Background: Immunocompromised patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection often have prolonged viral shedding, and some are clinically suspected of reinfection with different SARSCoV-2 variants. However, data on this issue are limited. This study investigated the SARS-CoV-2 variants in serially collected respiratory samples from immunocompromised patients with prolonged viral shedding for over 12 weeks or relapsed viral shedding after at least 2 weeks of viral clearance. Materials and Methods: From February 2022 to September 2023, we prospectively enrolled immunocompromised patients with coronavirus disease 2019 who had hematologic malignancies or had undergone transplantation and were admitted to a tertiary hospital. Weekly saliva or nasopharyngeal swabs were collected from enrolled patients for at least 12 weeks after diagnosis. Genomic RNA polymerase chain reaction (PCR) was performed on samples, and those testing positive underwent viral culture to isolate the live virus. Spike gene full sequencing via Sanger sequencing and real-time reverse transcription-PCR for detecting mutation genes were conducted to identify SARSCoV-2 variants. Results: Among 116 enrolled patients, 20 with prolonged or relapsed viral shedding were screened to identify the variants. Of these 20 patients, 7 (35%) exhibited evidence of re-infection; one of 8 patients with prolonged viral shedding and 6 of 12 with relapsed viral shedding were reinfected with SARS-CoV-2. Conclusion Our data suggest that approximately one-third of immunocompromised patients with persistent or relapsed viral shedding had reinfection with different variants of SARS-CoV-2.

Background: Immunocompromised patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection often have prolonged viral shedding, and some are clinically suspected of reinfection with different SARSCoV-2 variants. However, data on this issue are limited. This study investigated the SARS-CoV-2 variants in serially collected respiratory samples from immunocompromised patients with prolonged viral shedding for over 12 weeks or relapsed viral shedding after at least 2 weeks of viral clearance. Materials and Methods: From February 2022 to September 2023, we prospectively enrolled immunocompromised patients with coronavirus disease 2019 who had hematologic malignancies or had undergone transplantation and were admitted to a tertiary hospital. Weekly saliva or nasopharyngeal swabs were collected from enrolled patients for at least 12 weeks after diagnosis. Genomic RNA polymerase chain reaction (PCR) was performed on samples, and those testing positive underwent viral culture to isolate the live virus. Spike gene full sequencing via Sanger sequencing and real-time reverse transcription-PCR for detecting mutation genes were conducted to identify SARSCoV-2 variants. Results: Among 116 enrolled patients, 20 with prolonged or relapsed viral shedding were screened to identify the variants. Of these 20 patients, 7 (35%) exhibited evidence of re-infection; one of 8 patients with prolonged viral shedding and 6 of 12 with relapsed viral shedding were reinfected with SARS-CoV-2. Conclusion Our data suggest that approximately one-third of immunocompromised patients with persistent or relapsed viral shedding had reinfection with different variants of SARS-CoV-2.

Background: Immunocompromised patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection often have prolonged viral shedding, and some are clinically suspected of reinfection with different SARSCoV-2 variants. However, data on this issue are limited. This study investigated the SARS-CoV-2 variants in serially collected respiratory samples from immunocompromised patients with prolonged viral shedding for over 12 weeks or relapsed viral shedding after at least 2 weeks of viral clearance. Materials and Methods: From February 2022 to September 2023, we prospectively enrolled immunocompromised patients with coronavirus disease 2019 who had hematologic malignancies or had undergone transplantation and were admitted to a tertiary hospital. Weekly saliva or nasopharyngeal swabs were collected from enrolled patients for at least 12 weeks after diagnosis. Genomic RNA polymerase chain reaction (PCR) was performed on samples, and those testing positive underwent viral culture to isolate the live virus. Spike gene full sequencing via Sanger sequencing and real-time reverse transcription-PCR for detecting mutation genes were conducted to identify SARSCoV-2 variants. Results: Among 116 enrolled patients, 20 with prolonged or relapsed viral shedding were screened to identify the variants. Of these 20 patients, 7 (35%) exhibited evidence of re-infection; one of 8 patients with prolonged viral shedding and 6 of 12 with relapsed viral shedding were reinfected with SARS-CoV-2. Conclusion Our data suggest that approximately one-third of immunocompromised patients with persistent or relapsed viral shedding had reinfection with different variants of SARS-CoV-2.

Our study analyzed 95 solid organ transplant (SOT) and 78 hematopoietic stem cell transplant (HSCT) recipients with prior coronavirus disease 2019 (COVID-19). Patients who underwent transplantation within 30 days of COVID-19 infection comprised the early group, and those who underwent transplantation post-30 days of COVID-19 infection comprised the delayed group. In the early transplantation group, no patient, whether undergoing SOT and HSCT, experienced COVID-19-associated complications. In the delayed transplantation group, one patient each from SOT and HSCT experienced COVID-19-associated complications. Additionally, among early SOT and HSCT recipients, two and six patients underwent transplantation within seven days of COVID-19 diagnosis, respectively. However, no significant differences were observed in the clinical outcomes of these patients compared to those in other patients. Early transplantation following severe acute respiratory syndrome coronavirus 2 infection can be performed without increased risk of COVID-19-associated complications. Therefore, transplantation needs not be delayed by COVID-19 infection.

Background: The pathophysiological mechanisms underlying the post-acute sequelae of severe acute respiratory syndrome coronavirus 2 infection (PASC) are not well understood.Our study aimed to investigate various aspects of theses mechanisms, including viral persistence, immunological responses, and laboratory parameters in patients with and without PASC. Methods: We prospectively enrolled adults aged ≥ 18 years diagnosed with coronavirus disease 2019 (COVID-19) between August 2022 and July 2023. Blood samples were collected at three time-points: within one month of diagnosis (acute phase) and at 1 month, and 3 months post-diagnosis. Following a recent well-designed definition of PASC, PASC patients were defined as those with a questionnaire-based PASC score ≥ 12 persisting for at least 4 weeks after the initial COVID-19 diagnosis. Results: Of 57 eligible COVID-19 patients, 29 (51%) had PASC, and 28 (49%) did not. The PASC group had significantly higher nucleocapsid protein (NP) antigenemia 3 months after COVID-19 diagnosis (P = 0.022). Furthermore, several cytokines, including IL-2, IL-17A, VEGF, RANTES, sCD40L, IP-10, I-TAC, and granzyme A, were markedly elevated in the PASC group 1 and/or 3 month(s) after COVID-19 diagnosis. In contrast, the median values of several serological markers, including thyroid markers, autoimmune indicators, and stress-related hormones, were within the normal range. Conclusion Levels of NP antigen and of various cytokines involved in immune responses become significantly elevated over time after COVID-19 diagnosis in PASC patients compared to non-PASC patients. This suggests that PASC is associated with prolonged immune dysregulation resulting from heightened antigenic stimulation.

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models.METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning.RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively.CONCLUSIONS: ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.
Classification ; Dataset ; Diagnosis ; Humans ; Lacrimal Apparatus Diseases ; Learning ; Logistic Models ; Machine Learning ; Nuclear Medicine ; Programming Languages ; Radionuclide Imaging

PURPOSE: The optimal cytotoxic regimens have not been established for patients with non-small cell lung cancer (NSCLC) who develop disease progression on first-line epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI). MATERIALS AND METHODS: We conducted a multi-center randomized phase II trial to compare the clinical outcomes between pemetrexed plus cisplatin combination therapy followed by maintenance pemetrexed (PC) and pemetrexed monotherapy (P) after failure of first-line EGFR-TKI. The primary objective was progression-free survival (PFS), and secondary objectives included overall response rate (ORR), overall survival (OS), health-related quality of life (HRQOL), and safety and toxicity profiles. RESULTS: A total of 96 patientswere randomized, and 91 patientswere treated at 14 centers in Korea. The ORR was 34.8% (16/46) for the PC arm and 17.8% (8/45) for the P arm (p=0.066). With 23.4 months of follow-up, the median PFS was 5.4 months in the PC arm and 6.4 months in the P arm (p=0.114). The median OS was 17.9 months and 15.7 months in PC and P arms, respectively (p=0.787). Adverse events ≥ grade 3 were reported in 12 patients (26.1%) in the PC arm and nine patients (20.0%) in the P arm (p=0.491). The overall time trends of HRQOL were not significantly different between the two arms. CONCLUSION: The outcomes of pemetrexed therapy in NSCLC patients with disease progression after firstline EGFR-TKI might not be improved by adding cisplatin.
Arm ; Carcinoma, Non-Small-Cell Lung ; Cisplatin ; Disease Progression ; Disease-Free Survival ; Epidermal Growth Factor ; Follow-Up Studies ; Humans ; Korea ; Lung Neoplasms ; Lung ; Pemetrexed ; Protein-Tyrosine Kinases ; Quality of Life ; Receptor, Epidermal Growth Factor ; Tyrosine

PURPOSE: This study was conducted to evaluate the relationship between epidermal growth factor receptor (EGFR) mutation and clinical outcomes in patients with stage III non-squamous cell lung cancer treated with definitive concurrent chemoradiotherapy (CCRT). MATERIALS AND METHODS: From January 2008 to December 2013, the medical records of 197 patients with stage III non- squamous non-small cell lung cancer treated with definitive CCRT were analyzed to determine progression-free survival (PFS) and overall survival (OS) according to EGFR mutation status. RESULTS: Among 197 eligible patients, 81 patients were EGFR wild type, 36 patients had an EGFR mutation (exon 19 Del, n=18; L858R, n=9, uncommon [G719X, L868, T790M], n=9), and 80 patients had unknown EGFR status. The median age was 59 years (range, 28 to 80 years) and 136 patients (69.0%) were male. The median follow-up duration was 66.5 months (range, 1.9 to 114.5 months). One hundred sixty-four patients (83.2%) experienced disease progression. Median PFS was 8.9 months for the EGFR mutation group, 11.8 months for EGFR wild type, and 10.5 months for the unknown EGFR group (p=0.013 and p=0.042, respectively). The most common site of metastasis in the EGFR mutant group was the brain. However, there was no significant difference in OS among the three groups (34.6 months for EGFR mutant group vs. 31.9 months for EGFR wild type vs. 22.6 months for EGFR unknown group; p=0.792 and p=0.284). A total of 29 patients (80.6%) with EGFR mutation were treated with EGFR tyrosine kinase inhibitor (gefitinib, n=24; erlotinib, n=3; afatinib, n=2) upon progression. CONCLUSION: EGFR mutation is associatedwith short PFS and the brain is the most common site of distant metastasis in patients with stage III non- squamous cell lung cancer treated with CCRT.
Brain ; Carcinoma, Non-Small-Cell Lung ; Chemoradiotherapy ; Disease Progression ; Disease-Free Survival ; Epithelial Cells ; Erlotinib Hydrochloride ; Follow-Up Studies ; Humans ; Lung Neoplasms ; Lung ; Male ; Medical Records ; Neoplasm Metastasis ; Protein-Tyrosine Kinases ; Receptor, Epidermal Growth Factor

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models. METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. CONCLUSIONS ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.