A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

Erratum: Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis.

[This corrects the article DOI: 10.3389/fpubh.2024.1393677.].

Autotransplantation with Adjunctive Application of Enamel Matrix Derivatives Using a Digital Workflow: A Prospective Case Series.

OBJECTIVES: Digital protocols and bioactive materials may reduce complications and improve tooth autotransplantation (ATT) success and survival rates. This prospective study assesses the performance of a fully digital autotransplantation protocol of close-apex molars with the adjunctive application of Enamel Matrix Derivatives (EMD). METHODS: Twelve adult patients with 13 hopeless molar teeth were replaced with autotransplantation of closed apex third molars. Outcomes, including success and survival rates, clinical, endodontic, radiographic, patient-reported outcome measures (PROMs), and digital image assessments, were conducted over a two-year follow-up period. RESULTS: Survival and success rates were 100% and 91.2%, respectively, with no progressive inflammatory or replacement root resorption (ankylosis) except for one tooth presenting radiographic furcation involvement. A significant probing depth reduction of 2.4 ± 2.58 mm and CAL gains of 2.8 ± 3.03 mm were observed in transplanted teeth compared to the hopeless receptor teeth. Radiographic bone levels remained stable throughout the study period (-0.37 ± 0.66 mm), and digital image assessments showed minimal alveolar ridge width changes (-0.32 to -0.7 mm) and gingival margin changes (-0.95 to -1.27 mm) from baseline to last visit. PROMs indicated very high patient satisfaction. CONCLUSION: The use of a digital ATT protocol with adjunctive use of EMD in closed-apex third molars demonstrated promising short-term high success and survival rates. Additionally, this type of therapy adequately preserves the dimensions of the alveolar ridge in the receptor site.

Melanin-based color variation in response to changing climates in snakes.

Melanism, the process of heavier melanin deposition, can interact with climate variation at both micro and macro scales, ultimately influencing color evolution in organisms. While the ecological processes regulating melanin production in relation to climate have been extensively studied, intraspecific variations of melanism are seldom considered. Such scientific gap hampers our understanding of how species adapt to rapidly changing climates. For example, dark coloration may lead to higher heat absorption and be advantageous in cool climates, but also in hot environments as a UV or antimicrobial protection mechanism. To disentangle such opposing predictions, here we examined the effect of climate on shaping melanism variation in 150 barred grass snakes (Natrix helvetica) and 383 green whip snakes (Hierophis viridiflavus) across Italy. By utilizing melanistic morphs (charcoal and picturata in N. helvetica, charcoal and abundistic in H. viridiflavus) and compiling observations from 2002 to 2021, we predicted that charcoal morphs in H. viridiflavus would optimize heat absorption in cold environments, while offering protection from excessive UV radiation in N. helvetica within warm habitats; whereas picturata and abundistic morphs would thrive in humid environments, which naturally have a denser vegetation and wetter substrates producing darker ambient light, thus providing concealment advantages. While picturata and abundistic morphs did not align with our initial humidity expectations, the charcoal morph in N. helvetica is associated with UV environments, suggesting protection mechanisms against damaging solar radiation. H. viridiflavus is associated with high precipitations, which might offer antimicrobial protection. Overall, our results provide insights into the correlations between melanin-based color morphs and climate variables in snake populations. While suggestive of potential adaptive responses, future research should delve deeper into the underlying mechanisms regulating this relationship.

Did the COVID-19 quarantine policies applied in Cochabamba, Bolivia mitigated cases successfully? an interrupted time series analysis.

BACKGROUND: The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies' impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited. OBJECTIVE: To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia. METHODS: Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020-2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods. RESULTS: The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies. CONCLUSION: This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures' intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.

Main findings: The findings highlight the importance of stringent quarantine measures in managing infectious disease outbreaks, offering valuable insights for policymakers worldwide in strategizing effective public health interventions.Added knowledge: By providing a detailed analysis of testing disparities and quarantine policies' effectiveness within a specific Latin American context, our research fills a critical gap in understanding their impacts on health system responses and disease control.Global health impact for policy and action: The findings highlight the importance of stringent quarantine measures in managing infectious disease outbreaks, offering valuable insights for policymakers worldwide in strategizing effective public health interventions.

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm.

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches-multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012-2018) were used as a training set, and 3 years (2019-2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3-10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

A causal machine-learning framework for studying policy impact on air pollution: A case-study in COVID-19 lockdowns.

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies and opening or closing an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case-study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect. We outline the assumptions required to identify causal effects, showing that many common methods rely on strong assumptions that are relaxed by machine learning models. For empirical validation, we also propose a simple diagnostic criterion, guarding against false effects in baseline years when there was no intervention. The framework is applied to study the impact of COVID-19 lockdowns on NO2 in the eastern US. The machine learning approaches guard against false effects better than common methods and suggest decreases in NO2 in Boston, New York City, Baltimore, and Washington D.C. The study showcases the importance of our validation framework in selecting a suitable method and the utility of a machine learning based CITS model for studying causal changes in air pollution time series.

Clinical Characteristics and Patterns of Immune Responses in COVID-19 Patients From a Rural Community Hospital.

Background Although demographic and clinical factors such as age, certain comorbidities, and sex have been associated with COVID-19 outcomes, these studies were largely conducted in urban populations affiliated with large academic medical centers. There have been very few studies focusing on rural populations that also characterize broader changes in inflammatory cytokines and chemokines. Methodology A single-center study was conducted between June 2020 and March 2021 in Abilene, Texas, USA. Patients were included if they presented to the hospital for treatment of COVID-19, had extra biological materials from routine care available, and were between the ages of 0 to 110 years. There were no exclusion criteria. Patient characteristics, symptom presentation, and clinical laboratory results were extracted from electronic health records. Blood specimens were analyzed by protein microarray to quantitate 40 immunological biomarkers. Results A total of 122 patients were enrolled, of whom 81 (66%) were admitted to the general non-critical inpatient unit, 37 (30%) were admitted to the intensive or critical care units, and four (3.2%) were treated outpatient. Most hospitalized COVID-19 patients in this rural population were elderly, male, obese, and retired individuals. Predominant symptoms for non-critical patients were shortness of breath, fever, and fatigue. Ferritin levels for outpatient patients were lower on average than those in an inpatient setting and lactate dehydrogenase (LDH) levels were noted to be lower in non-critical and outpatient than those in the intensive care unit setting. Inflammatory biomarkers were positively correlated and consistent with inflammatory cascade. Interleukin (IL)-10 was positively correlated while platelet-derived growth factor was negatively correlated with inflammatory biomarkers. Patients ≥65 years had significantly higher levels of LDH and seven cytokines/chemokines (granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin IL-1b, IL-6, IL-10, IL-11, macrophage inflammatory protein (MIP)-1d, and IL-8) while levels of five other immune molecules (intercellular adhesion molecule 1 (ICAM-1), monocyte chemoattractant protein 1 (MCP-1), tissue inhibitor of metalloproteinase 2 (TIMP-2), IL-2, and IL-4) were significantly lower compared to those <65 years. Females had significantly higher levels of LDH and 10 cytokines/chemokines (GM-CSF, IL-1b, IL-6, IL-10, IL-11, IL-15, IL-16, MIP-1a, MIP-1d, and IL-8) while levels of TIMP-2 and IL-4 were significantly lower than male patients. Conclusions The clinical characteristics of this rural cohort of hospitalized patients differed somewhat from nationally reported data. The contributions of social, environmental, and healthcare access factors should be investigated. We identified age and sex-associated differences in immunological response markers that warrant further investigation to identify the underlying molecular mechanisms and impact on COVID-19 pathogenesis.

Hybrid convergent procedure with proactive oesophageal cooling for the treatment of long-standing persistent atrial fibrillation: a case series.

Background: The hybrid convergent procedure is approved to treat symptomatic patients with long-standing persistent atrial fibrillation (AF). Despite direct visualization during surgical ablation as well as the use of luminal oesophageal temperature (LET) monitoring, oesophageal injury is still possible. A dedicated device for proactive oesophageal cooling has recently been cleared by the Food and Drug Administration to reduce the likelihood of ablation-related oesophageal injury resulting from radiofrequency cardiac ablation procedures. This report describes the first uses of proactive oesophageal cooling for oesophageal protection during the epicardial ablation portion of hybrid convergent procedures. Case summary: Five patients with long-standing persistent AF underwent hybrid convergent ablations with the use of proactive oesophageal cooling as means of oesophageal protection. All cases were completed successfully with no adverse effects. Most notably, cases were shorter when compared to cases using LET monitoring, likely due to lack of pauses for overheating of the oesophagus that would otherwise be required to prevent damage to the oesophagus. Discussion: This report describes the first uses of proactive oesophageal cooling for oesophageal protection during the epicardial ablation portion of five hybrid convergent procedures. Use of cooling enabled uninhibited deployment of lesions without the need to pause energy delivery due to elevated temperatures in the oesophagus, providing a feasible alternative to LET monitoring.

IgG4-related disease with nasopharyngeal malignancy-like manifestations.

Background: IgG4-related disease (IgG4-RD) was characterized by single or multiple masses in organs, which may mimic various inflammatory and malignant diseases. Here, we summarize 4 patients with aggressive manifestations of IgG4-RD that mimic nasopharynx cancer to provide some new sights for the diagnosis of IgG4-RD. Case summary: Four patients were included in our series. The age ranged from 53 to 64 years old, and the duration of the disease ranged from 4 to 6 months. The chief complaints included headache, rhinorrhea, or diplopia. All patients had more than 10 IgG4+ plasma cells/HPF in immunohistochemistry with plasma lgG4 levels ranging from 218 mg/dL to 765 mg/dL. All of them met the diagnostic criteria of lgG4-RD. Conclusion: The described case is highly similar to the clinical manifestations of nasopharyngeal carcinoma. Although pathology is the gold standard, there are still limitations. Serological IgG4 can help confirm the diagnosis. Timely diagnosis of IgG4-RD is of great significance in preventing secondary organ damage in patients with active diseases.

Leveraging VQ-VAE tokenization for autoregressive modeling of medical time series.

In this work, we present CodeAR, a medical time series generative model for electronic health record (EHR) synthesis. CodeAR employs autoregressive modeling on discrete tokens obtained using a vector quantized-variational autoencoder (VQ-VAE), which addresses key challenges of accurate distribution modeling and patient privacy preservation in the medical domain. The proposed model is trained with next-token prediction instead of a regression problem for more accurate distribution modeling, where the autoregressive property of CodeAR is useful to capture the inherent causality in time series data. In addition, the compressive property of the VQ-VAE prevents CodeAR from memorizing the original training data, which ensures patient privacy. Experimental results demonstrate that CodeAR outperforms the baseline autoregressive-based and GAN-based models in terms of maximum mean discrepancy (MMD) and Train on Synthetic, Test on Real tests. Our results highlight the effectiveness of autoregressive modeling on discrete tokens, the utility of CodeAR in causal modeling, and its robustness against data memorization.

Pathologic features of head and neck adult rhabdomyosarcoma: Case series.

INTRODUCTION: Adult Head and neck Rhabdomyosarcomas (HNRMS) are exceedingly rare and remain challenging for pathologists. CASES PRESENTATION: Five cases of adult HNRMS (≥19 years) were retrieved from the archives of the Department of Pathology of Hospital of Specialities in Rabat (HSR) in Morocco, over 5 years. Clinical and pathologic findings from hematoxylin and eosin slides and immunohistochemistry for Desmin and Myogenin were reviewed. CLINICAL DISCUSSION: The median age was 33, with a men's predominance (3 M/2F). Histological analysis revealed three cases of Alveolar Rhabdomyosarcoma (RMS), one Pleomorphic RMS, and one spindle cell/sclerosing RMS. In addition to the typical histology observed in each RMS, we found tricky growth patterns that could be a source of misdiagnosis. All five cases demonstrated variable positivity for Desmin and Myogenin. CONCLUSION: HNRMS cases have different pathological features than pediatric RMS cases. We identified rare subtypes such as pleomorphic and spindle cell/sclerotic RMS, which exhibit unusual morphological patterns.

The Role of Robotics in Meeting Institutional Goals: A Unified Strategy to Facilitate Program Excellence.

PURPOSE: The rapid expansion of robotic surgical equipment necessitates a review of the needs and challenges faced by hospitals introducing robots for the first time to compete with experienced institutions. The aim of this study was to analyze the impact of robotic surgery on our hospital compared to open and laparoscopic surgery, examine internal transformations, and assess regional, domestic, and international implications. METHODS: A retrospective review was conducted of electronic medical records (EMRs) from 2019 to 2022 at Inha University Hospital, including patients who underwent common robotic procedures and equivalent open and laparoscopic operations. The study investigated clinical and operational performance changes in the hospital after the introduction of robotic technology. It also evaluated the operational effectiveness of robot implementation in local, national, and international contexts. To facilitate comparison with other hospitals, the data were transmitted to Intuitive Surgical, Inc. for analysis. The study was conducted in compliance with domestic personal information regulations and received approval from our Institutional Review Board. RESULTS: We analyzed EMR data from 3,147 patients who underwent surgical treatment. Over a period of 3.5 years, the adoption of robotic technology in a hospital setting significantly enhanced the technical skills of all professors involved. The introduction of robotic systems led to increased patient utilization of conventional surgical techniques, as well as a rise in the number of patients choosing robotic surgery. This collective trend contributed to an overall increase in patient numbers. This favorable evaluation of the operational effectiveness of our hospital's robot implementation in the context of local, national, and global factors is expected to positively influence policy changes. CONCLUSION: Stakeholders should embrace data science and evidence-based techniques to generate valuable insights from objective data, assess the health of robot-assisted surgery programs, and identify opportunities for improvement and excellence.

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. METHODS: Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. RESULTS: The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. CONCLUSION: Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.

Promoting fairness in activity recognition algorithms for patient's monitoring and evaluation systems in healthcare.

Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance. The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis. Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects' characteristics on activity recognition performance, providing valuable insights into the algorithm's robustness across diverse populations. This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.

Ion-specific ice provides a facile approach for reducing ice friction.

HYPOTHESIS: Ice friction plays a crucial role in both basic study and practical use. Various strategies for controlling ice friction have been developed. However, one unsolved puzzle regarding ice friction is the effect of ion-ice interplay on its tribological properties. EXPERIMENTS AND SIMULATIONS: Here, we conducted ice friction experiments and summarized the specific effects of hydrated ions on ice friction. By selecting cations and anions, the coefficient of ice friction can be reduced by more than 70 percent. Experimental spectra, low-field nuclear magnetic resonance (LF-NMR), density functional theory (DFT) calculations, and Molecular dynamics (MD) simulations demonstrated that the addition of ions could break the H-bonds in water. FINDINGS: The link between the charge density of ions and the coefficients of ice friction was revealed. A part of the ice structure was changed from an ice-like to a liquid-like interfacial water structure with the addition of ions. Lower charge density ions led to weaker ionic forces with the water molecules in the immobilized water layer, resulting in free water molecules increasing in the lubricating layer. This study provides guidance for preparing ice-making solutions with low friction coefficients and a fuller understanding of the interfacial water structure at low temperatures.

Predictability of ecological and evolutionary dynamics in a changing world.

Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.

Ion-Specific Interactions Engender Dynamic and Tailorable Properties in Biomimetic Cationic Polyelectrolytes.

Biomaterials such as spider silk and mussel byssi are fabricated by the dynamic manipulation of intra- and intermolecular biopolymer interactions. Organisms modulate solution parameters, such as pH and ion co-solute concentration, to effect these processes. These biofabrication schemes provide a conceptual framework to develop new dynamic and responsive abiotic soft material systems. Towards these ends, the chemical diversity of readily available ionic compounds offers a broad palette to manipulate the physicochemical properties of polyelectrolytes via ion-specific interactions. In this study, we show for the first time that the ion-specific interactions of biomimetic polyelectrolytes engenders a variety of phase separation behaviors, creating dynamic, thermal and ion responsive soft matter. that exhibits a spectrum of physical properties, spanning viscous fluids, to viscoelastic and viscoplastic solids. These ion dependent characteristics are further rendered general by the merger of lysine and phenylalanine into a single, amphiphilic vinyl monomer. The unprecedented breadth, precision, and dynamicity in the reported ion dependent phase behaviors thus introduce a broad array of opportunities for the future development of responsive soft matter, properties that are poised to drive developments in critical areas such as chemical sensing, soft robotics, and additive manufacturing.

COVID-19-Associated Cerebellar Ataxia: A Case Series.

COVID-19-associated cerebellar ataxia has rarely been reported and its clinical characteristics remain understudied. This study aims to report patients with COVID-19-associated cerebellar ataxia from our institution. COVID-19-associated cerebellar ataxia was diagnosed based on the prodromal COVID-19 infection and the exclusion of other causes. This study provides a summary of the patients' clinical presentations, neuroimaging features, and the results of anti-cerebellar antibody examinations. Our study included 11 patients and 4 were male. The median onset age was 38 years. Five patients also demonstrated signs of encephalopathy. Brain magnetic resonance imaging (MRI) was either unremarkable (n = 6) or showed bilateral cerebellar lesions (n = 5), which were typically transient, although brain atrophy could be observed later in the disease course. Anti-Homer-3 and anti-Yo antibodies were each detected in one patient, respectively. All patients received immunotherapy and nine improved. Compared with the late-onset group, individuals who exhibited ataxia earlier following COVID-19 onset (interval<5 days) were significantly younger [median age 18 (15.5-31) vs. 53.5 (44-64.8) years, p = 0.009] and more likely to present with encephalopathy (5/5 vs. 0/6, p = 0.002).They also experienced more severe symptoms [median modified Rankin scale (mRS) score at zenith 5 (5-5) vs. 2 (1.75-2.75), p = 0.017] and had a less favorable prognosis [median mRS score at the last follow-up 4 (2-5) vs. 1 (0-1.25), p = 0.009]. COVID-19-associated cerebellar ataxia can appear with encephalopathy. Brain MRI may show transient bilateral cerebellar lesions and brain atrophy later. Patients who exhibited ataxia earlier following COVID-19 were younger, had more severe symptoms and poorer outcomes.