Joint effects of prenatal exposure to air pollution and pregnancy-related anxiety on birth weight: A prospective birth cohort study in Ma'anshan, China.

BACKGROUND: A growing number of studies have shown that prenatal exposure to chemical and non-chemical stressors has effects on fetal growth. The co-exposure of both better reflects real-life exposure patterns. However, no studies have included air pollutants and pregnancy-related anxiety (PrA) as mixtures in the analysis. METHOD: Using the birth cohort study method, 576 mother-child pairs were included in the Ma'anshan Maternal and Child Health Hospital. Evaluate the exposure levels of six air pollutants during pregnancy using inverse distance weighting (IDW) based on the pregnant woman's residential address and air pollution data from monitoring stations. Prenatal anxiety levels were assessed using the PrA Questionnaire. Generalized linear regression (GLR), quantile g-computation (QgC) and bayesian kernel machine regression (BKMR) were used to assess the independent or combined effects of air pollutants and PrA on birth weight for gestational age z-score (BWz). RESULT: The results of GLR indicate that the correlation between the six air pollutants and PrA with BWz varies depending on the different stages of pregnancy and pollutants. The QgC shows that during trimester 1, when air pollutants and PrA are considered as a whole exposure, an increase of one quartile is significantly negatively correlated with BWz. The BKMR similarly indicates that during trimester 1, the combined exposure of air pollutants and PrA is moderately correlated with a decrease in BWz. CONCLUSION: Using the method of analyzing mixed exposures, we found that during pregnancy, the combined exposure of air pollutants and PrA, particularly during trimester 1, is associated with BWz decrease. This supports the view that prenatal exposure to chemical and non-chemical stressors has an impact on fetal growth.

The effects of gestational diabetes mellitus on fetal growth: is it different for low-risk and medium-high-risk pregnant women?

BACKGROUND: It has been suggested that gestational diabetes mellitus (GDM) alters the growth trajectory of a fetus and increases the risk of abnormal birth weight. In spite of this, there is still a significant debate regarding the mode and optimal timing of diagnosing this condition. Our aim was to determine fetal growth velocity and birth biometry in pregnant women with GDM at varying risk levels. METHODS: We conducted a cohort study involving 1023 pregnant women at a maternity hospital in Ma'anshan, China. All women completed an oral glucose tolerance test at 24-28 weeks' gestation. We measured fetal head circumference (HC), femoral length (FL), abdominal circumference (AC), biparietal diameter (BPD), and estimate fetal weight (EFW) by ultrasound at 17, 24, 31, and 35 weeks' gestation, respectively. RESULTS: Overall, 5115 ultrasound scans were performed. Among both low-risk and medium-high-risk pregnant women at 17-24 weeks' gestation, GDM exposure was associated with an increase in fetal growth velocity. Neonates born to women with GDM at medium-high risk had significantly larger birth weights than those born to women without GDM, while this was not observed in women at low risk. CONCLUSION: In medium-high-risk pregnant women, exposure to GDM has a greater effect on the fetus, leading to abnormal fetal growth velocity that lasts beyond week 24. It is evident from our results that the effects of GDM on fetal growth differ between medium-high-risk pregnant women and low-risk pregnant women, and therefore a different screening program based on the risk factor for GDM is warranted.

The modification of meteorological factors on the relationship between air pollution and periodontal diseases: an exploration based on different interaction strategies.

We aimed to characterize the association between air pollutants exposure and periodontal diseases outpatient visits and to explore the interactions between ambient air pollutants and meteorological factors. The outpatient visits data of several large stomatological and general hospitals in Hefei during 2015-2020 were collected to explore the relationship between daily air pollutants exposure and periodontal diseases by combining Poisson's generalized linear model (GLMs) and distributed lag nonlinear model (DLNMs). Subgroup analysis was performed to identify the vulnerability of different populations to air pollutants exposure. The interaction between air pollutants and meteorological factors was verified in both multiplicative and additive interaction models. An interquartile range (IQR) increased in nitrogen dioxide (NO2) concentration was associated with the greatest lag-specific relative risk (RR) of gingivitis at lag 3 days (RR = 1.087, 95% CI 1.008-1.173). Fine particulate matter (PM2.5) exposure also increased the risk of periodontitis at the day of exposure (RR = 1.049, 95% CI 1.004-1.096). Elderly patients with gingivitis and periodontitis were both vulnerable to PM2.5 exposure. The interaction analyses showed that exposure to high levels of NO2 at low temperatures was related to an increased risk of gingivitis, while exposure to high levels of NO2 and PM2.5 may also increase the risk of gingivitis and periodontitis in the high-humidity environment, respectively. This study supported that NO2 and PM2.5 exposure increased the risk of gingivitis and periodontitis outpatient visits, respectively. Besides, the adverse effects of air pollutants exposure on periodontal diseases may vary depending on ambient temperature and humidity.

Liriogerphines A-D, a Class of Sesquiterpene-Alkaloid Hybrids from the Rare Chinese Tulip Tree Plant.

Liriogerphines A-D (1-4, respectively), an unprecedented class of hybrids of germacranolide-type sesquiterpenoids and aporphine-type alkaloids, were isolated from the rare medicinal plant Liriodendron chinense. Their structures were elucidated by comprehensive spectroscopic analyses combined with electronic circular dichroism calculations and X-ray crystallographic data. Biosynthetically, an aza-Michael addition reaction is proposed to be involved in the assemblies of this class of hybrids. Compound 4 exhibited cytotoxicity against leukemia cells via inducing apoptosis and inhibiting Bcl-2 expression.

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph.

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

ZYZ-803, a novel hydrogen sulfide-nitric oxide conjugated donor, promotes angiogenesis via cross-talk between STAT3 and CaMKII.

Endothelial angiogenesis plays a vital role in recovery from chronic ischemic injuries. ZYZ-803 is a hybrid donor of hydrogen sulfide (H2S) and nitric oxide (NO). Previous studies showed that ZYZ-803 stimulated endothelial cell angiogenesis both in vitro and in vivo. In this study, we investigated whether the signal transducer and activator of transcription 3 (STAT3) and Ca2+/CaM-dependent protein kinase II (CaMKII) signaling was involved in ZYZ-803-induced angiogenesis. Treatment with ZYZ-803 (1 µM) significantly increased the phosphorylation of STAT3 (Tyr705) and CaMKII (Thr286) in human umbilical vein endothelial cells (HUVECs), these two effects had a similar time course. Pretreatment with WP1066 (STAT3 inhibitor) or KN93 (CAMKII inhibitor) blocked ZYZ-803-induced STAT3/CAMKII activation and significantly suppressed the proliferation and migration of HUVECs. In addition, pretreatment with the inhibitors significantly decreased ZYZ-803-induced tube formations along with the outgrowths of branch-like microvessels in aortic rings. In the mice with femoral artery ligation, administration of ZYZ-803 significantly increased the blood perfusion and vascular density in the hind limb, whereas co-administration of WP1066 or KN93 abrogated ZYZ-803-induced angiogenesis. By using STAT3 siRNA, we further explored the cross-talk between STAT3 and CaMKII in ZYZ-803-induced angiogenesis. We found that STAT3 knockdown suppressed ZYZ-803-induced HUVEC angiogenesis and affected CaMKII expression. ZYZ-803 treatment markedly enhanced the interaction between CaMKII and STAT3. ZYZ-803 treatment induced the nuclear translocation of STAT3. We demonstrated that both STAT3 and CaMKII functioned as positive regulators in ZYZ-803-induced endothelial angiogenesis and STAT3 was important in ZYZ-803-induced CaMKII activation, which highlights the beneficial role of ZYZ-803 in STAT3/CaMKII-related cardiovascular diseases.

Liriogerphines E-U, further unique sesquiterpene-alkaloid hybrids from the rare Chinese tulip tree.

Seventeen undescribed sesquiterpene-alkaloid hybrids (liriogerphines E-U, 1-17) were isolated and identified during a further phytochemical investigation on the branches and leaves of Chinese tulip tree (Liriodendron chinense), a rare medicinal and ornamental plant endemic to China. These unique heterodimers are conjugates of germacranolide-type sesquiterpenoids with structurally diverse alkaloids [i.e., aporphine- (1-15), proaporphine- (16), and benzyltetrahydroisoquinoline-type (17)] via the formation of a C-N bond. The previously undescribed structures were elucidated by comprehensive spectroscopic data analyses and electronic circular dichroism calculations. Such a class of sesquiterpene-alkaloid hybrids presumably biosynthesized via an aza-Michael addition is quite rare from terrestrial plants. In particular, the sesquiterpene-benzyltetrahydroisoquinoline hybrid skeleton has never been reported until the present study. All the isolates were evaluated for their cytotoxic effects against a small panel of leukemia cell lines (Raji, Jeko-1, Daudi, Jurkat, MV-4-11 and HL-60), and some of them exhibited considerable activities.

Bioactive polyketides and tryptophol alkaloids from the endophytic fungus Botryosphaeria dothidea LE-07 of Chinese tulip tree.

Two new tetraketide-derived phenol rhamnosides [botryrhamnosides A (1) and B (2)] and a new rhamnosylated tryptophol alkaloid (botryrhamnoside C, 3), along with seven related known compounds (4-10) were isolated from the solid culture of Botryosphaeria dothidea LE-07, an endophytic fungus residing in the leaves of the rare medicinal plant Chinese tulip tree (Liriodendron chinense). Their structures with the absolute configurations were determined by a combination of spectroscopy methods, comparing specific rotations, electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Compounds 1 and 2 are rare tetraketide-derived resorcinols incorporating a l-rhamnose moiety, while 3 represents the first example of rhamnose-bound tryptophol derivatives produced by microorganisms. These metabolites were evaluated in vitro for their antimicrobial and anti-neuroinflammation activities. The rhamnosylated derivatives 1-5 displayed potent antibacterial activity against Escherichia coli, with MIC values in the range of 8-16 µg/mL. Compound 2 attenuated neuroinflammation in lipopolysaccharide (LPS)-induced BV-2 microglial cells, by decreasing the level of pro-inflammatory mediators [nitric oxide (NO), tumor necrosis factor-α (TNF-α), and interleukin 6 (IL-6)] and down-regulating the mRNA expression of inducible nitric oxide synthase (iNOS). In addition, compound 8 exhibited remarkable inhibitory effect against the ATP-citrate lyase (ACL), an emerging drug target for hyperlipidemia and related glycolipid metabolic disorders, with an IC50 value of 5.32 µM.

Precision Medicine for Apical Lesions and Peri-Endo Combined Lesions Based on Transfer Learning Using Periapical Radiographs.

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. This image enhancement method is used with convolutional neural networks (CNN) to classify apical lesions, peri-endo combined lesions, and asymptomatic cases, and to compare with You Only Look Once-v8-Oriented Bounding Box (YOLOv8-OBB) disease detection results. The contributions lie in the utilization of database augmentation and adaptive histogram equalization on individual tooth images, achieving the highest comprehensive validation accuracy of 95.23% with the ConvNextv2 model. Furthermore, the CNN outperformed YOLOv8 in identifying apical lesions, achieving an F1-Score of 92.45%. For the classification of peri-endo combined lesions, CNN attained the highest F1-Score of 96.49%, whereas YOLOv8 scored 88.49%.

Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth's bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.

Association of urinary heavy metal combined exposure with periodontitis among US adults from NHANES 2011-2014.

Some heavy metals are associated with periodontitis; whereas most of these associations have focused on individual metal, there are no specific studies on the effects of combined heavy metal exposure on periodontitis. We conducted an analysis on the association between urinary heavy metal exposure and periodontitis in participants aged 30 years and older using multiple logistic regression and Bayesian kernel machine regression (BKMR). This analysis was performed on data from the National Health and Nutrition Examination Survey from 2011 to 2014. The study found that using logistic regression, the 4th quartile of urinary lead and molybdenum and the 3rd quartile of urinary strontium were positively associated with periodontitis compared to the reference quartile after adjusting for covariates. Odds ratio (OR) with 95% confidence interval (CI) was 1.738 (1.069-2.826), 1.515 (1.025-2.239), and 1.498 (1.010-2.222), respectively. The 3rd and 4th quartiles of urinary cobalt were negatively associated with periodontitis, and their ORs and 95% CIs were 0.639 (0.438-0.934) and 0.571 (0.377-0.964), respectively. The BKMR model showed that urinary barium, lead, and molybdenum were positively associated with periodontitis in a range of concentrations and urinary cobalt, manganese, tin, and strontium were negatively correlated with periodontitis. Furthermore, the overall association between urinary heavy metals and periodontitis was positive. Our study provides evidence for an association between exposure to multiple urinary heavy metals and periodontitis. However, further longitudinal studies are needed to explore the specific mechanisms involved.

The Role of Self-Esteem in the Relationship Between Psychological Capital and Anxiety of Left-Behind Experience College Students During COVID-19 Pandemic: An Online Study.

Background: There is growing evidence that the COVID-19 pandemic has had a dramatic impact on public mental health. However, less attention has been paid to left-behind experience college students (LBEs). This online study aimed to investigate the relationship between psychological capital (PsyCap) and anxiety among LBEs during COVID-19 pandemic, and further analyze the mediation role of self-esteem between them. Methods: A total of 9990 students were chosen using the stratified cluster sampling method. Three self-reported questionnaires were used to assess the PsyCap, self-esteem, and anxiety, respectively. All the statistical analyses were conducted using SPSS 23.0 and R, and to further investigate the mediation effect of self-esteem in the association of PsyCap with anxiety, AMOS 23.0 was used to build a structural equation model. Results: PsyCap, self-esteem, and anxiety were significantly correlated among LBEs during the COVID-19 pandemic. PsyCap affects anxiety directly (ß = -0.22, SE = 0.051, 95% CI: -0.27, -0.17, P < 0.05). In addition, self-esteem partially mediated the relationship between PsyCap and anxiety (mediating effect value = -0.16, 95% CI: -0.20, -0.13, P < 0.05). Conclusion: During the pandemic of COVID-19, left-behind experience had a negative influence on the PsyCap and self-esteem of college students. In addition, for LBEs, self-esteem plays an important mediating role between PsyCap and anxiety. Therefore, from the perspective of PsyCap and self-esteem, schools should translate them into practical educational strategies to enhance the mental health and mitigate the anxiety levels of LBEs.

Effect of gaseous pollutant and greenness exposure on mortality during treatment of newly treated tuberculosis patients: a provincial population-based cohort study.

BACKGROUND: Previous studies addressing the impact of environmental factors on TB prognosis are scarce, with only some studies examining the effect of particulate pollutants on TB mortality. Moreover, few studies have evaluated the effects of multiple gaseous pollutants and greenness exposures on newly treated TB patients on a large population scale. METHODS: Through the Centers for Disease Control and Prevention, data were collected from January 1, 2015 to December 31, 2020 for newly treated TB patients in Anhui Province, China. Data on gaseous pollutants sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone were collected through the National Earth System Science Data Center of China. Normalized vegetation index data were obtained through NASA. The Cox proportional risk model was also applied to calculate the hazard ratios of SO2, NO2, CO, O3, and NDVI with 95% confidence intervals for mortality among newly treated TB patients. RESULTS: Multifactorial Cox regression analysis showed that for every 0.10 µg/m3 increase in SO2, the risk of death among newly treated TB patients increased by 13.2% (HR = 1.132, 95% CI: 1.045-1.1.225), for every 10 µg/m3 increase in NO2, the risk of death among newly treated TB patients increased by 11.4%, and for each 0.1 mg/m3 increase in CO, the risk of death among newly treated TB patients increased by 5.8%. For each 0.1 increase in NDVI 250m-buffer and 500m-buffer, the risk of death among newly treated TB patients decreased by 8.5% and 6.4%, respectively. The effect of gaseous pollutants on mortality decreased progressively with elevated greenness exposure when greenness exposure was grouped from low to high. CONCLUSION: Gaseous pollutants are a risk factor during the treatment of newly treated TB patients and greenness exposure is a protective factor. Higher greenness exposure reduces the risk of death due to exposure to gaseous pollutants.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Short-term effect of sulfur dioxide (SO2) change on the risk of tuberculosis outpatient visits in 16 cities of Anhui Province, China: the first multi-city study to explore differences in occupational patients.

A growing number of biological studies suggest that exogenous sulfur dioxide (SO2) at a certain concentration may promote human resistance to Mycobacterium tuberculosis. However, the results of most relevant studies are inconsistent, and few studies have explored the relationship between SO2 exposure and tuberculosis risk at provincial level. In addition, occupational exposure has long been considered to have a certain impact on the human body, so for the first time, we discussed the differences between different occupations in the study on the relationship between air pollutant exposure and tuberculosis risk, and evaluated the impact of occupational exposure. This study aimed to explore the association between short-term SO2 exposure and the risk of outpatient visits to tuberculosis in Anhui province and 16 prefecture-level cities from 2015 to 2020. We used several models for multi-stage analysis, including distributed lag nonlinear model (DLNM), Poisson generalized linear regression model, and random-effects model. The association was assessed using the 28-day cumulative lag effect RR and 95%CI for each 10-unit increase in SO2 concentration. We divided all patients into the following six occupations: Worker, Farmer, Retired people, Children and Students, Cadre and Office clerk, and Service staff (catering, business, etc.). Sex, age, and season were analyzed by subgroup. Finally, the robustness of the multi-pollutant model was tested. At provincial level, the overall effect value of SO2 was RR=0.8191 (95%CI: 07702~0.8712); after grouping all patients by occupation, the association found only among Farmers (RR = 0.7150, 95%CI: 0.6699-0.7632, lag 0-28 days) and Workers (RR = 0.8566, 95%CI: 0.7930-0.9930, lag 0-4 days) was still statistically significant. Estimates for individual cities and using random-effects models to estimate average associations showed that SO2 exposure was associated with a reduced risk of outpatient TB visits in 14 municipalities, which remained significant when aggregated (RR = 0.9030, 95%CI: 0.8730-0.9340). Analysis of patients grouped by occupation in each municipality showed that statistical significance was again observed only in the Farmer (RR = 0.8880, 95%CI: 0.8610-0.9160) and Worker (RR = 0.8250, 95%CI: 0.7290-0.9340) groups. Stratified analysis of age, sex, and season showed that the effect of SO2 exposure was greater for middle-aged people (18-64 years old) and males, and less for seasonal changes. In summary, we found that exposure to SO2 reduces the risk of outpatient visits to tuberculosis, with farmers and workers more susceptible to SO2. Gender and age had a greater impact on the risk of TB outpatient visits than seasonal variations.