Purpose: Numerous patients experience long-term complications after hematopoietic stem cell transplantation (HSCT). This study aimed to identify the frequency and risk factors for psychiatric and endocrine complications following HSCT through big data analyses. Materials and Methods: We established a cohort of patients with hematologic disease who underwent HSCT in Korea between 2010 and 2012 using the Health Insurance Review & Assessment Service data. A total of 3,636 patients were identified, and insurance claims were tracked using psychiatric and endocrine diagnostic International Classification of Diseases, 10th Revision codes for the ensuing decade. We identified the incidence rates of long-term complications based on the baseline disease and HSCT type. Prognostic factors for each complication were scrutinized using logistic regression analysis. Results: A total of 1,879 patients underwent allogeneic HSCT and 1,757 patients received autologous HSCT. Post-HSCT, 506 patients were diagnosed with depression, 465 with anxiety disorders, and 659 with diabetes. The highest incidence of long-term complications occurred within the first year post-HSCT (12.2%), subsequently decreasing over time. Risk factors for depressive disorders after allogeneic HSCT included female sex, a total body irradiation–based conditioning regimen, and cyclosporine. Identified risk factors for diabetes mellitus comprised old age, total body irradiation–based conditioning regimen, and non-antithymocyte globulin protocol. Regarding autologous HSCT, only female sex was identified as a risk factor for depressive disorders, whereas elderly patients and those with multiple myeloma were identified as poor prognostic factors for diabetes mellitus. Conclusion The incidence of long-term psychiatric and endocrine complications post-HSCT remains high, and patients with risk factors for these complications require vigilant follow-up.

Randomized controlled trials (RCTs) are considered the most rigorous study design for testing hypotheses and the gold standard for evaluating intervention effectiveness. However, RCTs are often conducted under the assumption of ideal conditions that may differ from real-world scenarios in which various issues, such as loss to follow-up, mistakes in participant enrollment or intervention, and low subject compliance or adherence, may occur. There are various group-defining strategies for analyzing RCT data, including the intention-to-treat (ITT), as-treated, and per-protocol (PP) approaches. The ITT principle involves analyzing all participants according to their initial group assignments, regardless of study completion and compliance or adherence to treatment protocols. This approach aims to replicate real-world clinical settings in which several anticipated or unexpected conditions may occur with regard to the study protocol. For the PP approach, only participants who meet the inclusion criteria, complete the interventions according to the study protocols, and have primary outcome data available are included. This approach aims to confirm treatment effects under optimal conditions. In general, the ITT principle is preferred for superiority and inequality trials, whereas the PP approach is preferred for equivalence and non-inferiority trials. However, both analytical approaches should be conducted and their results compared to determine whether significant differences exist. Overall, using both the ITT and PP approaches can provide a more complete picture of the treatment effects and ensure the reliability of the trial results.

The diversity of A mating type in wild strains of Lentinula edodes was extensively analyzed to characterize and utilize them for developing new cultivars. One hundred twenty-three A mating type alleles, including 67 newly discovered alleles, were identified from 106 wild strains collected for the past four decades in Korea. Based on previous studies and current findings, a total of 130 A mating type alleles have been found, 124 of which were discovered from wild strains, indicating the hyper-variability of A mating type alleles of L. edodes. About half of the A mating type alleles in wild strains were found in more than two strains, whereas the other half of the alleles were found in only one strain. About 90% of A mating type combinations in dikaryotic wild strains showed a single occurrence. Geographically, diverse A mating type alleles were intensively located in the central region of the Korean peninsula, whereas only allele A17 was observed throughout Korea. We also found the conservation of the TCCCAC motif in addition to the previously reported motifs, including ATTGT, ACAAT, and GCGGAG, in the intergenic regions of A mating loci. Sequence comparison among some alleles indicated that accumulated mutation and recombination would contribute to the diversification of A mating type alleles in L. edodes. Our data support the rapid evolution of A mating locus in L. edodes, and would help to understand the characteristics of A mating loci of wild strains in Korea and help to utilize them for developing new cultivars.

Tables and figures are commonly adopted methods for presenting specific data or statistical analysis results. Figures can be used to display characteristics and distributions of data, allowing for intuitive understanding through visualization and thus making it easier to interpret the statistical results. To maximize the positive aspects of figure presentation and increase the accuracy of the content, in this article, the authors will describe how to choose an appropriate figure type and the necessary components to include. Additionally, this article includes examples of figures that are commonly used in research and their essential components using virtual data.

Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson’s disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson’s disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

General medical journals such as the Korean Journal of Anesthesiology (KJA) receive numerous manuscripts every year. However, reviewers have noticed that the tables presented in various manuscripts have great diversity in their appearance, resulting in difficulties in the review and publication process. It might be due to the lack of clear written instructions regarding reporting of statistical results for authors. Therefore, the present article aims to briefly outline reporting methods for several table types, which are commonly used to present statistical results. We hope this article will serve as a guideline for reviewers as well as for authors, who wish to submit a manuscript to the KJA.

Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.

General medical journals such as the Korean Journal of Anesthesiology (KJA) receive numerous manuscripts every year. However, reviewers have noticed that the tables presented in various manuscripts have great diversity in their appearance, resulting in difficulties in the review and publication process. It might be due to the lack of clear written instructions regarding reporting of statistical results for authors. Therefore, the present article aims to briefly outline reporting methods for several table types, which are commonly used to present statistical results. We hope this article will serve as a guideline for reviewers as well as for authors, who wish to submit a manuscript to the KJA.

Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.