Clinical manifestations of pain in patients suffering from COVID-19 infected with Delta variant of SARS-Cov-2

Mohammadian Erdi, Ali; Zakavi, Minoo; Amani, Mohammad; Fooladi, Shahnaz; Abedi, Ali

doi:10.3389/fpain.2023.1282527

Cited by 3 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In symptomatic COVID and even in patients who survive its initial stage but with persistent symptoms, often referred to as “long COVID” or “post‐acute sequelae of SARS‐CoV‐2 infection” (PASC), arthralgia or rheumatic complaints are frequently reported, 8,43–45 but fewer efforts have been put forward to explore the underlying factors behind SARS‐CoV‐2‐induced arthralgia. Recent studies indicate that hypocalcemia, characterized by decreased blood calcium levels, was observed as a common condition in SARS‐CoV‐2 patients, with lower levels correlating with heightened disease severity and poorer clinical outcomes 27,46,47 .…”

Section: Discussionmentioning

confidence: 99%

The abortive SARS‐CoV‐2 infection of osteoclast precursors promotes their differentiation into osteoclasts

Sviercz,

Jarmoluk,

Godoy Coto

et al. 2024

Journal of Medical Virology

View full text Add to dashboard Cite

The Coronavirus Disease 2019 (COVID‐19) pandemic has resulted in the loss of millions of lives, although a majority of those infected have managed to survive. Consequently, a set of outcomes, identified as long COVID, is now emerging. While the primary target of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is the respiratory system, the impact of COVID‐19 extends to various body parts, including the bone. This study aims to investigate the effects of acute SARS‐CoV‐2 infection on osteoclastogenesis, utilizing both ancestral and Omicron viral strains. Monocyte‐derived macrophages, which serve as precursors to osteoclasts, were exposed to both viral variants. However, the infection proved abortive, even though ACE2 receptor expression increased postinfection, with no significant impact on cellular viability and redox balance. Both SARS‐CoV‐2 strains heightened osteoclast formation in a dose‐dependent manner, as well as CD51/61 expression and bone resorptive ability. Notably, SARS‐CoV‐2 induced early pro‐inflammatory M1 macrophage polarization, shifting toward an M2‐like profile. Osteoclastogenesis‐related genes (RANK, NFATc1, DC‐STAMP, MMP9) were upregulated, and surprisingly, SARS‐CoV‐2 variants promoted RANKL‐independent osteoclast formation. This thorough investigation illuminates the intricate interplay between SARS‐CoV‐2 and osteoclast precursors, suggesting potential implications for bone homeostasis and opening new avenues for therapeutic exploration in COVID‐19.

show abstract

Section: Discussionmentioning

confidence: 99%

The abortive SARS‐CoV‐2 infection of osteoclast precursors promotes their differentiation into osteoclasts

Sviercz,

Jarmoluk,

Godoy Coto

et al. 2024

Journal of Medical Virology

View full text Add to dashboard Cite

show abstract

Integrating Statistical Methods and Machine Learning Techniques to Analyze and Classify COVID-19 Symptom Severity

Raddad,

Hasasneh,

Abdallah

et al. 2024

BDCC

View full text Add to dashboard Cite

Background/Objectives: The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), led to significant global health challenges, including the urgent need for accurate symptom severity prediction aimed at optimizing treatment. While machine learning (ML) and deep learning (DL) models have shown promise in predicting COVID-19 severity using imaging and clinical data, there is limited research utilizing comprehensive tabular symptom datasets. This study aims to address this gap by leveraging a detailed symptom dataset to develop robust models for categorizing COVID-19 symptom severity, thereby enhancing clinical decision making. Methods: A unique tabular dataset was created using questionnaire responses from 5654 individuals, including demographic information, comorbidities, travel history, and medical data. Both unsupervised and supervised ML techniques were employed, including k-means clustering to categorize symptom severity into mild, moderate, and severe clusters. In addition, classification models, namely, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), random forest, and a deep neural network (DNN) were used to predict symptom severity levels. Feature importance was analyzed using the random forest model for its robustness with high-dimensional data and ability to capture complex non-linear relationships, and statistical significance was evaluated through ANOVA and Chi-square tests. Results: Our study showed that fatigue, joint pain, and headache were the most important features in predicting severity. SVM, AdaBoost, and random forest achieved an accuracy of 94%, while XGBoost achieved an accuracy of 96%. DNN showed robust performance in handling complex patterns with 98% accuracy. In terms of precision and recall metrics, both the XGBoost and DNN models demonstrated robust performance, particularly for the moderate class. XGBoost recorded 98% precision and 97% recall, while DNN achieved 99% precision and recall. The clustering approach improved classification accuracy by reducing noise and dimensionality. Statistical tests confirmed the significance of additional features like Body Mass Index (BMI), age, and dominant variant type. Conclusions: Integrating symptom data with advanced ML models offers a promising approach for accurate COVID-19 severity classification. This method provides a reliable tool for healthcare professionals to optimize patient care and resource management, particularly in managing COVID-19 and potential future pandemics. Future work should focus on incorporating imaging and clinical data to further enhance model accuracy and clinical applicability.

show abstract

Bilateral Shoulder Infections and Delayed Recognition of Spinal Discitis in an Adult Male With Mild COVID-19 and Staphylococcus aureus Bacteremia: Case Report and Literature Review

Skedros,

Montgomery,

Cronin

et al. 2024

Cureus

View full text Add to dashboard Cite

Clinical manifestations of pain in patients suffering from COVID-19 infected with Delta variant of SARS-Cov-2

Cited by 3 publications

References 47 publications

The abortive SARS‐CoV‐2 infection of osteoclast precursors promotes their differentiation into osteoclasts

The abortive SARS‐CoV‐2 infection of osteoclast precursors promotes their differentiation into osteoclasts

Integrating Statistical Methods and Machine Learning Techniques to Analyze and Classify COVID-19 Symptom Severity

Bilateral Shoulder Infections and Delayed Recognition of Spinal Discitis in an Adult Male With Mild COVID-19 and Staphylococcus aureus Bacteremia: Case Report and Literature Review

Contact Info

Product

Resources

About