Maxillary Central Incisor Eruption Failure Due to Supernumerary Teeth and Pericoronal Hamartoma: A Report of a Rare Case.

Impaction of permanent teeth during the replacement period is a relatively common occurrence in clinical practice. Tooth impaction occurs in the presence of factors that inhibit tooth eruption, such as supernumerary teeth or tumors. This is a report of permanent tooth impaction due to supernumerary teeth and pericoronal myxofibrous hyperplasia (PMH), a type of pericoronal hamartomatous lesion. An eight-year-old girl was diagnosed with an unerupted right maxillary central incisor. An inverted supernumerary tooth was present on the palatal side of the impacted central incisor, and PMH developed on the labial side of the central incisor. Interestingly, the alveolar bone on the labial side had completely disappeared. After the extraction of the supernumerary tooth and the removal of the PMH, the central incisors erupted, and the labial alveolar bone regenerated normally. Treatment for impacted teeth typically involves the removal of any existing lesions. This case is unique in that the alveolar bone of the impacted tooth regenerated following the extraction of the supernumerary tooth and removal of the PMH.

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

Impaction of Mandibular First Molar by Tooth-Like Hard Tissue.

Impaction of permanent teeth during the mixed dentition stage is relatively common in clinical practice, but impaction of mandibular first molars is rare. This case report presents an impaction of the mandibular first molar due to a tooth-like hard tissue lesion. An 8-year-old girl was diagnosed with an impacted mandibular first molar. The roots of the impacted molars were almost completely developed. A spherical tooth-like hard tissue with a diameter of approximately 2 mm was observed at the alveolar crest between the impacted mandibular first and second molars. The lesion causing the impaction was excised, and the first molar was fenestrated and allowed to erupt naturally. We showed that even if the tooth root is almost complete, natural eruption can be expected if the lesion is removed and space for eruption is secured.

Effects of forest thinning on sap flow dynamics and transpiration in a Japanese cedar forest.

Historically, forest thinning in Japan was conducted to obtain high-quality timber from plantations. Today, in contrast, thinning is also motivated by forest water balance and climate change considerations. It is in this context that the present study examines the effects of thinning on the ecophysiological responses of remaining trees, which are inadequately understood, especially in relation to changes in the magnitude and duration of transpiration. Sap flux densities were measured in both outer and inner sapwood to obtain stand-scale transpiration for two years in the pre-thinning state and three years post-thinning. The effects of thinning on transpiration were quantitatively evaluated based on canopy conductance models. The larger increases in outer sap flux density were found in the first year after the treatment, while those in inner sap flux density were detected in the second and third years. The remaining trees required a few of years to adjust to improved light conditions of the lower crown, resulting in a delayed response of inner sap flux density. As a result of this lag, transpiration was reduced to 71 % of the pre-thinning condition in the first year, but transpiration recovered to the pre-thinning levels in the second and third years due to compensating contributions from inner sap flow. In terms of more accurately chronicling the thinning effect, the distribution of sap flux density with respect to its radial pattern, is necessary. Such measurements are key to more comprehensively examining the ecophysiological response of forest plantations to thinning and, ultimately, its effect on the forest water balance.

A Stepwise Progression of Acute Bilateral Visual Loss Due to Onodi Cell Sinusitis.

Acute visual loss in an immunocompromised patient may be caused by acute invasive fungal sinusitis (AIFS), even if symptoms include only mild headache and computed tomography (CT) shows only mild sinusitis, especially of the Onodi cell. Herein, we report a case of a 71-year-old man with a medical history of dermatomyositis and type 2 diabetes mellitus who presented with a stepwise progression of acute bilateral visual loss, mild headache, and altered consciousness. Initially, as the plain cranial CT showed only mild fluid retention in the posterior ethmoid sinus without bone destruction, the sinusitis was considered unrelated to the visual loss. Afterward, however, contrast-enhanced cranial magnetic resonance imaging (MRI) showed mucosal thickening, fluid retention in the posterior ethmoid sinus, and spread of the contrast medium over the dura around the right posterior ethmoid sinus and bilateral optic nerve tracts. Aspergillus fumigatus was identified from endoscopic drainage of the sinus. The patient was diagnosed with AIFS and treated with amphotericin B 350 mg/day. The altered sensorium and headache rapidly improved, and his left visual acuity improved to counting fingers. Although AIFS is rare, it can cause severe sequela or death due to vascular or direct intracranial invasion. Therefore, immediate drainage of the sinus and intravenous antifungal therapy are essential for AIFS. Our findings will help physicians make accurate and rapid diagnoses of AIFS in future cases.

Effects of sturgeon fillet intake on top-ranked Japanese female long-distance runners.

AIMS: The aim of this study is to investigate whether consumption of sturgeon fillets reduces the oxidative stress marker urinary 8-hydroxy-2'-deoxyguanosine (8OHdG) in top-ranked Japanese female long-distance runners. METHODS: In a before-and-after study, nine professional long-distance female athletes ate 100 g/day of sturgeon fillets for 2 weeks. Urinalysis (8OHdG, an oxidative stress marker, and creatinine), blood tests (fatty acids and 25-hydroxyvitamin D [25OHD]), exercise intensity, subjective fatigue, muscle elasticity, muscle mass, body fat mass, and nutritional intake using image-based dietary assessment (IBDA) were compared before, immediately after, and 1 month after the intervention. RESULTS: Consumption of sturgeon fillets suppressed 8OHdG (p < 0.05) in the increased exercise intensity female athletes. Eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and 25OHD levels in blood increased from before to immediately after and 1 month after the intervention (p < 0.05). IBDA showed that intake of n-3 fatty acid increased after and one month after the intervention, whereas DHA, imidazole dipeptide and vitamin D intake increased after the intervention (p < 0.05) and then decreased after 1 month (p < 0.05). There were no significant changes in subjective fatigue, muscle elasticity, muscle mass, and body fat. CONCLUSIONS: The results suggest that eating sturgeon fillets during intense training may increase blood levels of EPA, DHA, and 25OHD, which may suppress urinary oxidative stress (8OHdG) in top-ranked Japanese long-distance runners.

Hybrid PBL and Pure PBL: Which one is more effective in developing clinical reasoning skills for general medicine clerkship?-A mixed-method study.

This study aims to compare the effectiveness of Hybrid and Pure problem-based learning (PBL) in teaching clinical reasoning skills to medical students. The study sample consisted of 99 medical students participating in a clerkship rotation at the Department of General Medicine, Chiba University Hospital. They were randomly assigned to Hybrid PBL (intervention group, n = 52) or Pure PBL group (control group, n = 47). The quantitative outcomes were measured with the students' perceived competence in PBL, satisfaction with sessions, and self-evaluation of competency in clinical reasoning. The qualitative component consisted of a content analysis on the benefits of learning clinical reasoning using Hybrid PBL. There was no significant difference between intervention and control groups in the five students' perceived competence and satisfaction with sessions. In two-way repeated measure analysis of variance, self-evaluation of competency in clinical reasoning was significantly improved in the intervention group in "recalling appropriate differential diagnosis from patient's chief complaint" (F(1,97) = 5.295, p = 0.024) and "practicing the appropriate clinical reasoning process" (F(1,97) = 4.016, p = 0.038). According to multiple comparisons, the scores of "recalling appropriate history, physical examination, and tests on clinical hypothesis generation" (F(1,97) = 6.796, p = 0.011), "verbalizing and reflecting appropriately on own mistakes," (F(1,97) = 4.352, p = 0.040) "selecting keywords from the whole aspect of the patient," (F(1,97) = 5.607, p = 0.020) and "examining the patient while visualizing his/her daily life" (F(1,97) = 7.120, p = 0.009) were significantly higher in the control group. In the content analysis, 13 advantage categories of Hybrid PBL were extracted. In the subcategories, "acquisition of knowledge" was the most frequent subcategory, followed by "leading the discussion," "smooth discussion," "getting feedback," "timely feedback," and "supporting the clinical reasoning process." Hybrid PBL can help acquire practical knowledge and deepen understanding of clinical reasoning, whereas Pure PBL can improve several important skills such as verbalizing and reflecting on one's own errors and selecting appropriate keywords from the whole aspect of the patient.

Association Between Physician Empathy and Difficult Patient Encounters: a Cross-Sectional Study.

BACKGROUND: Physicians frequently experience patients as difficult. Our study explores whether more empathetic physicians experience fewer patient encounters as difficult. OBJECTIVE: To investigate the association between physician empathy and difficult patient encounters (DPEs). DESIGN: Cross-sectional study. PARTICIPANTS: Participants were 18 generalist physicians with 3-8 years of experience. The investigation was conducted from August-September 2018 and April-May 2019 at six healthcare facilities. MAIN MEASURES: Based on the Jefferson Scale of Empathy (JSE) scores, we classified physicians into low and high empathy groups. The physicians completed the Difficult Doctor-Patient Relationship Questionnaire-10 (DDPRQ-10) after each patient visit. Scores ≥ 31 on the DDPRQ-10 indicated DPEs. We implemented multilevel mixed-effects logistic regression models to examine the association between physicians' empathy and DPE, adjusting for patient-level covariates (age, sex, history of mental disorders) and with physician-level clustering. KEY RESULTS: The median JSE score was 114 (range: 96-126), and physicians with JSE scores 96-113 and 114-126 were assigned to low and high empathy groups, respectively (n = 8 and 10 each); 240 and 344 patients were examined by physicians in the low and high empathy groups, respectively. Among low empathy physicians, 23% of encounters were considered difficulty, compared to 11% among high empathy groups (OR: 0.37; 95% CI = 0.19-0.72, p = 0.004). JSE scores and DDPRQ-10 scores were negatively correlated (r = -0.22, p < 0.01). CONCLUSION: Empathetic physicians were less likely to experience encounters as difficult. Empathy appears to be an important component of physician perception of encounter difficulty.

HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map.

Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).

Real-Time Action Recognition System for Elderly People Using Stereo Depth Camera.

Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we introduce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.

Deep time-delay Markov network for prediction and modeling the stress and emotions state transition.

To recognize stress and emotion, most of the existing methods only observe and analyze speech patterns from present-time features. However, an emotion (especially for stress) can change because it was triggered by an event while speaking. To address this issue, we propose a novel method for predicting stress and emotions by analyzing prior emotional states. We named this method the deep time-delay Markov network (DTMN). Structurally, the proposed DTMN contains a hidden Markov model (HMM) and a time-delay neural network (TDNN). We evaluated the effectiveness of the proposed DTMN by comparing it with several state transition methods in predicting an emotional state from time-series (sequences) speech data of the SUSAS dataset. The experimental results show that the proposed DTMN can accurately predict present emotional states by outperforming the baseline systems in terms of the prediction error rate (PER). We then modeled the emotional state transition using a finite Markov chain based on the prediction result. We also conducted an ablation experiment to observe the effect of different HMM values and TDNN parameters on the prediction result and the computational training time of the proposed DTMN.

A Study on the Gaze Range Calculation Method During an Actual Car Driving Using Eyeball Angle and Head Angle Information.

Car operation requires advanced brain function. Currently, evaluation of the motor vehicle driving ability of people with higher brain dysfunction is medically unknown and there are few evaluation criteria. The increase in accidents by elderly drivers is a social problem in Japan, and a method to evaluate whether elderly people can drive a car is needed. Under these circumstances, a system to evaluate brain dysfunction and driving ability of elderly people is needed. Gaze estimation research is a rapidly developing field. In this paper, we propose the gaze calculation method by eye and head angles. We used the eye tracking device (TalkEyeLite) made by Takei Scientific Instruments Cooperation. For our image processing technique, we estimated the head angle using the template matching method. By using the eye tracking device and the head angle estimate, we built a system that can be used during actual on-road car operation. In order to evaluate our proposed method, we tested the system on Japanese drivers during on-road driving evaluations at a driving school. The subjects were one instructor of the car driving school and eight general drivers (three 40-50 years old and five people over 60 years old). We compared the gaze range of the eight general subjects and the instructor. As a result, we confirmed that one male in his 40s and one elderly driver had narrower gaze ranges.

Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor.

A gaze estimation system is one of the communication methods for severely disabled people who cannot perform gestures and speech. We previously developed an eye tracking method using a compact and light electrooculogram (EOG) signal, but its accuracy is not very high. In the present study, we conducted experiments to investigate the EOG component strongly correlated with the change of eye movements. The experiments in this study are of two types: experiments to see objects only by eye movements and experiments to see objects by face and eye movements. The experimental results show the possibility of an eye tracking method using EOG signals and a Kinect sensor.

EOG-sEMG Human Interface for Communication.

The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as "dual-modality" for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%.