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Background: Although a direct association has been established between oral health management and maintaining military combat readiness and fulfilling defense duties, unmet dental-care needs have been consistently reported, and there has been little research on the role of mobile healthcare services in addressing this issue. This study explored the association between unmet dental-care needs and the intention to use mobile healthcare services among military personnel. Methods: This study was conducted on military personnel who visited a military hospital in Gyeonggi-do, Korea, from August 19 to August 31, 2024. A total of 150 self-administered questionnaires were distributed and 135 valid responses were analyzed. The analysis included general characteristics, dental care utilization, and intention to use mobile healthcare services. Statistical analyses, such as t-tests, ANOVA, and multiple regression, were employed to determine the factors influencing the intention to use mobile healthcare services. Results: This study found that military personnel with unmet dental-care needs had significantly higher intentions to use mobile healthcare services than those without such needs. The analysis revealed that the key factors influencing this intention were perceived health status and previous dental-care utilization. Specifically, participants who rated their overall health as better and those who had used dental care services in the past year showed a greater tendency to express interest in mobile healthcare services than those who did not. Furthermore, unmet dental-care needs were more prevalent among soldiers who reported limited access to dental facilities owing to time constraints or a lack of prioritization of oral health, which increased their inclination toward utilizing mobile healthcare solutions as an alternative. Conclusion: Mobile healthcare solutions can offer personalized and timely care as viable alternatives for improving oral health management in the military. Moreover, integrating mobile healthcare services into military health systems could significantly reduce unmet dental-care needs and enhance overall combat readiness by promoting better health outcomes.

Abstract Plant diseases pose significant threats to agriculture, impacting both food safety and public health. Traditional plant disease detection systems are typically limited to recognizing disease categories included in the training dataset, rendering them ineffective against new disease types. Although out-of-distribution (OOD) detection methods have been proposed to address this issue, the impact of fine-tuning paradigms on these methods has been overlooked. This paper focuses on studying the impact of fine-tuning paradigms on the performance of detecting unknown plant diseases. Currently, fine-tuning on visual tasks is mainly divided into visual-based models and visual-language-based models. We first discuss the limitations of large-scale visual language models in this task: textual prompts are difficult to design. To avoid the side effects of textual prompts, we futher explore the effectiveness of purely visual pre-trained models for OOD detection in plant disease tasks. Specifically, we employed five publicly accessible datasets to establish benchmarks for open-set recognition, OOD detection, and few-shot learning in plant disease recognition. Additionally, we comprehensively compared various OOD detection methods, fine-tuning paradigms, and factors affecting OOD detection performance, such as sample quantity. The results show that visual prompt tuning outperforms fully fine-tuning and linear probe tuning in out-of-distribution detection performance, especially in the few-shot scenarios. Notably, the max-logit-based on visual prompt tuning achieves an AUROC score of 94.8 $$\%$$ % in the 8-shot setting, which is nearly comparable to the method of fully fine-tuning on the full dataset (95.2 $$\%$$ % ), which implies that an appropriate fine-tuning paradigm can directly improve OOD detection performance. Finally, we visualized the prediction distributions of different OOD detection methods and discussed the selection of thresholds. Overall, this work lays the foundation for unknown plant disease recognition, providing strong support for the security and reliability of plant disease recognition systems. We will release our code at https://github.com/JiuqingDong/PDOOD to further advance this field.

Plant diseases significantly impact the quality and yield of agricultural products, leading to considerable economic losses. Most existing plant disease recognition systems are limited to identifying categories within the training set, which poses potential systemic risks. Rejecting unknown samples is crucial for the safety and reliability of practical applications. This study aims to harness the strong generalization capabilities of vision-language models to address plant disease anomaly detection. To this end, we comprehensively explore prompt tuning paradigms based on vision-language models. We observe that anomaly detection methods guided by textual concepts perform poorly in the fine-grained task of plant disease due to their focus on concept matching. We argue that visual information is crucial for anomaly detection in plant diseases. Therefore, we propose guiding the vision-language model with visual information to address this issue. Additionally, we find that utilizing the general knowledge extracted by the original vision-language model can further enhance anomaly detection performance. Extensive experimental results demonstrate that our method significantly improves the current baseline methods by incorporating visual information. Notably, deploying our method under vision-language prompt tuning achieved an AUROC score of 99.85% in the all-shot setting. Even in a challenge 2-shot setting, our approach achieves an AUROC score of 93.81%, significantly outperforming CoCoOp fine-tuned on the entire dataset (88.61%). We believe that our study will contribute to the community and, to fuel the field, our code will be released.

Interleukin-2-inducible tyrosine kinase (ITK) is a crucial cytoplasmic protein in the T-cell signaling pathway. Here, we aimed to demonstrate the anti-inflammatory effect of the selective IL-2-induced tyrosine kinase inhibitor BMS-509744 (BMS) on Graves’ orbitopathy (GO) in an in vitro model. ITK mRNA expression in orbital tissues from GO and normal controls was compared using real-time polymerase chain reaction (RT-PCR) and immunohistochemistry. Primary cultured orbital fibroblasts from each group were pretreated with BMS and stimulated with interleukin (IL)-1β to induce inflammatory reaction. ITK mRNA expression was evaluated using western blotting, and inflammatory cytokine production and downstream transcription factor expression were analyzed after pretreatment with BMS. ITK mRNA expression in GO tissues was significantly higher than that in normal control tissues. After stimulation with IL-1β, ITK phosphorylation significantly increased in both GO orbital and normal control tissues. BMS inhibited IL-1β-induced IL-8 expression in the GO orbital fibroblasts. BMS pretreatment significantly suppressed NF-κB phosphorylation in both GO and normal controls. The selective ITK inhibitor attenuates proinflammatory cytokine production and proinflammatory transcription factor phosphorylation in in vitro model of GO.

Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that this illusion essentially comes from the fact that current plant disease recognition datasets cater to deep learning methods and are far from real scenarios. Mitigating this illusion fundamentally requires an interdisciplinary perspective from both plant disease and deep learning, and a core question arises. What are the characteristics of a desired dataset? This paper aims to provide a perspective on this question. First, we present a taxonomy to describe potential plant disease datasets, which provides a bridge between the two research fields. We then give several directions for making future datasets, such as creating challenge-oriented datasets. We believe that our paper will contribute to creating datasets that can help achieve the ultimate objective of deploying deep learning in real-world plant disease recognition applications. To facilitate the community, our project is publicly available at https://github.com/xml94/PPDRD with the information of relevant public datasets.

IntroductionBruton’s tyrosine kinase (BTK) and interleukin (IL)-2 Inducible T-cell Kinase (ITK) inhibitors have anti-inflammatory properties. We investigated the therapeutic effect of ibrutinib, an orally bioavailable BTK/ITK inhibitor, in a mouse model of Graves’ orbitopathy (GO).MethodsGenetic immunization was performed through intramuscular administration of the recombinant plasmid, pCMV6-hTSHR cDNA, to 8-week-old female BALB/c mice. Serum levels of T3, T4, and thyroid-stimulating hormone receptor (TSHR) antibodies (TRAbs) were quantified using enzyme-linked immunosorbent assay. Histopathological changes in orbital tissues were examined using immunohistochemistry (IHC) staining for TSHR and various inflammatory markers. Following successful genetic immunization, ibrutinib was orally administered daily for 2 weeks in the GO model mice. After treatment, the mRNA and protein expression levels of BTK, ITK, IL-1β, and IL-6 in orbital tissues were evaluated using real-time PCR and Western blotting.ResultsIn total, 20 mice were sacrificed to confirm successful genetic immunization. The GO mouse group exhibited significantly increased serum T3, T4, and TRAb levels. IHC revealed increased expression of TSHR, IL-1β, IL-6, transforming growth factor-β1, interferon-γ, CD40, CD4, BTK, and ITK in the GO mouse model. The orbital inflammation was significantly attenuated in ibrutinib-treated mice. The mRNA and protein expression levels of BTK, ITK, IL-1β, and IL-6 in orbital tissue were lower in ibrutinib-treated GO mouse group compared to the phosphate-buffered saline-treated GO mouse group.ConclusionThe GO mouse model demonstrated enhanced BTK and ITK expression. Ibrutinib, a BTK/ITK inhibitor, suppressed the inflammatory cytokine production. These findings highlight the potential involvement of BTK/ITK in the inflammatory pathogenesis of GO, suggesting its role as a novel therapeutic target.

Background: This study aimed to investigate whether there is a difference in organizational member attitudes by workplace environment and workplace types in dental hygienists working at dental health centers.Methods: A face-to-face survey or online survey was administered to dental hygienists working at dental health centers, and a total of 95 subjects were included. The survey includes 13 items to ask factors affecting employee’s job satisfaction. Also, some questions were included to assess perception of organizational member attitude: five about organizational citizenship behavior, two about innovative behavior, and four about organizational commitment. Results: Dental hygienists working at dental clinics were more satisfied with their incomes and numbers of working days, while those from dental university hospitals and general hospitals were more satisfied with education support. In addition, hygienists working at dental hospitals were more satisfied with job autonomy, individual work capability, safe working environment, personal relation, potential for personal development, and positive labor-management relations, compared to those working at general and university hospitals (p＜0.05). Among the items about perception of organizational member attitudes, the scores of items about organizational citizenship behaviors were higher, whereas the scores of items about innovative behaviors were relatively lower. Individuals working at dental hospitals than those working at general and university hospitals, chiefs and team and department leaders than team members, and those with increased satisfaction with current workplace had more positive perception of organizational member attitudes. Conclusion: For dental hygienists to have positive attitudes as organizational members, working environment should be improved, and executives of dental healthcare centers should pay attention to improving job satisfaction of organizational members. Moreover, since dental hygienists need to improve their perception of innovative behaviors and citizenship behaviors to strengthen specialty of dental hygienists in a changing dental healthcare, relevant training should be addressed in refresher courses or school programs.

Background Phospholipase C-γ (PLC-γ) plays a crucial role in immune responses and is related to the pathogenesis of various inflammatory disorders. In this study, we investigated the role of PLC-γ and the therapeutic effect of the PLC-specific inhibitor U73122 using orbital fibroblasts from patients with Graves’ orbitopathy (GO). Methods The expression of phospholipase C gamma 1 (PLCG1) and phospholipase C gamma 2 (PLCG2) was evaluated using polymerase chain reaction in GO and normal orbital tissues/fibroblasts. The primary cultures of orbital fibroblasts were treated with non-toxic concentrations of U73122 with or without interleukin (IL)-1β to determine its therapeutic efficacy. The proinflammatory cytokine levels and activation of downstream signaling molecules were determined using Western blotting. Results PLCG1 and PLCG2 mRNA expression was significantly higher in GO orbital tissues than in controls (P

Background: This study aims to investigate the perceptions of dentists and dental hygienists regarding quality dental job conditions and to identify differences in perceptions based on job type. Methods: As a result of conducting face-to-face and online surveys, data from a total of 132 people were analyzed. In order to investigate the perception of quality job conditions, the importance of a total of 13 items was investigated, and the work policies and job satisfaction of the current workplace were examined. Since the data did not follow a normal distribution, a non-parametric test, the Mann–Whitney U test, was performed. Results: Both dentists and dental hygienists perceived income and working hours to be of priority importance for quality job conditions. Dental hygienists valued holiday support and welfare, human relations, and personal development potential more than dentists (p＜0.05). Looking at differences by job type, dental hygienists rated all conditions as more important than dentists except income, indicating a statistically significant difference (p＜0.05). In terms of the work policies, 96.2% of the practices in the study were required to have the four types of social security contributions, but fewer had flexible working hours (19.7%), healthcare support (23.5%), and incentives (25.0%). Of the participants, 60.6% had parental leave available at their workplace, and dental hygienists had statistically significantly higher job satisfaction when parental leave was available (2.57 points) than when it was not (p＜0.05). Conclusion: Quality dental jobs are an important factor in keeping workers happy and maintaining an efficient practice. Dental practice owners need to pay attention to the quality of jobs required by the dental workforce, provide flexible working hours and welfare programs such as parental leave, and create a workplace atmosphere and human resource management system that supports the use of these programs.

Abstract Pathological markers that can monitor the progression of gastric cancer (GC) may facilitate the diagnosis and treatment of patients with diffuse GC (DGC). To identify microRNAs (miRNAs) that can differentiate between early and advanced DGC in the gastric mucosa, miRNA expression profiling was performed using the NanoString nCounter method in human DGC tumors. Ectopic expression of miR-199a and miR-199b (miR-199a/b) in SNU601 human GC cells accelerated the growth rate, viability, and motility of cancer cells and increased the tumor volume and weight in a mouse xenograft model. To study their clinicopathological roles in patients with GC, miR-199a/b levels were measured in human GC tumor samples using in situ hybridization. High miR-199a/b expression level was associated with enhanced lymphovascular invasion, advanced T stage, and lymph-node metastasis. Using the 3′-untranslated region (UTR) luciferase assay, Frizzled-6 (FZD6) was confirmed to be a direct target of miR-199a/b in GC cells. siRNA-mediated depletion of FZD6 enhanced the motility of SNU601 cells, and addback of FZD6 restored cancer cell motility stimulated by miR-199a/b. In conclusion, miR-199a/b promotes DGC progression by targeting FZD6, implying that miR-199a/b can be used as prognostic and diagnostic biomarkers for the disease.

Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust detector. However, a good label set generally requires much expert knowledge and meticulous work, which is expensive and time-consuming. This paper aims to learn robust feature representations with inaccurate bounding boxes, thereby reducing the model requirements for annotation quality. Specifically, we analyze the distribution of noisy annotations in the real world. A teacher-student learning paradigm is proposed to correct inaccurate bounding boxes. The teacher model is used to rectify the degraded bounding boxes, and the student model extracts more robust feature representations from the corrected bounding boxes. Furthermore, the method can be easily generalized to semi-supervised learning paradigms and auto-labeling techniques. Experimental results show that applying our method to the Faster-RCNN detector achieves a 26% performance improvement on the noisy dataset. Besides, our method achieves approximately 75% of the performance of a fully supervised object detector when 1% of the labels are available. Overall, this work provides a robust solution to real-world location noise. It alleviates the challenges posed by noisy data to precision agriculture, optimizes data labeling technology, and encourages practitioners to further investigate plant disease detection and intelligent agriculture at a lower cost. The code will be released at https://github.com/JiuqingDong/TS_OAMIL-for-Plant-disease-detection.

Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement restricts the model’s adaptability when encountering samples from unseen disease categories. Additionally, there is a challenge of knowledge degradation for these static learning settings, as the acquisition of new knowledge tends to overwrite the old when learning new categories. To overcome these limitations, this study introduces a novel paradigm for plant disease detection called open-world setting. Our approach can infer disease categories that have never been seen during the model training phase and gradually learn these unseen diseases through dynamic knowledge updates in the next training phase. Specifically, we utilize a well-trained unknown-aware region proposal network to generate pseudo-labels for unknown diseases during training and employ a class-agnostic classifier to enhance the recall rate for unknown diseases. Besides, we employ a sample replay strategy to maintain recognition ability for previously learned classes. Extensive experimental evaluation and ablation studies investigate the efficacy of our method in detecting old and unknown classes. Remarkably, our method demonstrates robust generalization ability even in cross-species disease detection experiments. Overall, this open-world and dynamically updated detection method shows promising potential to become the future paradigm for plant disease detection. We discuss open issues including classification and localization, and propose promising approaches to address them. We encourage further research in the community to tackle the crucial challenges in open-world plant disease detection. The code will be released at https://github.com/JiuqingDong/OWPDD.

Abstract As the world’s population is aging, sarcopenia is recognized as essential to assess people’s lifelong condition and do appropriate early intervention. Senile blepharoptosis is also a problem in old age deteriorating visual function and causing a cosmetic decline. We investigated the association between sarcopenia and the prevalence of senile blepharoptosis, using a nationwide representative survey in Korea. A total of 11,533 participants were recruited. We used the body mass index (BMI)- adjusted appendicular skeletal muscle (ASM) definition as the muscle mass index (MMI, ASM [kg] divided by BMI [kg/m2]). The association between blepharoptosis prevalence and MMI was analyzed using multivariate logistic regression. Sarcopenia, defined as the lowest MMI quintile group in both men and women, was also associated with the prevalence of blepharoptosis (ORs 1.92, 95% CI 1.17–2.16; p

Background: The purpose of this study was to confirm the major motivation for and self-efficacy of career decisions among dental hygiene students and to analyze differences in career decision self-efficacy according to the major motivation.Methods: An online survey was conducted among 194 dental hygiene students. To investigate career decision self-efficacy, 18 questions were posed, including 5 questions on future planning, 4 questions on self-evaluation, 4 questions on job information collection, 3 questions on goal setting, and 2 questions on problem solving. The survey was conducted using a 5-point scale. Results: The motives for choosing a major were a high employment rate (68.6%), a desire to become a professional (36.1%), according to aptitude (27.8%), recommendations by others (26.3%), and according to grades (12.9%). The career decision self-efficacy of the study participants was in the order of self-evaluation factors (3.75 points), problem solving factors a (3.62 points), job information collection factors (3.59 points), future planning factors (3.46 points), goal setting factors (3.44 points) score was high. The career decision self-efficacy scores were higher when the major motivation was “because I want to become a professional” and “because I think I have an aptitude for it.” Conclusion: It is necessary to give students who wish to major in dental hygiene an opportunity to consider whether they have an aptitude for it by providing information on not only major subjects but also the duties and roles of dental hygienists. In addition, it is necessary to develop a career education program to help dental hygiene students set their career goals.

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning–based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.

Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, stems, and fruits. However, processing data in real field conditions, with challenges such as image blurring and occlusion, requires improvement. This paper proposes a deep learning-based approach for leaf instance segmentation with a local refinement mechanism to enhance performance in cluttered backgrounds. The refinement mechanism employs Gaussian low-pass and High-boost filters to enhance target instances and can be applied to the training or testing dataset. An instance segmentation architecture generates segmented masks and detected areas, facilitating the derivation of phenotypic information, such as leaf count and size. Experimental results on a tomato leaf dataset demonstrate the system’s accuracy in segmenting target leaves despite complex backgrounds. The investigation of the refinement mechanism with different kernel sizes reveals that larger kernel sizes benefit the system’s ability to generate more leaf instances when using a High-boost filter, while prediction performance decays with larger Gaussian low-pass filter kernel sizes. This research addresses challenges in real greenhouse scenarios and enables automatic recognition of phenotypic data for smart agriculture. The proposed approach has the potential to enhance agricultural practices, ultimately leading to improved crop yields and productivity.

Abstract Background A substantial proportion of the world’s population experienced social, economic, and mental health challenges, including considerable changes in everyday life, due to the COVID-19 pandemic. However, these challenges varied in intensity depending upon occupation type and working environment. In this context, this study helps shed light on the effects of occupation type on depression through the mediation of changes in daily life and anxiety as perceived by individual workers through their experiences of the COVID-19 pandemic. Methods In total, 68,207 adults (aged 19–65 years) working in the “office” or “service” sectors were analyzed based on the raw data extracted from the 2020 National Community Health Survey conducted by the Korea Disease Control and Prevention Agency. Data analysis was performed using PROCESS Macro (Model 6) for SPSS 25 to examine how depression is affected by occupation type through a dual mediation of the changes in daily life and anxiety caused by COVID-19 as perceived by individual workers during the pandemic. Results First, service workers perceived COVID-19-related changes in daily life more acutely than the office workers. Second, service workers felt more COVID-19-related anxiety than office workers, whereby the higher the level of COVID-19-related changes in daily life perceived by the workers, the higher the level of their COVID-19-related anxiety. Finally, service workers’ perceived COVID-19-related changes in daily life more acutely than office workers, which had a positive effect on the level of COVID-19-related anxiety, ultimately increasing depression. Conclusions It was found that the impact of a special disaster situation, such as the COVID-19 pandemic, on the perceived changes in daily life and anxiety varies depending on occupation type, which suggests that different occupations have different effects on mental health outcomes. This highlights the need to develop various customized services and policies to promote mental health according to the type of occupation, considering the working environment and work characteristics of those vulnerable to COVID-19 infection.

Abstract Various omics-based biomarkers related to the occurrence, progression, and prognosis of colorectal cancer (CRC) have been identified. In this study, we attempted to identify gut microbiome-based biomarkers and detect their association with host gene expression in the initiation and progression of CRC by integrating analysis of the gut mucosal metagenome, RNA sequencing, and sociomedical factors. We performed metagenome and RNA sequencing on colonic mucosa samples from 13 patients with advanced CRC (ACRC), 10 patients with high-risk adenoma (HRA), and 7 normal control (NC) individuals. All participants completed a questionnaire on sociomedical factors. The interaction and correlation between changes in the microbiome and gene expression were assessed using bioinformatic analysis. When comparing HRA and NC samples, which can be considered to represent the process of tumor initiation, 28 genes and five microbiome species were analyzed with correlation plots. When comparing ACRC and HRA samples, which can be considered to represent the progression of CRC, seven bacterial species and 21 genes were analyzed. When comparing ACRC and NC samples, 16 genes and five bacterial species were analyzed, and four correlation plots were generated. A network visualizing the relationship between bacterial and host gene expression in the initiation and progression of CRC indicated that Clostridium spiroforme and Tyzzerella nexilis were hub bacteria in the development and progression of CRC. Our study revealed the interactions of and correlation between the colonic mucosal microbiome and host gene expression to identify potential roles of the microbiome in the initiation and progression of CRC. Our results provide gut microbiome-based biomarkers that may be potential diagnostic markers and therapeutic targets in patients with CRC.

Abstract Background In recent years, female drinking has been on the rise worldwide, and this trend can be observed in Korea as well. Accordingly, this study aimed to examine the heterogeneous longitudinal changes in drinking patterns among Korean women, while also exploring the determinants of these changes. In particular, the study identified the gender perspective-related determinants of the classified patterns of problem drinking. Methods Data on 4615 adult women who participated in the Korea Welfare Panel Study (KOWEPS) for 3 years (2018–2020) were analyzed longitudinally using SPSS Statistics 22.0 and M-plus 7.0. The changes in female drinking patterns were analyzed using latent class growth analysis. Subsequently, multinomial logistic regression analysis was performed to identify the predictive factors affecting the changes in drinking patterns. Results Latent class analysis yielded three classes: “low problem drinking/decreased,” “moderate problem drinking/maintained,” and “high problem drinking/increased.” Of the participants, 80.4% were in the first class, 14.5% in the second, and 5.1% in the third. After controlling for sociodemographic and psychosocial factors, we found: i) domestic violence, work–family balance stress, and gender role perception were not statistically significant for the “moderate problem drinking/maintained” class; lower levels of depression (odds ratio; OR = .750, p

Background: Dental hygienists study dental radiology through the dental hygiene department and curriculum, and most of the radiography work in dental clinics is performed by dental hygienists; however, the legal work regulations for dental hygienists place restrictions on the type of radiography performed. This study aimed to identify the actual conditions of the radiographic work performed by dental hygienists and to determine the difference according to the type of hospital.Methods: This study included 195 dental hygienists working at dental medical institutions in the metropolitan area. A survey was conducted on regarding the radiographic work performed and the clinical career of the main performers. The radiography work was divided into periapical radiography, bite-wing radiography, occlusal radiography, panoramic radiography, computed tomography (CT), and cephalometric radiography.Results: The frequency of performing intraoral radiography was as follows: periapical radiography, 94.9%; bite-wing radiography, 93.8%; and occlusal radiography, 77.9%. The frequency of performing extraoral radiography was 94.4% for panoramic radiography, 89.7% for CT, and 73.3% for cephalometric radiography. The frequency of internal and external radiography performance was higher among hygienists in dental clinics than among those in dental hospitals and university hospitals. The analysis of the dental hygienists’ clinical experience in the areas of intraoral and extraoral radiography showed that those working at university hospitals, dental hospitals, and dental clinics had over 5 years, 2∼4 years, and 1 year of clinical experience, respectively. The hygienists with less than 1 year of clinical experience showed high performance frequency (p＜0.05).Conclusion: For the dental hygienists to perform radiography safely, a discussion regarding the revision of related laws and regulations is warranted.

Accurate identification of individual cattle is of paramount importance in precision livestock farming, enabling the monitoring of cattle behavior, disease prevention, and enhanced animal welfare. Unlike human faces, the faces of most Hanwoo cattle, a native breed of Korea, exhibit significant similarities and have the same body color, posing a substantial challenge in accurately distinguishing between individual cattle. In this study, we sought to extend the closed-set scope (only including identifying known individuals) to a more-adaptable open-set recognition scenario (identifying both known and unknown individuals) termed Cattle’s Face Open-Set Recognition (CFOSR). By integrating open-set techniques to enhance the closed-set accuracy, the proposed method simultaneously addresses the open-set scenario. In CFOSR, the objective is to develop a trained model capable of accurately identifying known individuals, while effectively handling unknown or novel individuals, even in cases where the model has been trained solely on known individuals. To address this challenge, we propose a novel approach that integrates Adversarial Reciprocal Points Learning (ARPL), a state-of-the-art open-set recognition method, with the effectiveness of Additive Margin Softmax loss (AM-Softmax). ARPL was leveraged to mitigate the overlap between spaces of known and unknown or unregistered cattle. At the same time, AM-Softmax was chosen over the conventional Cross-Entropy loss (CE) to classify known individuals. The empirical results obtained from a real-world dataset demonstrated the effectiveness of the ARPL and AM-Softmax techniques in achieving both intra-class compactness and inter-class separability. Notably, the results of the open-set recognition and closed-set recognition validated the superior performance of our proposed method compared to existing algorithms. To be more precise, our method achieved an AUROC of 91.84 and an OSCR of 87.85 in the context of open-set recognition on a complex dataset. Simultaneously, it demonstrated an accuracy of 94.46 for closed-set recognition. We believe that our study provides a novel vision to improve the classification accuracy of the closed set. Simultaneously, it holds the potential to significantly contribute to herd monitoring and inventory management, especially in scenarios involving the presence of unknown or novel cattle.

Abstract The role of β-catenin and Dickkopf-1 (DKK1) is dependent on the specific immunobiology of T cell inflammation in biliary tract cancer (BTC). We aimed to analyze the role of DKK1 or β-catenin as a prognostic factor in BTC, and determine the clinical associations of ß-catenin and DKK1 with CD8+ tumor-infiltrating lymphocytes (TIL). We used data from The Cancer Genome Atlas Research Network and the clinicopathological data of 145 patients with BTC who had undergone primary radical resection between 2006 and 2016. CD8+ TIL expression was a significant predictor of favorable overall survival (OS) and relapse-free survival (RFS) (median OS, 34.9 months in high-TIL, 16.7 months in low-TIL, P

To describe the presentation of both new-onset and reactivation of thyroid eye disease (TED) following COVID-19 vaccination. Single-institution retrospective case series of patients presenting with symptoms and signs of new or reactivated TED coinciding with recent COVID-19 vaccination. Data collected included patient age, gender, presenting symptoms, ocular history, clinical signs, and interval duration between vaccination and onset of ocular symptoms. Three female patients were identified. All patients were over 18 years of age (range 45–66 years). Patients received either the Moderna or Pfizer COVID-19 vaccine and presented with symptoms of TED within 24 h to 21 days of receiving their first or second dose. None of the patients had previous infections with severe acute respiratory syndrome coronavirus 2. Two patients had a history of inactive TED with stable thyroid function tests: One of these patients had stable disease for at least 15 years and the other had stable disease for 5 years. The third patient had no previous history of thyroid dysfunction or TED and presented with low levels of thyroid-stimulating hormone. All three cases presented with proptosis. In two of three cases, periorbital edema, eyelid retraction, and diplopia were present. None were current smokers. One had prior facial hyaluronic acid filler injections. Symptoms in all cases were improving at 4 to 8 months. While the possibility of unrelated TED flaring concurrently with COVID-19 vaccination exists, questions remain on the effects of the COVID-19 vaccine in patients with autoimmune ophthalmic diseases. Physicians should be aware of this potential association and counsel patients appropriately.

Thyroid eye disease (TED) is the most common extrathyroidal manifestation of autoimmune Graves' hyperthyroidism. TED is a debilitating and potentially blinding disease with unclear pathogenesis. Autoreactive inflammatory reactions targeting orbital fibroblasts (OFs) lead to the expansion of orbital adipose tissues and extraocular muscle swelling within the fixed bony orbit. There are many recent advances in the understating of molecular pathogenesis of TED. The production of autoantibodies to cross-linked thyroid-stimulating hormone receptor and insulin-like growth factor-1 receptor (IGF-1R) activates OFs to produce significant cytokines and chemokines and hyaluronan production and to induce adipocyte differentiation. In moderately severe active TED patients, multicenter clinical trials showed that inhibition of IGF-1R with teprotumumab was unprecedentedly effective with minimal side effects. The emergence of novel biologics resulted in a paradigm shift in the treatment of TED. We here review the literature on advances of pathogenesis of TED and promising therapeutic targets and drugs.

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improved the performance by ameliorating networks and optimizing the loss function. However, because of the vast influence of data annotation quality and the cost of annotation, the data-centric part of a project also needs more investigation. We should further consider the relationship between data annotation strategies, annotation quality, and the model’s performance. In this paper, a systematic strategy with four annotation strategies for plant disease detection is proposed: local, semi-global, global, and symptom-adaptive annotation. Labels with different annotation strategies will result in distinct models’ performance, and their contrasts are remarkable. An interpretability study of the annotation strategy is conducted by using class activation maps. In addition, we define five types of inconsistencies in the annotation process and investigate the severity of the impact of inconsistent labels on model’s performance. Finally, we discuss the problem of label inconsistency during data augmentation. Overall, this data-centric quantitative analysis helps us to understand the significance of annotation strategies, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection. Our work encourages researchers to pay more attention to annotation consistency and the essential issues of annotation strategy. The code will be released at: https://github.com/JiuqingDong/PlantDiseaseDetection_Yolov5 .

Deep learning has witnessed a significant improvement in recent years to recognize plant diseases by observing their corresponding images. To have a decent performance, current deep learning models tend to require a large-scale dataset. However, collecting a dataset is expensive and time-consuming. Hence, the limited data is one of the main challenges to getting the desired recognition accuracy. Although transfer learning is heavily discussed and verified as an effective and efficient method to mitigate the challenge, most proposed methods focus on one or two specific datasets. In this paper, we propose a novel transfer learning strategy to have a high performance for versatile plant disease recognition, on multiple plant disease datasets. Our transfer learning strategy differs from the current popular one due to the following factors. First, PlantCLEF2022, a large-scale dataset related to plants with 2,885,052 images and 80,000 classes, is utilized to pre-train a model. Second, we adopt a vision transformer (ViT) model, instead of a convolution neural network. Third, the ViT model undergoes transfer learning twice to save computations. Fourth, the model is first pre-trained in ImageNet with a self-supervised loss function and with a supervised loss function in PlantCLEF2022. We apply our method to 12 plant disease datasets and the experimental results suggest that our method surpasses the popular one by a clear margin for different dataset settings. Specifically, our proposed method achieves a mean testing accuracy of 86.29over the 12 datasets in a 20-shot case, 12.76 higher than the current state-of-the-art method’s accuracy of 73.53. Furthermore, our method outperforms other methods in one plant growth stage prediction and the one weed recognition dataset. To encourage the community and related applications, we have made public our codes and pre-trained model1.

Specific domains of the Sugar-Sweetened Media Literacy Scale (SSM-ML) have been shown to significantly assess sugar-sweetened beverage (SSB) calorie intake in the US population. This study aimed to describe the psychometric properties of the revised Chinese version of the SSB-ML (C-SSB-ML) and evaluate its validity and reliability. Results from 975 undergraduates at two of the largest universities in a province in southwest China showed that Cronbach's alphas for the overall scale, the three dimensions, and two-halves analysis were satisfactory (0.71–0.92). The criterion-related validity of the C-SSB-ML was positively associated with the e Health literacy scale (eHEALS). Confirmatory factor analysis showed that the three-factor model of the C-SSB-ML had adequate fit indices χ2 (153) = 4349.93, p < 0.001; Comparative fit index (CFI), Tucker-Lewis index (TLI), Incremental fit index (IFI) >0.90; Standardized Root Mean Square Residual (SRMR)

Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent years, however, there are still two issues. First, image-based plant growth prediction is currently taken either from time series or image generation viewpoints, resulting in a flexible learning framework and clear predictions, respectively. Second, deep learning-based algorithms are notorious to require a large-scale dataset to obtain a competing performance but collecting enough data is time-consuming and expensive. To address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms. To be more specific, we raise a new framework with a length-changeable time-series processing unit to generate images flexibly. A generative adversarial loss is utilized to optimize our model to obtain high-quality images. Furthermore, we first recognize three key points to perform time-series data augmentation and then put forward T-Mixup and T-Copy-Paste. T-Mixup fuses images from a different time pixel-wise while T-Copy-Paste makes new time-series images with a different background by reusing individual leaves extracted from the existing dataset. We perform our method in a public dataset and achieve superior results, such as the generated RGB images and instance masks securing an average PSNR of 27.53 and 27.62, respectively, compared to the previously best 26.55 and 26.92.

BackgroundThis study using multi-center health examination data from Korean adults was conducted to confirm changes in weight, and their related cardiometabolic parameters, before and after strengthening of social distancing regulations.MethodsA retrospective cohort study was conducted using health check-up data from 13 university hospitals. The study period was from January 2018 to July 2020. To examine the effect of systematic social distancing measures, participants who underwent a health check-up (Visit 3) between July 2020 and July 2021 (during full scale social distancing), and had undergone two previous health check-ups (Visits 1 and 2) between January 2018 and June 2020 (before social distancing), were selected. In total, data from 7,875 participants were analyzed. Linear mixed-effect models were used to calculate estimates of anthropometric indices and metabolic markers measured on Visits 2 and 3, compared with measurements from Visit 1.ResultsThere were no significant differences in body weight, body mass index, waist circumference, and body composition on Visit 3 than on Visits 1 and 2. However, the odds of metabolic syndrome and its components, including hypertension, high glucose, diabetes, hypercholesterolemia, hypertriglyceridemia, hyper-non-high-density lipoprotein cholesterolemia, and dyslipidemia were significantly higher on Visit 3 than on Visits 1 and 2. The increase in metabolic complications was marked, particularly in relatively young adults who visited health check-up centers located in the capital area.ConclusionMetabolic syndrome and its components were significantly worse after high level social distancing, although there were no significant increases in anthropometric indices and body fat levels. Healthcare providers need to prevent and manage worsening of metabolic parameters in subpopulations prone to be more sedentary and eat unhealthy food during the COVID-19 pandemic and associated social distancing measures.

Abstract Background Noggin and RNA-binding protein for multiple splicing 2 (RBPMS2) are known to regulate the expression of smooth muscle cells, endothelial cells, and osteoblasts. However, the prognostic role of combined Noggin and RBPMS2 expression in resected gastric cancer (GC) is unclear. Methods A total of 163 patients with GC who underwent gastrectomy were included in this study. The expression of Noggin and RBPMS2 proteins in tumor cells at the tumor center and invasive front of resected GC was evaluated by immunohistochemistry, and in conjunction with clinicopathological parameters the patient survival was analyzed. Results RBPMS2 protein expression was high at the tumor center (n = 86, 52.8%) and low at the invasive front (n = 69, 42.3%), while Noggin protein expression was high in both tumor center (n = 91, 55.8%) and the invasive front (n = 90, 55.2%). Noggin expression at the invasive front and tumor center was significantly decreased in advanced T stage, non-intestinal-type (invasive front, P = 0.008 and P

Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.

MicroRNA transcriptomes from fresh tumors and the adjacent normal tissues were profiled in 10 Korean patients diagnosed with lung adenocarcinoma using a next-generation sequencing (NGS) technique called miRNA-seq. The sequencing quality was assessed using FastQC, and low-quality or adapter-contaminated portions of the reads were removed using Trim Galore. Quality-assured reads were analyzed using miRDeep2 and Bowtie. The abundance of known miRNAs was estimated using the reads per million (RPM) normalization method. Subsequently, using DESeq2 and Wx, we identified differentially expressed miRNAs and potential miRNA biomarkers for lung adenocarcinoma tissues compared to adjacent normal tissues, respectively. We defined reliable miRNA biomarkers for lung adenocarcinoma as those detected by both methods. The miRNA-seq data are available in the Gene Expression Omnibus (GEO) database under accession number GSE196633, and all processed data can be accessed via the Mendeley data website.

Background/Aim: Interferon-induced transmembrane protein 1 (IFITM1) is known to be involved in breast cancer progression. We aimed to investigate its role in estrogen receptor (ER)-positive breast cancer cells with wildtype p53 and tamoxifen-resistant breast cancer cells. Materials and Methods: The ER-positive breast cancer cell lines, MCF7 with wild-type p53 and T47D with mutant p53, were used. We established an MCF-7-derived tamoxifen-resistant cell line (TamR) by long-term culture of MCF-7 cells with 4- hydroxytamoxifen. Results: IFITM1 inhibition in MCF-7 cells significantly decreased cell growth and migration. MCF-7 cells with suppression of IFITM1 using siRNA or ruxolitinib showed reduced cell viability after tamoxifen treatment compared with that in the control MCF-7 cells. Unexpectedly, mRNA and protein levels of IFITM1 were decreased in TamR cells compared with those in MCF-7 cells. TamR cells with suppression of IFITM1 using siRNA or ruxolitinib showed no change in cell viability after treatment with tamoxifen. P53 knockdown using siRNA reduced the mRNA levels of IRF9 and increased mRNA and protein levels of SOCS3 in MCF-7 cells, suggesting that loss or mutation of p53 can affect the induction of IFITM1 via the JAK/STAT signaling pathway in breast cancer. Furthermore, MCF-7 cells with p53 knockdown using siRNA showed no decrease in cell viability after tamoxifen treatment or IFITM1 inhibition, indicating that p53 status may be important for cell death after tamoxifen treatment or IFITM1 inhibition. Conclusion: IFITM1 inhibition may enhance the sensitivity to tamoxifen based on p53-dependent enhancement of IFN signaling in wild-type p53, ER-positive breast cancer cells. [ABSTRACT FROM AUTHOR]

Purpose: Orbital fibroblasts play key roles in the pathogenesis of Graves' orbitopathy (GO), and previous findings have shown that endoplasmic reticulum (ER) stress and autophagy also contribute to GO. In this study, we investigated the presently unclear roles of inositol-requiring enzyme 1 (IRE1) and related autophagy processes in the pro-fibrotic mechanism of GO. Materials and Methods: Orbital adipose/connective tissues were obtained from eight GO patients and six normal individuals during surgery. GO fibroblasts were transfected with IRE1 small-interfering RNA and treated with bafilomycin A1 (Baf-A1) to evaluate the inhibitory effects of ER stress and autophagy, and protein-expression levels were analyzed through western blotting after stimulation with transforming growth factor (TGF)-β. Results: TGF-β stimulation upregulated IRE1 in GO orbital fibroblasts, whereas silencing IRE1 suppressed fibrosis and autophagy responses. Similarly, Baf-A1, an inhibitor of late-phase autophagy, decreased the expression of pro-fibrotic proteins. Conclusion: IRE1 mediates autophagy and the pro-fibrotic mechanism of GO, which provides a more comprehensive interpretation of GO pathogenesis and suggests potential therapeutic targets. [ABSTRACT FROM AUTHOR]