The study included 3,586 patients who had been tested with a multiplex real-time PCR respiratory panel after initially testing negative for SARS-CoV-2. This design choice is important: because the COVID-19 pandemic dominated respiratory diagnostics from 2020 onward, patients negative for the coronavirus were typically then screened with broader panels capable of detecting a wide range of viruses and bacteria simultaneously. Multiplex PCR works by amplifying genetic material from multiple target organisms in a single reaction, allowing laboratories to identify pathogens such as influenza viruses, respiratory syncytial virus, rhinoviruses, adenoviruses, and bacterial species like Streptococcus pneumoniae and Haemophilus influenzae from one sample. The technique has become a cornerstone of modern respiratory surveillance, particularly in settings where clinical symptoms alone cannot distinguish between viral and bacterial causes of infection. In this cohort, 62.4 percent of the patients were children, reflecting the fact that young people bear a disproportionate burden of acute respiratory tract infections and are tested more frequently with these panels.

Overall, at least one pathogen was detected in 52.6 percent of the samples, but the identity of those pathogens was striking. The most frequently detected organism was Streptococcus pneumoniae, found in 36.7 percent of samples, followed by Haemophilus influenzae at 8.2 percent, rhinovirus at 6.1 percent, and human bocavirus and influenza A, each at 2.8 percent. The dominance of bacterial detections, particularly pneumococcus, is notable because multiplex PCR detects genetic material rather than viable organisms, and colonizing bacteria can be present in the airways without causing disease. Nevertheless, the pattern provides a snapshot of what was circulating in the population’s respiratory tracts during the study window. Positivity rates were dramatically higher in children than in adults, at 64.5 percent versus 33.0 percent, a difference that was highly statistically significant. Detection also peaked in autumn and winter, consistent with the well-documented seasonality of respiratory infections in temperate climates, where indoor crowding and lower humidity favor transmission.

The temporal trends across the four years were equally revealing. Overall positivity declined from 57.5 percent in 2021 to 34.8 percent in 2023, and then rebounded sharply to 60.7 percent in 2024. This trajectory mirrors patterns observed globally after the COVID-19 pandemic, when widespread masking, distancing, and lockdowns suppressed transmission of many common respiratory pathogens, only for them to return with unusual intensity once restrictions lifted. The dip in 2023 is particularly interesting because it coincides with the year of the earthquakes, which struck in February and upended daily life across the region. Yet the researchers found that the overall rate of pathogen detection was almost identical before and after the earthquakes, at 52.5 percent versus 52.7 percent. On the surface, the disaster appeared to have left the total burden of detectable respiratory pathogens essentially unchanged.

Beneath that stable overall figure, however, the composition of detected pathogens shifted in ways that initially seemed alarming. In unadjusted comparisons, bacterial positivity rose from 34.2 percent before the earthquakes to 44.9 percent afterward, while viral positivity fell from 31.2 percent to 19.8 percent. This pattern would be consistent with what disaster epidemiologists might predict: displacement into crowded shelters, interruption of routine healthcare, and stress-induced immune suppression could plausibly increase bacterial carriage and transmission, while reduced social mixing in some settings or altered testing patterns might suppress viral detection. When the researchers built multivariable logistic regression models adjusting for age group, sex, season, clinical setting, year of sampling, and PCR platform, the signal persisted in some analyses. Compared with 2021, the adjusted odds of bacterial detection in 2024 were 5.50 times higher, with a 95 percent confidence interval of 3.51 to 8.63, a substantial and statistically robust increase.

But the story took a decisive turn when the team applied sensitivity analyses designed to test the robustness of the earthquake association. When they restricted the post-earthquake period to just the first 12 months after the disaster, the increases in bacterial and pneumococcal detection disappeared. When they restricted the analysis to samples tested only with the Bio-Speedy platform, one of the PCR systems used in the laboratory during the study period, the increases were not reproduced either. These findings matter because they suggest that the apparent post-earthquake bacterial surge may have been an artifact of how the comparison windows were defined or of changes in testing platforms and laboratory workflows over time, rather than a genuine biological effect of the earthquakes. The authors explicitly state that the differences between pre- and post-earthquake periods varied according to the time window and analytic subset examined, and that the data do not support attributing these differences specifically to the earthquakes.

This methodological caution is one of the study’s most valuable contributions. Natural experiments like earthquakes generate enormous public health interest, and observational data from affected regions are often the only evidence available in the chaotic aftermath of disaster. Yet such data are vulnerable to confounding by indication, changes in who gets tested, shifts in laboratory capacity, and the simple passage of time. A patient tested in late 2024 differs from one tested in 2021 not only in earthquake exposure but also in pandemic-era immune history, vaccination status, healthcare access, and the diagnostic technology applied to their sample. By showing that a seemingly compelling association dissolved under alternative analytic choices, the study offers a template for how respiratory surveillance data should be interrogated before dramatic conclusions are drawn about disaster-driven outbreaks.

The findings also carry practical implications for laboratories and public health agencies. Multiplex PCR panels are powerful tools, but their results depend on the specific platform, the panel composition, and the clinical context in which testing is ordered. The fact that restricting the analysis to a single PCR platform changed the results underscores how technical factors can shape epidemiological signals. The authors argue that multiplex PCR-based surveillance may be valuable for monitoring respiratory pathogen dynamics during periods of epidemiological disruption, precisely because such periods are when conventional assumptions about seasonality and pathogen distribution break down. Continuous, standardized surveillance, they suggest, is the best defense against both missed outbreaks and false alarms.

The research was supported by the Scientific Research Projects Coordination Unit of Mersin University and approved by the university’s Health Sciences Research Ethics Committee, which waived the requirement for informed consent given the retrospective design and use of anonymized routine clinical data. As the region continues to recover from one of the deadliest earthquakes in modern history, the study provides reassurance that the disaster did not produce a measurable, sustained shift in the overall burden of detectable respiratory pathogens, while simultaneously demonstrating how much careful statistical work is required to separate genuine epidemiological change from the noise of a health system in upheaval. For infectious disease watchers, the message is twofold: respiratory pathogens in southern Türkiye followed their own temporal rhythms through pandemic and earthquake alike, and the tools we use to watch them must be scrutinized as rigorously as the pathogens themselves.

Subject of Research: Temporal patterns of respiratory pathogen detection by multiplex PCR in southern Türkiye before and after the 2023 earthquakes

Article Title: Temporal patterns of respiratory pathogen detection in southern Türkiye: a retrospective multiplex PCR study with pre- and post-earthquake comparisons

Article References: Gülbudak, H., Bozok, T., Bekci, A., Ülger, S. T., & Aslan, G. (2026). Temporal patterns of respiratory pathogen detection in southern Türkiye: a retrospective multiplex PCR study with pre- and post-earthquake comparisons. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14638-z

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14638-z

Keywords: respiratory pathogens, multiplex PCR, Streptococcus pneumoniae, earthquakes, Türkiye, epidemiology, respiratory tract infections, post-pandemic, surveillance, BMC Infectious Diseases, bacterial detection, rhinovirus

Community-acquired pneumonia, or CAP, remains one of the most common and consequential infectious diseases worldwide. It is defined as pneumonia acquired outside of hospitals or long-term care facilities, and it presents clinicians with a persistent diagnostic dilemma: the symptoms, fever, cough, sputum production and breathlessness, overlap heavily with other respiratory conditions, and the causative pathogen is often never identified. Standard laboratory markers such as C-reactive protein (CRP), procalcitonin and the erythrocyte sedimentation rate are informative but imperfect, offering only coarse indications of inflammation rather than specific insight into the nature of the infection. The Chinese team set out to test whether NETs, as a direct readout of neutrophil activation by pathogens, could fill part of that gap.

The study enrolled 222 patients with community-acquired pneumonia admitted to a tertiary hospital in Lanzhou between January 2021 and December 2022. From each patient the researchers collected paired samples: peripheral blood serum and bronchoalveolar lavage fluid, or BALF, the liquid retrieved from the deep airways after it has been washed across the alveolar surfaces where the infection resides. As a baseline, they also gathered serum from 100 age- and gender-matched healthy volunteers. NETs levels in every specimen were quantified using a double-antibody sandwich enzyme-linked immunosorbent assay, an ELISA format in which capture antibodies immobilize NET-associated antigens from the sample and detection antibodies generate a measurable signal proportional to the amount of trap material present. Statistical analysis was performed with SPSS 25.0, with Pearson correlation used to relate NETs concentrations to routine laboratory indicators and receiver operating characteristic (ROC) curves used to evaluate diagnostic performance.

The first and most fundamental finding was that NETs are markedly elevated in pneumonia. Serum NETs levels in CAP patients were significantly higher than in healthy controls, and within the patients themselves, serum concentrations were substantially greater than the paired BALF concentrations, a difference that held at the stringent threshold of P < 0.001. This pattern is biologically interesting: it suggests that NETosis in pneumonia is not confined to the local lung environment but spills over into systemic circulation, where trap components or their degradation products become detectable in the blood. That spillover is precisely what makes serum NETs attractive as a clinical biomarker, because a blood draw is far simpler, faster and less invasive than bronchoscopy with lavage.

The researchers then stratified patients by whether a pathogen had been detected. Patients in whom a pathogen was identified showed elevated NETs levels in both serum and BALF compared with patients in whom no pathogen was found, with P values below 0.05. This supports the idea that NETs release is not merely a nonspecific consequence of lung inflammation but is actively driven by microbial invasion. When the analysis turned to the type of pathogen, a nuanced picture emerged. NETs levels did not differ significantly between patients with single bacterial infections and those with single viral infections, indicating that both major classes of respiratory pathogens provoke comparable NETotic responses as measured by this assay. However, the mixed-infection group, patients harboring both bacterial and viral pathogens simultaneously, exhibited significantly higher NETs levels in both serum and BALF than either single-infection group.

This last result may prove to be the study’s most clinically valuable contribution. Mixed infections in pneumonia are notoriously difficult to recognize at the bedside, yet they carry important therapeutic implications: a patient co-infected with influenza and Streptococcus pneumoniae, for example, may need both antiviral and antibacterial coverage, and missing one component can be fatal. If a simple serum measurement could flag the likelihood of a mixed infection, it could guide empiric therapy decisions, prompt more aggressive microbiological workup, and help steward antibiotics more rationally. The finding that NETs are amplified specifically in mixed infections, rather than merely in bacterial ones, suggests the biomarker reflects the total burden of pathogen-driven neutrophil activation rather than the identity of any single pathogen.

The correlation analysis reinforced the mechanistic coherence of the findings. Serum and BALF NETs levels were strongly correlated with the peripheral neutrophil count, which is expected since neutrophils are the source of the traps, and with fibrin degradation products (FDP), a marker of fibrin turnover. The link to FDP is notable because NETosis and coagulation are deeply intertwined: NETs provide scaffolding for thrombus formation, activate platelets, and interact with the coagulation cascade, while fibrinolytic products may in turn reflect the inflammatory meshwork that NETs help create. NETs levels were moderately correlated with monocyte count and only weakly correlated with CRP, suggesting that NETs capture a dimension of the immune response, granulocyte-driven and coagulation-linked, that conventional inflammatory markers only partially overlap.

For diagnostic performance, the ROC analysis delivered the headline numbers. Serum NETs yielded an area under the curve (AUC) of 0.8293 for distinguishing CAP patients from healthy controls, with an optimal cut-off value of 362.2. At that threshold, the assay achieved a sensitivity of 63.96 percent but a specificity of 98.00 percent. In practical terms, this profile describes a rule-in test: a positive result above the cut-off makes pneumonia highly likely, even though a negative result does not exclude it. High specificity is a prized property in emergency departments, where distinguishing true bacterial or viral pneumonia from heart failure, pulmonary embolism or other mimics can change management immediately. Used alongside clinical assessment and existing markers, a highly specific NETs measurement could add genuine discriminative value.

The authors conclude that community-acquired pneumonia induces significant NETs release, that pathogen invasion further upregulates NETs expression, and that this upregulation is prominently amplified in mixed-infection conditions. They position serum NETs as reliable biomarkers for the auxiliary diagnosis of CAP and for the identification of mixed infection, with favorable prospects for clinical application. The study was approved by the Ethics Committee of Gansu Provincial Center for Disease Control and Prevention under approval number 013 of 2019, and all participants provided informed consent. The work received support from the National Natural Science Foundation of China and several Gansu provincial funding programs, and it was conducted with technical assistance from the Virus Laboratory of the Gansu Provincial Center for Disease Control and Prevention and the Department of Clinical Laboratory Center of Lanzhou University Second Hospital.

As with any biomarker study, the path from these findings to routine clinical use will require further validation in independent and more diverse cohorts, standardization of the ELISA platform across laboratories, and clarification of how NETs measurements perform in patients with chronic inflammatory diseases, autoimmune conditions or other states of neutrophil activation that could confound specificity. The study was published as an open-access article under a Creative Commons Attribution 4.0 license, with the accepted manuscript released early for rapid access and citable under its permanent DOI. Even so, the conceptual advance is clear: a century-old view of neutrophils as simple phagocytes has given way to an appreciation of them as engineers of extracellular scaffolds, and those scaffolds, it turns out, leave measurable traces in a vial of blood that speak volumes about what is happening deep in the lungs. For a disease as common, costly and diagnostically slippery as community-acquired pneumonia, that is a signal worth pursuing.

Subject of Research: Neutrophil extracellular traps as diagnostic biomarkers for community-acquired pneumonia and mixed respiratory infections

Article Title: Diagnostic value of neutrophil extracellular traps in community-acquired pneumonia and their potential in identifying mixed infection

Article References: Zhang, H., Yang, X., Zhou, J., Wang, B., Wang, M., Zhang, X., & Li, J. (2026). Diagnostic value of neutrophil extracellular traps in community-acquired pneumonia and their potential in identifying mixed infection. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14390-4

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14390-4

Keywords: neutrophil extracellular traps, community-acquired pneumonia, biomarkers, mixed infection, ELISA, serum diagnostics, neutrophils, bronchoalveolar lavage fluid, ROC analysis, respiratory pathogens, C-reactive protein, fibrin degradation products

The headline finding is a measure of just how busy the respiratory pathogen world is in this region. Of the 1,716 patients enrolled in the study, 957 — or 55.77 percent — tested positive for at least one pathogen. Viruses dominated the spectrum, accounting for a 40.44 percent detection rate, while bacterial pathogens were found in 28.03 percent of samples. Most infections were caused by a single organism, at 34.15 percent, but a striking 21.51 percent of patients carried mixed infections, meaning two or more pathogens were detected simultaneously. That co-infection rate underscores a growing recognition in respiratory medicine that the old model of one microbe, one disease is often an oversimplification, particularly in children and in patients whose symptoms are severe enough to bring them to a hospital.

Demographics turned out to matter enormously. Children bore the heaviest pathogen load by far, with a detection rate of 69.81 percent in the pediatric group — nearly double the 35.68 percent seen in adults aged 66 and older, who had the lowest rate of any age bracket. This pattern is consistent with immunological reality: young children encounter most respiratory viruses for the first time and have not yet built the antibody repertoires that older adults accumulate over decades of exposure. Sex, by contrast, made essentially no difference. Male and female detection rates were almost perfectly matched, at 55.78 and 55.75 percent respectively, a difference so small it carried no statistical significance.

Perhaps the most socially revealing result was the urban-rural divide. Patients from urban areas tested positive 62.10 percent of the time, while those from rural districts were positive in only 20.28 percent of cases — a gap so large that the chi-square statistic reached 132.522 with a P value below 0.005. The authors of the study note that detection rates differed significantly across regions, and while the retrospective design cannot fully disentangle the reasons, several plausible mechanisms suggest themselves. Urban patients may reach the sentinel hospital earlier and more often, giving laboratory tests a better chance to catch pathogens before the immune system clears them. Differences in healthcare access, testing frequency, and exposure patterns between city and countryside could all contribute to what is, on its face, one of the starkest disparities in the dataset.

Seasonality, as expected, proved to be a powerful organizing force. Influenza A virus, the perennial driver of winter epidemics, peaked in the winter months with a detection rate of 20.40 percent. Rhinovirus, the most common cause of the common cold, reached its highest detection in autumn at 15.63 percent. Every major pathogen in the study displayed a significant seasonal pattern, reinforcing the idea that the calendar is one of the most reliable predictors of which microbe a clinician is likely to encounter. For hospitals in Kaifeng and comparable cities, this kind of seasonally resolved baseline is more than an academic curiosity: it can guide when to stock particular diagnostics, when to anticipate surges in pediatric admissions, and when to prioritize vaccination campaigns against specific influenza strains.

The study’s second major aim was more ambitious — to test whether environmental conditions, specifically temperature, precipitation, and fine particulate matter known as PM2.5, correlate with the ebb and flow of individual pathogens. This question has long fascinated epidemiologists. Laboratory studies show that cold, dry air stabilizes influenza virions and dries out the mucosal defenses of the airway, while air pollution can damage the epithelial lining of the lungs and impair the clearance of inhaled microbes. If those mechanisms translate cleanly into population-level patterns, one would expect strong statistical associations between meteorological variables and pathogen detection rates.

At first glance, the raw correlations looked promising. Influenza B virus detection showed a moderate negative correlation trend with precipitation, with a Spearman rank coefficient of −0.591 and a P value below 0.05, suggesting the virus was less often detected in wetter periods. Bordetella pertussis, the bacterium responsible for whooping cough, showed a moderate positive correlation trend with PM2.5 concentration, with a coefficient of 0.597 and a P value below 0.05 — the kind of result that would seem to support the idea that polluted air and bacterial respiratory infection travel together.

But the researchers did something that separates careful science from headline-chasing: they corrected for multiple comparisons. Because the team tested fifteen pathogens against three environmental variables, the analysis generated forty-five separate statistical tests. Under those conditions, a handful of nominally significant results are expected by pure chance alone. Applying the Bonferroni correction — a stringent threshold that lowered the significance cutoff to 0.0011 — neither the influenza B–precipitation link nor the B. pertussis–PM2.5 link survived. In the end, thirteen of the fifteen pathogens examined showed no statistically significant correlation with temperature, precipitation, or PM2.5, and the two marginal associations were flagged by the authors themselves as likely chance findings given the number of comparisons performed.

This kind of statistical honesty is worth pausing on, because it stands in contrast to a large body of published work that reports weather–pathogen associations without adequately accounting for the multiplicity of tests. Spearman correlation, the method used here, is an exploratory tool: it measures whether two variables rise and fall together in rank order, but it cannot establish causation, control for confounders such as seasonal testing patterns or school holidays, or distinguish a genuine biological signal from coincidence. The Kaifeng team’s decision to present their marginal findings transparently, while explicitly cautioning against overinterpretation, sets a methodological example for local surveillance studies elsewhere.

The practical value of the study lies less in its null environmental results than in the epidemiological baseline it establishes. For a city in Central China — a region the authors note has been underrepresented in multi-pathogen surveillance literature — the data provide a reference point against which future outbreaks, vaccine effects, and shifts in pathogen dominance can be measured. The findings argue for prevention strategies that are targeted by population and season rather than by weather forecast: intensified pediatric diagnostics and vaccination, attention to the diagnostic gap suggested by low rural detection rates, and winter preparedness for influenza A. As respiratory pathogens continue to circulate in an era of post-pandemic surveillance, local studies like this one, grounded in routine hospital testing and disciplined statistics, are the quiet infrastructure on which effective public health responses are built.

Subject of Research: Associations between meteorological factors and the respiratory pathogen spectrum in Kaifeng, China

Article Title: Association between meteorological factors and the respiratory pathogen spectrum in Kaifeng, China from 2023 to 2025: a retrospective analysis

Article References: Li, H., Chen, L., Li, Q., Liang, S.-S., & Zhao, H.-Y. (2026). Association between meteorological factors and the respiratory pathogen spectrum in Kaifeng, China from 2023 to 2025: a retrospective analysis. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14522-w

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14522-w

Keywords: acute respiratory infection, respiratory pathogens, meteorological factors, PM2.5, influenza, rhinovirus, Bordetella pertussis, seasonality, epidemiology, PCR surveillance, Kaifeng, China