medical recommendation) presented a lower trust in information from health professionals, had a lower willingness to get vaccinated in the future, and avoided less social/family events than those in Cluster 1 (reasons centered in distrust on COVID-19 vaccines, conspiracy thoughts and complacency). Conclusions: It is important to promote information campaigns that provide reliable information and fight fake news and myths. Future vaccination intention differs in both clusters, so these results are important for developing strategies target to increase vaccination uptake for those who do not reject the COVID-19 vaccine completely.

Senior Americans' perceptions, attitudes, and safety concerns toward Autonomous Vehicles (AVs).

INTRODUCTION: Autonomous vehicles (AVs) are considered a promising solution to improve seniors' safety and mobility. However, to transition to fully automated transportation, especially among seniors, it is vital to assess their perception and attitude toward AVs. This paper investigates seniors' perceptions and attitudes to a wide range of AV options from the perspective of pedestrians and users in general, as well as during and after the COVID-19 pandemic. Underlying this objective is to examine older pedestrians' safety perceptions and behaviors at crosswalks in the presence of AVs. METHOD: A national survey collected data from a sample of 1,000 senior Americans. Using Principal Component Analysis (PCA) and Cluster Analysis, three clusters of seniors were identified with different demographic characteristics, perceptions, and attitudes toward AVs. RESULTS: PCA findings revealed that "risky pedestrian crossing behavior," "cautious pedestrian crossing behavior in the presence of AVs," "positive perception and attitude toward shared AVs," and "demographic characteristics" were the main components explaining most of the variation within the data, respectively. The PCA factor scores were used in the cluster analysis, which resulted in the identification of three distinctive groups of seniors. Cluster one included individuals with lower demographic scores and a negative perception and attitude toward AVs from the perspective of users and pedestrians. Clusters two and three included individuals with higher demographic scores. Cluster two included individuals with a positive perception toward shared AVs from the user perspective, but a negative attitude toward pedestrian-AV interaction. Cluster three included those with a negative perception toward shared AVs but a somewhat positive attitude toward pedestrian-AV interaction. The findings of this study provide valuable insights to transportation authorities, AV manufacturers, and researchers regarding older American's perception and attitude toward AVs as well as their willingness to pay and use Advanced Vehicle Technologies.

Molecular Characterization and Cluster Analysis of SARS-CoV-2 Viral Isolates in Kahramanmaras City, Turkey: The Delta VOC Wave within One Month.

The SARS-CoV-2 pandemic has seriously affected the population in Turkey. Since the beginning, phylogenetic analysis has been necessary to monitor public health measures against COVID-19 disease. In any case, the analysis of spike (S) and nucleocapsid (N) gene mutations was crucial in determining their potential impact on viral spread. We screened S and N regions to detect usual and unusual substitutions, whilst also investigating the clusters among a patient cohort resident in Kahramanmaras city, in a restricted time span. Sequences were obtained by Sanger methods and genotyped by the PANGO Lineage tool. Amino acid substitutions were annotated comparing newly generated sequences to the NC_045512.2 reference sequence. Clusters were defined using phylogenetic analysis with a 70% cut-off. All sequences were classified as Delta. Eight isolates carried unusual mutations on the S protein, some of them located in the S2 key domain. One isolate displayed the unusual L139S on the N protein, while few isolates carried the T24I and A359S N substitutions able to destabilize the protein. Phylogeny identified nine monophyletic clusters. This study provided additional information about SARS-CoV-2 epidemiology in Turkey, suggesting local transmission of infection in the city by several transmission routes, and highlighting the necessity to improve the power of sequencing worldwide.

Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

Post COVID-19 condition after Wildtype, Delta, and Omicron SARS-CoV-2 infection and prior vaccination: Pooled analysis of two population-based cohorts.

BACKGROUND: Post COVID-19 condition (PCC) is an important complication of SARS-CoV-2 infection, affecting millions worldwide. This study aimed to evaluate the prevalence and severity of post COVID-19 condition (PCC) with novel SARS-CoV-2 variants and after prior vaccination. METHODS: We used pooled data from 1350 SARS-CoV-2-infected individuals from two representative population-based cohorts in Switzerland, diagnosed between Aug 5, 2020, and Feb 25, 2022. We descriptively analysed the prevalence and severity of PCC, defined as the presence and frequency of PCC-related symptoms six months after infection, among vaccinated and non-vaccinated individuals infected with Wildtype, Delta, and Omicron SARS-CoV-2. We used multivariable logistic regression models to assess the association and estimate the risk reduction of PCC after infection with newer variants and prior vaccination. We further assessed associations with the severity of PCC using multinomial logistic regression. To identify groups of individuals with similar symptom patterns and evaluate differences in the presentation of PCC across variants, we performed exploratory hierarchical cluster analyses. RESULTS: We found strong evidence that vaccinated individuals infected with Omicron had reduced odds of developing PCC compared to non-vaccinated Wildtype-infected individuals (odds ratio 0.42, 95% confidence interval 0.24-0.68). The odds among non-vaccinated individuals were similar after infection with Delta or Omicron compared to Wildtype SARS-CoV-2. We found no differences in PCC prevalence with respect to the number of received vaccine doses or timing of last vaccination. The prevalence of PCC-related symptoms among vaccinated, Omicron-infected individuals was lower across severity levels. In cluster analyses, we identified four clusters of diverse systemic, neurocognitive, cardiorespiratory, and musculoskeletal symptoms, with similar patterns across variants. CONCLUSION: The risk of PCC appears to be lowered with infection by the Omicron variant and after prior vaccination. This evidence is crucial to guide future public health measures and vaccination strategies.

Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis.

The normalized compression distance (NCD) is a similarity measure between a pair of finite objects based on compression. Clustering methods usually use distances (e.g., Euclidean distance, Manhattan distance) to measure the similarity between objects. The NCD is yet another distance with particular characteristics that can be used to build the starting distance matrix for methods such as hierarchical clustering or K-medoids. In this work, we propose Zgli, a novel Python module that enables the user to compute the NCD between files inside a given folder. Inspired by the CompLearn Linux command line tool, this module iterates on it by providing new text file compressors, a new compression-by-column option for tabular data, such as CSV files, and an encoder for small files made up of categorical data. Our results demonstrate that compression by column can yield better results than previous methods in the literature when clustering tabular data. Additionally, the categorical encoder shows that it can augment categorical data, allowing the use of the NCD for new data types. One of the advantages is that using this new feature does not require knowledge or context of the data. Furthermore, the fact that the new proposed module is written in Python, one of the most popular programming languages for machine learning, potentiates its use by developers to tackle problems with a new approach based on compression. This pipeline was tested in clinical data and proved a promising computational strategy by providing patient stratification via clusters aiding in precision medicine.

Consumer preference analysis on the attributes of samgyeopsal Korean cuisine and its market segmentation: Integrating conjoint analysis and K-means clustering.

Samgyeopsal is a popular Korean grilled dish with increasing recognition in the Philippines as a result of the Hallyu. The aim of this study was to analyze the preferability of Samgyeopsal attributes which includes the main entree, cheese inclusion, cooking style, price, brand, and drinks using Conjoint Analysis and market segmentation using k-means clustering. A total of 1018 responses were collected online through social media platforms by utilizing a convenience sampling approach. The results showed that the main entrée (46.314%) was found to be the most important attribute, followed by cheese (33.087%), price (9.361%), drinks (6.603%), and style (3.349%). In addition, k-means clustering identified 3 different market segments: high-value, core, and low-value consumers. Furthermore, this study formulated a marketing strategy that focused on enhancing the choice of meat, cheese, and price based on these 3 market segments. This study has significant implications for enhancing Samgyeopsal chain businesses and helping entrepreneurs with consumer preference on Samgyeopsal attributes. Finally, conjoint analysis with k-means clustering can be utilized and extended for evaluating food preferences worldwide.