Primary cilia function as critical sensory organelles that mediate multiple signaling pathways, including the Hedgehog (Hh) pathway, which is essential for organ patterning and morphogenesis. Disruptions in Hh signaling have been implicated in supernumerary tooth formation and molar fusion in mutant mice. Cilk1, a highly conserved serine/threonine-protein kinase localized within primary cilia, plays a critical role in ciliary transport. Loss of Cilk1 results in severe ciliopathy phenotypes, including polydactyly, edema, and cleft palate. However, the role of Cilk1 in tooth development remains unexplored. In this study, we investigated the role of Cilk1 in tooth development. Cilk1 was found to be expressed in both the epithelial and mesenchymal compartments of developing molars. Cilk1 deficiency resulted in altered ciliary dynamics, characterized by reduced frequency and increased length, accompanied by downregulation of Hh target genes, such as Ptch1 and Sostdc1, leading to the formation of diastemal supernumerary teeth. Furthermore, in Cilk1^-/-;PCS1-MRCS1^△/△ mice, which exhibit a compounded suppression of Hh signaling, we uncovered a novel phenomenon: diastemal supernumerary teeth can be larger than first molars. Based on these findings, we propose a progressive model linking Hh signaling levels to sequential changes in tooth patterning: initially inducing diastemal supernumerary teeth, then enlarging them, and ultimately leading to molar fusion. This study reveals a previously unrecognized role of Cilk1 in controlling tooth morphology via Hh signaling and highlights how Hh signaling levels shape tooth patterning in a gradient-dependent manner.
Animals ; Hedgehog Proteins/physiology* ; Mice ; Signal Transduction/physiology* ; Tooth, Supernumerary ; Molar ; Cilia/physiology* ; Odontogenesis/physiology* ; Patched-1 Receptor ; Protein Serine-Threonine Kinases/physiology* ; Mice, Knockout ; Adaptor Proteins, Signal Transducing

and Relevance: Chromatin accessibility is crucial in gene regulation in domestic animals, influencing development, immune response, and productivity. Despite the progress, more comprehensive epigenomic datasets and cross-species analytical tools are needed to harness chromatin accessibility in domestic animal research. Understanding these mechanisms has practical applications in improving livestock traits, advancing breeding programs, and developing disease-resistant animals, highlighting the importance of integrating epigenetic and genomic tools for enhancing animal health and productivity.

and Relevance: Chromatin accessibility is crucial in gene regulation in domestic animals, influencing development, immune response, and productivity. Despite the progress, more comprehensive epigenomic datasets and cross-species analytical tools are needed to harness chromatin accessibility in domestic animal research. Understanding these mechanisms has practical applications in improving livestock traits, advancing breeding programs, and developing disease-resistant animals, highlighting the importance of integrating epigenetic and genomic tools for enhancing animal health and productivity.

and Relevance: Chromatin accessibility is crucial in gene regulation in domestic animals, influencing development, immune response, and productivity. Despite the progress, more comprehensive epigenomic datasets and cross-species analytical tools are needed to harness chromatin accessibility in domestic animal research. Understanding these mechanisms has practical applications in improving livestock traits, advancing breeding programs, and developing disease-resistant animals, highlighting the importance of integrating epigenetic and genomic tools for enhancing animal health and productivity.

and Relevance: Chromatin accessibility is crucial in gene regulation in domestic animals, influencing development, immune response, and productivity. Despite the progress, more comprehensive epigenomic datasets and cross-species analytical tools are needed to harness chromatin accessibility in domestic animal research. Understanding these mechanisms has practical applications in improving livestock traits, advancing breeding programs, and developing disease-resistant animals, highlighting the importance of integrating epigenetic and genomic tools for enhancing animal health and productivity.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Background: and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

Purpose: To assess and compare the diagnostic efficacies of magnetic resonance arthrography (MRA) and computed tomography arthrography (CTA) in identifying labral and chondral lesions within the hip joints. To analyze the diagnostic confidence levels of radiologists when interpreting images obtained from MRA and CTA. Materials and Methods: Labral and chondral lesions in 100 hips of 95 patients on 3.0 tesla MRA, CTA (128- and 160-slice multi-detector computed tomography), and arthroscopic findings were retrospectively reviewed by three radiologists. We analyzed the sensitivity, specificity, accuracy, positive predictive value, and area under the curve (AUC) using receiver operating characteristic curves. The diagnostic confidence of the radiologists was also assessed during image interpretation. Results: Both MRA and CTA showed good diagnostic performances in the assessment of labral and chondral lesions. MRA showed higher sensitivity and accuracy, with a higher AUC for labral lesions than CTA (MRA/CTA: 97.3%/84.8% [p < 0.001], 93.8%/89.8%, and 0.945/0.896 [p = 0.003], respectively). CTA outperformed MRA in diagnostic performance in terms of sensitivity, specificity, accuracy, and AUC in acetabular cartilage lesions (MRA/ CTA: 75.8%/93.9% [p = 0.020], 67.6%/88.2% [p < 0.001], 73.0%/92.0% and 0.717/0.911 [p < 0.001], respectively) and sensitivity, accuracy and AUC in femoral cartilage lesions (60.5%/78.9% [p = 0.020], 80.0%/87.0%, and 0.762/0.854 [p = 0.018], respectively). The diagnostic confidence was higher with MRA for labral lesions (p = 0.002) and with CTA for chondral lesions (p < 0.001). Conclusion Both MRA and CTA showed strong diagnostic abilities for hip joint lesions, with MRA being better for labral lesions and CTA for chondral lesions. Radiologists showed greater confidence in diagnosing labral lesions using MRA and chondral lesions using CTA.