This study evaluates the biomechanical effects of acellular dermal matrix (ADM) patch augmentation in a rotator cuff repair model and determines the effect of medial knot tying on time-zero stability. Methods: A biomechanical study was conducted using a porcine flexor digitorum profundus tendon model. Eighteen specimens were assigned to three groups: non-patched repair with medial row knot tying (n=5), patch-augmented repair without medial row knot tying (n=4), and patch-augmented repair with medial row knot tying (n=4). All repairs were performed using a standardized double-row suture bridge technique. Biomechanical testing assessed elongation, strain, load to failure, and construct stiffness at time zero. Results: Patch augmentation without medial row knot tying resulted in significantly greater tendon elongation (12.9±6.5 mm) than nonpatched repairs (5.0±5.3 mm, P=0.016) and patch-augmented repairs with medial row knot tying (4.4±4.4 mm, P=0.027). Similarly, strain was significantly higher in the patch-augmented group without medial knot tying (75.8%±42.9%) than in the non-patched repair (35.2%±38.9%, P=0.028) and patch-augmented repair with medial knot tying groups (29.1%±28.9%, P=0.052). However, load to failure did not differ significantly among the groups (P>0.05). Conclusions: ADM patch augmentation did not enhance time-zero mechanical strength in the tested conditions, though biomechanical advantages might emerge after integration to the tendon. In addition, patch augmentation without medial row knot tying showed the highest elongation and strain among groups, potentially reducing the stability of the repair. Level of evidence: V, controlled laboratory study

Purpose: The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat­ment strategies for patients with rectal cancer. Methods: This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation. Results: AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer­ing noninvasive and cost-effective alternatives for identifying genetic mutations. Conclusion Image-based AI studies for rectal can­cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

OBJECTIVES: The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review. METHODS: We first established a concept and definition of “hospitalization episode,” taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms’ accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events. RESULTS: We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively. CONCLUSIONS We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, patients with myasthenia gravis (MG) were more susceptible to poor outcomes owing to respiratory muscle weakness and immunotherapy. Several studies conducted in the early stages of the COVID-19 pandemic reported higher mortality in patients with MG compared to the general population. This study aimed to investigate the clinical course and prognosis of COVID-19 in patients with MG and to compare these parameters between vaccinated and unvaccinated patients in South Korea. Methods: This multicenter, retrospective study, which was conducted at 14 tertiary hospitals in South Korea, reviewed the medical records and identified MG patients who contracted COVID-19 between February 2022 and April 2022. The demographic and clinical characteristics associated with MG and vaccination status were collected. The clinical outcomes of COVID-19 infection and MG were investigated and compared between the vaccinated and unvaccinated patients. Results: Ninety-two patients with MG contracted COVID-19 during the study. Nine (9.8%) patients required hospitalization, 4 (4.3%) of whom were admitted to the intensive care unit. Seventy-five of 92 patients were vaccinated before contracting COVID-19 infection, and 17 were not. During the COVID-19 infection, 6 of 17 (35.3%) unvaccinated patients were hospitalized, whereas 3 of 75 (4.0%) vaccinated patients were hospitalized (P < 0.001). The frequencies of ICU admission and mechanical ventilation were significantly lower in the vaccinated patients than in the unvaccinated patients (P = 0.019 and P = 0.032, respectively). The rate of MG deterioration was significantly lower in the vaccinated patients than in the unvaccinated patients (P = 0.041). Logistic regression after weighting revealed that the risk of hospitalization and MG deterioration after COVID-19 infection was significantly lower in the vaccinated patients than in the unvaccinated patients. Conclusion This study suggests that the clinical course and prognosis of patients with MG who contracted COVID-19 during the dominance of the omicron variant of COVID-19 may be milder than those at the early phase of the COVID-19 pandemic when vaccination was unavailable. Vaccination may reduce the morbidity of COVID-19 in patients with MG and effectively prevent MG deterioration induced by COVID-19 infection.

Cancer immunotherapy, in the form of vaccination, adoptive cellular transfer, or immune checkpoint inhibitors, has emerged as a promising practice within the field of oncology. However, despite the developing field's potential to revolutionize cancer treatment, the presence of immunotherapeutic-resistant tumor cells in many patients present a challenge and limitation to these immunotherapies. These cells not only indicate immunotherapeutic resistance, but also show multi-modal resistance to conventional therapies, abnormal metabolism, stemness, and metastasis. How can immunotherapeutic-resistant tumor cells render multi-malignant phenotypes? We reasoned that the immune-refractory phenotype could be associated with multi-malignant phenotypes and that these phenotypes are linked together by a factor that acts as the master regulator. In this review, we discussed the role of the embryonic transcription factor NANOG as a crucial master regulator we named “common factor” in multi-malignant phenotypes and presented strategies to overcome multi-malignancy in immunotherapeutic-resistant cancer by restraining the NANOG-mediated multi-malignant signaling axis. Strategies that blunt the NANOG axis could improve the clinical management of therapy-refractory cancer.
Humans ; Immunotherapy ; Metabolism ; Neoplasm Metastasis ; Phenotype ; Transcription Factors ; Vaccination

Cancer immunotherapy, in the form of vaccination, adoptive cellular transfer, or immune checkpoint inhibitors, has emerged as a promising practice within the field of oncology. However, despite the developing field's potential to revolutionize cancer treatment, the presence of immunotherapeutic-resistant tumor cells in many patients present a challenge and limitation to these immunotherapies. These cells not only indicate immunotherapeutic resistance, but also show multi-modal resistance to conventional therapies, abnormal metabolism, stemness, and metastasis. How can immunotherapeutic-resistant tumor cells render multi-malignant phenotypes? We reasoned that the immune-refractory phenotype could be associated with multi-malignant phenotypes and that these phenotypes are linked together by a factor that acts as the master regulator. In this review, we discussed the role of the embryonic transcription factor NANOG as a crucial master regulator we named “common factor” in multi-malignant phenotypes and presented strategies to overcome multi-malignancy in immunotherapeutic-resistant cancer by restraining the NANOG-mediated multi-malignant signaling axis. Strategies that blunt the NANOG axis could improve the clinical management of therapy-refractory cancer.

BACKGROUND/AIMS: We investigated the risk of multidrug-resistant, gram-negative bacteria (MDRGNB) in hospitalized elderly patients from non-hospital long-term care facilities (LTCFs) and the antibiotic prescription pattern. METHODS: All clinical cultures obtained within 48 hours of hospitalization from elderly patients of at least 55 years of age arriving at a 623-bed, public teaching hospital in Seoul, Republic of Korea from LTCFs between April 1, 2011 and April 1, 2012 were collected retrospectively. RESULTS: During this period, 365 elderly persons from 13 LTCFs were hospitalized. This study enrolled 135 patients who had cultures performed. In this group, 27.4% harbored MDRGNB at hospitalization. The presence of MDRGNB during prior hospitalization was the only risk factor that predicted harboring it (p = 0.043, odds ratio = 5.00, confidence interval = 1.049-23.834). Combinations of antibiotics or carbapenems were used initially in 35.6% of the patients, and this did not affect the mortality rate in this population. CONCLUSIONS: Hospitalized elderly patients from non-hospital LTCFs need more attention. Judicious antibiotic selection is needed according to the risk factor of harboring MDRGNB for antibiotics stewardship.
Aged ; Anti-Bacterial Agents ; Carbapenems ; Drug Resistance, Multiple, Bacterial ; Gram-Negative Bacteria ; Hospitalization ; Hospitals, Teaching ; Humans ; Long-Term Care ; Mortality ; Odds Ratio ; Prescriptions ; Republic of Korea ; Retrospective Studies ; Risk Factors ; Seoul

BACKGROUND/AIMS: We investigated the risk of multidrug-resistant, gram-negative bacteria (MDRGNB) in hospitalized elderly patients from non-hospital long-term care facilities (LTCFs) and the antibiotic prescription pattern. METHODS: All clinical cultures obtained within 48 hours of hospitalization from elderly patients of at least 55 years of age arriving at a 623-bed, public teaching hospital in Seoul, Republic of Korea from LTCFs between April 1, 2011 and April 1, 2012 were collected retrospectively. RESULTS: During this period, 365 elderly persons from 13 LTCFs were hospitalized. This study enrolled 135 patients who had cultures performed. In this group, 27.4% harbored MDRGNB at hospitalization. The presence of MDRGNB during prior hospitalization was the only risk factor that predicted harboring it (p = 0.043, odds ratio = 5.00, confidence interval = 1.049-23.834). Combinations of antibiotics or carbapenems were used initially in 35.6% of the patients, and this did not affect the mortality rate in this population. CONCLUSIONS Hospitalized elderly patients from non-hospital LTCFs need more attention. Judicious antibiotic selection is needed according to the risk factor of harboring MDRGNB for antibiotics stewardship.