Respiratory Infectious Diseases Research Articles

Exploring post-COVID-19 health effects and features with advanced machine learning techniques

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson’s coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1

BackgroundInfluenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. MethodsThe generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA—GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. ResultsConsidering the MAPE, RMSE, and R squared values, the ARMA—GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models’ predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. ConclusionsOur study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Design of convertible patient care unit for both non-pandemic and pandemic times: Prototype, building spatial layout, and ventilation design

Convertible patient care units can accommodate non-respiratory infectious disease patients in non-pandemic times and provide isolated treatment for respiratory infectious disease patients during pandemic times. A prototype convertible patient care unit covering a floor area of 1518 m2, featuring 16 wards, and accommodating 40 beds was developed to examine the impact of building spatial layout and ventilation design on the inter-zonal airborne transmission and energy use intensity (EUI) during both non-pandemic and pandemic times. Building spatial layout referred to the presence of patient corridors and anterooms, while ventilation design referred to the location of air terminals and the calculation method of residual air volume (namely, difference between supply and exhaust airflow rates). Adding anterooms provided an 18 % reduction in pressure gradient fluctuations during door-opening events. The absence of exhaust in the medical corridor, anterooms, and wards increased contamination leakages to the patient corridors. Using the differential pressure method to calculate residual air volume was more effective in controlling the inter-zonal pressure gradient than the fixed residual air volume method. Various conversion methods resulted in an 84%–134 % increase of EUI compared to non-pandemic times, without, however, corresponding reduction in inter-zonal airborne transmission risk. The condition of adding both patient corridors and anterooms, exhaust-only patient corridors, anterooms with balanced supply-exhaust airflow rates, and differential pressure method to calculate residual air volume were effective conversion strategies to simultaneously address both the inter-zonal airborne transmission and EUI. These findings provide fundamental information for designing convertible patient care units and revising related guidelines in China.

Investigating the Impact of Green Space Ratio and Layout on Bioaerosol Concentrations in Urban High-Density Areas: A Simulation Study in Beijing, China

The COVID-19 pandemic significantly impacted global development. Through bioaerosols emitted by human respiration, respiratory infectious diseases, including COVID-19, are transmitted. The bioaerosol concentrations can be affected by the urban climate and morphology. However, the effects of urban green spaces on bioaerosol concentrations remain unclear. Focusing on the dormitory area of Beijing Forestry University, this study first investigated the influence of different green space ratios on the average bioaerosol concentrations using the ENVI-met software. Moreover, both overall and local green space layouts were analyzed for their impact on bioaerosol concentrations. The results indicated that ventilation conditions were the primary factor influencing bioaerosol concentrations. During peak congestion, a 10% increase in the green space ratio resulted in a 2% rise in the average bioaerosol concentration. Furthermore, a distributed layout resulted in a 1.3% higher average bioaerosol concentration than a concentrated layout with an equivalent green space ratio. Enacting strategies such as Roadside Green Spaces Retreat, Road Spaces Expansion, and Intersection Green Spaces Chamfering led to reductions in local bioaerosol concentrations by up to 17.7%, 18.44%, and 12.69%, respectively. This study highlights the importance of adjusting green space layouts in urban high-density areas after the pandemic, reducing the risk of population exposure to bioaerosol concentrations.

Fim3–24/ptxP-3 genotype is associated to whooping cough outbreak in Brazilian Midwest: The selection of Bordetella pertussis strains driven by vaccine immunization

Whopping cough (or Pertussis) is an acute infectious respiratory disease caused by Bordetella pertussis bacteria. The disease is highly transmissible and can be fatal in children under two years old. Since the introduction of vaccine immunization in 1940, Pertussis incidence decreased worldwide. In Brazil, the immunization was introduced in 1977 using the whole cell (wP) vaccine. Despite the high vaccination coverage, an unexpected increase in the number of observed Pertussis cases was observed in 2012. In this year, 2257 cases were reported exceeding the average incidence rate of <1000 cases per year until 2010. This outbreak reached a peak level in 2014 and ended in 2018 according to the Brazilian National Surveillance System (SINAN). To understand the relationship between the outbreak and the vaccination, bacterial isolates (n = 136) from the Brazilian Midwest region obtained during the outbreak were submitted to genotyping of two vaccine loci: ptxP and fim3. Most of isolates (102) were obtained from nursing children (29 days to 2 years old). Genotyping of 94 isolates revealed that fim3–24/ptxP-3 was the most prevalent genotype (68%) associated with the outbreak peak. Two additional genotypes were also observed: fim3–1/ptxP-3 (15%) and fim3–3/ptxP-3 (17%). Conversely, the fim3–1/ptxP-2 genotype, which is harbored by the strain used in the wP vaccine (Bp137), was not observed. These results showed that B. pertussis circulating strains in the outbreak analyzed were different from the strain used for Pertussis immunization in Brazil. These observations provide insights that could be used to target vaccination programs to prevent future whooping cough outbreaks in Brazil.

Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7

Wearing masks is an effective protective measure for residents to prevent respiratory infectious diseases when going out. Due to issues such as a small target size, target occlusion leading to information loss, false positives, and missed detections, the effectiveness of face mask-wearing detection needs improvement. To address these issues, an improved YOLOv7 object detection model is proposed. Firstly, the C2f_SCConv module is introduced in the backbone network to replace some ELAN modules for feature extraction, enhancing the detection performance of small targets. Next, the SPPFCSPCA module is proposed to optimize the spatial pyramid pooling structure, accelerating the model convergence speed and improving detection accuracy. Finally, the HAM_Detect decoupled detection head structure is introduced to mitigate missed and false detections caused by target occlusion, further accelerating model convergence and improving detection performance in complex environments. The experimental results show that improved YOLOv7 achieved an mAP of 90.1% on the test set, a 1.4% improvement over the original YOLOv7 model. The detection accuracy of each category improved, effectively providing technical support for mask-wearing detection in complex environments.

Gut microbial features may influence antiviral IgG levels after vaccination against viral respiratory infectious diseases: the evidence from two-sample bidirectional mendelian randomization

BackgroundVaccination is effective in preventing viral respiratory infectious diseases through protective antibodies and the gut microbiome has been proven to regulate human immunity. This study explores the causal correlations between gut microbial features and serum-specific antiviral immunoglobulin G (IgG) levels.MethodsWe conduct a two-sample bidirectional Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data to explore the causal relationships between 412 gut microbial features and four antiviral IgG (for influenza A, measles, rubella, and mumps) levels. To make the results more reliable, we used four robust methods and performed comprehensive sensitivity analyses.ResultsThe MR analyses revealed 26, 13, 20, and 18 causal associations of the gut microbial features influencing four IgG levels separately. ​Interestingly, ten microbial features, like genus Collinsella, species Bifidobacterium longum, and the biosynthesis of L-alanine have shown the capacity to regulate multiple IgG levels with consistent direction (rise or fall). The ​reverse MR analysis suggested several potential causal associations of IgG levels affecting microbial features.ConclusionsThe human immune response against viral respiratory infectious diseases could be modulated by changing the abundance of gut microbes, which provided new approaches for the intervention of viral respiratory infections.

Genomic analysis of canine pneumoviruses and canine respiratory coronavirus from New Zealand

ABSTRACT Aims To isolate canine respiratory coronavirus (CRCoV) and canine pneumovirus (CnPnV) in cell culture and to compare partial genomic sequences of CRCoV and CnPnV from New Zealand with those from other countries. Methods Oropharyngeal swab samples from dogs affected by canine infectious respiratory disease syndrome that were positive for CnPnV (n = 15) or CRCoV (n = 1) by virus-specific reverse transcriptase quantitative PCR (RT-qPCR) in a previous study comprised the starting material. Virus isolation was performed in HRT-18 cells for CRCoV and RAW 264.7 and Vero cells for CnPnV. The entire sequence of CnPnV G protein (1,266 nucleotides) and most (8,063/9,707 nucleotides) of the 3’ region of CRCoV that codes for 10 structural and accessory proteins were amplified and sequenced. The sequences were analysed and compared with other sequences available in GenBank using standard molecular tools including phylogenetic analysis. Results Virus isolation was unsuccessful for both CRCoV and CnPnV. Pneumovirus G protein was amplified from 3/15 (20%) samples that were positive for CnPnV RNA by RT-qPCR. Two of these (NZ-048 and NZ-049) were 100% identical to each other, and 90.9% identical to the third one (NZ-007). Based on phylogenetic analysis of the G protein gene, CnPnV NZ-048 and NZ-049 clustered with sequences from the USA, Thailand and Italy in group A, and CnPnV NZ-007 clustered with sequences from the USA in group B. The characteristics of the predicted genes (length, position) and their putative protein products (size, predicted structure, presence of N- and O-glycosylation sites) of the New Zealand CRCoV sequence were consistent with those reported previously, except for the region located between open reading frame (ORF)3 (coding for S protein) and ORF6 (coding for E protein). The New Zealand virus was predicted to encode 5.9 kDa, 27 kDa and 12.7 kDa proteins, which differed from the putative coding capacity of this region reported for CRCoV from other countries. Conclusions This report represents the first characterisation of partial genomic sequences of CRCoV and CnPnV from New Zealand. Our results suggest that the population of CnPnV circulating in New Zealand is not homogeneous, and that the viruses from two clades described overseas are also present here. Limited conclusions can be made based on only one CRCoV sequence, but the putative differences in the coding capacity of New Zealand CRCoV support the previously reported variability of this region. The reasons for such variability and its biological implications need to be further elucidated.

Intranasal neomycin evokes broad-spectrum antiviral immunity in the upper respiratory tract

Respiratory virus infections in humans cause a broad-spectrum of diseases that result in substantial morbidity and mortality annually worldwide. To reduce the global burden of respiratory viral diseases, preventative and therapeutic interventions that are accessible and effective are urgently needed, especially in countries that are disproportionately affected. Repurposing generic medicine has the potential to bring new treatments for infectious diseases to patients efficiently and equitably. In this study, we found that intranasal delivery of neomycin, a generic aminoglycoside antibiotic, induces the expression of interferon-stimulated genes (ISGs) in the nasal mucosa that is independent of the commensal microbiota. Prophylactic or therapeutic administration of neomycin provided significant protection against upper respiratory infection and lethal disease in a mouse model of COVID-19. Furthermore, neomycin treatment protected Mx1 congenic mice from upper and lower respiratory infections with a highly virulent strain of influenza A virus. In Syrian hamsters, neomycin treatment potently mitigated contact transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In healthy humans, intranasal application of neomycin-containing Neosporin ointment was well tolerated and effective at inducing ISG expression in the nose in a subset of participants. These findings suggest that neomycin has the potential to be harnessed as a host-directed antiviral strategy for the prevention and treatment of respiratory viral infections.

Epidemiological and etiological characteristics of 1266 patients with severe acute respiratory infection in central China, 2018–2020: a retrospective survey

Epidemiological and etiological characteristics of 1266 patients with severe acute respiratory infection in central China, 2018–2020: a retrospective survey

A Markov chain-based approach for assessing respiratory infection risk in a multi-zone office building

Most respiratory infectious disease transmission events occur in indoor environments, and examining the infection risk in different types of multi-zone buildings is critical to the safe reopening and use of public buildings. Appropriate space design influences occupant behavior and virus spread, thereby aiding in mitigating indoor infection risk. This study aims to quickly evaluate the multi-route infection risk in multi-zone buildings with relatively stable occupant populations, and to quantify the effectiveness of spatial interventions at different levels. A Markov chain-based approach was proposed to characterize occupants' time-activity trajectories with low time cost and relatively high accuracy. Four transmission routes are considered, especially for the fine-grained characterization of close contact. A real university office building was selected as a case study to quantitatively evaluate the effects of three non-pharmacological interventions including spatial layout, ventilation, and occupant behavioral control. Results showed that short-range airborne transmission dominated, and optimized combination spatial design interventions reduced the indoor infection risk by up to 10 %. Zone layout intervention was the most effective, with the division of the open-plan office area into three subzones, reducing the risk by 9.6 %, which was superior to the intervention of maintaining a one-meter interpersonal distance. Followed by the furniture layout, without changing the seating capacity and without desktop baffles, the L-shaped table layout worked better reducing the risk by 2.78 %. This study can help architects and managers better understand the risk of indoor transmission in multizone buildings from a spatial design perspective, to select appropriate interventions to reduce the risk.

Dynamics of respiratory infectious diseases under rapid urbanization and COVID-19 pandemic in the subcenter of Beijing during 2014–2022

ObjectiveThe study analyzed the impact of urbanization on epidemiological characteristics of respiratory infectious disease in Tongzhou District, Beijing during 2014–2022 to provide reference for prevention and control priorities of respiratory infectious diseases during the innovative urbanization process in China. MethodsThe incidence data of notifiable respiratory infectious diseases (NRIDs) in Tongzhou Beijing during 2014–2022 were summarized. The trend of incidence rate was analyzed by Joinpoint regression model, and entropy method was performed to construct the comprehensive index of urbanization (CIU) and generalized linear model was used to analyze the influence of CIU on the incidence rate of respiratory infectious diseases. ResultsTotally 72616 NRIDs cases were reported in Tongzhou District during 2014–2022, and the incidence rate of NRIDs was higher during 2017–2019 (153/100 000) than during 2014–2016 (930/100 000) and during 2020–2022 (371/100 000), respectively (both P < 0.001). The CIU constantly increased with slight fluctuation in 2016 and 2018, respectively. The incidence rate of NRIDs showed an increase along with the CIU during 2014–2019 (r = 0.95, P = 0.004), while the incidence rate's tendency was interrupted by COVID-19 during 2020 with slight decrease in 2020–2021 and rebounded in 2022. For the patients aged <15 years, the incidence rate of NRIDs revealed a very sharp rise at the urbanization period without COVID-19 pandemic compared with that under pre-urbanization period (RR = 7.93, 95 % CI 7.63−8.24), and dropped off to the similar level as of pre-urbanization period when COVID-19 pandemic spread. ConclusionsUrbanization process may increase the incidence of NRIDs but constrained by COVID-19. Certain measures should be taken to prevent and control the effects by urbanization process, such as good natural environment with less population density, ecological environment with good air quality, promoted hand hygiene, mask wearing, keeping interpersonal distance, vaccination, media publicity for NRIDs’ prevention and control.

Occupation and COVID-19: Lessons From the Pandemic

Recognition that an individual's job could affect the likelihood of contracting coronavirus disease 2019 created challenges for investigators who sought to understand and prevent the transmission of severe acute respiratory syndrome coronavirus 2. Considerable research resources were devoted to separating the effects of occupational from nonoccupational risk factors. This commentary highlights results from studies that adjusted for multiple nonoccupational risk factors while estimating the effects of occupations and occupational risk factors. Methods used in these studies will prove useful in future infectious disease epidemics and pandemics and may potentially enrich studies of other occupational infectious and noninfectious respiratory diseases.

Acid etching post-treatment enhanced fungal sterilization performance of copper-manganese-cerium oxide in liquid and aerosol: Materials and molecular biological mechanisms

Bioaerosol is one of the main ways to spread respiratory infectious diseases. In order to further improve the sterilization efficiency of copper-manganese-cerium oxide (CuMnCeOx), the post-treatment method based on acid etching was adopted. The results showed that sterilization efficiency of the treated CuMnCeOx could reach 99% in aerosol with space velocity of 1400 h−1. L(+)-ascorbic acid successfully promoted the formation of Cu+, oxygen vacancies and the generation of reactive oxygen species (ROS) on the surface of the treated CuMnCeOx. During sterilization in liquid system, the transcriptome identified 316 differentially expressed genes, including 270 up-regulated genes and 46 down-regulated genes. Differentially expressed genes were significantly enriched in cell wall (GO:0005618) and external encapsulating structure (GO:0030312). Up-regulated genes were shown in regulation of reactive oxygen species biosynthetic processes (GO:1903409, GO:1903426, GO:1903428) and positive regulation all of reactive oxygen species metabolic process (GO:2000379), indicating that ROS induced cell death by destroying cell wall.

Air Pollution and Respiratory Infectious Diseases

Recent research suggests that short-term exposure to air pollution is associated with an elevated prevalence of respiratory infectious disease. In this paper, we examine the relationship between the air quality index and weekly cases of COVID-19 and influenza-like illnesses (ILI) in the United States. We address potential bias from omitted variables and measurement error with an instrumental variable approach using atmospheric temperature inversions. Unlike other recent studies, we find no relationship between air quality and either COVID-19 or ILI cases.

Effectiveness of Rehabilitation Interventions in Individuals With Emerging Virtual Respiratory Tract Infectious Disease: A Systematic Review and Meta-Analysis

Objective Assessing rehabilitation effectiveness for persistent symptoms post-infection with emerging viral respiratory diseases. Data sources Systematic review of seven databases (MEDLINE, EMBASE, Cochrane Library, PEDro, MedRxiv, CNKI, Wanfang) until 30 December 2023. Review methods Evaluated 101 studies (9593 participants) on respiratory function, exercise capacity, and quality of life. Methodological quality was assessed using the Cochrane Collaboration's Risk of Bias tool for randomized controlled trials (RCTs), the Newcastle-Ottawa Scale (NOS) for observational studies and non-RCTs, and the NIH Quality Assessment Tools for before-after studies. Results The most common rehabilitation program combined breathing exercises with aerobic exercise or strength training. Rehabilitation interventions significantly enhanced respiratory function, as evidenced by improvements on the Borg Scale (MD, −1.85; 95% CI, −3.00 to −0.70, low certainty), the mMRC Dyspnea Scale (MD, −0.45; 95% CI, −0.72 to −0.18, low certainty), and the Multidimensional Dyspnoea-12 Scale (MD, −4.64; 95% CI, −6.54 to −2.74, moderate certainty). Exercise capacity also improved, demonstrated by results from the Six-Minute Walk Test (MD, 38.18; 95% CI, 25.33–51.03, moderate certainty) and the Sit-to-Stand Test (MD, 3.04; 95% CI, 1.07–5.01, low certainty). Conclusion Rehabilitation interventions are promising for survivors of viral respiratory diseases, yet gaps in research remain. Future investigations should focus on personalizing rehabilitation efforts, utilizing remote technology-assisted programs, improving research quality, and identifying specific subgroups for customized rehabilitation strategies to achieve the best outcomes for survivors.

Cell entry of bovine respiratory syncytial virus through clathrin-mediated endocytosis is regulated by PI3K-Akt and Src-JNK pathways.

Bovine respiratory syncytial virus (BRSV) is an RNA virus with envelope that causes acute, febrile, and highly infectious respiratory diseases in cattle. However, the manner and mechanism of BRSV entry into cells remain unclear. In this study, we aimed to explore the entry manner of BRSV into MDBK cells and its regulatory mechanism. Our findings, based on virus titer, virus copies, western blot and IFA analysis, indicate that BRSV enters MDBK cells through endocytosis, relying on dynamin, specifically via clathrin-mediated endocytosis rather than caveolin-mediated endocytosis and micropinocytosis. We observed that the entered BRSV initially localizes in early endosomes and subsequently localizes in late endosomes. Additionally, our results of western blot, virus titer and virus copies demonstrate that BRSV entry through clathrin-mediated endocytosis is regulated by PI3K-Akt and Src-JNK signaling pathways. Overall, our study suggests that BRSV enters MDBK cells through clathrin-mediated endocytosis, entered BRSV is trafficked to late endosome via early endosome, BRSV entry through clathrin-mediated endocytosis is regulated by PI3K-Akt and Src-JNK signaling pathways.

Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.

Precision medicine for respiratory diseases: A current viewpoint.

In the realm of respiratory illnesses, despite the immense costs and efforts invested in diagnosis and treatment, numerous patients with chronic respiratory conditions or malignancies do not respond well to existing therapies. Delayed diagnoses and inadequate treatments contribute to these challenges, along with adverse reactions or treatment limitations due to side-effects. However, recent advancements in understanding respiratory diseases have paved the way for personalized medical treatments, considering individual genetic, molecular and environmental factors. Precision medicine, which accommodates individual differences in disease susceptibility and response to treatments, aims to improve patient care by aligning medical research with tailored therapies. Innovative technologies, such as genomic sequencing and biomarker identification contribute to this approach, allowing for customized treatments and the identification of effective therapies. Additionally, the application of precision medicine in lung cancer treatment exemplifies the forefront of individualized care within respiratory medicine. Several studies have explored the role of precision medicine in managing respiratory infectious diseases, asthma and idiopathic pulmonary fibrosis, aiming to categorize diseases more accurately and design targeted therapies. The ultimate goal is to enhance treatment effectiveness, minimize adverse events, and shift towards a patient-centered approach to managing respiratory conditions. Despite limitations, precision medicine holds promise for improving patient outcomes and emphasizing personalized care in respiratory medicine.

An association between PM2.5 components and respiratory infectious diseases: A China's mainland-based study

The particulate matter with diameter of less than 2.5 µm (PM2.5) is an important risk factor for respiratory infectious diseases, such as scarlet fever, tuberculosis, and similar diseases. However, it is not clear which component of PM2.5 is more important for respiratory infectious diseases. Based on data from 31 provinces in mainland China obtained between 2013 and 2019, this study investigated the effects of different PM2.5 components, i.e., sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and organic matter (OM), and black carbon (BC), on respiratory infectious diseases incidence [pulmonary tuberculosis (PTB), scarlet fever (SF), influenza, hand, foot, and mouth disease (HFMD), and mumps]. Geographical probes and the Bayesian kernel machine regression (BKMR) model were used to investigate correlations, single-component effects, joint effects, and interactions between components, and subgroup analysis was used to assess regional and temporal heterogeneity. The results of geographical probes showed that the chemical components of PM2.5 were associated with the incidence of respiratory infectious diseases. BKMR results showed that the five components of PM2.5 were the main factors affecting the incidence of respiratory infectious diseases (PIP>0.5). The joint effect of influenza and mumps by co-exposure to the components showed a significant positive correlation, and the exposure-response curve for a single component was approximately linear. And single-component modelling revealed that OM and BC may be the most important factors influencing the incidence of respiratory infections. Moreover, respiratory infectious diseases in southern and southwestern China may be less affected by the PM2.5 component. This study is the first to explore the relationship between different components of PM2.5 and the incidence of five common respiratory infectious diseases in 31 provinces of mainland China, which provides a certain theoretical basis for future research.