Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: Although patients with lymphoma appear particularly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the clinical evolution of coronavirus disease 2019 (COVID-19) in a patient with lymphoid malignancies has been under-represented, especially in relation to chemo-, chemo-immunotherapy. Materials and Methods: Among adult patients with lymphoma receiving treatment in a specialized lymphoma center at a 500-bed, university-affiliated hospital, we retrospectively reviewed the medical records of patients diagnosed with SARS-CoV-2 infection from January 2020 to April 2022. Results: A total of 117 patients with a median age of 53 years were included. One hundred twelves (95.7%) were non-Hodgkin lymphoma. Eighty-six patients (73.5%) were on active chemotherapy and 9 were post stem cell transplant state. Sixty-one patients had more than one comorbidity and 29 had hypogammaglobulinemia. Thirty-four patients (29.1%) had never received a COVID-19 vaccine. During a median follow-up of 134 days, COVID-19 pneumonia developed in 37 patients (31.6%). Excluding three patients who died before the 30 days, 31 out of 34 patients had ongoing symptomatic COVID-19. Eleven patients (9.4%) had post COVID-19 lung condition that persisted 90 days after COVID-19 diagnosis. Overall mortality was 10.3% (12 of 117), which was higher in patients with pneumonia. In multivariate analyses, age 65 years or older, follicular lymphoma, receiving rituximab maintenance therapy, and lack of vaccination were significantly associated with the development of COVID-19 pneumonia. Conclusion Patients with lymphoma are at high risk for developing pneumonia after SARS-CoV-2 infection and suffer from prolonged symptoms. More aggressive vaccination and protective measures for patients with lymphoma who have impaired humoral response related to rituximab maintenance therapy and hypogammaglobulinemia are needed.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: Although patients with lymphoma appear particularly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the clinical evolution of coronavirus disease 2019 (COVID-19) in a patient with lymphoid malignancies has been under-represented, especially in relation to chemo-, chemo-immunotherapy. Materials and Methods: Among adult patients with lymphoma receiving treatment in a specialized lymphoma center at a 500-bed, university-affiliated hospital, we retrospectively reviewed the medical records of patients diagnosed with SARS-CoV-2 infection from January 2020 to April 2022. Results: A total of 117 patients with a median age of 53 years were included. One hundred twelves (95.7%) were non-Hodgkin lymphoma. Eighty-six patients (73.5%) were on active chemotherapy and 9 were post stem cell transplant state. Sixty-one patients had more than one comorbidity and 29 had hypogammaglobulinemia. Thirty-four patients (29.1%) had never received a COVID-19 vaccine. During a median follow-up of 134 days, COVID-19 pneumonia developed in 37 patients (31.6%). Excluding three patients who died before the 30 days, 31 out of 34 patients had ongoing symptomatic COVID-19. Eleven patients (9.4%) had post COVID-19 lung condition that persisted 90 days after COVID-19 diagnosis. Overall mortality was 10.3% (12 of 117), which was higher in patients with pneumonia. In multivariate analyses, age 65 years or older, follicular lymphoma, receiving rituximab maintenance therapy, and lack of vaccination were significantly associated with the development of COVID-19 pneumonia. Conclusion Patients with lymphoma are at high risk for developing pneumonia after SARS-CoV-2 infection and suffer from prolonged symptoms. More aggressive vaccination and protective measures for patients with lymphoma who have impaired humoral response related to rituximab maintenance therapy and hypogammaglobulinemia are needed.

Background: Although patients with lymphoma appear particularly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the clinical evolution of coronavirus disease 2019 (COVID-19) in a patient with lymphoid malignancies has been under-represented, especially in relation to chemo-, chemo-immunotherapy. Materials and Methods: Among adult patients with lymphoma receiving treatment in a specialized lymphoma center at a 500-bed, university-affiliated hospital, we retrospectively reviewed the medical records of patients diagnosed with SARS-CoV-2 infection from January 2020 to April 2022. Results: A total of 117 patients with a median age of 53 years were included. One hundred twelves (95.7%) were non-Hodgkin lymphoma. Eighty-six patients (73.5%) were on active chemotherapy and 9 were post stem cell transplant state. Sixty-one patients had more than one comorbidity and 29 had hypogammaglobulinemia. Thirty-four patients (29.1%) had never received a COVID-19 vaccine. During a median follow-up of 134 days, COVID-19 pneumonia developed in 37 patients (31.6%). Excluding three patients who died before the 30 days, 31 out of 34 patients had ongoing symptomatic COVID-19. Eleven patients (9.4%) had post COVID-19 lung condition that persisted 90 days after COVID-19 diagnosis. Overall mortality was 10.3% (12 of 117), which was higher in patients with pneumonia. In multivariate analyses, age 65 years or older, follicular lymphoma, receiving rituximab maintenance therapy, and lack of vaccination were significantly associated with the development of COVID-19 pneumonia. Conclusion Patients with lymphoma are at high risk for developing pneumonia after SARS-CoV-2 infection and suffer from prolonged symptoms. More aggressive vaccination and protective measures for patients with lymphoma who have impaired humoral response related to rituximab maintenance therapy and hypogammaglobulinemia are needed.

Background: Transient receptor potential canonical (TRPC) channels are non-selective cationic channels with perme‑ ability to ­Ca2+ and ­Na+ . Despite their importance, there are currently few studies on TRPC in the periodontal ligament (PDL) and bone cells in the dental field. To provide biological information regarding TRPC in PDL cells and periodontal tissue, we evaluated TRPC channels expression in the osteoblast differentiation of PDL cells and periodontitis-induced tissue. Human PDL cells were cultured in osteogenic differentiation media for 28 days, and the expression of Runx2, osteocalcin (OCN), and TRPC1, 3, 4, and 6 was evaluated by real-time PCR. In ligature-induced periodontitis mice, the alveolar bone and osteoid areas, the osteoclast number, and the expression of Runx2, OCN, TRPC3, and TRPC6 was evaluated by H&E staining, TRAP staining, and immunohistochemistry, respectively. Results: In the PDL cell differentiation group, TRPC6 expression peaked on day 7 and TRPC3 expression generally increased during differentiation. During the 28 days of periodontitis progression, alveolar bone loss and osteoclast numbers increased compared to the control group during the experimental period and the osteoid area increased from day 14. TRPC6 expression in the periodontitis group increased in the PDL area and in the osteoblasts compared to the control group, whereas TRPC3 expression increased only in the PDL area on days 7 and 28. Conclusions These results indicate changes of TRPC3 and TRPC6 expression in PDL cells that were differentiating into osteoblasts and in periodontitis-induced tissue, suggesting the need for research on the role of TRPC in osteo‑ blast differentiation or periodontitis progression.

Purpose: This study used a mixed methods research design in an attempt to verify the effects of robot-assisted rehabilitation on the gait ability of stroke patients with hemiplegia, and thereby further understand the benefits and challenges of stroke patients’ experiences relying on robot-assisted rehabilitation. Methods: An exploratory sequential mixed methods study design was used in order to combine both quantitative and qualitative data. For the quantitative data collection, a total of 30 stroke patients with hemiplegia were recruited from one rehabilitation hospital. Qualitative data were collected through individual interviews using semi-structured questionnaires for a group of 15 patients who were currently undergoing robot-assisted rehabilitation. The data were analyzed through qualitative content analysis. Results: As a result of the quantitative analysis, there were significant differences between the two groups in terms of daily living activity patterns, total number of steps, and average walking speed. As a result of the qualitative analysis, the four main themes derived consisted of, ‘curiosity about the usage of robot-assisted rehabilitation,’ ‘pleasure experienced while using the robots,’ ‘insufficient information about robots,’ and ‘a lack of education about robot-assisted rehabilitation.’ Conclusions Robot-assisted rehabilitation had a significant effect on the walking ability of stroke patients with hemiplegia. Additionally, stroke patients with hemiplegia experienced difficulty during the course of their robot-assisted rehabilitation, due to a lack of sufficient information on correct usage techniques. These quantitative and qualitative findings could provide the basic foundation for the development of an educational program on robot-assisted rehabilitation.

Purpose: This study was conducted to examine the effects of an educational program based on mobile SMS and counseling for colonoscopy in elderly people. Methods: The study utilized a non-equivalent control group with a non-synchronized design as a quasiexperimental research. The study subjects consisted of 72 elderly people (36 elderly people in the experimental group and 36 in the control group). Among the subjects, 49 were male (68.1%) and 23 were female (31.9%), and the age was 69.04± 6.14 years. Fortyfive subjects (62.5%) answered that they had underlying diseases, eight subjects (11.1%) had a family history of colon disease, and 61 subjects (84.7%) had undergone a colonoscopy. The experimental group was provided with an educational program based on mobile SMS and counseling when three days and one day before the test. The control group received verbal explanations using information sheet. The data were analyzed using descriptive statistics, the Chi-squares test, and the independent t-test with SPSS/WIN 21.0. Results Significant differences were found in bowel cleanliness scores (t= 3.60, p= .003) between the groups. No significant differences were seen between the groups in the sedation drug dosage (propofol (t= 1.02, p= .730), midazolam (t= 0.66, p= .380)) and time for colonoscopy (t= 1.08, p= .235). Conclusion: Based on the results of this study, an educational program based on mobile SMS and counseling for colonoscopy was proposed as an educational program before colonoscopy.