In dental trauma, reattachment of the original tooth fragment improves the reproduction of original tooth shape, texture, color, and radiolucency; thus, it provides good aesthetics.A 9-year-old boy was referred due to complicated crown-root fracture of the maxillary right central incisor. Although it had poor prognosis due to severe coronal damage and subcrestal fracture, reattachment of the tooth fragment was chosen due to the patient's age. One-visit apexification with mineral trioxide aggregate (MTA) was performed, followed by osteotomy and reattachment of the tooth fragment with post placement.Regular observation revealed no clinical signs or symptoms and no radiologic complications.
Apexification ; Child ; Esthetics ; Humans ; Incisor ; Male ; Miners ; Osteotomy ; Pemetrexed ; Prognosis ; Reproduction ; Tooth

This study aimed to determine whether the curing times of Xtra Power and High Power modes of high-power light emitting diode (LED) curing light are sufficient for polymerization of resin sealants. The specimens were prepared and their microhardness values were measured and compared with those of specimens polymerized under conventional LED curing light.The filled sealant polymerized for 8 seconds in the High Power mode and for 3 seconds in the Xtra Power mode showed significantly lower microhardness than the control specimen (p = 0.000). The unfilled sealant polymerized for 8, 12 seconds in the High Power mode and for 6 seconds in the Xtra Power mode showed significantly lower microhardness than the control specimen (p = 0.000).The results of this study suggest that the short curing time with the Xtra Power and High Power modes of highpower LED curing light are not sufficient for adequate polymerization of sealants under specific conditions, taking into account the curing times and the type of sealant.
Polymerization ; Polymers

OBJECTIVES: Noise-induced hearing lose(NIHL) is characterized by bilaterally symmetrical hearing loss, but some screening audiometries of employees who were exposed noise showed asymmetry. Therefore, this study was carried out to evaluate distribution of asymmetrical hearing loss and factors influencing asymmetries. METHODS: Study subjects were 294 male employees who have participated in 1st and 2nd noise-specific health examination. RESULTS: The interaural threshold difference of 20 dB or more at 4,000 Hz was classified as asymmetry. Among 294 NIHL employees. 19% had left asymmetric hearing loss. 22% had right asymmetric hearing loss. and overall asymmetry were 41%. Prevalence of asymmetry at 4,000 Hz significantly more decreased in middle(80~84 dB (A)) and high-level noise exposure groups(> or =85 dB(A)) than low-level exposure group and significantly increased as greater hearing threshold level in the worse ear. Prevalence of left asymmetry at 4,000 Hz significantly increased as greater hearing threshold level in the worse ear than right asymmetry. The interaural difference was significantly greater in the left asymmetry. CONCLUSIONS: Noise exposure produced asymmetric hearing loss(interaural asymmetry was 20 dB and more in 41% of case) and left ear was more susceptible to noise demage than right ear.
Audiometry ; Ear ; Hearing Loss* ; Hearing* ; Humans ; Male ; Mass Screening ; Noise ; Prevalence

PURPOSE: To investigate the effects of estrogen on the expression of the alpha1 receptor and nitric oxide synthase (NOS) in rat urethra and bladder after oophorectomy. MATERIALS AND METHODS: Forty-five mature female Sprague-Dawley rats (aged 10-11 weeks, 235-250 g) were randomly assigned to one of three groups: control group, oophorectomy group (Opx), or oophorectomy and estradiol replacement group (Opx+ Est). The degree of expression of alpha1 receptor (alpha1A and D) and NOS (neuronal NOS [nNOS] and endothelial NOS [eNOS]) in bladder and urethral tissues was investigated by using immunohistochemical staining and Western blotting. RESULTS: In the bladder, the expression rates of alpha1 receptor (alpha1A and alpha1D) increased in the Opx group but decreased in the Opx+Est group. These changes were not statistically significant. The alpha1A and alpha1D receptor of the urethra decreased in the Opx group but increased in the Opx+Est group. These changes were not statistically significant. In the bladder and urethra, the expression rates of nNOS and eNOS significantly increased in the Opx group but decreased in the Opx+Est group (p<0.05). CONCLUSIONS: These data suggest that estrogen depletion increases NOS and alpha1 receptor expression in the rat bladder. However, these changes could be restored by estrogen replacement therapy.
Animals ; Collagen/metabolism ; Estradiol/analogs & derivatives/blood/pharmacology ; Estrogen Replacement Therapy/*methods ; Female ; Muscle, Smooth/pathology ; Nitric Oxide Synthase/*metabolism ; Ovariectomy ; Rats, Sprague-Dawley ; Receptors, Adrenergic, alpha-1/*metabolism ; Urethra/drug effects/*metabolism/pathology ; Urinary Bladder/drug effects/*metabolism/pathology

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Objective: To estimate the projected cancer risk attributable to diagnostic cone-beam computed tomography (CBCT) performed under different exposure settings for orthodontic purposes in children and adults. Methods: We collected a list of CBCT machines and their specifications from 38 orthodontists. Organ doses were estimated using median and maximum exposure settings of 105 kVp/156.8 mAs and 130 kVp/200 mAs, respectively. The projected cancer risk attributable to CBCT procedures performed 1–3 times within 2 years was calculated for children (aged 5 and 10 years) and adult (aged 20, 30, and 40 years) male and female patients. Results: For maximum exposure settings, the mean lifetime fractional ratio (LFR) was 14.28% for children and 0.91% for adults; this indicated that the risk to children was 16 times the risk to adults. For median exposure settings, the mean LFR was 5.25% and 0.58% for children and adults, respectively. The risk of cancer decreased with increasing age. For both median and maximum exposure settings, females showed a higher risk of cancer than did males in all age groups. Cancer risk increased with an increase in the frequency of CBCT procedures within a given period. Conclusions The projected dental CBCT-associated cancer risk spans over a wide range depending on the machine parameters and image acquisition settings. Children and female patients are at a higher risk of developing cancer associated with diagnostic CBCT. Therefore, the use of diagnostic CBCT should be justified, and protective measures should be taken to minimize the harmful biological effects of radiation.

Objective: To estimate the projected cancer risk attributable to diagnostic cone-beam computed tomography (CBCT) performed under different exposure settings for orthodontic purposes in children and adults. Methods: We collected a list of CBCT machines and their specifications from 38 orthodontists. Organ doses were estimated using median and maximum exposure settings of 105 kVp/156.8 mAs and 130 kVp/200 mAs, respectively. The projected cancer risk attributable to CBCT procedures performed 1–3 times within 2 years was calculated for children (aged 5 and 10 years) and adult (aged 20, 30, and 40 years) male and female patients. Results: For maximum exposure settings, the mean lifetime fractional ratio (LFR) was 14.28% for children and 0.91% for adults; this indicated that the risk to children was 16 times the risk to adults. For median exposure settings, the mean LFR was 5.25% and 0.58% for children and adults, respectively. The risk of cancer decreased with increasing age. For both median and maximum exposure settings, females showed a higher risk of cancer than did males in all age groups. Cancer risk increased with an increase in the frequency of CBCT procedures within a given period. Conclusions The projected dental CBCT-associated cancer risk spans over a wide range depending on the machine parameters and image acquisition settings. Children and female patients are at a higher risk of developing cancer associated with diagnostic CBCT. Therefore, the use of diagnostic CBCT should be justified, and protective measures should be taken to minimize the harmful biological effects of radiation.