Background: Understanding the virulence and pathogenicity of invasive nontyphoidal Salmonella (iNTS) in children may support timely treatment and enable closer monitoring of chronic infections. iNTS epidemiology in Asia remains inadequately described. We analyzed the genetic diversity and virulence genes associated with extra-intestinal invasion in Korean children. Methods: Salmonella isolates from children < 18 yrs of age diagnosed with moderate-tosevere salmonellosis between January 2019 and December 2021 were subjected to antibiotic susceptibility testing and whole-genome sequencing. Results: In total, 58 cases were included. We identified 20 serotypes, the most prevalent being Salmonella Enteritidis (N = 21), followed by Infantis (N = 6), I 4,[5],12:i:- (N = 5), and Bareilly (N = 5). Extra-intestinal invasion occurred in 12 (20.7%) cases involving Salmonella Oranienburg (2/2), Give (1/1), Javiana (1/1), Paratyphi B var. L(+) tartrate+ (1/1), Schwarzengrund (1/1), Singapore (1/1), Montevideo (1/2), Saintpaul (1/2), I 4:b:- (1/2), Infantis (1/6), and Enteritidis (1/21). While the numbers of total virulence genes and genes belonging to major virulence categories did not significantly differ between iNTS and noniNTS, several genetic factors, including Salmonella pathogenicity island (SPI)-1 (P = 0.039), SPI-2 (P = 0.020), SPI-5 (P = 0.014), SPI-13 (P = 0.010), cytolethal distending toxin-related genes (P = 1.4 × 10 –4 ), fepC (P = 0.021), and tcpC (P = 0.040) were more frequent in invasive isolates. Conclusions Salmonella Enteritidis-ST11 predominated in infections among Korean children, but invasive isolates were rare. Early detection of genetic factors associated with extra-intestinal invasion will be helpful for prompt and appropriate treatment.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.