Lag Period Research Articles

Are Larger Banks Performing Better than Smaller Banks in Bangladesh?

This paper examines the relationship among bank size, net profit after tax and net interest income of 41 commercial banks in Bangladesh from 2012 to 2021. The study develops six hypotheses to diagnose the relationship between bank size and net profit or net interest income from the perspective of lag period data, generation wise data, and yearly data. Convenience sampling has been used, and the data is collected for the period 2012 to 2021 that considers all four generations of banks and thus the data set includes 410 firm year observations. After conducting OLS regression, it turns out that bank size positively impacts net interest income and net profit after tax. Overall, the correlation between net interest income and net profit after tax with bank size is significant revealed by examining the value of R2 and adjusted R2. We suggest testing with some other relevant variables (qualitative) which may produce some insightful thoughts and thus opens up the door of future research. Finally, it can be concluded that bank size is a self-sustaining attribute

Impacts of environmental factors on the aetiological diagnosis and disease severity of community-acquired pneumonia in China: a multicentre, hospital-based, observational study.

Environmental exposures are known to be associated with pathogen transmission and immune impairment, but the association of exposures with aetiology and severity of community-acquired pneumonia (CAP) are unclear. A retrospective observational study was conducted at nine hospitals in eight provinces in China from 2014 to 2019. CAP patients were recruited according to inclusion criteria, and respiratory samples were screened for 33 respiratory pathogens using molecular test methods. Sociodemographic, environmental and clinical factors were used to analyze the association with pathogen detection and disease severity by logistic regression models combined with distributed lag nonlinear models. A total of 3323 CAP patients were included, with 709 (21.3%) having severe illness. 2064 (62.1%) patients were positive for at least one pathogen. More severe patients were found in positive group. After adjusting for confounders, particulate matter (PM) 2.5 and 8-h ozone (O3-8h) were significant association at specific lag periods with detection of influenza viruses and Klebsiella pneumoniae respectively. PM10 and carbon monoxide (CO) showed cumulative effect with severe CAP. Pollutants exposures, especially PM, O3-8h, and CO should be considered in pathogen detection and severity of CAP to improve the clinical aetiological and disease severity diagnosis.

Characterization of a Pseudomonas sp. Isolated from Langkawi Capable of Degrading Glyphosate

Bioremediation of soil contaminants, including glyphosate, is an economically viable and environmentally friendly technique. Glyphosate, one of the most widely used herbicides for weed management, poses significant risks to wildlife and their habitats when it contaminates the environment. This study focused on the bioremediation potential of soil Pseudomonas spp. isolated from a paddy field with a long history of glyphosate application. The most promising isolate was tentatively identified as Pseudomonas sp. strain UPM-2009 through partial identification methods. This bacterium showed significant potential for glyphosate degradation under optimal conditions. Experiments demonstrated that Pseudomonas sp. degrades glyphosate most effectively at pH 7.0, a glyphosate concentration of 0.5 g/L, temperatures between 30 and 35°C, and an inoculum size of 1% (v/v). Notably, the bacterium exhibited a two-day lag period at 0.5 g/L glyphosate, achieving nearly 90% degradation after six days of incubation. Heavy metals such as Hg(II), Ag(I), and Cd(II) significantly inhibited bacterial growth, with inhibition rates of 99%, 95%, and 66%, respectively. This study underscores the potential of Pseudomonas sp. for bioremediation of glyphosate-contaminated environments. It highlights the need for further research, particularly molecular identification techniques, to fully characterize and optimize this bioremediation strategy. This approach can significantly contribute to mitigating the environmental impact of glyphosate pollution, promoting healthier ecosystems and sustainable agricultural practices.

Modelling the Effect of mercury on the Growth Rate of an SDS-degrading Pseudomonas sp. strain Maninjau1

The SDS-degrading bacterium Pseudomonas sp. strain Maninjau1 experienced significant inhibition by mercury. Observations of bacterial growth at varying mercury concentrations revealed a sigmoidal pattern, with lag periods extending from 7 to 12 hours. Increasing mercury concentrations progressively impeded growth, with a concentration of 1.0 mg/L nearly halting bacterial activity. To analyze these effects, the modified Gompertz model was employed to determine growth rates across different mercury concentrations. These rates were then subjected to several models, including the modified Han-Levenspiel, Wang, Liu, Shukor, modified Andrews, and Amor models. Among these, the Amor model failed to appropriately fit the growth curves. Statistical analysis indicated that the Shukor model performed the best, evidenced by the lowest values for Root Mean Square Error (RMSE) and Akaike’s Information Criterion corrected (AICc), the highest adjusted correlation coefficient (adR2), and values of Accuracy Factor (AF) and Bias Factor (BF) closest to unity. The parameters obtained from the Shukor model, which are mmax (h-1) and Sm (mg L-1) and n which represent maximum growth rate, critical heavy metal ion concentration and empirical constant values were 0.187, 1.126 and 2.406, respectively. The Shukor model allows for the prediction of the critical heavy metals concentration which can completely inhibited bacterial growth. This robust modeling approach underscores the Shukor model's suitability for predicting the impact of mercury on the growth dynamics of Pseudomonas sp. strain Maninjau1 under toxic stress conditions.

Primary Mathematical Modeling of the Growth of Diesel by a Bacterium Isolated from a Hydrocarbon-contaminated Soil

Mathematical modeling of microbial growth via nonlinear regression is essential for determining key parameters such as the maximum specific growth rate, which are foundational for secondary modeling. Models such as modified Gompertz, modified Logistic, modified Richards, Buchanan-3-phase, Baranyi-Roberts, modified Schnute, von Bertalanffy, Morgan-Mercer-Flodin (MMF), and Huang elucidate the impact of substrates on bacterial growth and biotransformation processes, vital for biotechnological applications like wastewater treatment and bioremediation. A previously isolated diesel-degrading Pseudomonas sp. strain Neni-4 growth on diesel was modeled using the aforementioned primary models. Experimental data showed that diesel concentrations from 0.25 to 3.5% (v/v) are toxic, slowing bacterial growth and increasing lag periods from 3 to 15 hours. Among the primary models tested, the Baranyi-Roberts model provided the best fit, evidenced by a high adjusted coefficient of determination, low RMSE and AICc values, and favorable accuracy (AF) and bias factors (BF). The reliability of the Baranyi-Roberts model underscores its suitability for modeling bacterial growth under toxic conditions, offering valuable insights for optimizing biotechnological processes involving bacterial adaptation and growth under stress conditions.

Primary Modeling of Microbial Growth under Toxic Conditions with the Modified Schnute Model

Primary modeling of microbial growth is essential for determining key parameters such as the maximum specific growth rate (μm), which are foundational for secondary modeling. These models, including those by Monod, Haldane, Aiba, and Teissier, elucidate the impact of substrates on bacterial growth and biotransformation processes, vital for biotechnological applications like wastewater treatment and bioremediation. Experimental data showed that acrylamide from 250 to 1250 mg/L as a sole nitrogen source is toxic, slowing bacterial growth at higher concentrations resulting in an increase in lag periods ranging from 3 to 9 hours. Various primary models were tested, with the modified Schnute model providing the best fit based on statistical analysis, normality tests, and key parameters such as adjusted coefficient of determination near to unity, lowest values for RMSE and AICc values and good values of accuracy (AF) and bias factors (BF). The modified Schnute model's reliability underscores its suitability for modeling bacterial growth under toxic conditions, offering valuable insights for optimizing biotechnological processes involving bacterial adaptation and growth under stress conditions.

Incidence and survival of secondary malignancies after external beam radiotherapy for prostate cancer in the SEER database.

We investigated the incidence of secondary bladder (BCa) and rectal cancers (RCa) after external beam radiotherapy (EBRT) for prostate cancer (PCa) compared to radical prostatectomy (RP) alone, and compared cancer-specific survival (CSS) of these secondary neoplasms to their primary counterparts. This retrospective cohort study included men in the SEER cancer registry with a diagnosis of non-metastatic, clinically node-negative PCa treated with either RP or EBRT from 1995-2011 and allowed a minimum five-year lag period for the development of secondary BCa or RCa. Patients were divided into two eras, 1995-2002 and 2003-2011, to examine differences in incidence of secondary malignancies over time. Univariable and multivariable competing risk analyses with Fine-Gray subdistribution hazard and cause-specific hazard models were used to examine the risk of developing a secondary BCa or RCa. Competing risks analyses were used to compare CSS of primary vs. secondary BCa and RCa. A total of 198 184 men underwent RP and 190 536 underwent EBRT for PCa. The cumulative incidence of secondary BCa at 10 years was 1.71% for RP, and 3.7% for EBRT (p<0.001), while that of RCa was 0.52% for RP and 0.99% for EBRT (p<0.001). EBRT was associated with almost twice the risk of developing a secondary BCa and RCa compared to RP. The hazard of secondary BCa following EBRT delivered during 2003-2011 was 20% less than from 1995-2002 (p<0.09, Fine-Gray model), while that of secondary RCa was 31% less (p<0.001) (hazard ratio 0.78, p<0.001) for Fine-Gray and cause-specific hazard models. In the Fine-Gray model, the risk of death from BCa was 27% lower for secondary BCa after RP compared to primary BCa, while the risk of death was 9% lower for secondary BCa after EBRT compared to primary BCa. There was no difference in RCa-specific survival between primary or secondary RCa after RP or EBRT. The risk of BCa and RCa is almost twice as high for men undergoing EBRT for localized PCa vs. RP, but that risk is declining, likely reflecting advances in radiation delivery. The development of secondary RCa or BCa does not confer elevated risk of death compared to their primary counterparts.

Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model

Abstract Accurate prediction of monthly runoff is critical for effective water resource management and flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) model, Fourier transform long short-term memory (FT-LSTM), to improve the prediction accuracy of monthly discharge time series in the Brahmani river basin at Jenapur station. We compare the performance of FT-LSTM with three popular DL models: LSTM, recurrent neutral network, and gated recurrent unit, considering different lag periods (1, 3, 6, and 12). The lag period, representing the interval between the observed data points and the predicted data points, is crucial for capturing the temporal relationships and identifying patterns within the hydrological data. The results of this study show that the FT-LSTM model consistently outperforms other models across all lag periods in terms of error metrics. Furthermore, the FT-LSTM model demonstrates higher Nash–Sutcliffe efficiency and R2 values, indicating a better fit between predicted and actual runoff values. This work contributes to the growing field of hybrid DL models for hydrological forecasting. The FT-LSTM model proves effective in improving the accuracy of monthly runoff forecasts and offers a promising solution for water resource management and river basin decision-making processes.

Potential risk of proton pump inhibitors for Parkinson’s disease: A nationwide nested case-control study

Proton pump inhibitor (PPI) use is a potential risk factor for neurodegenerative disease development; however, its role in Parkinson’s disease (PD) remains unclear. This study aimed to investigate the association between PPI use and PD risk. A total of 31,326 patients with newly diagnosed PD were matches by age, sex, body mass index, diabetes, and hypertension with 125,304 controls at a ratio of 1:4. The data were collected from the Korean National Health Insurance Services Database from January 2010 to December 2019. Cumulative defined daily doses of PPIs were extracted from treatment claims. We examined the association between PPI use and PD risk using conditional logistic regression. To prevent protopathic bias, we excluded patients diagnosed with PD within a 1-year lag period after PPI exposure. We applied 2- and 3-year lag periods for sensitivity analysis. PPI use was associated with an increased risk of PD when a 1-year lag period was applied between PPI exposure and PD development (adjusted odds ratio, 1.10; 95% confidence interval, 1.07–1.13). A significant positive dose-response relationship existed between the cumulative defined daily doses of PPIs and PD development (P&lt;0.001). Similar results were obtained for the 2- or 3-year lag periods. The association did not vary based on gender. Older age, a higher Charlson Comorbidity Index score, no alcohol consumption, and a non-smoking status were associated with a significantly increased PD risk with PPI use. We observed an association between PPI use and PD risk, although long-term follow-up studies are necessary to verify this association.

Associations between Australian climate drivers and extreme weekly fire danger

Aims We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year. Methods We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times. Key results The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest. Conclusions These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger. Implications The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia.

Time to initiation of modern contraceptive method use after childbirth and its predictors in Southern Ethiopia: a retrospective follow-up study

BackgroundTimely initiation of modern contraceptive use is vital to prevent unintended pregnancy and its related morbidities and mortalities. However, there is a scarcity of evidence about the duration of time elapsing from childbirth to initiating modern contraceptive use following childbirth and its associated factors in the study area for evidence-based interventions. Therefore, we aimed to assess the time to initiation of modern contraceptive method use and its predictors in Hossana town, southern Ethiopia.MethodsA retrospective follow-up study was conducted at public health facilities in Hosanna town. A total of 503 study participants were included in the study using a systematic random sampling technique. The Box and Whisker plot was used to estimate the time to initiation of modern contraceptive use. A Weibull regression model was applied to identify predictors of time to initiation of modern contraceptive use. Adjusted Hazard Ratio (AHR) with a 95% confidence interval (CI) was used to interpret the strength of the association.ResultsThe median time to initiation of modern contraceptive use was 6 months, with an interquartile range of 3 months. Husband/partner attending higher education [AHR = 1.64, 95% CI: 1.04, 2.57], women who had parity of more than two [AHR = 1.93, 95% CI: 1.01, 3.67], and women who had communicated with their husband/partner about modern contraceptive methods [AHR = 3.03, 95% CI: 1.41, 6.67] were more likely to initiate modern contraceptive method use within six months after childbirth. In contrast, women with an older age of greater than or equal to 30 years [AHR = 0.32, 95% CI: 0.13, 0.82] and who did not resume sexual intercourse after childbirth [AHR = 0.02, 95% CI: 0.01, 0.03] were less likely to initiate modern contraceptive method use within six months after childbirth.ConclusionsThe median time to initiation of modern contraceptive method use after childbirth in the postpartum period was delayed from the World Health Organization recommendation of at most 6 weeks. Emphasis should be given to older women, women with lower parity, and men’s participation in contraceptive communication to improve timing for initiation of modern contraceptive use after childbirth and to curb the five-month lag periods.

Vector Autoregressive Model of Maize Production in Northern Region of Ghana

Agricultural growth plays a crucial role in the Comprehensive African Agriculture Development Programme (CAADP, 2009) agenda. The program recognizes that increasing agricultural productivity is essential for reducing poverty, meeting food production targets, and lowering production costs and food prices for the impoverished. This study aimed to develop two types of models. The first model employed a vector autoregressive (VAR) approach, which involved regressing the production of maize in one district against the production of maize in other districts at various lags. The second model utilized a VAR framework where the maize production in each district was regressed against the production of maize in other districts and the corresponding climate conditions at different lag periods. The available data spanned from 1968 to 2018 and were recorded on an annual basis. Six climatic variables were included in the analysis. A lag order of 3 was selected for the models. The results of the autocorrelation test using the portmanteau test indicated no serial autocorrelation across all lag periods. The test for normality revealed that the residuals followed a normal distribution. Additionally, there was no evidence of heteroscedasticity in the data. Furthermore, a Granger causality test was conducted on the selected districts to explore causal relationships. Variance decomposition analysis was performed to assess the variance relation in the data and understand the contribution of different factors. Based on adjusted R-squared, mean absolute error (MAE), and root mean squared error (RMSE) values, the models that incorporated climatic variables were found to be the most suitable for forecasting maize production in the selected districts. The VAR model, which captures the interdependencies between the variables, was utilized in this analysis. All variables in the VAR model were treated symmetrically, meaning that their relationships were considered equally important.

Low ambient temperature and air pollution are associated with hospitalization incidence of coronary artery disease: Insights from a cross-sectional study in Northeast China

Abstract Background Previous studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease (CAD). However, there remains a need to explore and clarify the evidence for associations between meteorological changes and hospitalization incidences related to CAD and its subtypes, especially in cold regions. This study aimed to systematically investigate the relationship between exposure to meteorological changes, air pollutants, and hospitalization for CAD in cold regions. Methods We conducted a cross-sectional study using hospitalization records of 86,483 CAD patients between January 1, 2009, and December 31, 2019. Poisson regression analysis, based on generalized additive models, was applied to estimating the influence of hospitalization for CAD. Results Significant associations were found between low ambient temperature [-10°C, RR = 1.65; 95% CI: (1.28–2.13)] and the incidence of hospitalization for CAD within a lag of 0–14 days. Furthermore, O3 [95.50 μg/m3, RR = 12; 95% CI: (1.03–1.21)] and NO2 [48.70 μg/m3, RR = 1.0895% CI: (1.01–1.15)] levels were identified as primary air pollutants affecting the incidence of CAD, ST-segment-elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) within the same lag period. Furthermore, O3 [95.50 μg/m3, RR = 1.12; 95% CI: (1.03–1.21)] and NO2 [48.70 μg/m3, RR = 1.0895% CI: (1.01–1.15)] levels were identified as primary air pollutants affecting the incidence of CAD, ST-segment-elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) within the same lag period. The effect curve of CAD hospitalization incidence significantly increased at lag days 2 and 4 when NO2 and O3 concentrations were higher, with a pronounced effect at 7 days, dissipating by lag 14 days. No significant associations were observed between exposure to PM, SO2, air pressure, humidity, or wind speed and hospitalization incidences due to CAD and its subtypes. Conclusion Our findings suggest a positive correlation between short-term exposure to low ambient temperatures or air pollutants (O3 and NO2) and hospitalizations for CAD, STEMI, and NSTEMI. These results could aid the development of effective preparedness strategies for frequent extreme weather events and support clinical and public health practices aimed at reducing the disease burden associated with current and future abnormal weather events.

Early metformin initiation was not associated with incident dementia: a population‐based cohort study

AbstractBackgroundMetformin is the first‐line pharmacotherapy for type 2 diabetes. Although metformin was previously suggested to prevent dementia, most existing epidemiologic studies did not adequately address immortal time bias or confounding due to disease severity. We aim to determine the association of metformin with dementia risk using various bias mitigation strategies.MethodThis population‐based retrospective cohort study utilized data housed at ICES (https://www.ices.on.ca/). Using a validated algorithm for diabetes (Specificity = 99.1%, PPV = 91.4%), residents of Ontario, Canada ≥66 years newly diagnosed with diabetes from January 1, 2008 to December 31, 2017 entered the cohort. We excluded potential type 1 diabetes or advanced diabetes cases by excluding people with insulin as the first received glucose‐lowering medication. To select people at an early disease stage who could either initiate metformin or start lifestyle interventions alone, we excluded people with hemoglobin A1c &lt;6.5% or ≥8.0% or estimated glomerular filtration rate (eGFR) &lt;45 mL/min/1.73m2. Using a landmark analysis to address immortal time bias, the exposure was classified into “metformin monotherapy initiation within 180 days after new diabetes diagnosis” or “no glucose‐lowering medications within 180 days”. To mitigate reverse causality, a 1‐year lag period was applied to the end of the 180‐day landmark period, and therefore follow‐up started 1.5 year after cohort entry. Incident dementia was ascertained using a validated algorithm for Alzheimer’s disease and related dementias (Sensitivity = 79.3%, Specificity = 99.1%). Adjusted hazard ratios (aHR) and confidence intervals (CI) were obtained from propensity‐score weighted Cox proportional hazard models.ResultCompared to no or delayed metformin initiation (N = 22,369; 1,768 events; 117,414 person‐years), metformin initiation within 180 days (N = 12,331; 978 events; 65,761 person‐years) showed no association (aHR [95% CI] = 1.047 [0.956‐1.147]) with dementia risk over mean follow‐up of 5.28 years. The sensitivity analysis with a 1‐year landmark period also showed no association (aHR [95% CI] = 1.040 [0.942‐1.148]) when comparing metformin initiation within 1 year (N = 13,902; 1,047 events; 68,404 person‐years) to no or delayed initiation (N = 19,078; 1,433 events; 91,917 person‐years).ConclusionEarly metformin initiation was not associated with a lower dementia risk. The present study does not support metformin as a drug to prevent dementia.

Study on the Relationship Between the Number of Adverse Drug Reactions of Essential Drugs and Visits: Based on Vector Autoregressive Model.

To analyse the relationship between the adverse drug reactions (ADRs) of essential drugs and visits, based on the recorded annual increase in ADRs associated with essential medicines in China, to provide a reliable theoretical basis for further analysis and optimization of the safety of essential drugs. The data of adverse reactions of essential drugs in China from 2011 to 2020, time series analysis was conducted, and vector autoregressive (VAR) model was established. The relationship between the number of ADRs and visits was explored empirically through Granger causality test, impulse response function and variance decomposition. There was a long-term cointegration relationship and one-way causality between the number of visits and ADRs caused by essential medicines. In the initial stage, the ADR response to the number of visits increased sharply, but with an increase in the number of lag periods, the impact remained basically stable, even showing a slight decreasing trend. The number of visits impacts ADRs caused by essential medicines, but this impact remains basically stable after reaching a certain level.

Assessment of Cataract Risk after Diagnostic Head CT Scan Radiation Exposure in Ontario, Canada.

Ionizing radiation is one of the known risk factors for cataract development, however, there is still debate regarding the level of risk after low dose exposures. One of the largest sources of radiation exposure to the lens of the eye is diagnostic CT scans. The aim of this study was to examine whether ionizing radiation associated with head CT scans increases cataract risk in residents of Ontario, Canada. Data were collected from January 1, 1994 to December 31, 2015 (22 years) from anonymized Ontario Health Insurance Plan (OHIP) medical records for over 16 million subjects. A lens dose was estimated for each CT scan using the National Cancer Institute dosimetry system for CT (NCICT) program combined with Canada-specific CTDIvol data. Multivariate Cox proportional hazards analysis was performed with cataract extraction surgery as the primary outcome and lens dose as the main variable of interest, with inclusion of various medical and demographic covariates. Lag periods of 3, 5 and 7 years were incorporated. When lens dose was treated as a continuous variable, hazard ratios (per 100 mGy) ranged from 0.82 (0.80-0.84) to 1.10 (1.09-1.11) depending on the lag period. As a secondary analysis, when individuals were binned based on their total cumulative dose, no significant dose response pattern was observed in the low dose region. Overall, within the bounds of this study, the data do not support an increased risk of vision impairing cataracts after diagnostic head CT scan radiation exposure.

Introducing activation functions into segmented regression model to address lag effects of interventions

The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regarding methods that account for lag effects of interventions.To address this, we introduced activation functions (ReLU and Sigmoid) to into the classic segmented regression (CSR) of the ITS design during the lag period. This led to the proposal of proposed an optimized segmented regression (OSR), namely, OSR-ReLU and OSR-Sig. To compare the performance of the models, we simulated data under multiple scenarios, including positive or negative impacts of interventions, linear or nonlinear lag patterns, different lag lengths, and different fluctuation degrees of the outcome time series. Based on the simulated data, we examined the bias, mean relative error (MRE), mean square error (MSE), mean width of the 95% confidence interval (CI), and coverage rate of the 95% CI for the long-term impact estimates of interventions among different models.OSR-ReLU and OSR-Sig yielded approximately unbiased estimates of the long-term impacts across all scenarios, whereas CSR did not. In terms of accuracy, OSR-ReLU and OSR-Sig outperformed CSR, exhibiting lower values in MRE and MSE. With increasing lag length, the optimized models provided robust estimates of long-term impacts. Regarding precision, OSR-ReLU and OSR-Sig surpassed CSR, demonstrating narrower mean widths of 95% CI and higher coverage rates.Our optimized models are powerful tools, as they can model the lag effects of interventions and provide more accurate and precise estimates of the long-term impact of interventions. The introduction of an activation function provides new ideas for improving of the CSR model.

Interactive effect of air pollutant and meteorological factors on seasonal influenza transmission, Shanghai, China

BackgroundPrevious studies have provided evidence of seasonal influenza transmission linked to various meteorological and environmental factors. However, the joint effect from both air pollutant exposure and meteorological factors that modify the seasonal influenza incidence is not well discussed. MethodsData on weekly laboratory-confirmed influenza positive case numbers were obtained from Pudong New Area, Shanghai, China between January 1, 2011 and December 31, 2019. Weekly average weather data (absolute humidity (AH), minimum temperature (Min Temp) and particulate matter <2.5 μm (PM2.5) concentration levels) at the same period were collected from Copernicus Atmosphere Monitoring Services (CAMS).A General additive model (GAM) and distributed lag nonlinear model (DLNM) with Quasi-Poisson likelihood were used to evaluate the independent and interaction effect of weather factors and PM2.5 on seasonal influenza transmission along with lag effects. ResultsOur research showed that both AH and Min Temp had U-shape interactive relationships with PM2.5 at high PM2.5 concentration level. Interaction between PM2.5 and AH had positive relative risk (RR) effect at lag of 5 weeks when AH = 5 g/m3and 15 g/m3, as well as the same lag period when Min Temp = 5 °C and 20 °C. ConclusionOur study found that an interactive effect between air pollutant and weather factors exists and significantly affects influenza transmission. For future studies, lag interactive effects among air pollutant and temperature or humidity should be considered in the evaluation of the environment-influenza association, as such interactions might change the disease response to short-term effects.

The Effects of Air Pollutants on Mortality in the Elderly at Different Ages: A Case of the Prefecture with Most Serious Aging in China

The elderly population is the main group sensitive to air pollution; however, the risks of multiple air pollutants on the elderly at different ages are not very clear. For this study, daily concentrations of five major air pollutants (PM10, NO2, SO2, CO, and O3) and daily mortality information of 173,537 people who died between 2014 to 2017 in Nantong, a prefecture with the most serious aging rate in China in 2000, 2010, and 2020, were collected; the aging people were divided into 2–6 groups according to age. A generalized additive model with a thin-plate spline function was used to study the exposure–response relationship, the excess risk, and the lag period of various pollutants on each group of the elderly, and the synergistic effect between these pollutants and temperature was demonstrated. The results revealed that, after controlling confounding factors such as temperature, humidity, and wind speed, the lag period and relative risk of most pollutants increased with age, and the adverse effect of air pollutants on the elderly lasted for 2–3 days. Among the pollutants, NO2, SO2, CO, and O3 had a longer lag period compared with PM10. Linear and non-linear exposure–response relationships were observed between the pollutants and mortality. Women were at higher risk than men for the same pollutant concentrations. Synergistic effects were observed between the five pollutants and temperature. This study could contribute to the formulation of a strategy to mitigate the effects of air pollution on the elderly at different ages and play a positive role in reducing the negative impact of air pollution on aging societies.

TMET-17. LEVERAGING METABOLIC VULNERABILITIES INDUCED BY TERT EXPRESSION FOR GLIOBLASTOMA THERAPY IN VIVO

Abstract Telomerase reverse transcriptase (TERT) is essential for telomere maintenance and tumor immortality. Although TERT is an attractive therapeutic target, targeting TERT directly is hampered by toxicity to stem cells that also rely on TERT for proliferation and the lag period before telomere attrition is observed. Targeting metabolic reprogramming associated with TERT is an alternate strategy. Our studies indicate that TERT is essential for redox homeostasis in glioblastoma cells (GBM; GBM6 and U251). Specifically, TERT increases levels of glutathione (GSH) via upregulation of the catalytic subunit of glutamate-cysteine ligase (GCLC), the rate-limiting enzyme in de novo GSH synthesis. Inhibiting GCLC using buthionine sulfoximine (BSO) or siRNA reduces synthesis of 13C-GSH from [U-13C]-glutamine and inhibits viability of GBM6 and U251 cells. However, GCLC inhibition causes a compensatory increase in synthesis of glutamate, aspartate, and pyrimidine nucleotides from [U-13C]-glutamine via upregulation of two key glutamine-utilizing enzymes i.e., glutaminase (GLS), the enzyme that generates glutamate from glutamine, and carbamoyl phosphate synthetase II (CPSII), the enzyme that converts glutamine to carbamoyl phosphate during pyrimidine biosynthesis. We then examined the therapeutic potential of inhibiting GLS and CPSII in combination with GCLC in GBMs. 6-diazo-5-oxy-L-norleucin (DON), a potent inhibitor of glutamine-utilizing enzymes including GLS and CPSII, synergizes with BSO in inhibiting viability of GBM6 and U251 cells. This effect is specific to TERT-expressing GBM cells because the combination of DON and BSO does not affect viability of normal astrocytes or astrocytomas that use the ALT pathway for telomere maintenance. Importantly, in vivo stable isotope tracing confirmed that combined treatment with JHU-083 (a brain-penetrant prodrug of DON) and BSO abrogates synthesis of GSH, aspartate, and pyrimidine nucleotides from [U-13C]-glutamine and induces apoptotic death in mice bearing intracranial GBM6 tumors.Collectively, our studies highlight the therapeutic potential of targeting metabolic vulnerabilities induced by TERT expression in GBMs in vivo.