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.

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.

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.

Purpose: Surgical resection, the primary treatment for colorectal cancer (CRC), is often linked with postoperative complications that adversely affect the overall survival rates (OS). The pan-immune-inflammation value (PIV), a novel biomarker, is promising in evaluating cancer prognoses. We aimed to explore the impact of preoperative immune inflammation status on postoperative and long-term oncological outcomes in patients with CRC. Methods: A retrospective analysis of 203 patients with CRC who underwent surgery (January 2016–June 2020) was conducted. The preoperative PIV was calculated as [(neutrophil count + platelet count + monocyte count) / lymphocyte counts]. The PIV optimal cutoff value was determined based on the OS using the Contal and O’Quigley methods. Results: A PIV value ≥155.90 was defined as high. Patients were categorized into low-PIV (n = 85) and high-PIV (n = 118) groups. Perioperative clinical outcomes (total operation time, time to gas out, sips of water, soft diet, and hospital stay) were not significantly different between the groups. The high-PIV group exhibited more postoperative complications (P = 0.024), and larger tumor size compared with the low-PIV group. Multivariate analysis identified that American Society of Anesthesiologists grade III (P = 0.046) and high-PIV (P = 0.049) were significantly associated with postoperative complications. The low-PIV group demonstrated higher OS (P = 0.001) and disease-free survival rates (DFS) (P = 0.021) compared with the high-PIV group. Advanced N stage (P = 0.005) and high-PIV levels (P = 0.047) were the identified independent prognostic factors for OS, whereas advanced N stage (P = 0.045) was an independent prognostic factor for DFS. Conclusion Elevated preoperative PIV was associated with an increased incidence of postoperative complications and served as an independent prognostic factor for OS.

Background: Minimalist transcatheter aortic valve replacement (TAVR) under monitored anesthesia care (MAC) emphasizes early recovery. Remimazolam is a novel benzodiazepine with a short recovery time. This study hypothesized that remimazolam is non-inferior to dexmedetomidine in terms of recovery after TAVR. Methods: In this retrospective observational study, remimazolam was compared to dexmedetomidine in patients who underwent TAVR under MAC at a tertiary academic hospital between July 2020 and July 2022. The primary outcome was timely recovery after TAVR, defined as discharge from the intensive care unit within the first day following the procedure. Propensity score matching was used to compare timely recovery between remimazolam and dexmedetomidine, applying a non-inferiority margin of -10%. Results: The study included 464 patients, of whom 218 received remimazolam and 246 received dexmedetomidine. After propensity score matching, 164 patients in each group were included in the analysis. Regarding timely recovery after TAVR, remimazolam was non-inferior to dexmedetomidine (152 of 164 [92.7%] in the remimazolam group versus 153 of 164 [93.3%] in the dexmedetomidine group, risk difference [95% CI]: −0.6% [−6.7%, 5.5%]). The use of remimazolam was associated with fewer postoperative vasopressors/inotropes (21 of 164 [12.8%] vs. 39 of 164 [23.8%]) and temporary pacemakers (TPMs) (76 of 164 [46.3%] vs. 108 of 164 [65.9%]) compared to dexmedetomidine. Conclusions In patients undergoing TAVR under MAC, remimazolam was non-inferior to dexmedetomidine in terms of timely recovery. Remimazolam may be associated with better postoperative recovery profiles, including a lesser need for vasopressors/inotropes and TPMs.

Purpose: Poloxamer-407 (P-407) is used to induce hyperlipidemia. Exercise is effective in improving arteriosclerosis and cognitive impairment. In this research, the effect of treadmill running on short-term memory in the P-407-treated hyperlipidemia rats was studied focusing on neuroinflammation. Methods: Rats were classified in normal group, normal and treadmill exercise group, P-407-treated group, and P-407-treated and treadmill exercise group. Hyperlipidemia rats were made by single intraperitoneal injection with P-407 (500 mg/kg). Treadmill exercise was conducted for 30 minutes once a day, 5 days per week during 28 days. Step-down avoidance task was done to measure short-term memory. Glial fibrillary acidic protein and ionized calcium binding adaptor molecule 1 were assessed by immunohistochemistry. Expression of adhesion molecules and proinflammatory cytokines was determined by western blot analysis. Results: Treadmill exercise alleviated lipid profiles in the P-407-induced hyperlipidemia rats. Treadmill exercise improved short-term memory, inhibited reactive astrogliosis and microglia activation, and suppressed expression of adhesion molecules and proinflammatory cytokines in the hyperlipidemic rats. Conclusions Treadmill exercise exerts alleviating effect on memory deficits by inhibiting hippocampal neuroinflammation in the hyperlipidemia. The current results suggest that treadmill running serves as the treatment strategy for the cognitive dysfunction caused by hyperlipidemia.

Purpose: Wnt pathway is closely related to neurodevelopmental process associated with cognitive function. After administration of valproic acid to the pregnant mice, the effect of swimming exercise of pregnant mice on the memory, neuronal production, and apoptosis of pups was studied in relation with Wnt/β-catenin signaling pathway. Methods: On day 12 of pregnancy, mice were injected subcutaneously with 400-mg/kg valproic acid. The pregnant mice in the control with swimming exercise group and in the valproic acid injection with swimming exercise group were allowed for swimming for 30 minutes one time per a day, repeated 5 days per a week, during 3 weeks. Step-through avoidance task and Morris water maze task for memory function, immunohistochemistry for 5-bromo-2’-deoxyuridine (BrdU)-positive cells and western blot for brain-derived neurotrophic factor (BDNF), Wnt, β-catenin, Bcl-2 related X protein (Bax), B-cell lymphoma 2 (Bcl-2), cleaved caspase-3 were carried out. Results: Maternal swimming exercise during pregnancy improved memory function, increased BDNF expression, and neuronal proliferation in the valproic acid injected pups. Maternal swimming exercise during pregnancy suppressed Wnt expression and phosphorylation of β-catenin in the valproic acid injected pups. Maternal swimming exercise inhibited Bax and cleaved caspase-3 expression and increased Bcl-2 expression in the valproic acid injected pups. Conclusions Maternal swimming exercise during pregnancy improved memory function by increasing cell proliferation and inhibiting apoptosis through Wnt/β-catenin signaling cascade activation in the valproic acid injected pups. Maternal swimming exercise during pregnancy may have a protective effect on factors that induce autism in the fetus.

Growth hormone (GH) deficiency is caused by congenital or acquired causes and occurs in childhood or adulthood. GH replacement therapy brings benefits to body composition, exercise capacity, skeletal health, cardiovascular outcomes, and quality of life. Before initiating GH replacement, GH deficiency should be confirmed through proper stimulation tests, and in cases with proven genetic causes or structural lesions, repeated GH stimulation testing is not necessary. The dosing regimen of GH replacement therapy should be individualized, with the goal of minimizing side effects and maximizing clinical improvements. The Korean Endocrine Society and the Korean Society of Pediatric Endocrinology have developed a position statement on the diagnosis and treatment of GH deficiency. This position statement is based on a systematic review of evidence and expert opinions.