Influence of chronic disease long prescription management on treatment efficacy in hypertensive patients during COVID-19 epidemic period.

Objective To explore the influence of chronic disease long prescription management on the treatment effect of the patients with hypertension during the novel coronavirus pneumonia（COVID-19) epidemic period. Methods Eighty patients with hypertension chronic disease and with relatively stable condition, suitable for long medication and documented management in the outpatient department of a hospital from May 2020 to April 2021 were selected and randomly divided into the long term prescription treatment group and conventional treatment group, 40 cases in each group by using a simple randomized sampling method. The two groups conducted the standard management and 1-year follow up.The systolic blood pressure, diastolic blood pressure, body mass index (BMI),satisfaction rate, visit time and cumulative visit times were compared between the two groups, and then the blood pressure control rate was calculated. Results There were no statistically significant differences in the systolic and diastolic blood pressure, blood pressure control and BMI at the first time and final follow up between the two groups（P＞0.05）;the satisfaction rate of the patients in the long prescription treatment group was significantly higher than that in the routine treatment group（P＜0.05）,while the visiting time and cumulative visiting number in the long prescription treatment group were significantly lower than those in the routine treatment group, and the difference was statistically significant（P＜0.05）.Conclusion Compared with the conventional treatment method, the chronic disease long prescription will not have an impact on the treatment effect and safety in the patients with hypertension, but it could effectively shorten the visiting time of the patients, reduce the number of visits, effectively improve the satisfaction rate of the patients, help to improve the treatment compliance of the patients and promote the prognosis of the disease. (English) [ FROM AUTHOR] 目的 探索新型冠状病毒感染疫情防控期间慢病长处方管理对高血压患者疗效的影响. 方法 选取2020年5月至2021年4月在某院门诊就诊的病情相对稳定、适合长期服药, 并且已建档管理的高血压慢性病患者80例, 通过简单随机化抽样方法, 将抽样样本随机分为长处方治疗组和常规治疗组, 每组40例. 2组均规范管理, 随访1年. 比较2组患者收缩压、舒张压、体重指数、满意率、就诊时间和累积就诊次数, 计算血压控制率. 结果 2组患者首次及终末随访收缩压、舒张压、血压控制情况、体重指数比较, 差异均无统计学意义（P＞0.05）;长处方治疗组患者满意率明显高于常规治疗组, 就诊时间和累积就诊次数均明显低于常规治疗组, 差异均有统计学意义（P＜0.05) . 结论 与常规治疗方法比较, 慢病长处方不会对高血压患者疗效、安全性产生影响, 但能有效缩短患者就诊时间, 减少其就诊次数, 有效改善患者的满意度, 有助于提高患者治疗的依从性, 促进疾病转归. (Chinese) [ FROM AUTHOR] Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Post-exertional malaise may persist in Long COVID despite learning STOP-REST-PACE

Background Objective Methods Results Conclusions Physical activity used in rehabilitation can trigger post-exertional malaise (PEM) in people with Long COVID. Concerns remain if the STOP-REST-PACE approach promoted by patient communities and professional organizations can be safely administered and contributes to return to usual activities.(1) To observe PEM over 12 weeks of telerehabilitation based on the STOP-REST-PACE approach. (2) To describe the changes in health-related quality of life (HRQoL), respiratory symptoms, fatigue and return to work.This was an observational prospective cohort of participants with Long COVID referred to a telerehabilitation service. Participants received up to 14 h of physiotherapy and occupational therapy over 12 weeks based on the STOP-REST-PACE approach. Frequency was personalized, up to two sessions weekly. An independent coordinator assessed PEM, HRQoL, respiratory symptoms, fatigue and return to work.Thirty-four participants were included and 30 completed the 12 weeks of telerehabilitation. Participants had an average of eight impairments. We found PEM in all participants at baseline. After 12 weeks, PEM remained present for 19 out of 30 participants. Respiratory symptoms significantly improved (COPD Assessment Test: 19.2 ± 7.3 vs 13.8 ± 7.7, p < .001). Fatigue and HRQoL did not significantly improve (p = 0.32 and p = 0.20, respectively). Only four participants were able to work full time.PEM persisted for close to two-third of participants despite learning the STOP-REST-PACE approach through physical and occupational therapy sessions over 12 weeks. Respiratory symptoms improved, but we did not observe a difference in fatigue and HRQoL. Return to work was out of reach for most participants. [ FROM AUTHOR] Copyright of Fatigue: Biomedicine, Health & Behavior is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Pleurisy and pericarditis as a cause of atypical chest pain in patients with in early post-COVID-19 period.

During the pandemic COVID-19, there has been an increase in the number of patients with non-anginal chest pain at cardiologist appointments. Objective. To assess the incidence of signs of pleurisy and pericarditis after COVID-19 in non-comorbid patients with atypical chest pain and describe their characteristics according to echocardiography and magnetic resonance imaging. Materials and methods. From February 2021 to January 2022, 200 outpatients were prospectively enrolled in the study, all of them suffered from a discomfort in the heart region for the first time after SARS-CoV-2 infection. Inclusion criteria: 18-50 years old, 5-12 weeks after SARS-CoV-2 infection, non-anginal chest pain. Exclusion criteria: pneumonia or signs of pulmonary thromboembolism, coronary heart disease, congestive heart failure or kidney disease, clinical or laboratory signs of myocarditis, oncopathology, radiation or chemotherapy of the chest in past medical history. A survey was conducted (yes/no) for the presence of general malaise, quality of life deterioration, hyperthermia, cough. Ultrasound examination of the pericardium and pleura to detect effusion or post-inflammatory changes was performed in accordance with the recommendations. Magnetic resonance imaging was performed if ultrasound imaging was poor or there was no evidence of pericardial or pleural involvement in patients with typical symptoms. Results. 82 women and 118 men were included. Median of age 39 [28-46] years old. Pericarditis was diagnosed in 152 (76%) patients, including effusive pericarditis in 119 (78%), myocarditis in 6 (3%) and myopericarditis in 49 (25%) patients, pleurisy was detected in 22 (11%) patients, exudative pleurisy - in 11 (5.5%) patients with a predominant unilateral lesion of the mediastinal-diaphragmatic region adjacent to the heart. Hyperthermia was recorded in 2.5% of cases, general malaise - in 60% and a decrease in the quality of life - in 84%. Conclusion. Serositis as a cause of atypical chest pain among young non-comorbid patients in early postCOVID was identified in 87% of patients. In the coming years, it is probably worthwhile to perform ultrasound of the pericardium and pleura in all patients with chest pain.Copyright © 2022 Infectious Diseases: News, Opinions, Training.

Long-COVID sequelae are associated with oxidative stress in hemodialysis patients

The pathophysiology of long-COVID sequelae in the general population of SARS-CoV-2-infected patients has been shown to be strongly influenced by oxidative stress. However, the potential role of oxidative stress in the development of long-COVID sequelae in hemodialysis patients (HD) has never been investigated. The present study aimed to evaluate the oxidative status of HD patients 3.5 months after SARS-CoV-2 infection in relation to the presence of long-COVID sequelae and the severity of the acute phase COVID-19. Methods. This cross-sectional cohort study included 63 HD patients with a median age of 55 (43-62.5) years and a dialysis vintage of 42 (25-73) months who had been infected with COVID-19 at least 3 months before recruitment. Patients were divided into two groups according to the occurrence of long-COVID sequelae: Group 1 included 31 (49.2%) HD patients with sequelae, while Group 2 included 32 (50.8%) fully recovered individuals. At 3.5 (3.2-4.6) months after the acute phase of COVID-19, malondialdehyde (MDA) and erythrocyte levels (MDAe), sulfhydryl groups (SH -groups), serum catalase activity, transferrin, and ceruloplasmin were measured. A comparison of the obtained data was performed using the Student's test or the Mann-Whitney test according to the data distribution. A correlation was evaluated with the Spearman test. Results. HD patients with persistent long-COVID sequelae had significantly higher concentrations of MDAs (p = 0.002), MDAe (p = 0.0006), and CTs (p = 0.02), and lower serum levels of SH-groups (p = 0.03) and ceruloplasmin (p = 0.03) compared with Group 2. The concentration of most studied indicators of pro- and antioxidant status did not depend on the severity of the acute phase COVID-19, and only catalase activity was statistically significantly related to the need for hospitalization (r = 0.59;p = 0.001), oxygen support (r = 0.44;p = 0.02), and the percentage of lung injury according to computed tomography (p = 0.03). Although the serum concentration of transferrin did not differ between the studied groups, the individual analysis showed that its value was statistically higher in HD patients with severe COVID-19 even 3.5 months after infection (p < 0.0001). Conclusions. Long-term COVID-19 sequelae in HD patients are associated with oxidative stress. High levels of catalase activity and serum transferrin 3.5 months after COVID-19 may be a consequence of the severe course of the acute phase of the disease. The obtained data suggest that the use of antioxidants may be one of the possible strategies to treat the long-term consequences of COVID in HD patients. © N. Stepanova, L. Korol, L. Snisar, A. Rysyev, T. Ostapenko,V. Marchenko, O. Belousova, O. Popova, N. Malashevska, M. Kolesnyk, 2023. All rights reserved.

Cardiometabolic and psychocognitive features of the post-COVID period in patients with atrial fibrillation.

The number of people with long-term consequences of COVID-19 is increasing worldwide. The long-term prognosis for patients remains poorly understood. Objective. To study cardiometabolic and psychocognitive features in comorbid elderly patients with atrial fibrillation (AF), de-pending on the presence of post-COVID syndrome (PCS). Material and methods. The observational analytical cohort study included 223 patients with AF and comorbidity (coronary artery disease, hypertension, obesity, type 2 diabetes mellitus) aged 60-74, who were divided into two groups: group 1 included 123 patients without COVID-19 and group 2 included 110 patients with a history of COVID-19 and the presence of PCS. The study evaluated laboratory and instrumental tests, and a general clinical study assessing psychocognitive disorders using the SPMSQ and HADS questionnaires was conducted. Results and discussion. In COVID-19 survivors, compared with patients of group 1, there were more pronounced atherogenic changes in total cholesterol (TC) (p=0.003), low-density lipoprotein cholesterol (p<0.001), and triglycerides (p=0.011). Lower dia-stolic blood pressure was found in COVID-19 survivors (p<0.001). In addition, patients in group 2 had higher median pulse pressure (p<0.001) and heart rate (p<0.001). In group 2 patients, a larger ascending aorta diameter was observed (p<0.001). The anx-iety-depressive syndrome was more common in COVID-19 survivors with comorbidities, and a statistically significant difference was found in clinical anxiety (24%, p=0.041) and subclinical depression (21%, p=0.015). When assessing cognitive function, mod-erate cognitive impairment was detected in 22% (p=0.005) of patients with PCS and severe cognitive impairment in 2% (p=0.007). Conclusion. In comorbid elderly patients with the post-COVID syndrome, a high prevalence of psychocognitive disorders and adverse cardiometabolic changes were observed, supporting the need for long-term monitoring of the general clinical condition and psychocognitive status of COVID-19 survivors.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.

Characterization of Demographics and Concomittant Symptoms in Patients with Post COVID-19 Conditions or Long COVID

Objectives: Post COVID-19 conditions or long COVID continues to burden the healthcare system. With the introduction of new code in October 2021 to appropriately capture this condition (U09.9), we have enough data to understand the detailed demographic and clinical characterization of the patients with long COVID. As this new clinical entity continues to evolve, our study will provide insights for care management and planning. Method(s): We conducted a retrospective cohort study from a large deidentified database of US health insurance claims. The study population included all individuals with at least one ICD-10 code for COVID (U07.1) between June 1, 2021, and November 30, 2022. Individuals with at least one ICD-10 code for long COVID (U09.9), at least 7 days after COVID diagnosis were termed "Long COVID" patients. Index date was defined as the first long COVID diagnosis date. We also assessed the most prevalent diagnosis codes within the 30 days pre- and post-index to understand top symptoms. Result(s): A cohort of 253,145 patients (62% female patients;38% male patients) were identified. Among this cohort, 3.2% were pediatric patients aged 0 - 17 years;73.3 % aged 18 - 64 years and 23.5 % aged 65+ years. Most prevalent symptoms that increased in the 30 day pre- and post-index: Nervous system symptoms (6 fold), fatigue (7 fold), Dyspnea (4.3 fold), esophagitis (1.6 fold) chronic kidney disease (1.3 fold) among others. Conclusion(s): Our findings indicate that long COVID is more prevalent in females, with fatigue and dyspnea emerging as top symptoms. These findings are consistent with the published literature. However, we uncovered additional symptoms such as nervous system symptoms, chronic kidney disease among others. Additional analysis is planned to evaluate the association of these symptoms with sociodemographic features to understand the health inequity aspects of long COVID.Copyright © 2023

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Post COVID-19 Conditions: The New Challenge to Mankind

After the waves of acute COVID-19 that swept mankind in 2020 and 2021, now we are confronted with the challenge of post COVID-19 conditions. According to the definition, post COVID-19 conditions comprise all signs and symptoms of COVID-19 that persist after the acute phase (3-4 weeks), without an upper limit of duration (as for the present state of knowledge). The symptoms of post COVID-19 conditions are highly variable, could affect every system, often overlap, and typically fluctuate and change over time. In regard to this disease and its long-term burden, the Bulgarian Cardiac Institute initiated a campaign "Life after COVID-19" and the data we gathered showed that a substantial proportion of patients having suffered from COVID-19 continue to have persistent symptoms that require special medical attention. Our biggest concern was the acute vascular manifestations of post COVID-19 conditions, such as acute coronary syndromes and acute pulmonary embolism, and for these we shared our personal experience. Post COVID-19 conditions have and will have a major significance for the healthcare and economic systems in the upcoming years. This derives from the simple facts that it is highly prevalent, affects people regardless of age (including young and active people) or severity of the acute illness (even asymptomatic cases), and that we still must learn a lot about its pathogenesis, natural history, treatment, and prognosis. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

Clinician-initiated written communication for families of patients at a long-term acute care hospital

Objective To assess the experience of families and clinicians at a long term acute care hospital (LTACH) after implementing a written communication intervention. Methods Written communication templates were developed for six clinical disciplines. LTACH clinicians used templates to describe the condition of 30 mechanically ventilated patients at up to three time points. Completed templates were the basis for written summaries that there were sent to families. Impressions of the intervention among families (n = 21) and clinicians (n = 17) were assessed using a descriptive correlational design. Interviews were analyzed using thematic content analysis. Results We identified four themes during interviews with families: Written summaries 1) facilitated communication with LTACH staff, 2) reduced stress related to COVID-19 visitor restrictions, 3) facilitated understanding of the patient condition, prognosis, and goals and 4) facilitated communication among family members. Although clinicians understood why families would appreciate written material, they did not feel that the intervention addressed their main challenge – overly optimistic expectations for patient recovery among families. Conclusion Written communication positively affected the experience of families of LTACH patients, but was less useful for clincians. Innovation Use of written patient care updates helps LTACH clinicians initiate communication with families.

Subjective and Objective Cognitive Deficits in Patients with Post-COVID Syndrome A Challenge for Neuropsychologists

Cognitive impairment is a prominent symptom of the post-COVID syndrome (PCS). However, the correspondence between subjective cognitive complaints (SCC) and objective results is inconsistent. Here, we investigated this discrepancy. This longitudinal study included N = 42 individuals who reported SCC as PCS after mild infection at inclusion. Data collection comprised questionnaires and neuropsychological assessment at baseline and follow-up (FU). At FU - on average 15 months after acute COVID-19 - 88 % of patients reported persisting SCC. There was an approx. 40 % discrepancy between subjective report and test results at both visits. Patients with SCC and objective impairment indicated elevated fatigue and reduced quality of life compared to patients without SCC at FU. A growing number of patients is anticipated to request neuropsychological assessments even after mild infections.

Healthcare Resource Utilization and Direct Medical Costs Associated with Post COVID-19 Condition in Colombia, 2020-2021

Objectives: This study aimed to estimate the direct medical costs of patients with post COVID-19 condition in a Colombian insurance company with more than 2.5 million affiliates. Method(s): We conducted a bottom-up cost-of-illness study of adults with persistent symptoms after at least three months of hospital discharge due to COVID-19. We surveyed patients that were hospitalized between March 2020 and August 2021. We asked about healthcare resource utilization (HCRU), which included laboratories and images, medications, consults, rehospitalizations, and others, associated with post COVID-19 condition. The answers were verified using the company's outpatient and inpatient service authorization records. Costs were estimated from the third payer perspective and expressed in American dollars using an exchange rate of 1USD$=3,743COP. Result(s): We included 202 participants, 51.5% were male, mean age of 55.6 years old, 49% had a comorbidity (41.9% hypertension), and 46 patients (22.8%) required an intensive care unit. A total of 159 (78.7%) patients reported at least one symptom after discharge. Of these, 132 (65.3%) persisted with at least one symptom during the telephone survey. Seventy-five (47.2%) of the 159 patients with persistent symptoms reported HCRU. Of these, 93.3% consulted a physician (mean consultations: 2.1 SD 1.1;mean consultations with specialists: 2.4 SD 2.0), and 9.3% were re-hospitalized. The average direct medical costs of post COVID-19 condition were US$824 (95%CI 195-1,454). Costs in outpatient were US$373 (95%CI 158-588), and in inpatient, US$3,285 (95%CI -167-6,738). Conclusion(s): It is crucial to follow up and identify patients discharged from the hospital who persist with symptoms after three months since we observed a greater HCRU, including prolonged recovery therapiesCopyright © 2023

COVID-19 Associated Brain Fog and Neurocognitive Assessment

BACKGROUND/AIMS: In this study, we aimed to make detailed neurocognitive assessments of patients who presented with brain fog after coronavirus disease-2019 (COVID-19) infection and to investigate their complaints after one-year of follow-up. MATERIALS AND METHODS: Patients who had COVID-19, which was not severe enough to require intensive care, and who subsequently applied to neurology due to cognitive complaints were included in this study. A neurocognitive test battery was applied to those patients who agreed to detailed examination (n=16). This battery consisted of the following tests: mini-mental test, enhanced cued recall test, phonemic fluency, categorical fluency, digit span, counting the months backwards, clock-drawing, arithmetic operations, trail-making, cube copying, intersecting pentagons, and the interpretation of proverbs and similes. At one year, the patients were called by phone and questioned as to whether their cognitive complaints had persisted. Those patients with ongoing complaints were invited to the hospital and re-evaluated via cognitive tests. The results are presented in comparison with age-matched healthy controls (n=15). RESULTS: Almost all of the patients' scores were within the "normal" range. The Spontaneous recall of the patients was statistically significantly lower than the controls (p=0.03). Although there were decreases in executive functions and central processing speed (trail making-A, trail making-B and reciting the months backwards tests) in the patient group, these differences were not statistically significant (p=0.07;p=0.14 and p=0.22, respectively) compared to the controls. We observed that the cognitive complaints of the patients had disappeared by the one-year follow-up. CONCLUSION: In our patients with brain fog, most of whom had mild COVID-19, we observed that among all cognitive functions, memory domain was most affected compared to the controls. At the one-year follow-up, COVID-related brain fog had disappeared.

Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

Long-Term Urban Epidemic and Disaster Resilience: The Planning and Assessment of a Comprehensive Underground Resilience Core

This study utilizes the enclosed and stable environment of underground space for long-term sustainable planning for urban epidemics and disasters. Owing to the COVID-19 epidemic, cities require long-term epidemic-disaster management. Therefore, this study proposed a strategy for integrating multiple functions to plan a comprehensive Underground Resilience Core (URC). A planning and assessment methods of URC were proposed. With this methodology, epidemic- and disaster- URCs were integrated to construct a comprehensive-URC in underground spaces. The results show: (1) Epidemic-resilient URCs adopting a joint progressive approach with designated hospitals can rapidly suppress an epidemic outbreak. (2) The regularity of the morphology of underground spaces determines the area of the URC. Bar-shaped underground spaces have the potential for planning disaster-URCs. (3) The URC planning efficiency ranking is as follows: Bar shapes lead overall, T shapes are second under seismic resilience, and Cross shapes are second under epidemic resilience. (4) The potential analysis of planning a comprehensive-URC in the underground parking in Chinese cities showed that the recovery time can be advanced from 29% to 39% and the comprehensive resilience can be improved by 37.63%. The results of this study can serve as sustainable urban planning strategies and assessment tools for long-term epidemic-disaster management.