Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Objective: To investigate the attention cognitive function and symptom correlations of school aged children with attention deficit hyperactivity disorder (ADHD)using event related potential (ERP) technology, so as to provide references for the early diagnosis of children with ADHD. Methods: A total of 52 school aged children diagnosed with ADHD at the outpatient department of the Third Affiliated Hospital of Zhengzhou University from September 2022 to September 2024 and 50 age /sex matched healthy controls were selected. The ERP experiment adopted the auditory Oddball task to conduct comparative analyses of the amplitude and latency of the mismatch negative(MMN) at the Fz, Cz, and Pz points of the scalp electrode and the P3a component respectively. The symptom assessment scales adopted the Swanson,Nolan,and Pelham-Ⅳ Rating Scale (SNAP-Ⅳ) and the Parent Symptom Questionnaire (PSQ), which were filled out by the parents. Spearman correlation analysis was used to analyze the correlation between ERP components and symptoms in schoolaged children with ADHD. Results: The latency of MMN components in the healthy control group on the Fz lead was (188.30±2.06)ms, and the amplitude was (-15.54±1.35)μV; the latency of the P3a component on the Pz lead was (312.82±7.80)ms, and the amplitude was (3.80±0.18)μV. The latency of MMN components in the ADHD group on the Fz lead was (188.94±1.39)ms, and the amplitude was (-14.78±1.40)μV; the latency of the P3a component on the Pz lead was (317.21±5.65)ms, and the amplitude was (3.70±0.13)μV. Compared with normal children, the MMN of children with ADHD had smaller amplitudes in the Fz and Cz leads, and the P3a had greater latency and smaller amplitudes in the Cz and Pz leads ( t =2.79,2.20；-2.04，-3.25；2.35,3.21， P <0.05). Correlation analysis showed that the latency of MMN in children with ADHD was positively correlated with the inattention score in the SNAP-Ⅳ( r =0.22), and the amplitude of MMN was negatively correlated with the inattention score in the SNAP-Ⅳ and the learning problem score in PSQ ( r = -0.26 , -0.34)( P <0.05). The latency of P3a was positively correlated with the scores of inattention in the SNAP-Ⅳ and the score of learning problems in the PSQ ( r =0.26 ,0.24); the amplitude of P3a was negatively correlated with the scores of attention deficit and hyperactivity/impulsivity in the SNAP-Ⅳ and the scores of learning problems and impulsivity/hyperactivity in the PSQ( r = -0.26 , -0.22, -0.25,-0.32)( P <0.05). Conclusions School aged ADHD children exhibit abnormal MMN/P3a components, indicating attention related cognitive dysfunction. Symptoms such as inattention, learning problems and hyperactivity/impulsivity in children with ADHD are related to abnormal components of MMN and P3a.

Dendrobium moniliforme (D. moniliforme) is a traditional medicinal herb widely cultivated in Asia. Flavonoids, one of the largest groups of secondary metabolites in plants, are significant medicinal components in Dendrobium species. Several subgroups of R2R3-MYB proteins have been validated to directly regulate flavonoid biosynthesis. Using PacBio sequencing technology, we assembled a high-quality chromosome-level D. moniliforme genome with a total length of 1.20 Gb and a contig N50 of 3.97 Mb. The BUSCO assessment of genome annotation was 91.4%. By integrating the genome and transcriptome, we identified biosynthesis pathway enzyme genes related to flavonoids, polysaccharides, carotenoids, and alkaloids. A total of 90 R2R3-MYBs were identified in D. moniliforme and classified into 21 subgroups. Studies on the functions of R2R3-MYB transcription factors revealed that R2R3-MYB in SG6 can up-regulate flavonoid biosynthesis. Various validation experiments, including subcellular localization, transient overexpression, UPLC-MS/MS, HPLC, yeast one-hybrid, and dual-luciferase assays, demonstrated that DMYB69 directly up-regulates the expression of enzyme genes involved in flavonoid biosynthesis, increasing the content of flavonoids such as anthocyanin, flavone, and flavonol. Additionally, DMYB44 was shown to directly up-regulate the expression of carotenoid biosynthesis enzyme genes, thereby increasing carotenoid content. This study provides an essential genome resource and theoretical basis for molecular breeding research in D. moniliforme.

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

ObjectiveTo establish a method using ultra-high performance liquid chromatography-tandem mass spectrometry （UHPLC-MS/MS） for the detection of six common trichothecene toxins in oats. MethodsOats were selected as the research subject in this study. Response surface design was used to optimize the QuEChERS extraction method. Additionally， a rapid and efficient strategy for sample extraction and purification was developed. Combined with UHPLC-MS/MS， six commonly co-occurring trichothecene toxins in oats were quantitatively analyzed simultaneously. ResultsThis method demonstrated good analytical performance for each analyte across the corresponding concentration ranges （r>0.99）， with accuracy ranging from 87.26% to 99.64%. The inter-day and intra-day relative standard deviations were less than 6.8% and 5.5%， respectively， indicating its potential for practical application. This method was used to detect mycotoxins in 12 oat samples from China， and it was found that one sample exceeded the standard limits for deoxynivalenol （DON）， and the co-contamination of trichothecene toxin was prevalent. ConclusionThe risk posed by these toxins has been underestimated. Ongoing， extensive monitoring is necessary to provide contamination data to assess the consumer risk.