The data originated from a long-term surveillance program of respiratory syncytial virus and other childhood lower respiratory tract infections conducted between January 2007 and August 2020. The surveillance program sampled children younger than five years who were febrile or presented with sepsis or pneumonia. For this secondary analysis, children born prematurely, younger than one week or with a history of congenital heart disease were excluded.

Because the investigators used an existing dataset, the participants did not take part in the design or conduct of the predictive analysis. Investigators classified 228 children as having probable viral pneumonia because multiplex real-time PCR detected one or more viral pathogens associated with pneumonia in nasopharyngeal or oropharyngeal swabs, with no evidence of another infection. The remaining 229 children were placed in a possible bacterial group.

This group included 93 children with a positive blood culture, 12 with bacterial-virus co-infections and 124 whose cause remained unknown after blood culture, viral PCR and malaria testing. The broader classification reflected situations in which bacterial infection could not be excluded, but it also introduced uncertainty into the outcome used to train and evaluate the model. Indeed, 124 of the 229 children in the possible bacterial group, or 54% of that group, had an unknown etiology.

Among the 228 probable viral cases, the most frequently detected pathogens were respiratory syncytial virus A in 39 children, parainfluenza virus 3 in 36, human metapneumovirus in 35, influenza A in 34 and adenovirus in 28. The most common bacterial isolates from blood cultures were Escherichia coli and Streptococcus pneumoniae, each detected in 17 children, followed by Staphylococcus aureus in 11 and Haemophilus influenzae in eight. Severe pneumonia was present in 63% of the study population, including 72% of the probable viral group and 54% of the possible bacterial group.

This difference in severity was relevant because exploratory analyses found that the predictive performance of several biomarkers varied between severe and non-severe pneumonia. The researchers tested C-reactive protein, or CRP, procalcitonin, or PCT, and six less commonly evaluated plasma proteins: angiotensinogen, or AGT; serpin family A member 1, or SERPINA1; serpin family A member 3, or SERPINA3; paraoxonase 1, or PON1; histidine-rich glycoprotein, or HRG; and lipopolysaccharide-binding protein, or LBP. The six less commonly assessed proteins had been selected after an earlier discovery analysis within the surveillance cohort quantified 418 plasma proteins.

A modified Poisson regression model assessed associations while accounting for age, and backward selection identified variables for the multivariable model. The dataset was divided into training and validation subsets, with 70% used for training and 30% for validation. The investigators also repeated the complete model-building procedure through five-fold cross-validation stratified by etiology.

When possible bacterial cases were compared with probable viral cases, AGT had the strongest individual discriminatory performance, but its AUC was only 0.60, with a 95% confidence interval of 0.53 to 0.66. PCT and SERPINA1 each had an AUC of 0.58, while LBP had an AUC of 0.56. CRP, HRG, PON1 and SERPINA3 did not distinguish the groups beyond chance in the primary analysis.

A threshold of at least 7.9144 mean fluorescent intensity units for AGT had 49% sensitivity, 72% specificity, a positive predictive value of 64% and a negative predictive value of 58%. A SERPINA1 threshold of at least 8.9741 mean fluorescent intensity units had 29% sensitivity, 91% specificity, a positive predictive value of 75% and a negative predictive value of 86%. The high specificity of some individual markers came with correspondingly low sensitivity, and the biomarker distributions overlapped substantially between the two clinical groups.

In age-adjusted analyses of the training data, lower chest-wall indrawing, cough, convulsions, crackles, AGT and SERPINA1 were associated with pneumonia etiology. Once age, chest-wall indrawing and the other candidate predictors were considered together, however, only chest-wall indrawing remained significantly associated with etiology, and it was more common in the probable viral group. The model containing age and chest-wall indrawing produced an AUC of 0.72 in the training dataset.

That result declined to 0.61 in validation, while five-fold cross-validation gave a mean AUC of 0.64, with individual fold values ranging from 0.54 to 0.68. Chest-wall indrawing was retained in three of the five folds, cough and SERPINA1 in two folds each, and crackles and LBP in one fold each. No predictor was retained in four or more folds, indicating that variable selection was not stable across resamples.

The validation calibration slope was 0.5688, and the investigators described the predictions on unseen observations as overly extreme. A separate analysis excluded children with infections of unknown etiology and bacterial-virus co-infections, leaving more clearly defined bacterial and viral groups. In that restricted setting, chest-wall indrawing, cough, crackles and AGT at or above 7.9144 mean fluorescent intensity units were significant predictors after adjustment for age.

The resulting model achieved an AUC of 0.91 in the training set and 0.78 in the validation set. Validation sensitivity was 61%, and specificity was 96%. The model had a calibration intercept of 0.0696 and a calibration slope of 0.6915, indicating that its predicted risks were miscalibrated in the validation data.

The researchers said the improved performance suggested that unknown infections had attenuated the performance of the broader model. The restricted analysis was conducted in a selected subset with clearer laboratory-defined outcomes, rather than in the full population used for the primary model. Two previously published approaches were also applied without modification to the validation dataset.

A model using CRP of at least 5.70 milligrams per litre, fever of at least 38 degrees Celsius and rainy season, developed in hospitalized Indonesian children, achieved an AUC of 0.59, with a 95% confidence interval of 0.46 to 0.71. A second model using CRP of at least 72 milligrams per litre, fever of at least 38 degrees Celsius and the presence or absence of rhinorrhea reached an AUC of 0.74, with a 95% confidence interval of 0.64 to 0.85, a sensitivity of 55% and a specificity of 86%. These external comparisons showed mixed performance, but neither published approach nor the full biomarker model offered strong and consistent discrimination across the validation measures reported in the study.

Several limitations constrain interpretation. A negative blood culture did not exclude bacterial infection, and PCR detected only a defined panel of viruses, leaving other respiratory pathogens, including human bocavirus, unassessed. Nasopharyngeal or oropharyngeal swabs served as a proxy for the site of lung infection, potentially misclassifying the cause of pneumonia.

Biomarker results were recorded as mean fluorescent intensity rather than conventional concentrations, limiting direct comparison with studies that use concentration-based thresholds. The hospital-based population was also more severely ill than many children treated in outpatient settings, so the findings cannot be generalized to all children with pneumonia. The authors concluded that future work could address weaknesses in infection classification, stratify analyses by disease severity and further examine potential variation in biomarker performance by age.

Their overall finding was that the broad combination of clinical observations and protein markers did not provide sufficiently accurate internal validation for the primary model intended to distinguish possible bacterial from probable viral pneumonia.

Subject of Research: Internal validation of clinical and plasma-biomarker models for distinguishing possible bacterial from probable viral childhood pneumonia in Kilifi, Kenya

Article Title: Predictive modelling to differentiate possible bacterial and viral cases of childhood pneumonia in Kilifi, Kenya using protein markers and clinical data

Article References: Matuli, C., Waeni, J. M., Gicheru, E. T., Sande, C. J., & Gallagher, K. (2026). Predictive modelling to differentiate possible bacterial and viral cases of childhood pneumonia in Kilifi, Kenya using protein markers and clinical data. Frontiers in Medicine, 13, Article 1943481. https://doi.org/10.3389/fmed.2026.1943481

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1943481

Keywords: childhood pneumonia, bacterial pneumonia, viral pneumonia, plasma biomarkers, predictive modelling, internal validation, Kilifi County, Kenya, paediatric diagnostics, clinical signs, Predictive, modelling

The research, led by Sukanta Kumar Tulo of the School of Electronics Engineering at Vellore Institute of Technology in India, together with colleagues at VIT and the Manipal Institute of Technology, takes a deliberately lightweight path. Instead of relying on massive deep neural networks, the team extracted a compact set of morphological imaging biomarkers from segmented lung and mediastinum masks and then fused the most informative ones using wrapper-based feature selection with classical machine learning classifiers. The approach is designed for settings where annotated data, computing power, and expert radiologist time are all in short supply.

The dataset consisted of 200 pediatric chest radiographs—100 bacterial and 100 viral pneumonia cases—drawn from a publicly available collection of images acquired at the Guangzhou Women’s and Children’s Medical Centre. The images, anteroposterior views of children under five years of age, had been reviewed by experts for quality assurance and were collected under ethical committee clearance in accordance with the Declaration of Helsinki. Each image was resampled to 512 by 512 pixels using bicubic interpolation to standardize the inputs for analysis.

Segmentation is the foundation of the pipeline, and the authors built a hybrid model that pairs two level set techniques, each suited to a different anatomical challenge. The lungs were delineated with the Reaction-Diffusion Level Set method, an edge-based approach that incorporates a diffusion term into the level set formulation and thereby eliminates the need for the slow and often unstable re-initialization step required by conventional level set contours. In the study, the method was configured with a Gaussian kernel of standard deviation 1.5, a fixed force of 0.7, a time increment of 0.1, and 800 iterations. The mediastinum—the central compartment of the thoracic cavity bounded laterally by the lungs—was segmented with the Chan-Vese model, a region-based technique that partitions the image according to mean pixel intensities inside and outside a closed contour, with additional regularization terms to ensure boundary smoothness. The mediastinum segmentation ran for 900 iterations, a number chosen empirically.

Once the masks were extracted, the team computed 25 morphological biomarkers per region: 18 geometric descriptors and 7 Hu invariant moments. The geometric set includes area, perimeter, orientation, eccentricity, circularity, equivalent diameter, convex area, solidity, extent, major and minor axis lengths, maximum and minimum Feret diameters and their angles, and bounding box dimensions. Size-related measures such as area and equivalent diameter quantify how large a structure is, while eccentricity and orientation capture elongation and tilt. Compactness indicators such as solidity and circularity describe how closely a region resembles a regular form, and Feret diameters add directional information that is useful for irregular structures. Hu moments, constructed from second- and third-order normalized central moments, are invariant to translation, scale, and rotation, which makes them robust descriptors of shape regardless of how the structure sits in the image.

Statistical analysis with the Wilcoxon rank sum test revealed clear morphological differences between the two pneumonia types. In bacterial cases, the mean lung area was significantly smaller than in viral cases by 14.3 percent, while the mean mediastinum area was 29.6 percent larger, and the mean minor axis length of the mediastinum was 41.9 percent higher, indicating a widened central compartment. The authors attribute the mediastinal enlargement to abnormalities such as bronchial dilatation and mediastinal lymphadenopathy, which enlarge the airways and lymph nodes, as well as pleural effusion, which can shift the mediastinum. In viral images, by contrast, irregular opacities appeared diffusely in both lungs, consistent with interstitial patterns. Roughly 46 percent of bacterial images showed a mediastinum area larger than the lung area, compared with only 21 percent of viral images. Biomarkers including area, minor axis length, eccentricity, circularity, equivalent diameter, minimum Feret diameter, and bounding dimensions were statistically significant for both the left lung and the mediastinum.

To squeeze the most discriminative power out of these measurements, the researchers applied wrapper-based biomarker selection, a strategy that repeatedly trains and tests a learning algorithm with different biomarker combinations to find the subset that yields the best predictive performance. Using forward selection starting from an empty set, they ran the procedure with two algorithms: Linear Discriminant Analysis, which seeks a linear projection that maximizes the ratio of between-class to within-class variance, and Support Vector Machine, which constructs an optimal hyperplane that maximizes the margin between classes. The optimal biomarkers from the left lung, right lung, and mediastinum were then concatenated in series to form fused biomarker sets, which were fed back into LDA and SVM classifiers with a linear kernel and a soft-margin penalty of one, evaluated with five-fold cross-validation.

The results showed a consistent pattern: fusing biomarkers from both regions outperformed using either region alone. With the SVM classifier, raw fused biomarkers achieved an F-measure of 82.9 percent, compared with 75.5 percent for mediastinum biomarkers alone and 81.3 percent for lung biomarkers alone. Wrapper selection improved things further. The LDA-based wrapper combined with the LDA classifier delivered F-measures of 76.5 percent for mediastinum biomarkers, 82.0 percent for lung biomarkers, and a peak of 87.3 percent for the fused set, with an accuracy of 87.0 percent, sensitivity of 89.0 percent, specificity of 85.0 percent, and an area under the ROC curve of 88.3 percent. The authors attribute the superiority of the LDA wrapper to its efficient dimensionality reduction, which helps it select biomarkers that generalize well.

Compared with deep learning studies on the same publicly available dataset, the biomarker fusion approach holds its own while demanding far less. The original Kermany benchmark using the Inception network reported 90.7 percent accuracy, and later deep models such as MD-Conv reached 91.0 percent, but these require large annotated datasets and powerful hardware. The new pipeline, by contrast, ran on an ordinary AMD Ryzen 7 system with 16 gigabytes of RAM in MATLAB, without GPU acceleration. Segmentation took roughly four seconds per image due to its iterative nature, while feature extraction and classification across the entire 200-image dataset required only about four seconds in total, or roughly 0.02 seconds per image. The authors note that their method achieved better accuracy and specificity than some deep learning approaches, including Inception, SqueezeNet, and VGG-19, although customized deep networks still lead in raw performance.

The practical appeal of the work lies in its suitability for resource-constrained environments, where deep learning infrastructure is often unavailable. Because the pipeline needs no large-scale training and runs on commodity hardware, the researchers suggest it could serve as a valuable tool for primary screening of pneumonia and as a decision-support system assisting radiologists in differentiating bacterial from viral infections in pediatric patients. The team plans to extend the framework with ensemble and hybrid learning approaches and explainable techniques, and to validate it on larger and more diverse datasets to assess generalizability. For now, the study offers a concrete demonstration that two anatomical regions are better than one, and that carefully chosen shape descriptors can rival heavyweight artificial intelligence at a fraction of the computational cost.

Subject of Research: Fusion of lung and mediastinum imaging biomarkers for differentiating bacterial and viral pneumonia in pediatric chest radiographs

Article Title: Detection of bacterial and viral pneumonia in pediatric chest radiographs using fusion of mediastinum and lung imaging biomarkers

Article References: Tulo, S. K., Kerketta, S. R., Rahul Manohar, O., & Martha, P. (2026). Detection of bacterial and viral pneumonia in pediatric chest radiographs using fusion of mediastinum and lung imaging biomarkers. Frontiers in Signal Processing, 6, Article 1831207. https://doi.org/10.3389/frsip.2026.1831207

Image Credits: AI Generated

DOI: 10.3389/frsip.2026.1831207

Keywords: pneumonia, pediatric chest radiographs, bacterial pneumonia, viral pneumonia, mediastinum, image segmentation, level set methods, Hu moments, biomarker fusion, machine learning, support vector machine, linear discriminant analysis

The patient arrived at the hospital with a low-grade fever and a facial rash, one week after close contact with his daughter, who had been suffering from upper respiratory symptoms. Two years earlier he had been diagnosed with interstitial lung disease, suspected to be bird-related chronic hypersensitivity pneumonitis, a form of inflammatory lung injury driven by exposure to avian antigens such as those from feather duvets and down jackets. His condition had been managed with systemic corticosteroids, which had been carefully tapered and discontinued several months before admission, and he had been rigorously counseled to avoid avian exposures. A former smoker with a 38 pack-year history, he initially received oral levofloxacin from his local physician without improvement, prompting referral to the authors’ hospital.

On admission the clinical picture was deceptively subtle. He was alert, his temperature was 37.0 degrees Celsius, and his oxygen saturation sat at 90 percent on room air while fine crackles could be heard bilaterally and a mild facial rash was visible. Laboratory evaluation showed only mild inflammation, with a C-reactive protein level of 0.89 mg/dL, and importantly, serum KL-6 and surfactant protein D, biomarkers commonly used to track interstitial lung disease activity, remained unchanged from his baseline values. An extensive infectious workup was entirely negative, including serum beta-D-glucan, cytomegalovirus antigenemia, Aspergillus IgG and a multiplex polymerase chain reaction panel for common respiratory pathogens. Given the recent sick contact in the family and the facial rash, clinicians performed parvovirus B19 serology, which returned positive IgM and IgG antibodies, indicating a recent primary infection.

Imaging told a more alarming story. Chest radiography revealed decreased radiolucency in the bilateral middle and lower lung fields compared with films obtained only three months earlier, and high-resolution computed tomography demonstrated newly developed bilateral ground-glass opacities, a finding that in a patient with underlying fibrotic lung disease immediately raises concern for an acute exacerbation. Broad-spectrum antibiotics were started on the assumption of bacterial pneumonia, yet the opacities continued to progress, forcing the team to look deeper. Bronchoscopy was performed, and bronchoalveolar lavage fluid collected from the left B5 segment yielded a total cell count of 1.42 x 10^5 cells per milliliter with striking lymphocytic predominance of 63.5 percent, along with histiocytes at 33.8 percent, neutrophils at 1.0 percent and eosinophils at 1.7 percent.

The decisive result came from molecular testing of that lavage fluid: polymerase chain reaction was positive for parvovirus B19 DNA, directly demonstrating the virus within the lower respiratory tract during the acute phase of illness. Testing for Pneumocystis jirovecii was negative, no pathogenic organisms were isolated in culture, and cytology excluded malignancy. With a compatible exposure history, serologic confirmation of acute infection and viral DNA recovered from the lung itself, the clinicians concluded that the acute respiratory deterioration was associated with parvovirus B19, a diagnosis essentially never made in immunocompetent adults. During hospitalization the serum KL-6 and SP-D levels rose and the patient’s oxygen requirement climbed transiently to 3 liters per minute, reflecting genuine physiologic worsening rather than an incidental imaging finding.

Treatment took the form of high-dose pulsed corticosteroids, with methylprednisolone at 1000 mg per day, which produced rapid clinical and radiological improvement. The patient was transitioned to tapering oral prednisolone and discharged on Day 19 of his admission. At four-month follow-up he remained clinically stable, with sustained resolution of the opacities on serial chest radiographs. The favorable response is notable, though as the authors caution, it does not by itself settle the underlying mechanism, since the suspected bird-related hypersensitivity pneumonitis is generally considered more steroid-responsive than progressive fibrotic diseases such as idiopathic pulmonary fibrosis.

The diagnostic reasoning in this case illuminates a genuine clinical dilemma. Acute exacerbation of interstitial lung disease is defined by rapid respiratory deterioration within one month and the appearance of new bilateral ground-glass opacities on high-resolution computed tomography, criteria that this patient met. Yet distinguishing primary viral pneumonia from infection-triggered acute exacerbation remains notoriously difficult because the two entities overlap substantially in their clinical and radiological features. Previous prospective work cited by the authors detected respiratory viral infections in 19.2 percent of acute exacerbation cases, and such infections are associated with poor short-term outcomes. In this instance, common culprits such as influenza virus and the herpesviruses were excluded, and the absence of any alternative infectious agent or cardiac failure strengthened the case for parvovirus B19 as the causative trigger, although the authors acknowledge that unrecognized avian antigen exposure and progression of the underlying hypersensitivity pneumonitis could not be completely excluded, particularly given the lymphocytic, rather than neutrophilic, lavage profile.

The mechanistic plausibility of parvovirus B19 lung injury rests on well-characterized virology. The virus is a small, non-enveloped, single-stranded DNA virus that enters cells by binding the P antigen, a globoside receptor expressed not only on erythroid progenitors but also on pulmonary endothelial cells. Viral interaction with this receptor may cause endothelial injury, increased vascular permeability and diffuse alveolar damage, the histological substrate of acute lung injury. Experimental studies add a pro-fibrotic dimension: the viral non-structural protein NS1 has been shown to exacerbate fibrotic processes through activation of the TGF-beta/Smad signaling pathway, while the VP1-unique region of the capsid induces inflammatory cytokine release and disrupts epithelial barriers. In a lung already scarred by chronic interstitial disease, these processes could act as a ‘second hit’ that tips a compensated patient into acute exacerbation, providing a coherent biological explanation for what the radiographs and lavage findings captured.

The authors, Tomoki Kishaba and Satoshi Marumo of the Department of Respirology at Kitano Hospital, conclude that although the relative contributions of direct viral lung injury, infection-triggered exacerbation and progression of the underlying bird-related hypersensitivity pneumonitis could not be definitively disentangled, the case highlights the importance of considering parvovirus B19 as a potential contributor to acute respiratory deterioration, particularly in patients with underlying interstitial lung disease. For clinicians, the practical lessons are concrete: a facial rash or known ‘slapped cheek’ contact in an deteriorating ILD patient should prompt parvovirus B19 serology, and bronchoalveolar lavage with viral PCR can identify pathogens that routine respiratory panels miss, since parvovirus B19 does not appear on standard multiplex panels. For virologists and pulmonologists alike, the report underscores how much remains unknown about a virus that infects roughly half of humanity by adulthood, and the authors call for further studies to clarify the mechanisms underlying parvovirus B19-associated pulmonary involvement before such life-threatening presentations are encountered again.

Subject of Research: Acute respiratory deterioration associated with parvovirus B19 infection in a patient with interstitial lung disease.

Article Title: Acute Respiratory Deterioration Following Parvovirus B19 Infection in a Patient With Interstitial Lung Disease: A Case Report

Article References: Kishaba, T., Marumo, S., Nishida, Y., Nohara, E., Higashi, J., Aoki, S., Okura, C., Jinno, S., Tashima, N., Morimoto, C., Kitajima, T., & Inoue, D. (2026). Acute Respiratory Deterioration Following Parvovirus B19 Infection in a Patient With Interstitial Lung Disease: A Case Report. Respirology Case Reports, 14(9), Article e70751. https://doi.org/10.1002/rcr2.70751

Image Credits: AI Generated

DOI: 10.1002/rcr2.70751

Keywords: parvovirus B19, interstitial lung disease, acute exacerbation, hypersensitivity pneumonitis, viral pneumonia, bronchoalveolar lavage, ground-glass opacities, corticosteroids, respiratory infection, lung fibrosis, Acute, Respiratory