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

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.