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Physical activity (PA) may be considered an alternative method to ameliorate the elevated mortality risks associated with cadmium exposure. In this prospective cohort study, a total of 20,253 participants (weighted mean age, 47.79 years), including 10,247 men (weighted prevalence: 50.1 %), aged 18 years or older, were selected from the National Health and Nutrition Examination Survey from 2007 to 2018. Multivariable Cox proportional hazards regression models were utilized to evaluate the associations between blood cadmium levels, PA, and the risks of mortality. Restricted cubic spline analyses were employed to investigate the nonlinear relationships between blood cadmium and PA levels and mortality risks. During a median follow-up of 7.6 years, a total of 2002 (9.89 %) all-cause deaths occurred, of which 581 (2.87 %) participants were due to cardiovascular disease (CVD) and 498 (2.46 %) died of cancer. J-shaped associations were observed for blood cadmium with risks of mortality (all P overall < 0.001; all P nonlinearity < 0.001). Blood cadmium and PA had multiplicative interactions on mortality risk (all P interaction < 0.05). Compared with the subgroup with the lowest quartile of blood cadmium and recommended PA, the combination of the highest quartile of blood cadmium and without recommended PA was associated with the highest risks of all-cause and cancer mortality, followed by those meeting recommended PA but in the highest quartile of blood cadmium (hazard ratios, 2.43; 95 % confidence interval, 1.95–3.02). Achieving recommended PA significantly attenuated the detrimental effects of blood cadmium on all-cause, CVD, and cancer mortality risks.

(1) Background: In recent years, large language models (LLMs) such as ChatGPT have gained significant attention in various fields, including dentistry. This scoping review aims to examine the current applications and explore potential uses of LLMs in the orthodontic domain, shedding light on how they might improve dental healthcare. (2) Methods: We carried out a comprehensive search in five electronic databases, namely PubMed, Scopus, Embase, ProQuest and Web of Science. Two authors independently screened articles and performed data extraction according to the eligibility criteria, following the PRISMA-ScR guideline. The main findings from the included articles were synthesized and analyzed in a narrative way. (3) Results: A total of 706 articles were searched, and 12 papers were eventually included. The applications of LLMs include improving diagnostic and treatment efficiency in orthodontics as well as enhancing communication with patients. (4) Conclusions: There is emerging research in countries worldwide on the use of LLMs in orthodontics, suggesting an upward trend in their acceptance within this field. However, the potential application of LLMs remains in its early stage, with a noticeable lack of extensive studies and tailored products to address specific clinical needs.

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher–student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet’s performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies.

Abstract Purpose Nodal size is an important imaging criterion for differentiating benign from malignant nodes in the head and neck cancer staging. This study evaluated the size of normal nodes in less well-documented nodal groups in the upper head and neck on magnetic resonance imaging (MRI). Methods Analysis was performed on 289 upper head and neck MRIs of patients without head and neck cancer. The short axial diameters (SAD) of the largest node in the parotid, submandibular, occipital, facial, retroauricular and Level IIb of the upper internal jugular nodal groups were documented and compared to the commonly used threshold of ≥ 10 mm for diagnosis of a malignant node. Results Normal nodes in the parotid, occipital, retroauricular and Level IIb groups were small with a mean SAD ranging from 3.8 to 4.4 mm, nodes in the submandibular group were larger with a mean SAD of 5.5 mm and facial nodes were not identified. A size ≥ 10 mm was found in 0.8% of submandibular nodes. Less than 10% of the other nodal group had a SAD of ≥ 6 mm and none of them had a SAD ≥ 8 mm. Conclusion To identify malignant neck nodes in these groups there is scope to reduce the size threshold of ≥ 10 mm to improve sensitivity without substantial loss of specificity.

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).

As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

IntroductionSubarachnoid hemorrhage (SAH) is a severe hemorrhagic stroke with high mortality. However, there is a lack of clinical tools for predicting in-hospital mortality in clinical practice. LAR is a novel clinical marker that has demonstrated prognostic significance in a variety of diseases.MethodsCritically ill patients diagnosed and SAH with their data in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) were included in our study. Multivariate logistic regression was utilized to establish the nomogram.ResultsA total of 244 patients with spontaneous SAH in the MIMIC-IV database were eligible for the study as a training set, and 83 patients in eICU-CRD were included for external validation. Data on clinical characteristics, laboratory parameters and outcomes were collected. Univariate and multivariate logistic regression analysis identified age (OR: 1.042, P-value: 0.003), LAR (OR: 2.592, P-value: 0.011), anion gap (OR: 1.134, P-value: 0.036) and APSIII (OR: 1.028, P-value: < 0.001) as independent predictors of in-hospital mortality and we developed a nomogram model based on these factors. The nomogram model incorporated with LAR, APSIII, age and anion gap demonstrated great discrimination and clinical utility both in the training set (accuracy: 77.5%, AUC: 0.811) and validation set (accuracy: 75.9%, AUC: 0.822).ConclusionLAR is closely associated with increased in-hospital mortality of patients with spontaneous SAH, which could serve as a novel clinical marker. The nomogram model combined with LAR, APSIII, age, and anion gap presents good predictive performance and clinical practicability.

Abstract Marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALToma) arises in extranodal sites in the head and neck. Chronic inflammatory, infectious or autoimmune conditions are implicated in its pathogenesis. Within the head and neck, MALToma is often multifocal and indolent and the imaging appearances may be mistaken for non-malignant disease in the head and neck. The aim of this article is to illustrate the varied radiological and clinical features of MALToma in the head and neck, an awareness of which is needed for timely and correct diagnosis to guide subsequent disease management.

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.

Abstract Background ERRα, a constitutive transcription factor that regulates energy metabolism, plays an important role in the progression of various tumours. However, its role in cell survival and proliferation and its implication in targeted therapy in colon cancer remains elusive. Methods The expression of ERRα in colon cancer tissues and cell lines was detected by using western blotting and immunohistochemistry. A wound healing assay and a transwell assay were performed to examine the migration and invasion of the colon cancer cells. A cell viability assay, clonogenic assay, western blot assay and the dual-luciferase reporter assay were employed to study the interaction between trametinib (inhibitor of MEK) and EGF treatment. Flow cytometry, western blotting, quantitative reverse-transcription polymerase chain reaction and xenograft studies were used to identify whether the combination of trametinib and simvastatin had a synergistic effect. Results ERRα positively regulated the cell proliferation, migration and invasion of colon cancer cells, and the suppression of ERRα completely reduced the EGF treatment-induced proliferation of colon cancer cells. Further investigation showed that trametinib partially restrained the up-regulation of ERRα induced by the EGF treatment, and ERRα inhibition increased the sensitivity of colon cancer cells to trametinib. At last, we combined trametinib with simvastatin, a common clinically used drug with a new reported function of transcriptional activity inhibition of ERRα, and found that this combination produced a synergistic effect in inhibiting the proliferation and survival of colon cancer cells in vitro as well as in vivo. Conclusions The present data indicated that ERRα acted as an oncogene in colon cancer cells, and the combined targeting of ERRα and MEK might be a promising therapeutic strategy for colon cancer treatment.

The new land observation satellite Sentinel-1A was launched on 25 April 2014 with a C-band synthetic aperture radar (SAR) sensor, which has the significant enhancements in terms of revisit period and high resolution. The Mw 6.1 Napa, California earthquake occurring on 24 August 2014, almost 4 months after the launch, is the first moderate earthquake imaged by the Sentinel-1A. This provides an opportunity to map the coseismic deformation of the event and evaluate the potential of Sentinel-1A SAR for earthquake study. Two techniques including the interferometric SAR (InSAR) and pixel offset-tracking (PO) are, respectively, employed to map the surface deformation along the radar line of sight (LOS), azimuth and slant-range directions. The cross comparison between Sentinel-1A InSAR LOS deformation and GPS observations indicates good agreement with an accuracy of ∼2.6 mm. We further estimate the earthquake source model with the external COSMO-SkyMed InSAR and GPS data as constraints, and forward calculate the surface deformation as cross validation with the Sentinel-1A observations. The comparison between the observed and modeled deformation shows that the Sentinel-1A measurement accuracy can achieve 1.6 cm for InSAR technique along LOS direction, and 6.3 and 6.7 cm for PO along azimuth and range directions, respectively.

The low carbon boron-bearing steel with 0.0045% boron and 0.0039% nitrogen was investigated, the thermodynamics and kinetics of precipitation and growth of BN during liquid steel solidification were studied. The effect of cooling conditions on the precipitation and growth of BN was experimentally investigated. The results show that BN can only precipitate at the solidifying front where the solid fraction is greater than the calculated critical value of 0.981 due to the segregation of boron and nitrogen. There is a serious segregation of boron and nitrogen with the increase of the solid fraction at the solidifying front. The diffusion of nitrogen in liquid steel is the rate-controlling step of BN growth because the boron content in the remaining liquid steel is much higher than that of nitro-gen when the solid fraction is close to 1. The change in cooling rate of liquid steel has negligible effect on the segregation of boron and nitrogen. However, the size of BN precipitates decreases greatly with increasing the cooling rate.

The spreading and wetting behavior of Mg-Zn-Al solder under the action of flux and ultrasonic vibration was conducted in atmosphere. The ultrasonic vibration time was 1, 2, 3, 4s, respectively. The spreading morphology and microstructure of the solder was investigated by optical microscope. The results show that the Mg-Zn-Al solder is forced spread under the effect of ultrasonic vibration along the base metal. The impact wave induced by ultrasonic cavitation can break the oxide film on the surface of base metal and the solder, which can promote the solder to wet the base metal. The depth of the dissolved base metal is only 0.12mm. When the ultrasonic time is 2s, the spreading area reaches its maximum. The ultrasonic cavitation can break the columnar crystals of Mg-Zn eutectic phase and the dendrite crystals of α-Mg, which can refine the microstructure of the Mg-Zn-Al solder.

Porous microcarriers were fabricated from synthesized poly(γ-benzyl-L-glutamate) (PBLG) polymer to engineer adipose tissue with lobule-like structure via the injectable approach. The adipogenic differentiation of human adipose-derived stem cells (hASCs) seeded on porous PBLG microcarriers was determined by adipogenic gene expression and glycerol-3-phosphate dehydrogenase enzyme activity. In vitro adipogenic cultivation was performed for 7 days, and induced hASC/PBLG complex (Adi-ASC/PBLG group) was subcutaneously injected into nude mice. Injections of PBLG microcarriers alone (PBLG group) and non-induced hASC/PBLG complex (ASC/PBLG group) served as controls. Newly formed tissues were harvested after 4 and 8 weeks. Generation of subcutaneous adipose tissue with typical lobule-like structure separated by fibrous septa was observed upon injection of adipogenic-induced hASC/microsphere complex. Adipogenesis significantly increased in the Adi-ASC/PBLG group compared with the control groups. The angiogenesis in the engineered adipose tissue was comparable to that in normal tissue as determined by capillary density and luminal diameter. Cell tracking assay demonstrated that labeled hASCs remained detectable in the neo-generated tissues 8 weeks post-injection using green fluorescence protein-labeled hASCs. These results indicate that adipose tissue with typical lobule-like structure could be engineered using injectable porous PBLG microspheres loaded with adipogenic-induced hASCs.