Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.

Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.

Associations between plasma metabolites and heavy metal exposure in residents of environmentally polluted areas.

Heavy metals are commonly released into the environment through industrial processes such as mining and refining. The rapid industrialization that occurred in South Korea during the 1960s and 1970s contributed significantly to the economy of the country; however, the associated mining and refining led to considerable environmental pollution, and although mining is now in decline in South Korea, the detrimental effects on residents inhabiting the surrounding areas remain. The bioaccumulation of toxic heavy metals leads to metabolic alterations in human homeostasis, with disruptions in this balance leading to various health issues. This study used metabolomics to explore metabolomic alterations in the plasma samples of residents living in mining and refining areas. The results showed significant increases in metabolites involved in glycolysis and the surrounding metabolic pathways, such as glucose-6-phosphate, phosphoenolpyruvate, lactate, and inosine monophosphate, in those inhabiting polluted areas. An investigation of the associations between metabolites and blood clinical parameters through meet-in-the-middle analysis indicated that female residents were more affected by heavy metal exposure, resulting in more metabolomic alterations. For women, inhabiting the abandoned mine area, metabolites in the glycolysis and pentose phosphate pathways, such as ribose-5-phosphate and 3-phosphoglycerate, have shown a negative correlation with albumin and calcium. Finally, Mendelian randomization(MR) was used to determine the causal effects of these heavy metal exposure-related metabolites on heavy metal exposure-related clinical parameters. Metabolite biomarkers could provide insights into altered metabolic pathways related to exposure to toxic heavy metals and improve our understanding of the molecular mechanisms underlying the health effects of toxic heavy metal exposure.

Visuospatial cueing differences as a function of autistic traits.

Atypical orienting of visuospatial attention in autistic individuals or individuals with a high level of autistic-like traits (ALTs) has been well documented and viewed as a core feature underlying the development of autism. However, there has been limited testing of three alternative theoretical positions advanced to explain atypical orienting - difficulty in disengagement, cue indifference, and delay in orienting. Moreover, research commonly has not separated facilitation (reaction time difference between neutral and valid cues) and cost effects (reaction time difference between invalid and neutral cues) in orienting tasks. We addressed these limitations in two experiments that compared groups selected for Low- and High-ALT levels on exogenous and endogenous versions of the Posner cueing paradigm. Experiment 1 showed that High-ALT participants exhibited a significantly reduced cost effect compared to Low-ALT participants in the endogenous cueing task, although the overall orienting effect remained small. In Experiment 2, we increased task difficulty of the endogenous task to augment cueing effects. Results were comparable to Experiment 1 regarding the finding of a reduced cost effect for High-ALT participants on the endogenous cueing task and additionally demonstrated a reduced facilitation effect in High-ALT participants on the same task. No ALT group differences were observed on an exogenous cueing task included in Experiment 2. These findings suggest atypical orienting in High-ALT individuals may be attributable to general cue indifference, which implicates differences in top-down attentional processes between Low- and High-ALT individuals. We discuss how indifference to endogenous cues may contribute to social cognitive differences in autism.

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Dexmedetomidine as an effective alternative tool for heart rate control in atrial fibrillation during general anesthesia-A case report.

Atrial fibrillation (AF) is a common arrhythmia requiring effective heart rate (HR) management. Conventional therapies may not always achieve the desired HR control during intraoperative conditions. We present two cases of AF patients in whom dexmedetomidine, an alpha-2 receptor agonist, was utilized during surgery when conventional treatments proved ineffective. In Case 1, a 65-year-old male with multiple comorbidities underwent surgery. Despite receiving intraoperative medications for AF, his HR remained uncontrolled. Dexmedetomidine successfully stabilized his HR without complications. In Case 2, a 75-year-old male with heart disease experienced a sudden HR surge during surgery, which remained uncontrolled despite conventional treatment. Dexmedetomidine effectively managed his HR, ensuring a safer surgical course. While the primary indication of dexmedetomidine is not arrhythmia management, this case report suggests its potential in challenging cases. Further research is needed to explore its therapeutic role in tachyarrhythmia management and establish appropriate dosing strategies.

Radiological manifestations and clinical findings of patients with oncologic and osteoporotic medication-related osteonecrosis of the jaw.

Medication-related osteonecrosis of the jaw (MRONJ) poses a challenging form of osteomyelitis in patients undergoing antiresorptive therapies in contrast to conventional osteomyelitis. This study aimed to compare the clinical and radiological features of MRONJ between patients receiving low-dose medications for osteoporosis and those receiving high-dose medications for oncologic purposes. The clinical, panoramic radiographic, and computed tomography data of 159 patients with MRONJ (osteoporotic group, n = 120; oncologic group, n = 39) who developed the condition after using antiresorptive medications for the management of osteoporosis or bone malignancy were analyzed. The osteoporotic group was older (75.8 vs. 60.4 years, p < 0.01) and had a longer duration of medication usage than the oncologic group (58.1 vs. 28.0 months, p < 0.01). Pus discharge and swelling were more common in the osteoporotic group (p < 0.05), whereas bone exposure was more frequent in the oncologic group (p < 0.01). The mandibular cortical index (MCI) in panoramic radiographs was higher in the osteoporotic group (p < 0.01). The mean sequestra size was larger in the oncologic group than in the osteoporotic group (15.3 vs. 10.6 mm, p < 0.05). The cured rate was significantly higher in the osteoporotic group (66.3% vs. 33.3%, p < 0.01). Oncologic MRONJ exhibited distinct clinical findings including rapid disease onset, fewer purulent signs, and lower cure rates than osteoporotic MRONJ. Radiological features such as sequestrum size on CT scan, and MCI values on panoramic radiographs, may aid in differentiating MRONJ in osteoporotic and oncologic patients.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Safety assessment and gastrointestinal retention of orally administered cerium oxide nanoparticles in rats.

Cerium oxide nanoparticles (CeO2 NPs, NM-212) are well-known for their catalytic properties and antioxidant potential, and have many applications in various industries, drug delivery, and cosmetic formulations. CeO2 NPs exhibit strong antimicrobial activity and can be used to efficiently remove pathogens from different environments. However, knowledge of the toxicological evaluation of CeO2 NPs is too limited to support their safe use. In this study, CeO2 NPs were orally administered to Sprague Dawley rats for 13 weeks at the doses of 0, 10, 100, and 1000 mg/kg bw/day, followed by a four week recovery period. The hematology values for the absolute and relative reticulocyte counts in male rats treated with 1000 mg/kg bw/day CeO2 NPs were lower than those in control rats. The clinical chemistry values for sodium and chloride in the treated male rat groups (100 and 1000 mg/kg/day) and total protein and calcium in the treated female rat groups (100 mg/kg/day) were higher than those in the control groups. However, these changes were not consistent in both sexes, and no abnormalities were found in the corresponding pathological findings. The results showed no adverse effects on any of the parameters assessed. CeO2 NPs accumulated in the jejunum, colon, and stomach wall of rats administered 1000 mg/kg CeO2 NPs for 90 days. However, these changes were not abnormal in the corresponding histopathological and immunohistochemical examinations. Therefore, 1000 mg/kg bw/day may be considered the "no observed adverse effect level" of CeO2 NPs (NM-212) in male and female SD rats under the present experimental conditions.

A complex case of posterior reversible encephalopathy syndrome after combined spinal epidural of preeclampsia parturient: A case report.

Posterior reversible encephalopathy syndrome (PRES) is a disorder characterized by vasogenic edema affecting the posterior brain region. We report a case of PRES in a 36-year-old woman with preeclampsia who underwent an emergency cesarean section with spinal anesthesia. After surgery, she developed right leg weakness, headache, and seizures. Imaging showed white matter edema consistent with PRES. The exact cause of PRES is unclear, but elevated blood pressure and endothelial dysfunction are implicated. Tight blood pressure control in PRES is crucial for management, and prompt recognition and treatment are essential for favorable outcomes.

Very long-lasting spinal anesthesia with dexmedetomidine: A report of two cases.

Spinal anesthesia usually lasts up to two hours, but an infusion of IV dexmedetomidine can prolong it to three to four hours. We report two cases where single spinal anesthesia with IV dexmedetomidine was maintained for more than six hours during tibia fracture surgery. The spinal anesthesia was maintained for 350 and 390 minutes without another medication, and the sensory level confirmed after the surgery was T10 and L1. Dexmedetomidine can very-prolong the duration of spinal anesthesia beyond what has been reported. However, longer infusion times can also result in longer recovery times.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.

OBJECTIVES: This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. METHODS: PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). RESULTS: The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. CONCLUSIONS: This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.

Clinical and radiological features of malformed mesiodens in the nasopalatine canal: an observational study.

OBJECTIVES: The purpose of this study is to investigate the morphological changes that occur when mesiodens is located within the nasopalatine canal, as well as clinical characteristics. METHODS: Clinical records and CT images of patients who had mesiodens in the nasopalatine canal were retrospectively analysed. In addition to demographic information, clinical symptoms and complications associated with extraction of mesiodens were recorded. Using CT images, number, location, size, and tooth morphology were evaluated. RESULTS: This study included 32 patients and 38 mesiodens within the nasopalatine canal. Supernumerary teeth exhibited a characteristic feature of thin and elongated shape in the canal (narrow width and elongation were observed in 96.6% and 53.3% of the patients, respectively). Fusion was found in 4 patients and dilaceration in 12. A complication occurred in 2 patients, which was tooth remnant, not a neurologic complication. Only 5 mesiodens could be detected in the nasopalatine canal on panoramic images. CONCLUSIONS: Morphological abnormalities in mesiodens within the nasopalatine canal were frequently detected, and these could be effectively diagnosed through 3D imaging analysis.

Novel Organoid Culture System for Improved Safety Assessment of Nanomaterials.

Over the past few decades, the increased application of nanomaterials has raised questions regarding their safety and possible toxic effects. Organoids have been suggested as promising tools, offering efficient assays for nanomaterial-induced toxicity evaluation. However, organoid systems have some limitations, such as size heterogeneity and poor penetration of nanoparticles because of the extracellular matrix, which is necessary for organoid culture. Here, we developed a novel system for the improved safety assessment of nanomaterials by establishing a 3D floating organoid paradigm. In addition to overcoming the limitations of two-dimensional systems including the lack of in vitro-in vivo cross-talk, our method provides multiple benefits as compared with conventional organoid systems that rely on an extracellular matrix for culture. Organoids cultured using our method exhibited relatively uniform sizing and structural integrity and were more conducive to the internalization of nanoparticles. Our floating culture system will accelerate the research and development of safe nanomaterials.

Factors Associated With People's Accessibility to Mental Healthcare Services in Ukraine: Focusing on Household Head Vulnerability.

Objectives: This study examines the factors associated with access to mental healthcare services among people living in the government-controlled areas (GCAs) of Donetsk and Luhansk oblasts in Ukraine. Methods: The 2020 Ukraine Multi-Sector Needs Assessment conducted by REACH was subjected to frequency analysis, percentage analysis, and binary logistic regression to confirm the factors associated with accessibility to mental healthcare services among Ukrainian household heads. Results: Older household heads, heads with high accessibility to healthcare facilities, and those with low health expenditures were highly likely to have low access to mental healthcare services. Household heads' awareness of household members' medical assistance eligibility was significantly and positively associated with the former's mental healthcare accessibility. Conclusion: This study revealed the mental health vulnerability of people living in GCAs in Ukraine, in which the situation progresses from conflict to war. The need for mental healthcare, which is adversely affected by armed conflict, is expected to increase. Accordingly, further studies should clarify the demand for and methods to enhance mental healthcare services to ensure the timely provision of these services in the future.

V2-Net: An Attention-guided Volumetric Regression Network for Tooth Landmark Localization on CT Images with Metal Artifacts.

For virtual surgical planning in orthognathic surgery, marking tooth landmarks on CT images is an important procedure. However, the manual localization procedure of tooth landmarks is time-consuming, labor-intensive, and requires expert knowledge. Also, direct and automatic tooth landmark localization on CT images is difficult because of the lower resolution and metal artifacts of dental images. The purpose of this study was to propose an attention-guided volumetric regression network (V2-Net) for accurate tooth landmark localization on CT images with metal artifacts and lower resolution. V2-Net has an attention-guided network architecture using a coarse-to-fine-attention mechanism that guided the 3D probability distribution of tooth landmark locations within anatomical structures from the coarse V-Net to the fine V-Net for more focus on tooth landmarks. In addition, we combined attention-guided learning and a 3D attention module with optimal Pseudo Huber loss to improve the localization accuracy. Our results show that the proposed method achieves state-of-the-art accuracy of 0.85 ± 0.40 mm in terms of mean radial error, outperforming previous studies. In ablation studies, we observed that the proposed attention-guided learning and a 3D attention module improved the accuracy of tooth landmark localization in CT images of lower resolution and metal artifacts. Furthermore, our method achieved 97.92% in terms of the success detection rate within the clinically accepted accuracy range of 2.0 mm.

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

OBJECTIVE: The aim of this study is to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN: A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS: The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS: The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.

Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images.

BACKGROUND: The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. METHODS: The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. RESULTS: The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. CONCLUSIONS: The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes.

Severe pulmonary edema occurred during endobronchial ultrasound under monitored anesthesia care - A case report.

BACKGROUND: Endobronchial ultrasound (EBUS) is widely used to diagnose lung cancer. Monitored anesthesia care (MAC) can enhance patient comfort and procedural conditions during EBUS. EBUS under MAC is usually safe but can lead to various complications. CASE: A 34-year-old male who had increased sputum for two months showed an enlarged paratracheal lymph node and planned for lymph node biopsy by EBUS. During EBUS under MAC, an unexpected oxygen saturation decline required intervention. After intubation, copious frothy fluid was suctioned from the bronchi, and oxygenation was recovered. A narrowed trachea and the EBUS bronchoscope might have resulted in upper airway obstruction, and suction performed under these conditions might have caused pulmonary edema. The patient received non-invasive ventilation and high-flow nasal cannula and recovered without complications. CONCLUSIONS: When there is an expected risk of upper airway obstruction during EBUS, careful preoperative evaluation and preparation are essential to prevent negative pressure pulmonary edema.