Background: Elevated active glucocorticoids (GCs) are implicated in skin barrier dysfunction, notably in aging and diabetes. The enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) converts inactive GCs to active forms, potentially exacerbating this dysfunction. Objective: We aimed to investigate the impact of a novel 11β-HSD1 inhibitor on skin inflammation using both in vitro and in vivo models. Methods: To elucidate the efficacy of a new 11β-HSD1 inhibitor in mitigating skin inflammation induced by various triggers, including dexamethasone treatment, ultraviolet B irradiation, and high glucose levels, in cultured human keratinocytes and the db/db mice as a type 2 diabetes murine model. In cultured keratinocytes, we assessed the effects of the 11β-HSD1 inhibitor on cortisol levels, 11β-HSD1 expression, and cytokine production under conditions simulating inflammation. In db/db mice, we evaluated the inhibitor’s impact on skin barrier function, hemoglobin A1c (HbA1c) levels, corticosterone levels, 11β-HSD1 expression, and cytokine profiles following a 2-week treatment regimen. Results: Our results demonstrated that both the novel 11β-HSD1 inhibitor and a known inhibitor reduced cortisol levels, 11β-HSD1 expression, and inflammatory cytokine production in cultured keratinocytes. In db/db mice, treatment with either inhibitor improved skin barrier function, lowered serum HbA1c levels, and decreased corticosterone, 11β-HSD1, and inflammatory cytokine expression. Conclusion A new 11β-HSD1 inhibitor, “11b-0048,” showed a significant inhibitory effect on the expression of 11β-HSD1 in keratinocytes activated by various conditions and diabetic skin.

Objective: Patients with panic disorder (PD) are approximately four times more likely to experience suicidal thoughts and attempts compared to healthy controls (HCs). Despite the elevated risk compared to HCs, the relationship between cortical thickness (CT) in PD and suicidality remains underexplored. Methods: We recruited 161 right-handed participants, including 82 PD patients and 79 HCs, and assessed them using a comprehensive battery of psychological scales, including the Scale for Suicidal Ideation (SSI), Early Trauma Inventory Self Report-Short Form, Neuroticism-Extraversion-Openness Personality Inventory-Neuroticism (NEO-N), State-Trait Anxiety Inventory-Trait Anxiety (STAI-T), Panic Disorder Severity Scale (PDSS), and Beck Depression Inventory. Results: In whole-brain vertex-wise group comparison, patients with PD demonstrated significantly lower CT values in the insula, lateral occipital sulcus, and precentral gyrus compared to HCs. Notably, paradoxical significant positive correlations were observed between SSI total scores and CT in the above-mentioned regions within the PD cohort.Pearson’s correlation analyses further indicated that CT in these regions may be linked to high levels of early trauma, trait anxiety (e.g., NEO-N, STAI-T), panic symptom severity (e.g., PDSS), and treatment response in patients with PD. Conclusion This study suggest that suicidality in PD may be associated with CT in specific EFN regions related to suicidal brain and that CT in these regions could play a critical role in anxiety symptomatology in PD.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Heart failure (HF) is a major cause of mortality and morbidity in South Korea, imposing substantial physical, emotional, and financial burdens on patients and society. Despite the high burden of symptom and complex care needs of HF patients, palliative care and hospice services remain underutilized in South Korea due to cultural, institutional, and knowledge-related barriers. This position statement from the Korean Society of Heart Failure emphasizes the need for integrating palliative and hospice care into HF management to improve quality of life and support holistic care for patients and their families. By clarifying the role of palliative care in HF and proposing practical referral criteria, this position statement aims to bridge the gap between HF and palliative care services in South Korea, ultimately improving patient-centered outcomes and aligning treatment with the goals and values of HF patients.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Dental clinicians and researchers have recently recommended oral microbial examinations to more accurately diagnose and treat oral diseases, including periodontitis and dental caries. Theoretical and experimental evidence suggests that oral microbiota may also be associated with non-oral diseases, such as gastrointestinal and extra-gastrointestinal diseases. This review highlights studies demonstrating microbial alterations in the oral cavity associated with malignant tumors including gastric, colorectal, esophageal, and lung cancers, implying that these alterations may serve as early indicators for non-invasive diagnosis and risk assessment of cancer development. Furthermore, we addressed the implications of oral microbial co-occurrence with malignant tumors, such as Streptococcus anginosus, Fusobacterium nucleatum, and Veillonella parvula, which are recognized as tumor-enriched oral pathogens involved in the development and progression of cancers in the stomach, colon, and lungs, respectively. Notably, we explored the immune and inflammatory mechanisms underlying reciprocal interactions between oral microbiota and tumors, underscoring that targeting these mechanistic pathways can contribute to preventing cancer development.

Dental clinicians and researchers have recently recommended oral microbial examinations to more accurately diagnose and treat oral diseases, including periodontitis and dental caries. Theoretical and experimental evidence suggests that oral microbiota may also be associated with non-oral diseases, such as gastrointestinal and extra-gastrointestinal diseases. This review highlights studies demonstrating microbial alterations in the oral cavity associated with malignant tumors including gastric, colorectal, esophageal, and lung cancers, implying that these alterations may serve as early indicators for non-invasive diagnosis and risk assessment of cancer development. Furthermore, we addressed the implications of oral microbial co-occurrence with malignant tumors, such as Streptococcus anginosus, Fusobacterium nucleatum, and Veillonella parvula, which are recognized as tumor-enriched oral pathogens involved in the development and progression of cancers in the stomach, colon, and lungs, respectively. Notably, we explored the immune and inflammatory mechanisms underlying reciprocal interactions between oral microbiota and tumors, underscoring that targeting these mechanistic pathways can contribute to preventing cancer development.