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.

OBJECTIVES: Long-term care facilities (LTCFs) are communal environments for patients with chronic diseases or older adults, making them particularly susceptible to significant harm during infectious disease outbreaks. Nonetheless, LTCFs have historically been subject to less stringent infection prevention and control (IPC) mandates. This study aimed to assess the current state of LTCFs and to develop an IPC system tailored for these facilities following the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We conducted an online survey of 11,366 LTCFs in Korea from December 30, 2022 to January 20, 2023, to evaluate the components of IPC in LTCFs. The infectious diseases targeted for IPC included COVID-19, influenza, and scabies. Additionally, we compared institution-based and home-based long-term care insurance facilities. RESULTS: Overall, 3,537 (31.1%) LTCFs responded to the survey, comprising 1,819 (51.4%) institution-based and 1,718 (48.6%) home-based facilities. A majority (87.4%, 2,376/2,720) of these facilities experienced COVID-19 outbreaks. However, only 42.2% of home-based facilities, in contrast to 90.6% of institution-based facilities, were equipped to manage concurrent COVID-19 cases. Similarly, while 92.1% of institution-based facilities were capable of managing influenza, only 50.5% of home-based facilities could do the same. The incidence of scabies was significantly higher in institution-based facilities than in home-based ones (26.1 vs. 4.3%). Additionally, 88.7% of institution-based facilities managed scabies cases effectively, compared to only 42.1% of home-based facilities. CONCLUSIONS Approximately half of the LTCFs had a basic capacity to respond to infectious diseases. However, there were differences in response capabilities between institution-based facilities and home-based facilities.

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.

OBJECTIVES: Long-term care facilities (LTCFs) are communal environments for patients with chronic diseases or older adults, making them particularly susceptible to significant harm during infectious disease outbreaks. Nonetheless, LTCFs have historically been subject to less stringent infection prevention and control (IPC) mandates. This study aimed to assess the current state of LTCFs and to develop an IPC system tailored for these facilities following the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We conducted an online survey of 11,366 LTCFs in Korea from December 30, 2022 to January 20, 2023, to evaluate the components of IPC in LTCFs. The infectious diseases targeted for IPC included COVID-19, influenza, and scabies. Additionally, we compared institution-based and home-based long-term care insurance facilities. RESULTS: Overall, 3,537 (31.1%) LTCFs responded to the survey, comprising 1,819 (51.4%) institution-based and 1,718 (48.6%) home-based facilities. A majority (87.4%, 2,376/2,720) of these facilities experienced COVID-19 outbreaks. However, only 42.2% of home-based facilities, in contrast to 90.6% of institution-based facilities, were equipped to manage concurrent COVID-19 cases. Similarly, while 92.1% of institution-based facilities were capable of managing influenza, only 50.5% of home-based facilities could do the same. The incidence of scabies was significantly higher in institution-based facilities than in home-based ones (26.1 vs. 4.3%). Additionally, 88.7% of institution-based facilities managed scabies cases effectively, compared to only 42.1% of home-based facilities. CONCLUSIONS Approximately half of the LTCFs had a basic capacity to respond to infectious diseases. However, there were differences in response capabilities between institution-based facilities and home-based facilities.

OBJECTIVES: Long-term care facilities (LTCFs) are communal environments for patients with chronic diseases or older adults, making them particularly susceptible to significant harm during infectious disease outbreaks. Nonetheless, LTCFs have historically been subject to less stringent infection prevention and control (IPC) mandates. This study aimed to assess the current state of LTCFs and to develop an IPC system tailored for these facilities following the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We conducted an online survey of 11,366 LTCFs in Korea from December 30, 2022 to January 20, 2023, to evaluate the components of IPC in LTCFs. The infectious diseases targeted for IPC included COVID-19, influenza, and scabies. Additionally, we compared institution-based and home-based long-term care insurance facilities. RESULTS: Overall, 3,537 (31.1%) LTCFs responded to the survey, comprising 1,819 (51.4%) institution-based and 1,718 (48.6%) home-based facilities. A majority (87.4%, 2,376/2,720) of these facilities experienced COVID-19 outbreaks. However, only 42.2% of home-based facilities, in contrast to 90.6% of institution-based facilities, were equipped to manage concurrent COVID-19 cases. Similarly, while 92.1% of institution-based facilities were capable of managing influenza, only 50.5% of home-based facilities could do the same. The incidence of scabies was significantly higher in institution-based facilities than in home-based ones (26.1 vs. 4.3%). Additionally, 88.7% of institution-based facilities managed scabies cases effectively, compared to only 42.1% of home-based facilities. CONCLUSIONS Approximately half of the LTCFs had a basic capacity to respond to infectious diseases. However, there were differences in response capabilities between institution-based facilities and home-based facilities.

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.

OBJECTIVES: Long-term care facilities (LTCFs) are communal environments for patients with chronic diseases or older adults, making them particularly susceptible to significant harm during infectious disease outbreaks. Nonetheless, LTCFs have historically been subject to less stringent infection prevention and control (IPC) mandates. This study aimed to assess the current state of LTCFs and to develop an IPC system tailored for these facilities following the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We conducted an online survey of 11,366 LTCFs in Korea from December 30, 2022 to January 20, 2023, to evaluate the components of IPC in LTCFs. The infectious diseases targeted for IPC included COVID-19, influenza, and scabies. Additionally, we compared institution-based and home-based long-term care insurance facilities. RESULTS: Overall, 3,537 (31.1%) LTCFs responded to the survey, comprising 1,819 (51.4%) institution-based and 1,718 (48.6%) home-based facilities. A majority (87.4%, 2,376/2,720) of these facilities experienced COVID-19 outbreaks. However, only 42.2% of home-based facilities, in contrast to 90.6% of institution-based facilities, were equipped to manage concurrent COVID-19 cases. Similarly, while 92.1% of institution-based facilities were capable of managing influenza, only 50.5% of home-based facilities could do the same. The incidence of scabies was significantly higher in institution-based facilities than in home-based ones (26.1 vs. 4.3%). Additionally, 88.7% of institution-based facilities managed scabies cases effectively, compared to only 42.1% of home-based facilities. CONCLUSIONS Approximately half of the LTCFs had a basic capacity to respond to infectious diseases. However, there were differences in response capabilities between institution-based facilities and home-based facilities.

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: Aging is one of major causes triggering neurophysiological changes in many brain substructures, including the hippocampus, which has a major role in learning and memory. Thioredoxin (Trx) is a class of small redox proteins. Among the Trx family, Trx2 plays an important role in the regulation of mitochondrial membrane potential and is controlled by TrxR2. Hitherto, age-dependent alterations in Trx2 and TrxR2 in aged hippocampi have been poorly investigated. Therefore, the aim of this study was to examine changes in Trx2 and TrxR2 in mouse and rat hippocampi by age and to compare their differences between mice and rats. Results: Trx2 and TrxR2 levels using Western blots in mice were the highest at young age and gradually reduced with time, showing that no significant differences in the levels were found between the two subfields. In rats, however, their expression levels were the lowest at young age and gradually increased with time. Nevertheless, there were no differences in cellular distribution and morphology in their hippocampi when it was observed by cresyl violet staining. In addition, both Trx2 and TrxR2 immunoreactivities in the CA1-3 fields were mainly shown in pyramidal cells (principal cells), showing that their immunoreactivities were altered like changes in their protein levels. Conclusions Our current findings suggest that Trx2 and TrxR2 expressions in the brain may be different according to brain regions, age and species. Therefore, further studies are needed to examine the reasons of the differences of Trx2 and TrxR2 expressions in the hippocampus between mice and rats.