Synergy of Information in Multimodal IoT Systems -- Discovering the impact of daily behaviour routines on physical activity level (preprint)

The intricate connection between daily behaviours and health necessitates robust behaviour monitoring, particularly with the advent of IoT systems. This study introduces an innovative approach, exploiting the synergy of information from various IoT sources, to assess the alignment of behaviour routines with health guidelines. We grouped routines based on guideline compliance and used a clustering method to identify similarities in behaviours and key characteristics within each cluster. Applied to an elderly care case study, our approach unveils patterns leading to physical inactivity by categorising days based on recommended daily steps. Utilising data from wristbands, smartphones, and ambient sensors, the study provides insights not achievable with single-source data. Visualisation in a calendar view aids health experts in understanding patient behaviours, enabling precise interventions. Notably, the approach facilitates early detection of behaviour changes during events like COVID-19 and Ramadan, available in our dataset. This work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous IoT-driven data analysis.

Evaluation of the dental clinics’ function based on the checklist dental services in the context of the COVID-19 pandemic (preprint)

Introduction: The risk of transmitting COVID-19 during dental procedures is one of the main concerns in dentistry. Given the rapid spread of the disease, the most effective way to prevent infection is to break the chain of disease transmission. The Alborz University of Medical Sciences developed and published a checklist of 46 items during the COVID-19 pandemic in April 2020. This study aimed to evaluate the principles of infection control in centers providing dental care during the COVID-19 pandemic based on the checklist prepared by Alborz University of Medical Sciences in Karaj, Iran.Method In this cross-sectional study, the number of 76 active dental clinics in the Alborz province was evaluated. The review began in June 2020 and the checklist was completed by the Alborz University of Medical Sciences team for each clinic while either the clinic manager or the clinic’s infection control team was present. Then, the adherence to the protocol was evaluated.Results Based on achieving at least 50% of the checklist score, clinics were divided into two groups: adherents and non-adherents. Out of a total of 76 clinics, 66 clinics (86/8%) followed the protocol. The highest level of adherence was related to principle number 12 (employees' restrooms are separate from patients') by a frequency of 81.6%. The lowest level was related to principle number 18 (the suspected or infected COVID-19 patients' triage and referral chart is visible to the reception personnel, and the triage is performed according to the protocol) by a frequency of 32.9%.Conclusion The results showed that most clinics followed the COVID-19 Dental Services protocol relatively well. Given the high rate of COVID-19 transmission, especially in its mutated strains, increasing the adherence level of dental clinics can be a valuable step toward reducing the transmission cycle of this disease. More frequent reviews of clinics, as well as referring non-relevant clinics to legal institutions could potentially help to reduce COVID-19 transmission, potentially.

Examining the Intersection of Inflammatory Bowel Disease and COVID-19: Insights from a National Inpatient Database Study (preprint)

Background: This study aimed to investigate the clinical indicators and outcomes of COVID-19-positive patients with inflammatory bowel disease (IBD), focusing on age, sex, socioeconomic status, and geographic location. The objective of this study was to fill the knowledge gap regarding determinants influencing outcomes in individuals with and without IBD who contracted COVID-19, thus impacting healthcare provision.Methods: This study utilized the nationwide inpatient sample (NIS) database for the period from January to December 2020. Patients were categorized into those with COVID-19 alone (controls) and those with both COVID-19 and IBD (cases). Demographic, clinical, and hospital-related variables were analyzed using statistical methods, including t tests and chi-square tests. Logistic and multivariate regression analyses were performed to assess factors affecting mortality.Results: Among COVID-19 patients with IBD, a sex disparity was observed, with more females in the IBD group than in the non-IBD group. The mean age was similar in both groups. Hospitalizations were concentrated in the age group of 65–84 years. Ethnically, Caucasians dominated both cohorts, and Medicare was the primary payer for a greater proportion of hospitalizations in the IBD group. Hospitalizations were prevalent in urban teaching hospitals, primarily in the southern and mid-western regions of the US. There were no significant differences in mortality rates, and clinical symptoms were comparable between the two groups. Factors associated with mortality included sex, age, and specific existing health conditions.Conclusion: Contrary to the initial hypothesis, the presence of IBD among COVID-19 patients did not significantly impact mortality rates. However, certain clinical indicators and outcomes are influenced by individual factors such as age, sex, and underlying health conditions. This study emphasizes the need for careful monitoring of COVID-19 patients with IBD, particularly those with additional risk factors. Further research is necessary to fully understand the biochemical interactions and implications of IBD in the context of COVID-19. This comprehensive study contributes valuable insights to healthcare authorities, aiding in patient management and outcome optimization.

Intensive care unit nurses' futile care perception in COVID-19 patients and its relationship with resilience and turnover intention: A cross-sectional descriptive study (preprint)

Background Futile care is a set of actions without creating a reasonable chance of benefiting critically ill patients. In the COVID-19 pandemic, there is a need to discuss futile care perception. This may cause unbearable pressure for nurses in terms of resilience and turnover intention.This study was designed to determine the ICU nurses' perception of futile care in COVID-19 patients and its relationship with resilience and turnover intention.Methods This cross-sectional descriptive study was conducted on 251 nurses working in COVID-19 ICUs, in 2021 who were recruited with the stratified random sampling method.The data collection tools included questionnaires for demographic information, futile care, resilience assessment, and turnover intention. Collected data were analyzed using SPSS v26 software.Results A high average level (64.78 ± 9.98) of futile care perception was found in the studied nurses, and their average levels of turnover intention (36.4 ± 5.1) and resilience (65.5 ± 14.1) were evaluated at a moderate level. A significant and inverse relationship was observed between futile care perception and resilience (P = 0.047, rs= -0.077). There was a significant and positive relationship between futile care perception and turnover intention (P = 0.026, rs= 0.151). The resilience and turnover intention variables were correlated inversely and significantly (rs= -0.158, P = 0.014).Conclusion The adoption of appropriate strategies by managers can direct the nursing care path from futile to useful care. Given the significant relationship between resilience and turnover intention, it is recommended to teach strategies in the teaching program of nurses to enhance their resilience.

Investigating the correlation between demographic data, laboratory results, intubation, and mortality rate in hospitalized COVID-19 patients: A retrospective study (preprint)

Background COVID-19 is a highly contagious infectious disease that rapidly escalated into a pandemic. This coronavirus pandemic has been associated with considerable morbidity and mortality. Multiple risk factors for severe, critical illness and death due to COVID-19 have been identified. This study investigated associations between demographic traits, laboratory test results, intubation status, and mortality among hospitalized COVID-19 patients.Methods In this cross-sectional descriptive study, 181 patients infected with COVID-19 who were admitted to Imam Reza Hospital in Tabriz, Iran, between March and September 2020 were reviewed using the census sampling method. Demographic data, laboratory results, and intubation history were extracted from the records. The data were analyzed using Chi-square, the independent samples t-test, Mann-Whitney U tests, and logistic regression using SPSS 27. P < 0.05 and the odds ratio with a 95% confidence interval were considered significant.Results Out of 181 patients infected with COVID-19, there were 110 (60.8%) males with a median age of 66 (16–96) years. There was a significant direct relationship between outcome and intubation, chest X-ray, CT-Scan, renal diseases, age, WBC, K, P, urea, Cr, and LDH, respectively (P < 0.0001, < 0.0001, 0.02, 0.01, < 0.0001, 0.002, 0.002, 0.008, < 0.0001, < 0.0001, and 0.008). In contrast, there was a significant inverse relationship between outcome and PH, HCO3, and SaO2 (P = 0.005, 0.002, and < 0.0001).Conclusion This study found that several factors increase the risk of mortality in patients infected with COVID-19, including intubation, abnormal lung CT and chest X-ray findings, underlying kidney disease, advanced age, and high levels of white blood cells, potassium, phosphorus, urea, creatinine, and lactate dehydrogenase. On the other hand, increased pH, bicarbonate, and oxygen saturation were protective factors against mortality. These results highlight important risk and protective factors for mortality in COVID-19 patients. The findings can inform clinical decision-making and resource allocation during future COVID-19 outbreaks and pandemics involving similar respiratory viruses.

Comprehensive Forecasting of California's Energy Consumption: A Multi-Source and Sectoral Analysis Using ARIMA and ARIMAX Models (preprint)

California's significant role as the second-largest consumer of energy in the United States underscores the importance of accurate energy consumption predictions. With a thriving industrial sector, a burgeoning population, and ambitious environmental goals, the state's energy landscape is dynamic and complex. This paper presents a comprehensive analysis of California's energy consumption trends and provides detailed forecasting models for different energy sources and sectors. The study leverages ARIMA and ARIMAX models, considering both historical consumption data and exogenous variables. We address the unique challenges posed by the COVID-19 pandemic and the limited data for 2022, highlighting the resilience of these models in the face of uncertainty. Our analysis reveals that while fossil fuels continue to dominate California's energy landscape, renewable energy sources, particularly solar and biomass, are experiencing substantial growth. Hydroelectric power, while sensitive to precipitation, remains a significant contributor to renewable energy consumption. Furthermore, we anticipate ongoing efforts to reduce fossil fuel consumption. The forecasts for energy consumption by sector suggest continued growth in the commercial and residential sectors, reflecting California's expanding economy and population. In contrast, the industrial sector is expected to experience more moderate changes, while the transportation sector remains the largest energy consumer.

Predictors of the chest CT score in COVID-19 patients: A cross-sectional study (preprint)

Background: Since the COVID-19 outbreak, pulmonary involvement was one of the most significant concerns in assessing patients. In the current study, we evaluated patient’s clinical and laboratory findings on the first visit to predict the severity of pulmonary involvement and their outcome. Methods: Four hundred seventy-eight COVID-19 patients with positive real-time reverse-transcriptase-polymerase chain reaction (RT-PCR) or highly suggestive symptoms with computed tomography(CT) imaging results with typical findings of COVID-19 were enrolled in the study. The clinical features, initial laboratory, CT findings, and short-term outcomes (ICU admission, mortality, length of hospitalization, and recovery time) were recorded. In addition, the severity of pulmonary involvement was assessed using a semi-quantitative scoring system (0-25). Results: Among 478 participants in this study, 353 (73.6%) were admitted to the hospital, and 57 (11.9%) patients were admitted to the ICU. A review of chest CT scans showed that Ground Glass Opacity (GGO) (58.5%) and consolidation (20.7%) were the most patterns of lung lesions. Among initial clinical and laboratory findings, anosmia (P = 0.01), respiratory rate (RR) ≥ 25 (P = 0.001), C-reactive protein (CRP) ≥ 91 (P = 0.002), white Blood Cell (WBC) >10,000 (P = 0.009), and SpO2 ≥ 93 (P = 0.04) was associated with higher chest CT score. Lung involvement and consolidation lesions on chest CT scans were also associated with more extended hospitalization and recovery period. Conclusions: Initial assessment of COVID-19 patients, including symptoms, vital signs, and routine laboratory tests, can predict the severity of lung involvement and unfavorable outcomes.

In the Line of Fire: A Systematic Review and Meta-Analysis of Job Burnout Among Nurses (preprint)

Using a systematic review and meta-analysis, this study investigates the impact of the COVID-19 pandemic on job burnout among nurses. We review healthcare articles following the PRISMA 2020 guidelines and identify the main aspects and factors of burnout among nurses during the pandemic. Using the Maslach Burnout questionnaire, we searched PubMed, ScienceDirect, and Google Scholar, three open-access databases, for relevant sources measuring emotional burnout, personal failure, and nurse depersonalization. Two reviewers extract and screen data from the sources and evaluate the risk of bias. The analysis reveals that 2.75% of nurses experienced job burnout during the pandemic, with a 95% confidence interval and rates varying from 1.87% to 7.75%. These findings emphasize the need for interventions to address the pandemic's effect on job burnout among nurses and enhance their well-being and healthcare quality. We recommend considering individual, organizational, and contextual factors influencing healthcare workers' burnout. Future research should focus on identifying effective interventions to lower burnout in nurses and other healthcare professionals during pandemics and high-stress situations.

Moral Sensitivity and Beliefs in Medical Students during the COVID-19 Pandemic (preprint)

Background Apart from healthcare concerns, the COVID-19 pandemic has also brought up ethical challenges. This study aimed to shed light on one of the ethical questions regarding medical students' responsibilities during the current pandemic.Methods An online anonymous questionnaire was applied to ask Iranian stagers and interns about their demographics and beliefs toward ethical responsibilities during the pandemic. Data were gathered in two phases; before and after COVID-19 vaccination. The SPSS software was used for statistical analysis.Results At the end of the study, 95 and 264 students participated in the first and second phases, respectively. The mean moral sensitivity score was 38.11 ± 4.88 in the first phase and 35.39 ± 4.9 in the second phase (p-value < 0.001). Among the participating students, 60 (63.2%) believed that it is their ethical responsibility to contribute to patient care at the hospital. The majority of students believed it was their ethical responsibility to contribute to the management of patients during the pandemic at both phases (63% and 71%, p-value = 0.15). In the multivariate analysis, age and ethical responsibility were related to moral sensitivity in the first phase. Age, sex, being worried about getting COVID-19, and ethical responsibility had a relationship with moral sensitivity score.Conclusion The moral sensitivity of medical students had a relationship with their belief in ethical responsibility. To improve moral reasoning and moral motivation, the adaptation of the medical ethics course with new challenges related to the COVID-19 pandemic and emphasizing their usefulness at the hospital during the crisis could be helpful.

Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights (preprint)

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use US county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.

Deep Neural Decision Forest: A Novel Approach for Predicting Recovery or Decease of COVID-19 Patients with Clinical and RT-PCR (preprint)

COVID-19 continues to be considered an endemic disease in spite of the World Health Organization's declaration that the pandemic is over. This pandemic has disrupted people's lives in unprecedented ways and caused widespread morbidity and mortality. As a result, it is important for emergency physicians to identify patients with a higher mortality risk in order to prioritize hospital equipment, especially in areas with limited medical services. The collected data from patients is beneficial to predict the outcome of COVID-19 cases, although there is a question about which data makes the most accurate predictions. Therefore, this study aims to accomplish two main objectives. First, we want to examine whether deep learning algorithms can predict a patient's morality. Second, we investigated the impact of Clinical and RT-PCR on prediction to determine which one is more reliable. We defined four stages with different feature sets and used interpretable deep learning methods to build appropriate model. Based on results, the deep neural decision forest performed the best across all stages and proved its capability to predict the recovery and death of patients. Additionally, results indicate that Clinical alone (without the use of RT-PCR) is the most effective method of diagnosis, with an accuracy of 80%. It is important to document and understand experiences from the COVID-19 pandemic in order to aid future medical efforts. This study can provide guidance for medical professionals in the event of a crisis or outbreak similar to COVID-19.

Usability of saliva as a reliable and non-invasive sample for SARS-CoV-2 detection in Durban, South Africa (preprint)

Background Saliva has been proposed as a potential more convenient, cost-effective, and easier sample for diagnosing SARS-CoV-2 infections, but there is limited knowledge of the impact of saliva volumes and stages of infection on its sensitivity and specificity.Methods In this study, we evaluated the performance of SARS-CoV-2 testing in 171 saliva samples across different volumes (50, 100, 300 and 500ul of saliva) and at different stages of disease (at screening, day 7, 14 and 28 post SARS-CoV-2 diagnosis) from 52 mostly mild symptomatic patients. Imperfect nasopharyngeal swab samples were used as a reference.Results Overall, 52 of the 171 samples were positive, with sensitivity of 73.2% and specificity of 81.0%. The sensitivity of saliva samples ranged from 70.6% for 50µl to 83.3% for 300µl of saliva collected. The specificity values ranged between 78.8% for 500µl and 86.4% for 100µl saliva. The overall percentage of positive results in nasopharyngeal swabs and saliva specimens remained comparable throughout the study visits. We observed no significant difference in cycle number values between saliva and nasopharyngeal swab specimens, irrespective of saliva volume tested.Conclusions The saliva collection offers a promising approach for population-based testing. Implementing robust saliva-based testing strategies could contribute significantly to controlling and managing the COVID-19 pandemic.

COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets (preprint)

Very large numbers of M-pox cases have, since the start of May 2022, been reported in non-endemic countries leading many to fear that the M-pox Outbreak would rapidly transition into another pandemic, while the COVID-19 pandemic ravages on. Given the similarities of M-pox with COVID-19, we chose to test the performance of COVID-19 models trained on South African twitter data on a hand-labelled M-pox dataset before and after fine-tuning. More than 20k M-pox-related tweets from South Africa were hand-labelled as being either positive, negative or neutral. After fine-tuning these COVID-19 models on the M-pox dataset, the F1-scores increased by more than 8% falling just short of 70%, but still outperforming state-of-the-art models and well-known classification algorithms. An LDA-based topic modelling procedure was used to compare the miss-classified M-pox tweets of the original COVID-19 RoBERTa model with its fine-tuned version, and from this analysis, we were able to draw conclusions on how to build more sophisticated models.

Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining (preprint)

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 patient's records, identified the most common signs and symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively.

COVID pandemic impact on hypertension management in North-East London: an observational cohort study using electronic health records (preprint)

Background: The COVID19 pandemic had a major impact on primary care management of long-term conditions such as hypertension. This observational cohort study of adults with hypertension registered in 193 primary care practices in North-East London between January 2019 and October 2022 investigated the impact of the COVID19 pandemic on the treatment and control of blood pressure including demographic and social inequities. Method and findings: In 224,329 adults with hypertension, the proportion with a blood pressure (BP) recorded within the preceding 1 year fell from a 91% pre-pandemic peak to 62% at the end of the pandemic lock-down phase and improved to 77% by the end of the study. The proportion with controlled hypertension (<80 years old, BP <=140/90mmHg; 80 or more years old: <=150/90mmHg) for the same time points was 81%, 50% and 60% respectively. Using 'blood pressure control' (which considered only patients with a valid blood pressure recording) as the indicator attenuated the reduction to 83%, 80% and 78% respectively. The study used multivariable logistic analysis at four representative time points (Pre-pandemic: April 2019; Pre lockdown: April 2020; Lockdown: April 2021; Post-lockdown: April 2022) to identify temporal, clinical and demographic influences on blood pressure monitoring and control. Pre-pandemic inequities in the management of hypertension were not significantly altered by the pandemic. Throughout the pandemic phases, in comparison to the White ethnic group, the Black ethnic group was less likely to achieve blood pressure control (ORs 0.81 [95% CI = 0.78 to 0.85, p-value<0.001] to 0.87 [95% CI = 0.84 to 0.91, p-value<0.001]). Conversely, the Asian ethnic group was more likely to have controlled blood pressure (ORs 1.09 [95% CI = 1.05 to 1.14, p-value<0.001] to 1.28 [95% CI = 1.23 to 1.32, p-value<0.001]). Younger, male, more affluent individuals, individuals with unknown or unrecorded ethnicity or those untreated were less likely to have blood pressure controlled to target throughout the study. Conclusion: The COVID pandemic had a greater impact on blood pressure recording than on blood pressure control. Although recording and control have improved, these had not returned to pre-pandemic levels by the end of the study period. Ethnic inequalities in blood pressure control persisted during the pandemic and remain outstanding.

SARS-CoV-2 Viral Clearance and Evolution Varies by Extent of Immunodeficiency (preprint)

Despite vaccination and antiviral therapies, immunocompromised individuals are at risk for prolonged SARS-CoV-2 infection, but the immune defects that predispose to persistent COVID- 19 remain incompletely understood. In this study, we performed detailed viro-immunologic analyses of a prospective cohort of participants with COVID-19. The median time to nasal viral RNA and culture clearance in the severe hematologic malignancy/transplant group (S-HT) were 72 and 21 days, respectively, which were significantly longer than clearance rates in the severe autoimmune/B-cell deficient (S-A), non-severe, and non-immunocompromised groups (P<0.001). Participants who were severely immunocompromised had greater SARS-CoV-2 evolution and higher risk of developing antiviral treatment resistance. Both S-HT and S-A participants had diminished SARS-CoV-2-specific humoral, while only the S-HT group had reduced T cell-mediated responses. This highlights the varied risk of persistent COVID-19 across immunosuppressive conditions and suggests that suppression of both B and T cell responses results in the highest contributing risk of persistent infection.

Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning (preprint)

Very few social media studies have been done on South African user-generated content during the COVID-19 pandemic and even fewer using hand-labelling over automated methods. Vaccination is a major tool in the fight against the pandemic, but vaccine hesitancy jeopardizes any public health effort. In this study, sentiment analysis on South African tweets related to vaccine hesitancy was performed, with the aim of training AI-mediated classification models and assessing their reliability in categorizing UGC. A dataset of 30000 tweets from South Africa were extracted and hand-labelled into one of three sentiment classes: positive, negative, neutral. The machine learning models used were LSTM, bi-LSTM, SVM, BERT-base-cased and the RoBERTa-base models, whereby their hyperparameters were carefully chosen and tuned using the WandB platform. We used two different approaches when we pre-processed our data for comparison: one was semantics-based, while the other was corpus-based. The pre-processing of the tweets in our dataset was performed using both methods, respectively. All models were found to have low F1-scores within a range of 45$\%$-55$\%$, except for BERT and RoBERTa which both achieved significantly better measures with overall F1-scores of 60$\%$ and 61$\%$, respectively. Topic modelling using an LDA was performed on the miss-classified tweets of the RoBERTa model to gain insight on how to further improve model accuracy.

DrugRep-HeSiaGraph: When heterogenous siamese neural network meets knowledge graphs for drug repurposing (preprint)

Background: The Drug repurposing is an approach that holds promise in identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues in constructing and embedding knowledge graphs. Results: This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-diseases knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-diseases knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.52%, a BS of 0.119, and an MCC of 69.12%. Conclusion: We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing.

SARS-CoV-2 virologic rebound with nirmatrelvir-ritonavir therapy (preprint)

Abstract Objective: To compare the frequency of replication-competent virologic rebound with and without nirmatrelvir-ritonavir treatment for acute COVID-19. Secondary aims were to estimate the validity of symptoms to detect rebound and the incidence of emergent nirmatrelvir-resistance mutations after rebound. Design: Observational cohort study. Setting: Multicenter healthcare system in Boston, Massachusetts. Participants: We enrolled ambulatory adults with a positive COVID-19 test and/or a prescription for nirmatrelvir-ritonavir. Exposures: Receipt of 5 days of nirmatrelvir-ritonavir treatment versus no COVID-19 therapy. Main Outcome and Measures: The primary outcome was COVID-19 virologic rebound, defined as either (1) a positive SARS-CoV-2 viral culture following a prior negative culture or (2) two consecutive viral loads [≥]4.0 log10 copies/milliliter after a prior reduction in viral load to <4.0 log10 copies/milliliter. Results: Compared with untreated individuals (n=55), those taking nirmatrelvir-ritonavir (n=72) were older, received more COVID-19 vaccinations, and were more commonly immunosuppressed. Fifteen individuals (20.8%) taking nirmatrelvir-ritonavir experienced virologic rebound versus one (1.8%) of the untreated (absolute difference 19.0% [95%CI 9.0-29.0%], P=0.001). In multivariable models, only N-R was associated with VR (AOR 10.02, 95%CI 1.13-88.74). VR occurred more commonly among those with earlier nirmatrelvir-ritonavir initiation (29.0%, 16.7% and 0% when initiated days 0, 1, and [≥]2 after diagnosis, respectively, P=0.089). Among participants on N-R, those experiencing rebound had prolonged shedding of replication-competent virus compared to those that did not rebound (median: 14 vs 3 days). Only 8/16 with virologic rebound reported worsening symptoms (50%, 95%CI 25%-75%); 2 were completely asymptomatic. We detected no post-rebound nirmatrelvir-resistance mutations in the NSP5 protease gene. Conclusions and Relevance: Virologic rebound occurred in approximately one in five people taking nirmatrelvir-ritonavir and often occurred without worsening symptoms. Because it is associated with replication-competent viral shedding, close monitoring and potential isolation of those who rebound should be considered.