Clinical Differences between Community-Acquired Mycoplasma pneumoniae Pneumonia and COVID-19 Pneumonia

Clinical Features of Long COVID Patients Coinfected With Mycoplasma pneumoniae

Background: Since the SARS-CoV-2 pandemic, many patients have suffered prolonged complications, called "long COVID." Mycoplasma pneumoniae is a common respiratory pathogen. Reports of simultaneous long COVID and M. pneumoniae infections are rare in the literature. Methods: We analyzed the clinical data of patients with long COVID-19 who visited the Respiratory Clinic of The Affiliated Hospital of Hangzhou Normal University between January 1 and January 31, 2023, together with their laboratory and radiographic findings, with Pearson's χ 2 test. Results: Fifty-two patients diagnosed with both long COVID and M. pneumoniae infection and 77 with long COVID only were compared. The ages, clinical symptoms, and comorbidities of the two groups did not differ significantly (p > 0.05). However, sex and imaging findings differed between the groups. Conclusions: Our study showed that long COVID-M. pneumoniae coinfection was more commonly seen in females and patients with typical chest computed tomography (CT) images.

Routine blood parameters as auxiliary diagnostic tools for Mycoplasma pneumoniae infection in children

Introduction. Recently, the incidence of Mycoplasma pneumoniae (M. pneumoniae) infection in children has been increasing annually. Early differential diagnosis of M. pneumoniae infection can not only avoid the abuse of antibiotics, but also is essential for early treatment and reduction of transmission.Gap statement. The change of routine blood parameters may have important clinical significance for the diagnosis of M. pneumoniae infection, but it has not been reported so far.Aim. This study aims to establish a predictive model for M. pneumoniae infection and explore the changes and clinical value of routine blood parameters in children with M. pneumoniae infection, serving as auxiliary indicators for the diagnosis and differentiation of clinical M. pneumoniae infection.Methodology. A total of 770 paediatric patients with respiratory tract infections were enrolled in this study, including 360 in the M. pneumoniae group, 40 in the SARS-CoV-2 group, 200 in the influenza A virus group, and 170 in the control group. The differences of routine blood parameters among all groups were compared, and risk factors were analysed using multivariate logistics analysis, and the diagnostic efficacy of differential indicators using ROC curves.Results. This study revealed that Mono% (OR: 3.411; 95% CI: 1.638-7.102; P=0.001) was independent risk factor associated with M. pneumoniae infection, and Mono% (AUC=0.786, the optimal cutoff at 7.8%) had a good discriminative ability between patients with M. pneumoniae infection and healthy individuals. Additionally, Mono% (OR: 0.424; 95% CI: 0.231-0.781; P=0.006) and Lymp% (OR: 0.430; 95% CI: 0.246-0.753; P=0.003) were independent risk factors for distinguishing M. pneumoniae infection from influenza A virus infection, and the Lymp% (AUC=0.786, the optimal cutoff at 22.1%) and Net% (AUC=0.761, the optimal cutoff at 65.2%) had good discriminative abilities between M. pneumoniae infection and influenza A infection. Furthermore, platelet distribution width (OR: 0.680; 95% CI: 0.538-0.858; P=0.001) was independent risk factor for distinguishing M. pneumoniae infection from SARS-CoV-2 infection. Meanwhile, the ROC curve demonstrated that PDW (AUC=0.786, the optimal cutoff at 15%) has a good ability to differentiate between M. pneumoniae infection and SARS-CoV-2 infection.Conclusion. This study demonstrates that routine blood parameters can be used as auxiliary diagnostic indicators for M. pneumoniae infection and provide reference for the diagnosis and differentiation of clinical M. pneumoniae infection.

Clinical differences between Mycoplasma pneumoniae pneumonia and Streptococcus pneumoniae pneumonia: a case control study

Introduction

Mycoplasma pneumoniae pneumonia (MPP) and Streptococcus pneumoniae pneumonia (SPP) are frequent causes of respiratory tract infection, the aims of the study were to explore the differences in clinical features between children with MPP and those with SPP.

Methods

This retrospective study included admitted children who were diagnosed with MPP or SPP over 5 years from January 2015 to January 2020. Children with MPP were compared to children with SPP in terms of clinical features.

Results

506 patients with MPP were compared to 311 patients with SPP in terms of clinical differences. The MPP group with a median age of 60 [29-89] months and the SPP group with a median age of 24 [10-40] months. Patients with MPP were older and had a higher occurrence of receiving antibiotics before admission, fever, dry cough, polypnea and diarrhea than patients with SPP (all p < 0.01). Patients with SPP were more likely to have wheezing, cyanosis and irritability (all p < 0.01). Laboratory findings in our study showed that there were significant differences between MPP and SPP patients in mean leucocyte count, neutrophil % (N%), lymphocyte % (L%), ALT levels, AST levels, LDH levels and incidence of accelerated procalcitonin (PCT) (all p < 0.01). Lower age, no dry cough, no polypnea, lower LDH levels, and higher PCT might lead to the diagnosis of SPP. Our study showed that age had a higher accuracy in predicting MPP than LDH levels, with an age >48.5 months shown to be an independent predictive factor for the early evaluation and identification of MPP.

Discussion

In conclusion, patients with MPP and SPP usually present with fever, cough and some nonspecific symptoms. Our study showed that age, dry cough, polypnea, LDH levels, and PCT levels were independent predictive factors associated with MPP and SPP.

miRNA, lncRNA and circRNA: targeted molecules with therapeutic promises in Mycoplasma pneumoniae infection

Mycoplasma pneumoniae (MP) is primarily recognized as a respiratory pathogen that causes community-acquired pneumonia, which can lead to acute upper and lower airway inflammation and extrapulmonary syndrome. Refractory pneumonia caused by MP can cause severe complications and even be life-threatening, particularly in infants and the elderly. It is well-known that non-coding RNAs (ncRNAs) represented by miRNAs, lncRNAs and circRNAs have been manifested to be widely involved in the regulation of gene expression. Growing evidence indicates that these ncRNAs have distinct differentiated expression in MP infection and affect multiple biological processes, playing an indispensable role in the initiation and promotion of MP infection. However, the epigenetic mechanisms involved in the development of MP infection remain unclear. This article reviews the mechanisms by which miRNAs, lncRNAs, and circRNAs mediate MP infection, such as inflammatory responses, apoptosis and pulmonary fibrosis. Focusing on miRNAs, lncRNAs and circRNAs associated with MP infection could provide new insights into this disease's early diagnosis and therapeutic approaches.

Development and validation of a nomogram for predicting Mycoplasma pneumoniae pneumonia in adults

The study aimed to explore predictors of Mycoplasma pneumoniae pneumonia (MPP) in adults and develop a nomogram predictive model in order to identify high-risk patients early. We retrospectively analysed the clinical data of a total of 337 adult patients with community-acquired pneumonia (CAP) and divided them into MPP and non-MPP groups according to whether they were infected with MP. Univariate and multivariate logistic regression analyses were used to screen independent predictors of MPP in adults and to developed a nomogram model. Receiver operating characteristic (ROC) curve, calibration curve, concordance index (C-index), and decision curve analysis (DCA) were used for the validation of the evaluation model. Finally, the nomogram was further evaluated by internal verification. Age, body temperature, dry cough, dizziness, CRP and tree-in-bud sign were independent predictors of MPP in adults (P < 0.05). The nomogram showed high accuracy with C-index of 0.836 and well-fitted calibration curves in both the training and validation sets. The area under the receiver operating curve (AUROC) was 0.829 (95% CI 0.774-0.883) for the training set and 0.847 (95% CI 0.768-0.925) for the validation set. This nomogram prediction model can accurately predict the risk of MPP occurrence in adults, which helps clinicians identify high-risk patients at an early stage and make drug selection and clinical decisions.