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In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of the radiographic defect angle of the intrabony defect. In this approach, radiographic defect angles of the intrabony defect greater than 37 degrees are classified as severe, while those less than 37 degrees are considered mild. This study employed a series of novel image enhancement techniques to significantly improve diagnostic accuracy. Before enhancement, the maximum accuracy was 78.85%, which increased to 95.12% following enhancement. YOLOv8 detects the affected tooth, and its mAP can reach 95.5%, with a precision reach of 94.32%. This approach assists dentists in swiftly assessing the extent of periodontal erosion, enabling timely and appropriate treatment. These techniques reduce diagnostic time and improve healthcare quality. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 IC 50 value of 5.32 μM. [Display omitted] [ABSTRACT FROM AUTHOR]