Purpose: Although some medications trigger the worsening of myasthenia gravis (MG), their clinical influence on patients with MG has not been significantly evaluated. We aimed to investigate whether the risk of clinical worsening of MG increases after administering cautionary drugs in patients with MG. Materials and Methods: This retrospective case-control study was based on the medical records of patients diagnosed with MG between 2007 and 2020. We analyzed the risk of MG worsening in patients exposed to cautionary drugs during the risk period, defined as 6 months from the first exposure to cautionary drugs. The risk of MG worsening in the exposed patients was compared to that in the non-exposed patients, who were individually matched in a 1:1 ratio with exposed cases for sex, age, thymoma, and autoantibodies. Results: Of the 2002 patients diagnosed with MG, 552 (27.6%) were exposed to cautionary drugs. Neuromuscular blocking agents (320 patients) and beta blockers (66123 person-days) were the most frequently prescribed medications. After exact matching, 220 exposed and 220 non-exposed patients were enrolled. The incidence rate of clinical worsening during the risk period was significantly higher in the exposed patients than in the non-exposed patients (odds ratio=4.09; 95% confidence interval, 1.88–8.90;p<0.001). Clinical worsening was observed in 31 (14.1%) of the exposed patients and in 8 (3.6%) of the non-exposed patients. Conclusion The administration of cautionary drugs increased the risk of clinical worsening in patients with MG. Clinicians should be aware of this risk when cautionary drugs need to be administered.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Disease outbreaks pose serious threats to humans, as highlighted by the recent pandemic, underscoring the need for an institutionalized multi-sectoral approach like One Health, encompassing human, animal, and environmental health. One Health has demonstrated efficacy in addressing emerging issues such as antimicrobial resistance and zoonotic disease spillover. While integrating the human-animal sector has yielded positive outcomes, the majority of zoonotic spillovers originate from wildlife, emphasizing the crucial role of environmental surveillance within global One Health systems. Additionally, climate change intensifies the frequency and emergence of infectious diseases and spillover events. Tackling the complexity and interconnectedness of health challenges necessitates integrated solutions that incorporate broader structural factors, aiding in the prevention, detection, and mitigation of disease outbreaks. Embracing One Health through multi-sectoral preparedness can effectively confront the escalating threats of pandemics and other emerging diseases.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Purpose: Romosozumab reportedly increases bone density in patients with severe osteoporosis; however, data on its clinical effects are limited. We conducted a multicenter retrospective survey to study the bone density-increasing effects of romosozumab and blood test-based predictive factors in patients with severe osteoporosis, examining its effects in clinical practice. Materials and Methods: This was a multicenter retrospective observational study. The subjects were patients with severe osteoporosis who were treated with romosozumab at the participating facilities. The increase in bone density was assessed by comparing bone density changes (as a percentage) in the lumbar spine, femoral neck, and total femur before and 12 months after administration using dual-energy X-ray absorptiometry. The association between changes in bone density at each site and pre-treatment bone metabolism markers (Tracp 5b, P1NP), serum calcium levels, nutritional status [Conut score: albumin, total cholesterol (TCho), and total lymphocyte count], and kidney function (eGFR) was assessed. Results: In both naïve patients and those switching from bone resorption inhibitors, the bone density increased significantly. In naïve patients, eGFR were positively associated with bone density in the total femur. In cases of switching from bone resorption inhibitors, correlations were found between Tracp 5b and lumbar spine bone mineral density (BMD), as well as between Tracp 5b, Alb, T-Cho, and eGFR in the total femur BMD. Conclusion Romosozumab administration significantly increases bone density in osteoporosis, and assessing key predictive factors is necessary to ensure clinical effectiveness.

Purpose: We investigated the clinical features, current negative-pressure wound therapy (NPWT) management strategies, and outcomes of pelvic-perineal soft tissue infection after open pelvic fractures. Materials and Methods: We analyzed the data of patients admitted to our trauma center with pelvic-perineal soft tissue after open pelvic fractures over a 7-year period. We investigated the injury severity score (ISS), medical costs, number of NPWTs, time required to reach definite wound coverage, complications, fracture classifications, transfusion requirements, interventions, length of stay (LOS) in hospital and intensive care unit (ICU), and prognosis. Results: Twenty patients with open pelvic fractures were treated with NPWT, and one patient who underwent NPWT died of pelvic sepsis during ICU treatment. The median LOS in hospital and medical costs were 98 [56–164] days and 106400 [65600–171100] USD, respectively. Patients treated with instillation NPWT (iNPWT, n=10) had a shorter NPWT duration (24 [13–39] vs. 46 [42–91] days, p=0.023), time to definite wound coverage (30 [21–43] vs. 49 [42–93] days, p=0.026), and hospital LOS (56 [43–72] vs. 158 [101–192] days, p=0.001), as well as lower medical costs (67800 [42500–102500] vs. 144200 [110400–236000] USD, p=0.009) compared to those treated with conventional NPWT. Conclusion NPWT is a feasible method for treating pelvic soft tissue infections in patients with open pelvic fractures. iNPWT can reduce the duration of NPWT, hospital LOS, and medical costs.

Purpose: Obesity and metabolic syndrome are acknowledged as key factors contributing to the development of non-alcoholic fatty liver disease (NAFLD). Vitamin D (VitD) is a multifaceted secosteroid hormone known for its anti-fibrotic and anti-inflammatory properties, with its deficiency often linked to obesity. Our study aimed to investigate whether VitD supplementation could mitigate the liver pathology associated with NAFLD. Materials and Methods: The NAFLD model was developed by subjecting male C57BL/6 mice to a high-fat diet (HFD) for 14 weeks.These mice were supplemented with VitD through intraperitoneal injection at a dosage of 7 μg/kg, administered three times per week for 7 weeks. Results: HFD resulted in VitD deficiency, insulin resistance, and increased liver weight. It elevated serum levels of liver aminotransferases and triglyceride, ultimately leading to steatohepatitis with fibrosis. This model exhibited increased levels of transforming growth factor (TGF)-β1, pro-inflammatory cytokines, HNF4α transcription factors, reactive oxygen species (ROS), renin-angiotensin system activity, and epithelial-mesenchymal transitions (EMT) within the liver. Supplementation with VitD resulted in the recovery of liver weight, improvement in histologic features associated with steatohepatitis, and reduction in alanine aminotransferases and triglyceride levels induced by the HFD. Additionally, it mitigated the HFD-induced over-expressions of TGF-β1 and fibrosis-related genes, along with pro-inflammatory cytokines and ROS. Notably, no adverse effect was found due to VitD supplementation in this model. Conclusion VitD ameliorates steatohepatitis within obesity-induced NAFLD through its multifaceted pathways. VitD supplementation emerges as a potentially safe, cost-effective, and direct treatment approach for NAFLD patients dealing with obesity or metabolic dysfunction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.