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

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.

Glutathione (GSH), the main cellular antioxidant, dynamically influences tumor growth, metastasis, and resistance to therapy in the tumor microenvironment (TME), which comprises cancer cells, immune cells, stromal cells, and non-cellular components, including the extracellular matrix, metabolites, hypoxia, and acidity. Cancer stem cells (CSCs) and T cells are minor but significant cell subsets of the TME. GSH dynamics influences the fate of CSCs and T cells.Here, we explored GSH dynamics in CSCs and T cells within the TME, as well as therapeutic approaches that could target these dynamics.

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.

Glutathione (GSH), the main cellular antioxidant, dynamically influences tumor growth, metastasis, and resistance to therapy in the tumor microenvironment (TME), which comprises cancer cells, immune cells, stromal cells, and non-cellular components, including the extracellular matrix, metabolites, hypoxia, and acidity. Cancer stem cells (CSCs) and T cells are minor but significant cell subsets of the TME. GSH dynamics influences the fate of CSCs and T cells.Here, we explored GSH dynamics in CSCs and T cells within the TME, as well as therapeutic approaches that could target these dynamics.

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.

Glutathione (GSH), the main cellular antioxidant, dynamically influences tumor growth, metastasis, and resistance to therapy in the tumor microenvironment (TME), which comprises cancer cells, immune cells, stromal cells, and non-cellular components, including the extracellular matrix, metabolites, hypoxia, and acidity. Cancer stem cells (CSCs) and T cells are minor but significant cell subsets of the TME. GSH dynamics influences the fate of CSCs and T cells.Here, we explored GSH dynamics in CSCs and T cells within the TME, as well as therapeutic approaches that could target these dynamics.

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.