Fine particulate matter (FPM) is a major component of air pollution and has emerged as a significant global health concern owing to its adverse health effects. Previous studies have investigated the correlation between bone health and FPM through cohort or review studies. However, the effects of FPM exposure on bone health are poorly understood. This study aimed to investigate the effects of FPM on bone health and elucidate these effects in vitro and in vivo using mice. Micro-CT analysis in vivo revealed FPM exposure decreased bone mineral density, trabecular bone volume/total volume ratio, and trabecular number in the femurs of mice, while increasing trabecular separation. Histological analysis showed that the FPM-treated group had a reduced trabecular area and an increased number of osteoclasts in the bone tissue. Moreover, in vitro studies revealed that low concentrations of FPM significantly enhanced osteoclast differentiation. These findings further support the notion that short-term FPM exposure negatively impacts bone health, providing a foundation for further research on this topic.

Background: Traditionally, dental implants require a healing period of 4 to 9 months for osseointegration, with longer recovery times considered when bone grafting is needed. This retrospective study evaluates the clinical efficacy of demineralized dentin matrix (DDM) combined with recombinant human bone morphogenetic protein-2 (rhBMP-2) during dental implant placement to expedite the osseointegration period for early loading. Methods: Thirty patients (17 male, 13 female; mean age 55.0 ± 8.8 years) requiring bone grafts due to implant fixture exposure (more than four threads; ≥ 3.2 mm) were included, with a total of 96 implants placed. Implants were inserted using a two-stage protocol with DDM/rhBMP-2 grafts. Early loading was initiated at two months postoperatively in the mandible and three months in the maxilla. Clinical outcomes evaluated included primary and secondary stability (implant stability quotient values), healing period, bone width, and marginal bone level assessed via cone-beam computed tomography. Results: All implants successfully supported final prosthetics with a torque of 50Ncm, without any osseointegration failures. The average healing period was 69.6 days in the mandible and 90.5 days in the maxilla, with significantly higher secondary stability in the mandible (80.7 ± 6.7) compared to the maxilla (73.0 ± 9.2, p < 0.001). Histological analysis confirmed new bone formation and vascularization. Conclusion DDM/rhBMP-2 grafting appears to significantly reduce the healing period, enabling early loading with stable and favorable clinical outcomes.

BACKGROUND: Embryo-endometrium cross-talk is one of the critical processes for implantation, and unsuccessful cross-talk leads to infertility. We established an endometrium-embryo (or embryoid bodies, hEBs) in vitro model in 2D and 3D conditions and assessed its potential through the fusion of embryos and the expression of specific markers. METHODS: C57BL/6 mouse embryos and human embryoid body (hEB) derived from embryonic stem cells were prepared as embryo models. Mouse endometrium (EM) and human endometrium cell line, HEC-1-A, were prepared, and 2D or 3D EMs were generated. The viability of the 3D endometrium was analyzed, and the optimal ratio of the gelation was revealed. The invasion of the embryos or hEBs was examined by immunostaining and 3D image rendering. RESULTS: The embryos and the alternative hEBs were effectively fused into 2D or 3D vitro EM models in both mouse and human models. The fused embryos and hEBs exhibited migration and further development. Notably, the established in vitro model expressed Oct4 and E-Cadherin, markers for early embryonic development; human CG Receptor and Progesterone Receptor, critical for implantation and pregnancy maintenance; and TSH Receptor, Epiregulin, and Prolactin, indicators of endometrial receptivity and embryo implantation. CONCLUSION This study marks a significant advancement in the field, as we have successfully established a novel in vitro model for studying embryo-endometrium cross-talk. This model, a crucial tool for understanding fertility and the causes of miscarriage due to failed implantation, provides a unique platform for investigating the complex processes of successful implantation and pregnancy, underscoring its potential impact on reproductive health.

High-flow nasal cannula (HFNC) is a noninvasive respiratory support system that delivers air that is heated at 31°C−38°C, humidified 100%, and oxygen-enriched at a constant high flow rate of 15−60 L/min. Because of its numerous physiological benefits, convenience, and minimal side effects, HFNC has been increasingly used over the past decade in patients with acute hypoxemic respiratory failure, yet the clinical benefits of long-term HFNC remain uncertain. Several studies have suggested its potential use as an alternative home oxygen therapy for patients with chronic stable lung diseases, such as chronic obstructive pulmonary disease (COPD), interstitial lung disease, and bronchiectasis. The use of long-term home HFNC in patients with chronic respiratory failure is an emerging area with promising potential. Despite limited clinical research, this review aims to describe the physiology of HFNC use and summarize the current evidence on its long-term application, to provide healthcare providers with insights and perspectives on the potential role of long-term home HFNC.

Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosing Birt-Hogg-Dubé (BHD) syndrome. This study aimed to analyze differences of lung cysts between BHD and other cystic lung diseases. Methods: From January 2020 to December 2022, patients with multiple lung cysts who underwent chest CT at Gangnam Severance Hospital were included. Results: Over a 3-year period (from January 2020 to December 2022), out of 52,823 patients who underwent a chest CT scan, 301 (0.6%) patients with multiple lung cysts were enrolled in this study. Of enrolled patients, 24 (8.0%) were diagnosed with BHD. In patients with BHD, 95.8% exhibited bilateral cysts, and 83.3% showed basal predominance. The cysts’ maximal diameter averaged 32.1 mm (interquartile range, 26.5 to 43.5). Additionally, 95.8% of patients with BHD had diverse cyst sizes and morphologies. Multivariate logistic regression analysis revealed that bilateral cysts (odds ratio [OR], 12.393; 95% confidence interval [CI], 1.613 to 274.682; p=0.038), basal predominance (OR, 8.511; 95% CI, 2.252 to 39.392; p=0.002), maximum diameter (OR, 1.053; 95% CI, 1.009 to 1.108; p=0.032), and diversity of morphology (OR, 19.513; 95% CI, 2.833 to 398.119; p=0.010) were significant factors associated with BHD diagnosis. A multivariate prediction model for BHD diagnosis demonstrated a sensitivity of 95.83%, a specificity of 81.22%, and an area under the receiver operating characteristic curve of 0.951 (95% CI, 0.914 to 0.987). Conclusion Distinguishing features of lung cysts from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape.

Connective tissue disease (CTD), comprising a range of autoimmune disorders, is often accompanied by lung involvement, which can lead to life-threatening complications. The primary types of CTDs that manifest as interstitial lung disease (ILD) include rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, mixed CTD, idiopathic inflammatory myopathies, and systemic lupus erythematosus. CTD-ILD presents a significant challenge in clinical diagnosis and management due to its heterogeneous nature and variable prognosis. Early diagnosis through clinical, serological, and radiographic assessments is crucial for distinguishing CTD-ILD from idiopathic forms and for implementing appropriate therapeutic strategies. Hence, we have reviewed the multiple clinical manifestations and diagnostic approaches for each type of CTD-ILD, acknowledging the diversity and complexity of the disease. The importance of a multidisciplinary approach in optimizing the management of CTD-ILD is emphasized by recent therapeutic advancements, which include immunosuppressive agents, antifibrotic therapies, and newer biological agents targeting specific pathways involved in the pathogenesis. Therapeutic strategies should be customized according to the type of CTD, the extent of lung involvement, and the presence of extrapulmonary manifestations. Additionally, we aimed to provide clinical guidance, including therapeutic recommendations, for the effective management of CTD-ILD, based on patient, intervention, comparison, outcome (PICO) analysis.

Background: Lung ultrasound (LUS) has proven valuable in the initial assessment of coronavirus disease 2019 (COVID-19), but its role in detecting pulmonary fibrosis following intensive care remains unclear. This study aims to assess the presence of pulmonary sequelae and fibrosis-like changes using LUS in survivors of severe COVID-19 pneumonia one month after discharge. Methods: We prospectively enrolled patients with severe COVID-19 who required mechanical ventilation in the intensive care unit (ICU) and conducted LUS assessments from admission to the outpatient visit after discharge. We tracked changes in key LUS findings and applied our proprietary LUS scoring system. To evaluate LUS accuracy, we correlated measured LUS values with computed tomography scores. Results: We evaluated B-line presence, pleural thickness, and consolidation in 14 eligible patients. The LUS scores exhibited minimal changes, with values of 19.1, 19.2, and 17.5 at admission, discharge, and the outpatient visit, respectively. Notably, the number of B-lines decreased significantly, from 1.92 at admission to 0.56 at the outpatient visit (p<0.05), while pleural thickness increased significantly, from 2.05 at admission to 2.48 at the outpatient visit (p≤0.05). Conclusion This study demonstrates that LUS can track changes in lung abnormalities in severe COVID-19 patients from ICU admission through to outpatient follow-up. While pleural thickening and B-line patterns showed significant changes, no correlation was found between LUS and high-resolution computed tomography fibrosis scores. These findings suggest that LUS may serve as a supplementary tool for assessing pulmonary recovery in severe COVID-19 cases.

Background: This study aimed to determine whether a shorter high-dose rifampicin regimen is non-inferior to the standard 6-month tuberculosis regimen. Methods: This multicenter, randomized, open-label, non-inferiority trial enrolled participants with respiratory specimen positivity by Xpert MTB/RIF assay or Mycobacterium tuberculosis culture without rifampicin-resistance. Participants were randomized at 1:1 to the investigational or control group. The investigational group received high-dose rifampicin (30 mg/kg/day), isoniazid, and pyrazinamide until culture conversion, followed by high-dose rifampicin and isoniazid for 12 weeks. The control group received the standard 6-month regimen. The primary outcome was the rate of unfavorable outcomes at 18 months post-randomization. The non-inferiority margin was set at <6% difference in unfavorable outcomes rates. The study is registered with ClinicalTrials.gov (NCT04485156) Results: Between 4 November 2020 and 3 January 2022, 76 participants were enrolled. Of these, 58 were included in the modified intention-to-treat analysis. Unfavorable outcomes occurred in 10 (31.3%) of 32 in the control group and 10 (38.5%) of 26 in the investigational group. The difference was 7.2% (95% confidence interval, ∞ to 31.9%), failing to prove non-inferiority. Serious adverse events and grade 3 or higher adverse events did not differ between the groups. Conclusion The shorter high-dose rifampicin regimen failed to demonstrate non-inferiority but had an acceptable safety profile.

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: 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.