Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models.

Background: Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions. Method: Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence. Results: From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033. Conclusion: This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.

BACKGROUND: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. METHOD: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. RESULTS: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. CONCLUSION: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study.

Background: In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs. Methods: We utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship. Results: In the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89-7.88; PIVW < 0.00625), while a potential association exists between PM2.5-10 and vitiligo (OR = 7.42; 95% CI: 1.02-53.94; PIVW < 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17-2.1e+03; PIVW < 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW > 0.025). There was no pleiotropy in the results. Conclusion: Our results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.

Evaluating the risk of developing hyperuricemia in patients with type 2 diabetes mellitus using least absolute shrinkage and selection operator regression and machine learning algorithm.

Background: Hyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease. Methods: Patients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated. Results: A total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%. Conclusions: We developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.

Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model.

Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.

Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.

miR-21 mediates nickel nanoparticle-induced pulmonary injury and fibrosis.

We and other groups have demonstrated that exposure to nickel nanoparticles (Nano-Ni) results in severe and persistent lung inflammation and fibrosis, but the underlying mechanisms remain unclear. Here, we propose that miR-21 may play an important role in Nano-Ni-induced lung inflammation, injury, and fibrosis. Our dose- and time-response studies demonstrated that exposure of C57BL/6J (WT) mice to Nano-Ni resulted in upregulation of miR-21, proinflammatory cytokines, and profibrotic mediators. Histologically, exposure to Nano-Ni caused severe pulmonary inflammation and fibrosis. Based on the dose- and time-response studies, we chose a dose of 50 µg of Nano-Ni per mouse to compare the effects of Nano-Ni on WT with those on miR-21 KO mouse lungs. At day 3 post-exposure, Nano-Ni caused severe acute lung inflammation and injury that were reflected by increased neutrophil count, CXCL1/KC level, LDH activity, total protein concentration, MMP-2/9 protein levels and activities, and proinflammatory cytokines in the BALF or lung tissues from WT mice, which were confirmed histologically. Although Nano-Ni had similar effects on miR-21 KO mice, the above-mentioned levels were significantly lower than those in WT mice. Histologically, lungs from WT mice exposed to Nano-Ni had infiltration of a large number of polymorphonuclear (PMN) cells and macrophages in the alveolar space and interstitial tissues. However, exposure of miR-21 KO mice to Nano-Ni only caused mild acute lung inflammation and injury. At day 42 post-exposure, Nano-Ni caused extensive pulmonary fibrosis and chronic inflammation in the WT mouse lungs. However, exposure of miR-21 KO mice to Nano-Ni only caused mild lung fibrosis and chronic lung inflammation. Our results also showed that exposure to Nano-Ni caused upregulation of TGF-ß1, phospho-Smad2, COL1A1, and COL3A1 in both WT and miR-21 KO mouse lungs. However, levels were significantly lower in miR-21 KO mice than in WT mice, except TGF-ß1, which was similar in both kinds of mice. Decreased expression of Smad7 was observed in WT mouse lungs, but not in miR-21 KO mice. Our results demonstrated that knocking out miR-21 ameliorated Nano-Ni-induced pulmonary inflammation, injury, and fibrosis, suggesting the important role of miR-21 in Nano-Ni-induced pulmonary toxicity.

Health Effects of Electromagnetic Fields on Reproductive-Age Female Operators of Plastic Welding Machines in Fuzhou, China.

OBJECTIVE: The aim of this study was to investigate the health effects of electromagnetic fields (EMFs) among female operators of plastic welding machines. METHODS: We examined 180 female operators in shoe factories as the exposed group, and 349 female workers from nearby supermarkets as the unexposed group. RESULTS: The mean radiation levels in the vicinity of the welding machines ranged from 51.3 to 368.9 V/m. The prevalence of neurovegetative symptoms increased with higher EMFs exposures (P < 0.05). The prevalence of menstrual disorder was 12.0% in the unexposed group, but was 26.8% and 33.8% in low and high-exposure groups, respectively (P < 0.01). Exposure-response relationships were found between cumulate exposure and neurovegetative symptoms and menstrual disorder (P < 0.05). Serum progesterone (P4) was significantly lower in the exposed groups (P < 0.01). CONCLUSIONS: EMFs exposure was associated with adverse health effects, including neurovegetative symptoms, menstrual disorder, and low level of P4.

[Effects of high-frequency electromagnetic field on morphology of hippocampal cells in female rats].

OBJECTIVE: To analyze the effects of high-frequency electromagnetic field (HF-EMF, 30 MHz, 0-1600 V/m) on the apoptosis and ultramicrostructure of the hippocamp and demonstrate the cytotoxicity of hippocamp. METHODS: 120 Wistar female adult rats were randomly divided into ten groups based on body weight with different levels of 30 MHz electromagnetic field (0, 25, 100, 400, 1600 V/m) for eight hours daily. Five group rats were irradiated for three days. The other five group rats were irradiated for fifty-six days. Weekly the rats were continuously exposed five days. The apoptotic rate of the hippocamp was detected with TUNEL System. Meanwhile, the ultramicrostructure was observed with the transmission electron microscope. RESULTS: (1) There was no significant difference on the apoptotic rate and pathological change of the hippocamp cell between the exposure and the control groups through short term experiment (P > 0.05). (2) The apoptotic rate of the granulocyte on the DG campus of the hippocamp in the 400 V/m group and the 1600 V/m group (0.165% +/- 0.049%, 0.189% +/- 0.049% respectively) were increased significantly (P < 0.01) through inferior chronic experiment compared with the control group (0.052% +/- 0.016%). Along with the increase of radiation dose, the ultramicrostructure of the neuron cell appeared more abnormal cells. Especially there were marked change on the neuron in the 1600 V/m group. CONCLUSIONS: There is no association between cell apoptotic rate of the hippocamp and short period exposure to HF-EMF (30 MHz, 25-1600 V/m). However inferior chronic exposures to HF-EMF might induce the cytotoxicity, especially in the high dose exposure (1600 V/m) under our experiment.

[Female genital toxicities of high-frequency electromagnetic field on rats].

OBJECTIVE: To study on the effects of sub-chronic exposures to high-frequency electromagnetic field (HF-EMF) on the estrous cycle, ovarian pathological changes and related hormones and preliminarily investigate the female genital toxicities of HF-EMF in rats. METHODS: 60 Wistar female adult rats were randomly divided into five groups based on body weight and radiated with different levels of 30 MHz electromagnetic field (0, 25, 100, 400 and 1600 V/m) for eight hours daily. Weekly the rats were continuously exposed five days. From the 48th day the four stage of estrus cycle were observed with smear method of the vaginal cell. Fifty-six days later the serum levels of sexual hormones were detected with the radioimmunoassay on estrus stage. The constituent ratio of the distinct follicle number on ovaries were observed with the HE staining and the ultrastructure was observed with the transmission electron microscope. Meanwhile, the ovarian humid weight and organ coefficient were observed. RESULTS: There was no significant difference in ovarian humid weight and organ coefficient between the exposure and the control groups. The time of proestrus in the 100 V/m group, the 400 V/m group and the 1600 V/m group was decreased significantly [(15.00 +/- 5.06), (11.40 +/- 2.05) and (10.56 +/- 0.96) h in the exposure group compared with (18.70 +/- 2.96) h in the control group, P < 0.01], and the time of Anestrum in the 400 V/m group and the 1600 V/m group were increased significantly [(101.20 +/- 17.81) and (115.33 +/- 19.28) h in the exposure group compared with (69.80 +/- 11.42) h in the control group, P < 0.01)]. Serum LH in the 400 V/m and 1600 V/m group was increased significantly [(11.02 +/- 1.11) and (14.70 +/- 1.94) mU/ml in the exposure groups compared with (8.70 +/- 0.53) mU/ml in the control group, P < 0.01], and serum E2 was decreased significantly [(57.16 +/- 31.56) and (50.57 +/- 25.16) pg/ml in the exposure groups compared with (95.04 +/- 32.62) pg/ml in the control group, P < 0.01]. The composition ratio of the corpus luteum/albicans number in the 400 V/m group and the 1600 V/m group was increased significantly (19.75% and 19.04% in the exposure groups compared with 14.01% in the control group, P < 0.05). The composition ratio of the atretic follicle number was increased significantly in the 100 V/m, the 400 V/m and the 1600 V/m group (8.45%, 9.95% and 11.70% in the exposure groups compared with 7.72% in the control group, P < 0.01). The composition ratio of the mature follicle and the pri/sec follicle was decreased significantly in the 400 V/m and the 1600 V/m group (1.50% and 1.55% in the exposure groups compared with 3.36% in the control group. 22.24% and 21.09% in the exposure groups compared with 26.60% in the control group, P < 0.01). Along with the increase of radiation dose, the ultrastructure of cell on the ovaries appeared more abnormal. CONCLUSIONS: The toxicities of female gonads are closely associated with exposures to HF-EMF. The nonage damage of female gonadal toxicities might emerge on the ovaries.

[High-frequency electromagnetic field exposure on reproductive and endocrine functions of female workers].

OBJECTIVE: To analyze the toxicity of high-frequency electromagnetic field (HF-EMF) on the reproductive and endocrine functions of female workers. METHODS: 180 female workers exposed to HF-EMF for more than one year were selected as the exposure group, and 349 female workers in the marketplace were selected as the control group. Their menstrual status and pregnancy outcomes were evaluated with historical cohort study. Meanwhile, the serum levels of reproductive hormones including follicle stimulation hormone (FSH), luteinizing hormone (LH), estradiol (E(2)) and progesterone (P(4)) from 30 workers respectively in the exposure group and the control group selected randomly (in periovulatory period) were detected with the radio immunoassays. RESULTS: With the increase of residue of HF-EMF, the incidence of menstrual disorder was increased (33.8% and 26.8% in the exposure group compared with 12.0% in the control group) (P < 0.01) and the incidence of menorrhagia in the exposure I group (16.9%) was significantly higher than that in the control group (8.0%) (P < 0.01). However, serum P(4) was significantly lower in the exposure group [(2.10 +/- 1.57) and (2.39 +/- 1.35) mg/L] than in the control group [(3.80 +/- 2.67) mg/L] (P < 0.01). There was no significant difference in serum E(2), LH and FSH between the exposure and the control groups. CONCLUSION: Increase of the incidence of menstrual disorder as well as that of menorrhagia, and decrease of levels of some reproductive hormones are closely associated with HF-EMF (25 approximately 30 MHz).

Comparative studies on the increase of uterine weight and related mechanisms of cadmium and p-nonylphenol.

The research was designed to compare the effect of cadmium and p-nonylphenol on the increase of uterine weight and to study the related mechanisms. It provided basic evidence for us to understand the possible different mechanisms among different EEDs. In this study, both ovaries of 60 Wistar rats (28 days age) were ectomized, and after 21 days recovery, the rats were randomly assigned into six groups and exposed to cadmium (0.12, 1.20mg/kg), NP (100, 200mg/kg), control (sterile PBS), and positive control (17beta-estradiol) per day for 3 days, respectively, then related indexes were detected. The results showed that the increase of uterine weight induced by cadmium was accompanied by the increase of the thickness of luminal epithelium cell and endometrium but the decrease of nuclear/cytoplasm of luminal epithelium cell and endometrium, while the increase of uterine weight induced by p-nonylphenol was accompanied by the increase of the thickness of luminal epithelium cell, endometrium, and myometrium but the decrease of nuclear/cytoplasm of luminal epithelium cell and endometrium. Cadmium could inhibit the positive expression of PCNA while p-nonylphenol prompted it. Exposure to cadmium and NP both could also stimulate phosphorylation of ERK mitogen-activated protein kinases, implying that this signal pathway had an effect on the increase of the uterine weight induced by cadmium and p-nonylphenol. The results indicate that cadmium may induce the increase of uterine weight, which is accompanied with toxic effect on endometrium, while NP's effect of the increase of uterine weight is due to cell proliferation of endometrium, the mechanisms of which are the same as estrogen, but they may both activate ERK signal pathway.