Binary logistic regression was used to test the association between elevated particulate matter and self-reported health; (3) Results: Of the multiple potential drivers of the particulate matter investigated, smoking and vaping were significantly associated with mean PM2.5. Following multivariate analysis, only smoking remained significantly associated with higher mean concentrations. Properties in which <15 cigarettes/day were smoked were predicted to have PM2.5 concentrations 9.06 µg/m3 higher  than those in which residents were non-smokers and 11.82 µg/m3 higher  where >15 cigarettes were smoked; (4) Conclusions: A total of 25% of social housing properties in this study experienced levels of indoor PM greater than WHO guideline levels for ambient air pollution. Although there are many factors that impact air quality, in this study the main driver was smoking. This highlights the importance of targeting smoking in indoor environments in future smoking cessation and control policy and practice and of understanding how pollutants interact in the home environment. There is also a need for further research into the impact on indoor air quality of vaping, particularly due to the rise in use and uncertainty of its long-term impact.(1) Background: Poor air quality affects health and causes premature death and disease. Outdoor air quality has received significant attention, but there has been less focus on indoor air quality and what drives levels of diverse pollutants in the home, such as particulate matter, and the impact this has on health; (2) Methods: This study conducts analysis of cross-sectional data from the Smartline project. Analyses of data from 279 social housing properties with indoor sensor data were used to assess multiple factors that could impact levels of particulate matter. T-Tests and Anova tests were used to explore associations between elevated PM Air pollution causes premature death and disease. In 2012 WHO estimated that 99,000 deaths in Europe were attributable to household air pollution ,4,5,6. T2.5 up to 2000 μg/m3 have been recorded, which is 2281% higher than the maximum hourly mean level of 84 μg/m3 recorded in 2019 on Euston Road in London [3 and 10 µg/m3, respectively, with the WHO recently halving their annual mean guideline level for PM2.5 to 5 µg/m3 [Reduced ventilation rates in winter months can further lead to elevated levels of PM from the sources described above . Where fn London ,36, alth 5 µg/m3 ,38. Indo 5 µg/m3 . Whole s 5 µg/m3  and cont 5 µg/m3 ,41,42.2.5) and ultrafine particles (less than 0.1 µm) is of interest because exposure to increased concentrations has been implicated in a range of adverse health effects such as asthma [Elevated indoor particulate matter  (accessed on 10 June 2020), led by the University of Exeter in partnership with Coastline Housing, Cornwall Council, Volunteer Cornwall, and the South West Academic Health Science Network. The project has recruited over 300 households in properties owned and managed by Coastline Housing with the aims of exploring the relationship between people, technology, and wellbeing in their home. The study location  is higher than the social 57.9%) and national (51%) averages from the English Housing Survey .9% and n.2.5-PM10 Transmitters supplied by Invisible Systems (https://www.invisible-systems.com/) (accessed on 2 December 2022). These are laser-based sensors that use the light scattering method to detect and count particles in the concentration range of 0 μg/m3 to 1000 μg/m3 in a given environment. The accuracy of the sensors is ±15 μg/m3 at 20–30 °C. Sensor data collection has previously been described in assessments of mould risk [2.5 was available for 64% (178/279) of the properties and outdoor sensor data for PM2.5 for 16% (44/279) of properties. Data from both internal and external sensors was available for 30 properties. The external sensors were placed using cluster analysis [2.5—measured in µg/m3 and a 12-month average was taken for household sensor data.Sensors were installed in 280 participants’ homes and of these, 279 were installed between October 2017 and November 2018 and were active during the study period. External sensors were installed in 53 locations in autumn 2018, which represented the geographic area where participant homes were located. The sensors used were LoRa PMuld risk . Due to analysis  to proviSeveral behavioural factors have been shown to be significantly associated with indoor PM, as described in the introduction. Self-reported data on smoking, vaping, cleaning, and occupant levels were available from the household questionnaire and was included in the analysis.2.5 were analysed for association with indoor concentrations. Distance to the nearest A-road was measured in metres from the latitude and longitude of the postal code for each home using the distance measurement tool provided in Google Maps [2.5 concentrations [Outdoor concentrations of PMgle Maps ,62. Propgle Maps , where 1trations . Buildintrations . HousehoComparing the buildings within the study sample to national and social rented averages, the distribution is broadly comparable to social rented sector averages for age, SAP score and property type . The hea2.5 concentrations and building, household and smoking and vaping characteristics. As indoor PM2.5 concentrations were positively skewed, the data were transformed using log base 10 to normalise the distribution, allowing for parametric testing. The data showed relative symmetry  with few large outliers that were retained as they were considered plausible. Prior to the transformation, skewness and kurtosis values were 5.6 and 45.2, respectively. Pearson and Spearman’s correlation were used where both variables were continuous. Following the analysis, the results were back-transformed to allow the accurate reporting of mean values.Analyses used T-tests and ANOVAs to explore associations between mean PM2.5 at the p < 0.05 level were included in a multivariate analysis. Backward elimination was used for the final model to create a parsimonious model by including only those variables identified as significant in the multivariate analysis. The assumptions of linearity, independence, distribution, and variance were checked following the production of the final model. Data analyses were carried out using IBM SPSS Statistics Version 26.Binary logistic regression was used to test the association between baseline responses for anyone in the home having self-reported wheeze/cough, asthma, or chronic bronchitis/emphysema/COPD. Variables identified as significantly associated with PMA total of 279 households were included in this study . Sixty-sJust over 40% (42%) of properties were occupied by a single person, 30.2% by two people, and 14.9% by 4 or more people. In over half of properties (56.2%) vacuuming took place more than twice per week. In 70.4% of households, no smoking took place inside the property. Vaping on the property was not common (14.7%). In 64.2% of households, there was neither smoking nor vaping. Among residents, 38.4% had a wheeze or cough, 28.0% had asthma and 11.5% had chronic bronchitis/emphysema/COPD.2.5 concentrations were 1.27 µg/m3  and 17.14 µg/m3 , respectively. 2.5 data were recorded, 25% had mean annual indoor PM2.5 concentrations exceeding the updated WHO guideline level of 5 µg/m3. Of the 44 properties where external sensor data were available, all had values exceeding this guideline level. The distance between the property and the nearest A-road was significantly associated with outdoor mean PM2.5 concentrations (p ≤ 0.001).The annual median indoor and outdoor PM2.5 . Smoking was significantly associated with mean indoor PM2.5, (p ≤ 0.001), irrespective of the number of times smoked/day (2.5 was 1.08 µg/m3 (SD 1.87) in properties with no smoking compared with 9.20 µg/m3 (SD 2.64) for those where <15 cigarettes per day were smoked and 9.81 µg/m3 (SD 2.03) for those where >15 cigarettes were smoked per day. Vaping inside the house was significantly associated with mean indoor PM2.5 ). The number of times vacuumed per month was not associated with mean indoor PM2.5 and no associations were found between PM2.5 concentrations and household size, or PM2.5 concentrations and mould growth.No association was found between indoor and outdoor concentrations of PMoked/day . Mean PM2.5 (p = 0.694). Fuel group was not associated with indoor mean PM2.5 (p = 0.841). There was no association between indoor PM2.5 concentrations and property type (p = 0.083), property construction date (p = 0.451), and SAP value (p = 0.705). No association was found between concentrations and mould growth (p = 0.776).The distance between the property and the nearest A-road was not associated with indoor mean PM2.5 and respiratory outcomes  than those in which residents were non-smokers or where no smoking took place inside the home. The PM2.5 concentration in properties where >15 cigarettes were smoked per day was 11.82 µg/m3 higher than in homes without smoking . These findings suggest that the effect on indoor PM2.5 is similar irrespective of the number of cigarettes smoked. The unadjusted model for both levels of smoking and vaping inside the house showed that mean indoor PMificant. . After a2.5 data were recorded had concentrations exceeding current WHO guideline levels for ambient air quality [2.5 concentration, which is consistent with existing literature [2.5 concentration in the final multivariate model, vaping has previously been found to increase PM2.5 indoors [In this novel study using survey and sensor data from 279 homes in Cornwall, South West of England, we found that a quarter of all properties where internal PM quality . Of the terature ,23,24,25terature . Vaping terature . Whilst It is not clear why many of the resident and building characteristics were found not to influence indoor PM, but this is likely to be a complex interaction between lifestyles, heating and ventilation practices, which is thought to partially explain the adverse impact of some housing interventions on health and wellbeing . There wPrevious studies ,18 have 2.5 concentration, but not for indoor PM2.5 concentrations. This may be because finer particle sizes were more likely to be found in higher quantities indoors [2.5 being more likely than for PM10 [2.5 inside buildings may be due to finer particles being able to penetrate structures, along with indoor activities such as smoking [2.5 may have been due to the properties in this study being located outside of major cities, property characteristics including air tightness and natural ventilation, or the behaviours of occupiers being different.Consistent with other findings ,12,13,50 indoors  with an for PM10 . Increas smoking . Other s smoking ,11,12 ha2.5. It is possible that indoor sensors were not placed in areas of the home where mould was present or that the effect of other activities, such as smoking, masked any impact of mould on measuring air quality. As the presence of mould was self-reported, there is the potential for bias within responses which also may have impacted results.Existing literature was inconsistent regarding the impact on PM of building specific factors such as construction date, property type and heating fuel ,19,20,502.5 concentrations and self-reported respiratory outcomes. This finding is consistent with a recent systematic review, which reported that there is insufficient evidence using objective sensor data to better understand whether elevated indoor PM increases the risk of asthma [2.5 levels exceeded guideline levels is of concern given the evidence in the literature linking levels of pollution and health [No association was found between PMf asthma . The sysf asthma . Consequd health ,47,48,492.5 concentrations and self-reported respiratory health outcomes, there is evidence of the link between PM and poor respiratory health [Whilst our study did not find any association between PMy health ,47. Thisy health . The spey health ,76. Indoy health ,32. Thisy health , and thoy health .2.5 levels over a one-year period helps to ensure that any short-term variations in concentrations do not have a significant impact on overall results. A significant strength of this study is the collaboration between the project partners, enabling access to a large number of homes and asset management records linked to those homes, such as energy efficiency data [The main strength of this study is the large sample size of 279 properties with sensor data recorded over long periods, both inside and outside dwellings. To fully explore the interaction between the diverse air pollution sources and exposures, future studies will likely require significantly larger sample sizes. The use of average PMncy data .2.5 values helped to reduce the impact of short-term variations on results, those short-term variations may also have shown subtle associations between variables and indoor air quality. There are several limitations to consider. The cross-sectional study design limits the possibility of evaluating causality. Self-reported questionnaires were the source of several variables and, although response rates were generally high, bias can occur as a result of the tendency for some participants not to respond to questionnaires and, within those that respond, there is a tendency for some to believe that certain factors, such as dampness and mould growth, impact health, influencing responses. The study focusses on properties owned by Coastline Housing and therefore, generalisability of findings is limited outside of the social housing sector in the South-West of England. Whilst using average annual PM2.5 concentrations. All variables had missing data, including PM2.5 concentrations in one-third of properties, and this may have affected the results. Where data was not available, it was marked as missing in SPSS and analysis was limited to those properties where complete data was available. Data were not available when properties were unoccupied so the effect of a property being vacant on PM2.5 concentrations could not be assessed. Further analysis of other sources of PM2.5  could be conducted, but this was not possible in the current study. Health data was restricted to binary responses on the presence of conditions at a single point in time, restricting the level of analysis. It would be useful to have more robust measures of health, such as asthma surveys or bronchodilator use, which could be linked to indoor air over time.The occupants themselves differed from national averages in terms of their characteristics, with higher rates of smoking  and higBuilding on an innovative, mixed-method study involving self-reported health and indoor air quality sensors, this study highlighted that smoking behaviour is related to increases in PM inside the home. It has been shown that 25% of social housing properties in the sample experienced levels of indoor PM greater than WHO guidelines for ambient air pollution. It highlights the importance of targeting smoking in indoor environments in future policy and practice, as well as the need for further research to better understand the interaction between smoking, vaping, and indoor air pollution. Models of higher complexity and larger sample size are needed to explore the diverse interactions between resident and building characteristics, resultant fluctuations in indoor air pollution and impact on health and wellbeing. It is also important that future studies analyse how different pollutants interact within specific environments, such as the home. Further work is also needed to consider indoor air pollution levels across different housing sectors, such as the owner-occupied and privately rented sectors and to consider different locations, particularly those with more heterogeneous outdoor air pollution. Given the climactic factors affecting air quality, further research is also needed in different regions of the world and contrasting rural and urban environments to better understand the drivers of poor indoor air quality. Increasing our understanding of factors driving indoor air pollution and resultant health outcomes will help inform future building design and housing retrofit interventions to avoid the potential unintended consequences resulting from inadequate heating and ventilation. A whole system approach is needed in the design of appropriate interventions—considering how humans interact with housing , and the wider determinants of indoor and external environments.


The aim of this study is to find the incidence, risks, and reasons for prolonged length of hospital stay, short, long‐term mortality, and the factors contributing to mortality of postoperative delirium in proximal femoral fractures.The data for the study was obtained from National Hip Fracture Database (NHFD) and internal hospital computer systems  between January 2018 and December 2019. One hundred seventy‐five patients were found have developed postoperative delirium. The outcomes measured were postoperative anemia, lower respiratory tract infection, urinary tract infection, acute kidney injury, urinary retention, cardiac event and stroke, alcohol or drug withdrawal, length of hospital stay, and 30 day and 1 year mortality.p < 0.05). The average length of stay after developing postoperative delirium was 19.69 days compared to 17.4 days for patients without delirium. The mortality at 30 days and 1 year was 10.9% and 37% in patients who had postoperative delirium compared to 2.1% and 2.8% to those without delirium, respectively.The patients who developed delirium were 68 (38.9%) with American Society of Anesthesiologists (ASA) grade 4 and 94 (22.3%) without delirium (Postoperative delirium is three times more common in hip fractures. Early detection and timely management are crucial in the improvement of functional outcomes and mortality. Delirium has got serious implications in the morbidity, mortality, length of inpatient stay, and postoperative rehabilitation. The aim of this study is to find the incidence, identifiable causes, reasons for prolonged length of hospital stay, calculate short and long‐term mortality, and the factors contributing to mortality of postoperative delirium in proximal femoral fractures. The study period was between January 2018 and December 2019. A total of 598 patients were admitted during the study period. The study was approved by the audit department of our hospital trust. This study used data from a public database and did not require ethical approval and patient consent.After screening, 175 patients were found have developed postoperative delirium. The diagnosis of delirium was made using the 4AT scoring system  grade, Abbreviated Mental Test (AMT) score at admission, type of anesthesia and procedure, comorbidities, complications, and mortality at 30 days and 1 year were recorded. Patients who had an AMT score of ≤6 were considered as having dementia. The comorbidities looked at are cardiovascular disease, mental illness, central nervous system disorders, chronic obstructive pulmonary disease (COPD), chronic kidney disease, malignancy, previous hip fractures, inflammatory arthritis, Parkinson’s disease, and chronic liver disease.All patients were treated operatively with a postoperative protocol consisting of early mobilization with weight bearing. Patients were assessed by an experienced geriatrician pre and postoperatively. Delirium was identified from hospital chart notes and using 4AT delirium assessment criteria which included alertness, 4AMT, attention and change in cognitive or mental function, a score of ≥4 is considered as possible delirium.The outcomes measured were postoperative anemia, lower respiratory tract infection, urinary tract infection, acute kidney injury, urinary retention, cardiac event, and stroke, alcohol or drug withdrawal, length of hospital stay, and 30 day and 1 year mortality. The effect of postoperative delirium on each of these outcomes was evaluated.2.1t test and Mann–Whitney U test for categorical, and parametric and nonparametric data sets, respectively. Logistic regression analysis was used to find the causes for postoperative delirium and also determining the factors contributing to mortality associated with delirium. A p value of <0.05 was considered significant.The statistical analysis was done using SPSS version 22. Comparison between patients who developed postoperative delirium and those without delirium were compared using chi‐squared, Student’s 3p < 0.05). There were 67 patients (38%) with intracapsular fractures and 108 (62%) had extracapsular fractures, and in patients without delirium there were 271 (64.1%) intracapsular fractures and 152 (35.9%) extracapsular fractures  scored a 4AT of four or more, which suggests possible postoperative delirium. There were 68 (38.9%) men and 107 (61.1%) women in the delirium group and 124 (29.3%) men and 299 (70.7%) women in the group without delirium. The mean age for patients with delirium was 84.82 years (67–100 years) compared to 81.23 years in patients without delirium  had dementia (patients who scored ≤6 on the AMT score) compared to 108 (25.5%) patients without delirium (The number of patients with pre admission comorbidities for delirium and non‐delirium were cardiovascular disease 125 (71.4%), 277 (65.5%), mental illness 102 (58.3%), 66 (15.6%), cerebrovascular disease including Parkinson’s disease 55 (31.4%), 102 (24.1%), COPD 49 (28%), 128 (30.2%), chronic kidney disease 33 (18.6%), 88 (21%), systemic malignancy 26 (14.9%), 26 (6.1%), previous hip fractures 13 (7.5%), 25 (5.9%), inflammatory arthritis 7 (4%), 21 (5%) and chronic liver disease 3 (1.7%), 8 (1.8%), respectively.There were no significant statistical differences between patients who had general anesthesia and spinal anesthesia or the type of procedure carried out for the hip fractures in the delirium group and in the group without delirium. The baseline characteristics of patients in both groups are summarized in Table p < 0.001), women , AMT scores ≤6 , ASA grade 4 , mental illness , postoperative anemia , systemic malignancy , lower respiratory tract infection , urinary tract infection , acute kidney injury , and urinary retention , acute kidney injury , sepsis , mental illness , and no cause were found in 16.4% , postoperative anemia , lower respiratory tract infection , urinary tract infection , acute kidney injury , cardiovascular disease , and general anesthesia (OR = 1.24, 95% CI = 0.72–2.01, p = 0.031; Table The mortality at 30 days and 1 year was 10.9% and 37% in patients who had postoperative delirium compared to 2.1% and 2.8% to those without delirium, respectively, which was statistically significant Table . The fac4Surgery for proximal femur fractures in elderly and frail patients can often lead to altered disturbance in their mental status. It is usually manifested as dementia, depression, and delirium.There is strong association of pre‐operative AMT score and the development of postoperative delirium. A one point increase of AMT score can lead to a fall in the odds of postoperative delirium by 0.6 fold.The functional reserve of the patients with hip fractures has been postulated as one of the most important factors leading to the development of postoperative delirium.In the 2018 NHFD report, it was suggested that there is an increased incidence of postoperative delirium in patients who had general anesthesia for the surgical repair of hip fractures.The average age of our patients who developed delirium was 84.82 years. Advancing age is one of the risk factors of delirium.Postoperative anemia in our study is one risk factor identified. There are a number of studies describing the incidence of low hemoglobin level to be associated with delirium and post blood transfusion to be associated with a lower delirium incidence.Delirium associated with increased incidence of postoperative complications including pneumonia, UTI, cardiovascular accident, and pulmonary complications in general leads to prolonged inpatient stay which in turn has got financial implications.The meta‐analysis done by Hamilton et al.5The development of postoperative delirium in hip fractures most often is associated with pre‐existing morbidities. In general, these factors can be identified and reversed by a multidisciplinary team approach. Our study highlights the risk factors in the perioperative period influencing the development and outcomes associated with postoperative delirium.6The authors wish to acknowledge Dr. Mike Wilkinson, Associate Specialist in Elderly Medicine, Queen Elizabeth Hospital, Gateshead, UK for delirium data assimilation and profiling.7Nothing to disclose.8Aysha Rajeev contributed to study design, analysis and writing of the manuscript. Catherine Railton contributed to the data collection. Kailash Devalia contributed to the review of the manuscript.


Background: Oral anticoagulants (OACs) are important in reducing the risk of ischaemic stroke in people with atrial fibrillation (AF). Although patients need to take their OAC continuously, it has been suggested that discontinuation is common in clinical practice, and this could predispose patients to thrombotic complications. Aims: To investigate the rate of OAC discontinuation and its predictors in patients with AF, using national data from Australian general practices. Methods: We analysed data obtained from NPS MedicineWise’s MedicineInsight dataset. We included patients with a recorded diagnosis of AF who newly started an OAC between 1 January 2013 and 31 December 2017. Patients were considered persistent if an OAC was prescribed continuously without discontinuing more than 60 days gap in therapy. The follow-up period was 12 months post-initiation. Multivariable models were used for the analysis of predictors. Results: Of 16,075 patients included in the cohort, 47.3% were females, and the mean age was 74.6 (SD 10.2) years. The overall OAC discontinuation rate was 13.2%  12.6–13.7%) by 12 months post-initiation. The discontinuation rates for warfarin, apixaban, dabigatran and rivaroxaban were 18.3% (95% CI 17.2–19.5%), 10.1% (95% CI 9.2–11.0%), 10.9% (95% CI 9.4–12.5%) and 12.2% (95% CI 11.4–13.2%), respectively. Warfarin had a significantly higher risk of discontinuation compared to direct-acting OACs. Factors that are known to increase the risk of stroke  were associated with better persistence. Conclusions: A relatively high proportion of patients with AF continued OAC therapy by 12 months post-initiation. Positively, patients with the highest risk of stroke and lowest risk of bleeds seemed to have better persistence. Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with substantial morbidity, mortality, and healthcare expenditure ,2. In 20AF increases the risk of stroke by almost five-fold, with 20–30% of all strokes being due to AF. It also increases the risk of death by 1.5-to 2-fold ,7. CompaStudies conducted using Australian general practice data showed a substantial increase in the initiation of OAC therapy for stroke prevention in patients with AF over the past decade ,11. OnceThe study was conducted using the NPS MedicineWise’s dataset, MedicineInsight. The dataset consists of de-identified longitudinal data from electronic medical records collected by consenting general practices, including patient demographics, diagnoses, pathology tests, prescribed medications, and clinical observations . The datWe included adults aged ≥ 18 years) with a documented diagnosis of AF who were newly commenced on an OAC between 1 January 2013 and 31 December 2017. Patients were considered new users if there was no evidence of OAC use in the 12 months before the index date (the date of the first OAC prescription identified during the study period)  years wi. PatientThe outcome of the study was the discontinuation of OAC therapy. We considered OAC therapy as being discontinued if the patient did not receive a new prescription of an OAC within 60 days after the end of the prior prescription coverage. On the Australian PBS, the number of days covered by a prescription with repeats varies with the type of OAC, ranging between 150 and 180 days. Since assessing warfarin’s days of coverage is challenging due to a high probability of subsequent dose adjustments depending on the international normalised ratio (INR) levels, 150 days of supply was considered with the assumption that a patient is taking one tablet per day at a stable dose .Both persistence with the index OAC and switching between OACs was considered. Patients who switched to another OAC were considered persistent with OAC therapy. When a patient received a prescription before the exhaustion of the previous prescription, the supply for the next prescription was adjusted for the leftover from the previous coverage. If the patient switched to another OAC, any leftover from the index OAC was discarded and discontinuation was assessed based on the prescription coverage of the second OAC for the days after switching. The proportion of patients who remained on OAC therapy was calculated at 6 and 12 months post-initiation. Discontinuation rates were measured as cumulative incidences. Sensitivity analysis was conducted by changing the discontinuation gap to 30 and 90 days.Age was calculated on the index date, while clinical information was collected for the baseline period. The most recent haemoglobin and estimated glomerular filtration rate (eGFR) values recorded on or before the index date were used.The general practice postcode provided by the Australian Statistical Geography Standard (ASGS) of the Australian Bureau of Statistics (ABS) was used to identify rurality. The ASGS classifies areas into five rurality categories based on relative access to services measured using the Accessibility and Remoteness Index of Australia (ARIA+). The ARIA+ score estimates the road distance of a point to the nearest urban centres and localities. The five categories are major cities (ARIA+ 0–0.20), inner regional ARIA+ 0.21–2.40), outer regional (ARIA+ 2.41–5.92), remote (ARIA+ 5.93–10.53) and very remote (ARIA+ > 10.53) –2.40, ou. The SEI2DS2-VASc (Congestive heart failure (1 point), Hypertension (1 point), Age ≥ 75 years (2 points), Diabetes mellitus (1 point), Stroke/transient ischaemic attack (2 points), Vascular disease (1 point), Age 65–74 years (1 point) and Sex category ) [Comorbidities were identified from the condition flags provided by MedicineInsight . Details point)) . The ris point))   for continuous variables. The proportion of patients who persisted with OAC therapy and those who discontinued was calculated. Chi-square and p-value of 0.1. Variables with a p-value < 0.1 were included in the final multivariable logistic regression model. Multicollinearity among variables was checked using a variance inflation factor (VIF). The absence of collinearity was declared if VIF was less than five. The CHA2DS2-VASc and ORBIT scores were not included in the model to minimise structural collinearity with individual components of the scores. Data were analysed using SAS version 9.4 , and statistical significance was considered at a two-sided p-value < 0.05 with a 95% confidence interval (CI).A binary logistic regression was used to identify factors associated with OAC discontinuation using a stepwise backward elimination at a threshold n = 4490), apixaban to 29.6% (n = 4761), dabigatran to 10.3% (n = 1651) and rivaroxaban to 32.2% (n = 5173) patients. Hypertension and arthritis were the most common comorbidities recorded, in 70.9% and 60.8% of patients, respectively. Most of the patients (91.2%) had a CHA2DS2-VASc score ≥ 2, with a mean value of 3.9 (SD 1.9), while the baseline bleeding risk was low in 66.3% of patients, with a mean ORBIT score of 1.9 (SD 1.4)  years) who were newly prescribed an OAC were included in the study. Warfarin was prescribed to 27.9% ((SD 1.4) .p < 0.001). Among patients who initiated apixaban, dabigatran and rivaroxaban, 10.1% (95% CI 9.2–11.0%), 10.9% (95% CI 9.4–12.5%) and 12.2% (95% CI 11.4–13.2%) discontinued their OAC by the end of one year, respectively  12.6–13.7%) discontinued their therapy within a year of initiation. The rates of discontinuation were 18.3% (95% CI 17.2–19.5%) for warfarin users and 11.2% (95% CI 10.6–11.8%) for DOAC users (ectively .The majority of OAC discontinuation (69.3%) occurred within six months post-initiation. At 6 and 12 months, persistence with an OAC averaged 90.9% (95% CI 90.4–91.3%) and 86.8% (95% CI 86.3–87.4%), respectively.On multivariable logistic regression, variables associated with better persistence with OAC were older age, and comorbidities such as hypertension, diabetes, and arthritis. Patients who started on warfarin were more likely to discontinue  compared to those patients who initiated dabigatran .We also performed a Cox proportional hazard to compare the risk of discontinuation between OACs by adjusting baseline differences. Accordingly, the discontinuation rate of warfarin was significantly higher than the three DOACs: apixaban (hazard ratio (HR) 1.78; 95% CI 1.54–2.06), dabigatran  and rivaroxaban . Rivaroxaban had also a slightly increased risk of discontinuation compared to apixaban . However, the rate of dabigatran discontinuation did not show a statistically significant difference compared to apixaban  or rivaroxaban .We conducted two sensitivity analyses to examine the effect of changing the maximum allowable gap days from 60 to 30 days and 60 to 90 days to define discontinuation. As expected, there was an increase in the discontinuation rate using a gap of 30 days (15.1% vs. 13.2%), with decreased discontinuation using 90 days (11.4% vs. 13.2%) relative to 60 days. These relatively small changes in the rates of discontinuation did not significantly affect the comparative risk of discontinuation between OACs in the Cox proportional model  and had In this retrospective cohort of 16,075 patients with AF who newly initiated either warfarin or a DOAC, more than one in eight patients discontinued their therapy by the end of 12 months post-initiation. Only a few studies from Australia, conducted using PBS data are available for comparison ,14, repoOn other hand, there are some studies around the world conducted using prescription data from general practices. These include ones from UK, Germany, and France  ,27,28. SAnother key finding was the timing of the majority discontinuation; more than two-thirds occurred during the first six months of treatment, particularly for DOACs. This signifies a need for more regular follow-up during the immediate post-initiation period . In the Among DOACs, the rate of discontinuation was comparable. Rivaroxaban had a slightly higher hazard of discontinuation  than apixaban, in line with a study from the United States   and the Our findings are in line with previous studies showing factors that increase the risk of stroke were associated with better OAC persistence ,40,41,42The study has some limitations to be considered when interpreting the findings. First, the definition of warfarin exposure is an approximation by assuming a defined daily dose. This is because the dataset lacks INR records that could be helpful to measure warfarin exposure. This may produce a bias, and it is difficult to be certain of the comparison with DOACs. Second, our study used prescribing data, which could not show whether the patient obtained the medication and took it as prescribed. Third, there is a possibility that patients may change their practice site and visit a general practice not registered in MedicineInsight, which may lead to the misclassification of patients as discontinued. To minimise this, we included patients who were regular attendees to a practice site and checked the presence of a visit to a single practice site after the patient was classified as discontinued. Fourth, the dataset captured prescriptions originating from general practices, while prescriptions from hospitals and specialist clinics were not included, and no linkage was available. Finally, the dataset did not provide data on the reasons for discontinuation and clinical outcomes following the discontinuation of OAC.The study demonstrated two key findings: a relatively low proportion of patients discontinued their OAC within 12 months post-initiation, and patients at the highest risk of stroke and lowest risk of bleeds seemed to have the highest persistence. Although there was a high persistence rate with OAC therapy, patient education would be helpful to further enhance the long-term continuity of therapy, especially in younger patients and those taking warfarin. Further study is needed to understand the reasons for discontinuation. Additionally, using linked data with prescription claims databases and hospital records could provide complete data to investigate medication possession and the clinical outcomes of discontinuation.


HPV has been detected in approximately 50% of invasive penile cancers but with a large span between 24 and 89%, most likely due to different types of tumors and various methods for HPV analysis. Most studies of HPV in penile cancer have been performed using paraffin-embedded tissue, argued to be at risk for contaminated HPV analysis. Viral activity of HPV, by the use of HPV mRNA expression is well studied in cervical cancer, but seldom studied in penile cancer.The aim was to determine prevalence of HPV types in fresh tissue of penile cancers compared to non-malignant age-matched penile controls. Additional aims were to analyze the viral expression and copy numbers of HPV16-positive tumors and 10 mm adjacent to the tumor.Fresh tissue from penile cancer cases was biopsied inside the tumor and 10 mm outside the tumor. Controls were males circumcised for non-malignant reasons, biopsied at surgery.PCR and Luminex assays were used for identification of HPV types. HPV16-positive samples were investigated for copy numbers and expression of HPV16-mRNA.n = 135) and age-matched controls (n = 105), HPV was detected in 38.5% (52/135) and 11.4% (12/105), respectively (p < 0.001), adjusted odds ratio 12.8 . High-risk HPV types were found in 35.6% (48/135) of tumors and 4.8% (5/105) of controls (p < 0.001). Among tumors and controls, HPV16 was present in 27.4% (37/135) and 1% (1/105), respectively (p < 0.001). Among HPV16-positive penile cancers, mean HPV16 viral copy/cell was 74.4 (range 0.00003–725.4) in the tumor and 1.6 (range 0.001–14.4) 10 mm adjacent from the tumor. HPV16-mRNA analysis of the tumors and 10 mm adjacent from the tumors demonstrated viral activity in 86.5% (32/37) and 21.7% (5/23), respectively.Among tumors  than among age-matched non-malignant penile samples (11.4%). HPV16 predominates (27.4%) in penile tumors. HPV16 expression was more common in penile cancer than in adjacent healthy tissue, strongly suggesting an etiological role for HPV16 in the development of penile cancer.The online version contains supplementary material available at 10.1186/s12885-022-10324-w. Penile cancer is a rare cancer worldwide, with an incidence of 0.45–1.7/100000 in Europe . HistoloTwo major pathways of malignant transformation to penile cancer have been described; one HPV-driven and the other derived from inflammatory skin diseases such as lichen sclerosus (LS) and lichen planus (LP) . Other rHPV has been detected in approximately 50% of invasive penile cancers but with a large span between 24 and 89%, most likely due to different types of tumors and various methods for HPV analysis –13. HPV Viral activity of HPV, by the use of HPV mRNA expression is well studied in cervical cancer , 20, butTo the best of our knowledge no study has been published analyzing HPV among biopsied fresh tissue from both penile cancer and from age-matched penile non-malignant samples.The primary aim of this study was to determine the prevalence of different HPV types in fresh tissue of invasive penile cancer and 10 mm adjacent to the tumor, and to compare that to corresponding HPV prevalence in fresh biopsies from age-matched non-malignant penile controls. A secondary aim was to conduct a blinded review of subtype of penile cancer and correlate penile cancer subtype to the prevalence of HPV. Furthermore, we aimed to determine copy numbers, status of integration and viral activity among HPV16-positive samples inside the tumor and 10 mm adjacent to the tumor.The study was approved by the ethical board in Lund with diary number 2015/907. Inclusion criteria were men over 18 years, having penile cancer surgery at Skane University Hospital (SUH) in Sweden between 22nd of June 2015 and 10th of August 2021. SUH is one of two national referral centers for penile cancer. Excised tumor tissues were transported fresh to the Department of Pathology, SUH and a 3 mm single-use punch biopsy was taken from the tumor and submerged in 1 mL RNAlater (Ambion). Another 3 mm single-use punch biopsy was taken 10 mm outside the macroscopic margins of the tumor and submerged in 1 mL RNAlater (Ambion).Controls comprised age-matched men over 18 years, circumcised for non-malignant reasons at urological departments in Skane. At surgery, a biopsy of approximately 5 mm was taken from excised penile skin, and submerged in 1 mL RNAlater (Ambion) as described .The biopsies were transported to the Department of Microbiology where they were transferred to 1 mL GITS-solution  and 1% mercaptoethanol) and incubated at room temperature overnight. Then DNA was extracted with the Total NA-kit  using MagNA Pure LC (200 μL input and 100 μL output). Sample adequacy was assessed by testing 5 μL of the sample for the human beta globin gene with a real-time PCR . SimultaThe integration of HPV-DNA into the human genome most frequently disrupts the E2 gene . As a suHistopathological diagnosis and classification of the subtype of penile cancer was retrospectively reviewed by two experienced pathologists subspecialized in uropathology and participating in the national multidisciplinary team conference of penile cancer in Sweden. A recent study showed that pathologists who have experience of penile cancer diagnostics have a good concordance in identifying HPV-related and non-HPV-related histological subtypes of penile cancer .The glass slides were retrieved from the pathology archive, and one representative slide of the tumor was chosen for each case in hematoxylin-eosin (H&E) stain. Assessment of histological subtype and histological grade was carried out using glass slides (106 cases) and high-resolution digital slides 40 cases). The slides were scanned using NanoZoomer S360 (S60 for large histologic sections) by Hamamatsu, and Sectra IDS7 software. The pathologists were blinded to the results of the HPV detection by PCR in the tumor material to avoid any bias in tumor assessment. The histological subtypes were determined according to the WHO criteria in “Classification of tumors of the urinary system” and the International Society of Urological Pathology recommendations (2016) 0 cases. . For claCircumcised skin was subjected to routine histopathological examination at the Department of Pathology at SUH. All specimens were stained with haematoxylin and eosin. All diagnoses were performed by the expert team of six uro-pathologists belonging to the Swedish national Penile Cancer Center in Malmö.All cases and controls completed a questionnaire, regarding medical history, medication, smoking habits, number of sexual partners and former symptoms and procedures performed on the penis . Having previously had a punch biopsy taken for histopathologic evaluation was more common among the penile cancer cases (20.5%) than the controls (2%), (p < 0.001). Previous surgery on the penis was more common among the penile cancer cases (31.5%) than among the controls (11.9%), (p = 0.001). Having had a diagnosis of penile cancer before was only seen in the penile cancer cases (11.6%) and not among the controls 0%.The study included 135 men with penile cancer, with a median age of 72 years (interquartile range 66–79 years)  (Table HPV was detected in 38.5% 52/135) of penile cancers and in 11.4% (12/105) of the controls  of the samples. HR HPV types were detected in 25.8% (34/132) of the samples, with HPV16 present in 17.4% (23/132). Only LR HPV types were seen in 4.5% (6/132) of the samples. All cases of HPV occurrence adjacent to the tumor also had at least one HPV type identical to that in the tumor, except for one case where HPV6 was adjacent to the tumor but HPV-negative in the tumor. In two samples 10 mm adjacent to the tumor, the HPV analysis was not adequate, due to no detectable human DNA in the sample. In one sample 10 mm adjacent to the tumor, the analysis was not performed.In all HPV16-positive cases, the E2/E7 ratio showed HPV16 to be episomal in 59.5% (22/37) of the tumors and in 69.6% (16/23) adjacent to the tumors. HPV was integrated in 27% (10/37) and 13% (3/23) in the tumor and adjacent to the tumor, respectively. Mixed forms of HPV16 were seen in 13.5% (5/37) in the tumor and in 4.3% (1/23) adjacent to the tumor. A weak E7 signal only was seen in one case 10 mm adjacent to the tumor. Viral activity shown by HPV16 mRNA expression was detected in 86.5% (32/37) of the tumors, with a mean mRNA expression of 15.1 (median 8.9) HPV16 mRNA copies/HPV16 DNA copy. Adjacent to the tumor, 21.7% (5/23) of the samples showed viral activity, with a mean mRNA expression of 73.1 (median 7.4) HPV16 mRNA copies/HPV16 DNA copy. In the HPV16-positive tumors the mean viral copy/cell was 74.4  and in the HPV16-positive adjacent tissue it was 1.6  (Supplementary p < 0.001). The subtype of penile cancer where HPV was second most common was in warty subtypes, where HPV was seen in 88.9% (8/9) compared to in 34.9% (44/126) of other subtypes (p = 0.002). Warty-basaloid subtypes of penile cancer were HPV-positive in 85.7% (18/21) compared to 29.8% (34/114) in other subtypes (p < 0.001) with an increased adjusted OR for HPV of 9.0 . In the usual subtype of penile cancer, HPV was found in 13.6% (11/81) (p < 0.001). Decreased risk for HPV was seen in the usual subtype of penile cancer with an adjusted OR of 0.03 .Classification of subtypes of penile cancer showed that the usual type was seen in 60% 81/135) of cases  and LP in 10.5% (11/105). Lichenoid dermatitis was seen in 7.6% (8/105). In LS, HPV was seen in 11.1% (7/63) and in 37.5% (3/8) of lichenoid dermatitis, but HPV was not found in LP  than in non-malignant controls (11.4%), and that HR HPV was seven times more common in invasive penile cancer (35.6%) than in non-malignant controls (4.8%). HPV16 was the predominant HPV type.Only a few other studies have searched for the presence of HPV in fresh penile tissue. Martins et al. found HPV in 89% (49/55) of penile cancer cases, which was substantially higher than our HPV prevalence (38.5%) among invasive penile cancer cases . In compIn our study, HPV16 was found to be the predominant HPV type, in agreement with other studies of HPV in penile cancer .p = 0.0573). Furthermore, our finding of a higher prevalence of HPV16 viral activity in penile cancer cases (86.5%), than in samples taken adjacent to the tumor (21.7%), suggests that HPV is more active in the tumor and probably has a role in the malignant transformation process.In the present study we analyzed HPV also outside of the tumor in order find out possible differences between tumors and adjacent healthy tissue. Interestingly, HPV was relatively frequently detected (30.3%) in the tissue adjacent to the tumors. However, there was a tendency of a higher proportion of HPV16 positive tumors compared to that of the adjacent tissue . Next-generation sequencing appears to yield a higher HPV integration rate than the E2/E7 ratio method used in our study, which is affected by limitations such as decreased detection of HPV integration when a large excess (at least 10-fold) of episomal HPV16 is simultaneously present [The observed mean HPV16 viral load (74.4 virus copies per cell) in the tumor was similar to that of Heideman et al., who found a mean HPV16 load of 72 copies per cell . Other s present .In our study, the HPV prevalence was highest in basaloid subtypes of penile cancer (100%), followed by warty subtypes (88.9%) and warty-basaloid subtypes (85.7%). This is in alignment, but slightly higher than former studies where a meta-analysis showed the pooled prevalence of HPV to be 84% in basaloid penile cancers, 58.7% in warty subtypes and 75.7% in warty-basaloid subtypes .The strengths of our study were that HPV was analyzed in fresh biopsies both in and adjacent to the tumor, and that biopsied age-matched controls were used. Another strength was that not only the presence of HPV16 DNA was studied; also the viral activity was investigated in HPV16-positive samples, both in and adjacent to the tumor. A further strength is that histological subtypes of penile tumors were reviewed by two experienced uropathologists, blinded to each other.One limitation was that compared to the controls, who completed the questionnaire at the hospital before the treatment, there was a relatively high proportion of missing questionnaires Table  from theKnowledge of HPV as a strong risk factor (adjusted OR 12.8) for invasive penile cancer is important in the work of preventing penile cancer. The nine-valent HPV vaccine targets the HPV types HPV6, 11, 16, 18, 31, 33, 45, 52 and 58, covering the majority of HR HPV types found here, which leads to the conclusion that vaccination of boys is important for preventing development of penile cancer. Knowledge about viral activity in penile cancer tumors, further indicates the important role of HR HPV in the malignant transformation.Additional file 1.Additional file 2.Additional file 3.


We examined whether long-term exposure to PM2.5 is associated with developing all strokes, ischemic stroke and hemorrhagic stroke.Evidences have shown that the stroke risk associated with long-term exposure to particulate matter with an aerodynamic diameter of ≤2.5 μm  and 95% confidence interval (CI) of developing stroke associated with 1-year mean levels of PM2.5 were 1.03 (1.00–1.06) for all stroke, 1.06 (1.02–1.09) for ischemic stroke, and 0.95 (0.89–1.10) for hemorrhagic stroke. The concentration-response curves estimated in the models with and without additional adjustments for other environmental measurements showed a positively linear association between baseline 1-year mean PM2.5 and ischemic stroke at concentrations greater than 30 μg/m3, under which no evidence of association was observed. There was an indication of an inverse association between PM2.5 and hemorrhagic stroke, but the association no longer existed after controlling for nitrogen dioxide or ozone. We found similar shape of the concentration-response association in the Cox regression models with time-varying PM2.5 exposures.During a median follow-up time of 6.0 years, 12,942 persons developed strokes, 9919 (76.6%) were ischemic. The adjusted HRs (95% CIs) per interquartile range increase in baseline 1-year mean PM2.5 might be associated with increased risk of developing ischemic stroke. The association with high PM2.5 concentrations remained significant after adjustment for other environmental factors.Long-term exposure to PMThe online version contains supplementary material available at 10.1186/s12940-022-00854-y. An elevated temperature was associated with a slightly increased risk of total stroke and ischemic stroke, with adjusted HRs (95% CI) of 1.02 (1.00–1.04) and 1.03 (1.01–1.05), respectively. No association with relative humidity was observed. We found no association between most of the environmental exposures and risk of developing hemorrhagic stroke except positive associations with NOx and CO at a borderline significance level. On the other hand, O3 was associated with reduced risk  after adjusting for all covariates. The main findings in time-varying Cox models showed similar relationships between environmental exposures and stroke risks, except that a weak inverse association appeared between PM2.5 and hemorrhagic stroke after adjusting for all covariates , and with other air pollutants were mostly weak.In the two-exposure models for 1-year air pollution exposure at baseline, the positive association between PM.5 Table . In the  Table S . HRs for Table S . Overall5 Fig. S . The Spe2.5 exposure at baseline and incidence of all stroke, ischemic stroke and hemorrhagic stroke in the models with or without adjustment for other air pollutants. In all models, we found a V-shaped association with ischemic stroke positive and significant at concentrations greater than 30 μg/m3, under which no evidence of association was observed  demonstrated that among people with PM2.5 level < 25 μg/m3 , long-term exposure to PM2.5 with 5 μg/m3 increment was associated with significantly elevated risk of cerebrovascular events [2.5 relating to the stroke risk, and the slope of the concentration-response curve was steeper at low concentrations than high concentrations [2.5 around 20 μg/m3, and found no elevated risk of ischemic stroke associated with further increased PM2.5 level [2.5 concentrations and the stroke incidence, and the estimated HRs were not statistically significant across the whole range of PM2.5 concentrations [2.5 level of less than 30 μg/m3. It is likely inappropriate to compare among studies for the exposure threshold because study populations, study regions and methods varied among studies. For example, the US-based Health Professionals Follow-up Study consisted of male healthcare professionals [Whether there is a possible threshold of PMtrations . The Dan.5 level . On the trations . In our ssionals  and the ssionals .2.5 exposure and stroke risk that men were more prone to a higher risk than women (Fig. S 2.5 concentrations. This observation was consistent with previous studies, in which the association between long-term PM2.5 exposure and stroke appeared stronger in men than in women [2.5 levels was not statistically significant.Our study suggested from shapes of exposure-response relation between PMin women , 42. How2.5 and the occurrence of stroke are not fully known. Several proposed pathways might explain our observation of the increased incidence of ischemic stroke associated with elevated PM2.5 exposure. Experimental studies have shown that chronic PM exposure may elicit adverse biological responses such as systemic inflammation and oxidative stress, which could result in endothelial dysfunction, development and progression of atherosclerosis, impaired metabolism, and enhanced coagulation/thrombosis [2.5 enhanced the acceleration of atherosclerosis and plaque vulnerability [2.5 was associated with decreased endothelial function and increased carotid artery intima-medial thickness [The mechanisms behind the association between long-term exposure to PMrombosis , 2. Atherability , 44. Humhickness , 46.2.5 reduced appreciably after adjusting for yearly mean temperature, and the temperature associations also attenuated to borderline significant trends after adjusting for PM2.5 (Table S 2.5 levels were positively associated with temperature, and the correlation of PM2.5 with temperature was stronger than that with other pollutants (Fig. S 2.5 or temperature was not likely due to multicollinearity. The observed association between time-dependent PM2.5 concentrations and stroke could be partly explained by temperature, and vice versa. It is worth noting that the correlation between PM2.5 concentrations and temperature varies by several factors such as time scales, geographic regions and components of PM2.5. Fu et al. reported the correlation between PM2.5 levels and temperature was positive at the daily scale, but was negative at the monthly scale in China [2.5 concentrations and temperature with varied strengths across regions [2.5 exposures and incidence of stroke in the long-term settings.In line with the observation of a systematic review , our ana Table S . Our dats Fig. S . The redin China . A multi regions . Further2.5 levels measured at the city/township/district level as a proxy for individuals’ exposure at their residence. In addition, we used the place of registered residence to assign the exposure level for cohort members. This would also result in exposure misclassification in subjects who had jobs or residence not in areas of their registered residence. However, a recent validation study showed that the registered residence was consistent with current residence in 85% of population in Taiwan [There are limitations to this study. First, the NHI claims data did not provide information on body mass index and lifestyles such as physical activity, alcohol consumption, diet, smoking status. The information on traffic noise levels was also unavailable. We were unable to evaluate whether these factors are associated with air pollution exposures and the risk of developing stroke. Second, misclassification of exposure might occur because data on indoor air pollution and personal daily-activity patterns were unavailable. However, personal daily activity patterns probably have minor impact on the associations in long-term settings. Third, we used PMn Taiwan . Further2.5. The association with high PM2.5 concentrations remained significant after adjustment for other air pollutants and temperature. Results derived from the present study broadens the scope of stroke prevention to include measures aiming to improve air quality applying PM2.5 as a key variable. Further studies are needed to clarify if there is a threshold level of long-term PM2.5 exposure associated with the risk of developing ischemic stroke.Using a population cohort of adults in Taiwan, we found an increased risk of developing ischemic stroke associated with long-term exposure to PMAdditional file 1. Online Tables S I –S VI. Online Figure S I –Figure S VII.


The physiological counterregulatory response to hypoglycaemia is reported to be organised hierarchically, with hormone responses usually preceding symptomatic awareness and autonomic responses preceding neuroglycopenic responses. To compare thresholds for activation of these responses more accurately between people with or without type 1 diabetes, we performed a systematic review on stepped hyperinsulinaemic–hypoglycaemic glucose clamps.A literature search in PubMed and EMBASE was conducted. We included articles published between 1980 and 2018 involving hyperinsulinaemic stepped hypoglycaemic glucose clamps among people with or without type 1 diabetes. Key exclusion criteria were as follows: data were previously published; other patient population; a clamp not the primary intervention; and an inadequate clamp description. Glycaemic thresholds for counterregulatory hormone and/or symptom responses to hypoglycaemia were estimated and compared using generalised logrank test for interval-censored data, where the intervals were either extracted directly or calculated from the data provided by the study. A glycaemic threshold was defined as the glucose level at which the response exceeded the 95% CI of the mean baseline measurement or euglycaemic control clamp. Because of the use of interval-censored data, we described thresholds using median and IQR.n=559; 67.4% male sex, aged 32.7±10.2 years, BMI 23.8±1.4 kg/m2) and 51 papers included participants without diabetes . Compared with non-diabetic control individuals, in people with type 1 diabetes, the median (IQR) glycaemic thresholds for adrenaline (3.8 [3.2–4.2] vs 3.4 [2.8–3.9 mmol/l]), noradrenaline (3.2 [3.2–3.7] vs 3.0 [2.8–3.1] mmol/l), cortisol (3.5 [3.2–4.2]) vs 2.8 [2.8–3.4] mmol/l) and growth hormone (3.8 [3.3–3.8] vs. 3.2 [3.0–3.3] mmol/l) all occurred at lower glucose levels in people with diabetes than in those without diabetes . Similarly, although both autonomic (median [IQR] 3.4 [3.4–3.4] vs 3.0 [2.8–3.4] mmol/l) and neuroglycopenic (median [IQR] 3.4 [2.8–N/A] vs 3.0 [3.0–3.1] mmol/l) symptom responses were elicited at lower glucose levels in people with type 1 diabetes, the thresholds for autonomic and neuroglycopenic symptoms did not differ for each individual subgroup.A total of 63 articles were included, whereof 37 papers included participants with type 1 diabetes (People with type 1 diabetes have glycaemic thresholds for counterregulatory hormone and symptom responses at lower glucose levels than people without diabetes. Autonomic and neuroglycopenic symptoms responses are generated at about similar levels of hypoglycaemia. There was a considerable variation in the methodology of the articles and the high insulin doses in most of the clamps may affect the counterregulatory responses.This article has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement no. 777460.This systematic review is registered in PROSPERO (CRD42019120083).The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-022-05749-8. Iatrogenic hypoglycaemia is a continuous threat for most people with type 1 diabetes, occurring weekly or even daily as a consequence of treatment with insulin .The hyperinsulinaemic clamp is a methodology that has been widely employed to study the impact of varying degrees of hypoglycaemia in counterregulatory responses and used to determine the threshold for activation of the individual components , 4. WithAlthough reported to be organised hierarchically, the glycaemic thresholds at which these responses occur are not fixed, showing inter- and intra-individual variability, and are influenced by prior exposure to hypo- and hyperglycaemia, as well as by age and duration of diabetes , 6. In ahttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=120083. All articles with stepped clamps from the systematic review were included in this article. Only English-language articles were included.The search string and selection of publications for this systematic review have been published previously . See eleA literature search was conducted in PubMed and EMBASE in November 2018. All articles available online in the databases and published from 1980 to 2018 were included. The search used a combination of free text words, MeSH (PubMed), and Emtree (EMBASE) terms. All titles and abstracts identified from the electronic search via PubMed and EMBASE were imported to COVIDENCE . To obtan=58). Symptom scores were classified as autonomic  or neuroglycopenic . See the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) flow diagram for an overview of the process . Statistical analyses were performed using IBM SPSS Statistics for Windows, version 25  and SAS 9.4 .Results are shown with standard descriptive statistical methods. Continuous data are shown as means with SD, which for readability reasons applies to both normal and non-normal distributed data. We used data provided by the articles on both the glucose value below and the glucose value above the determined glycaemic thresholds . When these were not provided by the article, we calculated these values from the plasma glucose level at which the counterregulatory hormone or symptom response first exceeded the 95% CI of the mean baseline measurement. When needed, we determined the 95% CI from the provided sample size. As the thresholds are not known by their value, but only up to the interval in which they are censored, statistical methods such as minology . TherefoThe 63 included studies involved 1332 participants with a median of 16 participants (range 6–90) per study, of which 11 exclusively enrolled people with type 1 diabetes, 26 only participants without diabetes, and 26 included both. The participants were generally young, had a normal BMI, and were more often male than female  and three . In the four-step clamps, the mean achieved plasma glucose levels were 4.4±0.1 mmol/l, 3.7±0.1 mmol/l, 3.1±0.1 mmol/l and 2.5±0.1 mmol/l, respectively. For the three-step clamps, the mean achieved plasma glucose levels were 4.6±0.4 mmol/l, 3.7±0.1 mmol/l and 3.0±0.1 mmol/l. Twenty-six articles used whole blood to measure blood glucose values and 37 measured glucose values in plasma.The hypoglycaemic clamps were designed with a variable number of steps, with the most frequently used number of steps after the normoglycaemic phase being four  was associated with the glycaemic threshold level for any of the measured hormones (ESM Table All but one  of the sp=0.01). Similarly, the glycaemic thresholds for the appearance of neuroglycopenic symptoms averaged 3.4 (2.8–N/A) mmol/l and 3.0 (3.0–3.1) mmol/l in people without and with type 1 diabetes, respectively (p=0.007)  was associated with the glycaemic threshold level for symptom responses in people with diabetes  provided data to either calculate or extract the glycaemic threshold for symptom responses to hypoglycaemia , 63, 68.p<0.001) and not for neuroglycopenic symptoms  : 3.0 (3.0–3.0 mmol/l) vs 3.5 (2.8–3.9 mmol/l) for adrenaline; 2.6 (2.6–N/A mmol/l) vs 3.1 (2.8–3.1 mmol/l) for noradrenaline; and N/A (2.6–N/A) vs 3.3 (3.2–3.7 mmol/l) for growth hormone. This difference did not reach statistical significance for cortisol (2.8 [2.5–3.0 mmol/l] vs 3.3 2.8–3.4 mmol/l])  When we restricted the analyses to either the three-step or four-step clamps alone, the differences in glycaemic thresholds between people with vs without diabetes for adrenaline, noradrenaline, cortisol and growth hormone remained largely unchanged Table . The sam1c and duration of diabetes. In both the subgroup with and the subgroup without type 1 diabetes, glycaemic thresholds for the emergence of autonomic or neuroglycopenic symptoms were about similar within each subgroup.This systematic review shows that in people without diabetes, the glycaemic thresholds for release of counterregulatory hormone responses ranged between 3.2 and 3.8 mmol/l, with upper limits of the IQR as high as 4.2 mmol/l, whereas lower glucose levels were required to elicit symptoms of hypoglycaemia. Compared with non-diabetic control individuals, the release of counterregulatory hormones and generation of symptoms occurred at lower glucose levels in people with type 1 diabetes, in part dependent on awareness status but independent of HbABased on hypoglycaemic glucose clamp studies dating back to the 1980s, it is commonly assumed that the physiological response to hypoglycaemia in healthy people occurs at glucose levels below 3.9 mmol/l, with the release of glucagon and adrenaline, whereas glucose levels below 3.3–3.5 mmol/l are reported to trigger symptomatic awareness . The curIn people with type 1 diabetes, we observed glycaemic thresholds at 0.2–0.7 and 0.4 mmol/l lower glucose levels for counterregulatory hormone and symptom responses, respectively, compared with people without diabetes, although with a wide range, suggesting greater variability in those with type 1 diabetes. This large variability in glycaemic thresholds in type 1 diabetes can probably be explained by different prior hypoglycaemia exposure rates between individuals , 10, blu1c, is sometimes viewed as a proxy for greater exposure to hypoglycaemia and higher HbA1c as the opposite. Our data underscore, however, that merely directing people with type 1 diabetes to allow their HbA1c to increase is not expected to alter glycaemic thresholds or the associated burden of hypoglycaemia unless the rate of hypoglycaemia drops considerably [We found no evidence that strict glycaemic control had an impact on glycaemic thresholds. This is important, since strict glycaemic control, as reflected by low HbAiderably , 32, 60.Remarkably, we found no evidence for different glycaemic thresholds for the appearance of autonomic or neuroglycopenic symptoms, neither in people with type 1 diabetes nor in those without. These results contrast with the commonly held assumption that the appearance of hypoglycaemic symptoms is hierarchically organised , 14. OneWe could not investigate the glycaemic threshold for the glucagon response in people with diabetes. Most studies did not report on glycaemic thresholds for this response to hypoglycaemia in participants with type 1 diabetes, whereas those that did described a large variety in thresholds, including absent responses. In addition, it is possible that some studies did not reach glucose levels that were sufficiently low to elicit a glucagon response. Although the glucagon response is probably retained in adults with a short duration of type 1 diabetes, contributing to their often good glycaemic control combined with low hypoglycaemia risk , 16, it In this systematic review, the calculation of glucose thresholds depended on the predefined glucose steps of the studies that were included in the survival curve analysis for interval-censored data. We included both the glucose level of the first counterregulatory response and the level prior to it to obtain a more realistic ‘glycaemic threshold value’. However, it could still be argued whether a stepped clamp is the optimal study design to determine glycaemic thresholds for counterregulatory responses to hypoglycaemia or whether gradually lowering glucose levels would be more precise for defining these cut-off values. Nevertheless, two studies dating back to the 1990s that used such a gradual glucose-lowering method to assess glycaemic thresholds in children with type 1 diabetes reported results that were in line with our findings , 18.Age and diabetes duration did not influence the glycaemic thresholds, which contrasts with previous studies that showed that older age and longer diabetes duration were associated with altered counterregulatory hormone and symptom responses –21. TherA strength of this systematic review is that the large number of participants provided more precision to estimate glycaemic thresholds for counterregulatory responses to hypoglycaemia with sufficient accuracy in both people with type 1 diabetes and people without diabetes. There are also limitations. First, the calculation of glycaemic thresholds depends on the level and number of glycaemic plateaus, the difference between the separate plateaus, and the number of participants, none of which were consistent across studies. However, we applied interval-censored data statistical methodology to control for these issues and two studies using a graded stepped hypoglycaemic clamp reported similar results , 19. AddThe ADA defines hypoglycaemia in people with diabetes non-numerically as ‘all episodes of an abnormally low plasma glucose concentration that expose the individual to potential harm’ . What coIn conclusion, this systematic review shows that counterregulatory hormone responses measured during stepped hyperinsulinaemic–hypoglycaemic glucose clamps are initiated at a median plasma glucose level of 3.8 mmol/l and that both autonomic and neuroglycopenic hypoglycaemic symptoms start at similar glucose levels of around 3.4 mmol/l in people without diabetes. In people with type 1 diabetes, the release of counterregulatory hormones and generation of warning symptoms occur at glucose levels that are 0.1–0.4 mmol/l lower than in people without diabetes, and sometimes at even lower levels in those with impaired awareness. These data may inform clinical practice as well as the conduction of future clinical trials and studies investigating hypoglycaemia, and contribute to discussions about refining the classification of hypoglycaemia.ESM 1(PDF 851 kb)


Anxiety and depression can negatively affect the management of asthma. The study aimed to assess the psychosocial effects of asthma patients during COVID-19 and analyze potential risk factors and interventions.In June 2022, the “Questionnaire Star” electronic questionnaire system was used to collect data. A total of 98 asthma patients from the affiliated hospital of the medical school of Ningbo University were invited to complete the questionnaires. According to our study, the prevalence of symptoms of anxiety and depression in the asthma patients in the institution was 91.8 and 77.6%, respectively. Patients who had an asthma exacerbation in the previous two months were more likely to have anxiety symptoms (OR = 0.142 95%CI 0.025–0.820), while patients who did not participate in asthma day activities were more likely to have anxiety symptoms than those who did (OR = 0.130 95%CI 0.022–0.762).This study found that routine disease educational lectures on asthma day can successfully alleviate asthma sufferers' anxiety and depression. In late March 2022, when the Omicron strain of COVID-19 hit Shanghai, the local government began two-month silent management (from 1 April to 31 May 2022),which included closing public places, canceling large events, and discouraging crowds from gathering [The capacity of Ningbo patients with asthma to visit their physicians or general practitioners has been seriously affected. It has been reported that anxiety and depression have increased significantly during the new coronavirus pandemic , 3, meanWe distributed online questionnaires to 120 asthma patients in the outpatient department. Subsequently, we received 98 responses accordingly with an effective recovery rate of 81.6%. At the beginning of the questionnaire, we informed participants that they would sign the consent by default if they completed the survey. All of the patients were invited to voluntarily participate in the online survey. Ethics approval was obtained from the Clinical Ethics Committee of the Affiliated Hospital of the medical school of Ningbo University.We used “Questionnaire Star” to collect data as previously described . The conWe employed the Chinese version of GAD-7 to assess the anxiety symptoms of patients . The GADPatients' depressive symptoms were assessed using the PHQ-9. The milWe designed the characteristics of the participants on the questionnaire, including gender, age, weight, smoking history, occupational status, and asthma history.Categorical variable statistics were used in this statistical analysis. The categorical variables were reported as percentages, and the chi-square test was used to determine statistical significance. To evaluate the results of the chi-square test p 0.05 in categorical variables, the binary logistic regression analysis was performed. The Hosmer–Lemeshow goodness-of-fit statistic was used to assess model discrimination and calibration. P = 0.05 on both sides was judged statistically significant. To acquire statistical data, these statistical instruments included SPSS v25.0 (IBM) and "questionnaire star."A total of 98 patients were investigated, including 52 males (53.06%) and 46 females (46.94%); 0 (0%) under the age of 18, and 4 (4.08%) between 18 and 25 years old. 20 (20.41%) aged 26–30; 28 (28.57%) aged 31–40, 23 (23.47%) aged 41–50, 18 (18.37%) aged 51–65; 5 aged over 65 (5.1%); 36 (36.73%) weighed 50–60 kg, 23 (23.46%) weighed 61-70 kg, 26 (26.53%) weighed 71–80 kg, 12 (12.24%) weighed 81-90 kg, 1 (1.02%) weighed 100 kg;7(7.14%) are students,5(5.10%) are in sales,7(7.14%) are public relations personnel,1(1.02%) is in the service industry,5(5.1%) are administrative staff, 20 (20.4%)are in professional jobs ,53(54.08%) 53(54.08%) are retirees or farmers; 24 (24.48%)had a smoking history, 74 (75.52%) had no smoking history, the 24 patients with a history of smoking were in the age group over 26 years, mainly in the 31–65 years age group; 36(36.73%) had an acute asthma attack in the last two months, and 62 (63.27%) had no attack, the 36 patients with acute attack of asthma ranged in age from 18 to 65 years, mainly in the 26–65 years age group.The questionnaire showed that 90 91.8%) people in this survey had anxiety-related symptoms, and the questionnaire showed that 76 (77.6%) people in this survey had depression-related symptoms , while patients who did not participate in asthma day activities were more likely to have anxiety symptoms than those who did (OR = 0.130 95 percent range 0.022–0.762)  and asthma day activities , and those who felt that they were Patients whose self-management was disrupted by the epidemic  were more likely to have depressive symptoms (Fig. Asthma, as a chronic heterogeneous illness, has been linked to psychiatric symptoms and mental problems. There is substantial evidence that persons with asthma have a worse quality of life and a higher frequency of mental problems , 12. EmoFour essential components of asthma management include patient education, monitoring of symptoms, control of triggering factors, and pharmacologic therapy. Patient education on asthma decreases exacerbations and improves control, and is a necessary and critical component . Asthma There was increased stress under lockdown, especially for patients with chronic diseases , and theThe asthma patients of Ningbo experienced high levels of anxiety and depression during the shanghai epidemic, and doctors' sharing and interpretation of chronic disease self-management knowledge eased patients' stress and depression levels. During the pandemic, doctors have mainly used the Internet or other media to disseminate knowledge about asthma to the general public. At the same time, they also provide personalized treatment for patients offline and spiritual support for those with psychological disorders.Therefore, during the COVID-19 epidemic, doctors are encouraged to provide professional education by different means to help patients.


Microbial aerosols from pig houses can be released into the environment, posing a serious threat to biosafety and public health. At present, there are few studies on the structural characteristics of aerosol bacteria in piggeries at different growth stages. It is important to understand the characteristics of aerosol bacteria in pig houses to solve the problems of air pollution and disease control in pig houses at different growth stages. In this study, bacterial aerosol concentrations and bacterial communities were compared in pig houses at different growth stages in Hebei Province, China. It was found that bacterial concentrations, community richness, and diversity in the air increased with the age of pigs. There are many pathogenic bacteria in the microbial aerosols of piggery. Our study highlights the importance of more comprehensive research and analysis of microbial aerosols in pig houses. Precautions for air pollution should be instituted in pig houses, including wearing masks, rigorous disinfection, and hygiene procedures.Acinetobacter, Erysipelothrix, Streptococcus, Moraxella, and Aerococcus in the microbial aerosols of pig houses have the potential risk of causing disease. These differences lead us to believe that disinfection strategies for pig houses should involve a situational focus on environmental aerosol composition on a case-by-case basis.With the development of modern pig raising technology, the increasing density of animals in pig houses leads to the accumulation of microbial aerosols in pig houses. It is an important prerequisite to grasp the characteristics of bacteria in aerosols in different pig houses to solve the problems of air pollution and disease prevention and control in different pig houses. This work investigated the effects of growth stages on bacterial aerosol concentrations and bacterial communities in pig houses. Three traditional types of closed pig houses were studied: farrowing (FAR) houses, weaning (WEA) houses, and fattening (FAT) houses. The Andersen six-stage sampler and high-volume air sampler were used to assess the concentrations and size distribution of airborne bacteria, and 16S rRNA gene sequencing was used to identify the bacterial communities. We found that the airborne bacterial concentration, community richness, and diversity index increased with pig age. We found that  With the development of modern pig raising technology, the increasing density of animals in pig houses has resulted in poor air quality in pig houses . The accAcinetobacter, Erysipelothrix, Streptococcus, etc.) in microbial aerosols of pig house bacteria have been widely studied by researchers [Previous studies have shown that the pig gut microbiome varies significantly at different growth stages, while the air microbiota in pig houses is mainly derived from feces ,15. Howeearchers ,20,21,22earchers .Pigs are an important group of commercial species raised in Hebei Province, China. However, because of the lack of related large-scale pig breeding enterprises, most pig farms are still based on traditional closed breeding, and the structure of the pig house is relatively simple. Furthermore, there is no automatic excrement cleaning or automatic control of the environment in pig house-related equipment. During the winter, there is an increase in respiratory illness among farmworkers, as microbial aerosol concentrations tend to increase with particulate concentrations because of reduced ventilation in pig houses ,25.In this study, the concentrations of aerosol bacteria and bacterial communities, as well as bacterial community diversity and community composition of three types of pig houses , were compared in three typical closed pig farms in Hebei Province. Our data provide a novel basis for the threat to public health of pig house aerosol bacteria.During the winter of 2019, samples were taken from three representative pig farms in Hebei Province, China, all of which belonged to the same swine industry corporation. Pig farms were anonymized according to the company’s requirements. Each pig farm had four types of pig houses: a gestating stable, a farrowing (FAR) stable, a weaning (WEA) stable, and a fattening (FAT) stable. According to the company’s requirements for the safety of pregnant sows, we did not sample gestation houses. The FAR stables consisted of post-farrowing sows and preweaning pig houses, the WEA stables raised postweaning pigs, and the FAT stables raised fattening pigs. For safe production, the FAR stables were positioned in a separate area, while the WEA and FAT stables were located in another. Nursery pigs could be transported from FAR stables to WEA stables through special transport channels.2/head for parturient sows, 0.5–0.8 m2/head for nursery pigs, and 1–1.5 m2/head for finishing pigs. Indoor structures of pig houses included sheet steel roofs and brick walls using thermal insulation material, with ventilation and a water curtain to maintain a relatively constant temperature and humidity. Each pig house occupied an area of approximately 60 × 15 m2 and was equipped with a double-row structure. Every 5 m, there was a corral in the pig house, and each corral was equipped with a deep pit with slats to collect pig manure, which was cleaned every 15 days. There were no disease outbreaks on any of the farms during our investigation, and samples were collected ten days after fecal cleaning.For the three types of pig houses (breed: Danish landrace), the stocking density was 3.7–4.2 mAn Andersen six-stage sampler  was used to identify the size distribution characteristics of culturable bacteria in airborne microorganisms. For each pig house, one sampling point was set in the middle of the passage of the pig house with a height of 0.5 m (the breathing height of pigs) above the ground. The sampling rate was 28.3 L/min for 3 min .2 Tissuquartz filters  was used to collect microbial aerosols in the pig house. The sampler was placed in the middle passage of the pig house at a height of 0.5 m above the ground, with a sampling efficiency of 1000 L/min and a sampling time of 12 h [g for 10 min at 4 °C. All the filters were sterilized by baking in a muffle furnace at 500 °C for 48 h before sampling. The filters were stored in a mobile refrigerator and transported to the laboratory after collection. Samples were stored at −80 °C until analysis.The method for the collection of microbial aerosols was performed according to our previous study . In brie of 12 h . A totalThe total genome DNA extraction method and PCR conditions of microbial aerosol samples were performed as described in previous studies ,28,29. Ap < 0.05). All analyses were conducted in triplicate and represented at least three separate experiments. The results are presented as means ± standard deviation (SD).QIIME software was employed to remove the poor-quality sequences and obtain high-quality data to control the sequence quality. OTUs  were clustered using UPARSE software (v7.0.1001), which had 97% identity for 16S rRNA sequences . Species3 CFU/m3, 15.669 ± 1.899 × 103 CFU/m3, and 35.027 ± 2.859 × 103 CFU/m3, respectively. Bacterial aerosol concentrations were significantly higher in the WEA and FAT stables than in the FAR stables (p < 0.001) and significantly higher in the FAT stables than in the WEA stables (p < 0.001).The concentrations of culturable bacteria in the air of the three pig house types  are shown in 3 and the OUTs of the WEA stables were 1.405 ± 0.219 × 103. In the FAT stables, the OUTs were 1.703 ± 0.295 × 103 (p < 0.01). The number of OUTs in the FAT stables was significantly higher than that in the FAR and the WEA stables (p < 0.05). At the same time, the dilution curves of all samples were infinitely close to saturation, indicating that most bacteria in the samples were detected, ensuring the reliability and stability of subsequent analysis.Nine air samples from three pig house types  were sequenced for the bacterial V3/V4 region. After filtering out the poor-quality sequences, 85,524 effective reads were obtained. The sequences were clustered into 3362 operational taxonomic units (OTUs) with 97% identity, among which the OUTs of the FAR stables were 1.026 ± 0.215 × 1095 × 103 A. Comparp < 0.001) (p < 0.05) (p < 0.05).The α-diversity and β-diversity of bacterial communities in the FAR, WEA, and FAT stables are shown in < 0.001) B,C. The  < 0.05) B,C. Figu < 0.05)  that theClostridiales (21.70%), Psychrobacter (16.07%), Streptococcus (6.78%), Terrisporobacter (4.96%), Lactobacillus (4.47%), Corynebacteriaceae (2.71%), Aerococcus (2.03%), Cyanobacteria (1.88%), Staphylococcus (0.58%), and Prevotellaceae (0.06%). The dominant bacterial genera in the WEA samples were Lactobacillus (16.24%), Streptococcus (12.78%), Clostridiales (5.48%), Prevotellaceae (4.73%), Corynebacteriaceae (4.14%), Cyanobacteria (2.54%), Aerococcus (1.70%), Terrisporobacter (1.60%), Staphylococcus (0.72%), and Psychrobacter (0.08%). The dominant bacterial genera in the FAT samples were Clostridiales (29.44%), Corynebacteriaceae (9.97%), Streptococcus (9.56%), Aerococcus (5.49%), Terrisporobacter (3.40%), Lactobacillus (2.12%), Psychrobacter (1.95%), Cyanobacteria (1.87%), Streptococcus (1.74%), and Prevotellaceae (0.12%). Heatmaps for the top 35 genera of all samples are shown in Acinetobacter, Streptococcus, and Erysipelothrix; the potentially pathogenic bacteria in the WEA stables were Streptococcus.At the phylum level, Firmicutes and Proteobacteria were the most abundant in the bacterial community composition, accounting for 68.86% and 20.23% in the FAR stables, 66.94% and 3.54% in the WEA stables, and 74.95% and 5.4% in the FAT stables, respectively. In this study, three types of pig houses  of three typical closed pig farms in Hebei Province were investigated. The aerosol bacterial concentration and bacterial community, as well as the bacterial community diversity and composition of the three types of pig houses, were compared to provide a data basis and reference for healthy pig breeding and the threat of microbial aerosols to public health in pig houses.p < 0.001). This may be due to the increased activity and excretion of pigs as they grow and increased feeding density, resulting in increased concentrations of bacterial aerosols. The numbers of culturable bacteria measured in the three types of stables  were 8.092 ± 1.089 × 103 CFU/m3, 15.669 ± 1.899 × 103 CFU/m3, and 35.027 ± 2.859 × 103 CFU/m3, respectively. According to studies, the culturable bacterial concentration in the WEA stables of Jiangsu, China, was 3.460× 103 CFU/m3 (1), and the culturable bacterial concentrations in the air of three types of pig houses  in Korea were 2.322 ± 1.352 × 103 CFU/m3, 6.124 ± 1.527 × 103 CFU/m3, and 12.470 ± 4.869 × 103 CFU/m3 [Previous studies have shown that bacteria dominate microbial aerosols in pig houses , so this3 CFU/m3 .Different particle sizes of bacterial aerosols enter the respiratory system in different locations. Particles of 4.7–7 µm in diameter can enter the nasal cavity and upper respiratory tract; particles of 2.1–4.7 µm can be deposited in the bronchi, causing asthma and other diseases; however, those less than 2 µm in diameter can penetrate the alveoli and enter the respiratory tract, causing alveolar inflammation and other diseases. The smallest fraction of the fine particles may have a disproportionately large role. Although fine particles are only a small proportion, they still pose a serious threat to the respiratory health of pigs. In the FAT stables, 71.9% of airborne bacteria had a particle size larger than 3.3 µm, and 28.1% had a particle size ranging from 0.6 to 3.3 µm. Aerosols with a particle size of less than 3.3 µm are defined as fine particles, and those larger than 3.3 µm are defined as coarse particles. Culturable bacteria in the air predominate in coarse particles, but not in fine particles, similar to our results . The smap < 0.001). PCA showed that the composition of the bacterial community also changed significantly (p < 0.05). As animals grow, their activity and excretion increase and their metabolic levels change, which could cause remarkable changes in the microbial community structure in the air of feedlots [We found that the bacterial community α-diversity index of microbial aerosols increased gradually with the age of pigs. The Chao1 index and Shannon index of the FAT stables were significantly higher than those of the FAR and WEA stables , the potentially pathogenic bacteria in the FAR stables were patients ,46,47. Mty risks . Erysipeg period ,50. Erysbacteria . Strepto country . Previou workers . Thus, ty system . In the tunistic . Aerococthogenic ,54,55. Ping sick . The souing sick ,46,56.This study showed that with the increase in weekly pig age, the concentration of airborne bacteria, the species, and the diversity index of the bacterial community in pig houses has also increased gradually. The pathogenic bacteria Acinetobacter, Erysipelothrix, Streptococcus, Moraxella, and Aerococcus were detected in three types of pig houses. These bacteria with potential risk to pig and human health should be monitored. The number of disinfections should be increased to 2–3 times a week or every other day. A disinfectant should be replaced after 2–3 weeks of continuous use for another disinfectant to avoid pathogenic microorganisms producing drug resistance and reduce the disinfection effect. Spray disinfection should be used to kill airborne microorganisms in piggeries to make the piggeries cleaner. These measures may help optimize the piggery environment. From the point of view of personnel, farmers should wear masks, hats, and overalls to combat the effects of air pollution when working on farms. In this study, we emphasize the necessity of long-term detection of microbial aerosols in pig houses and the importance of more comprehensive research and analysis of microbial aerosols in livestock and poultry houses.