Periodontal bone defects, primarily caused by periodontitis, are highly prevalent in clinical settings and manifest as bone fenestration, dehiscence, or attachment loss, presenting a significant challenge to oral health. In regenerative medicine, harnessing developmental principles for tissue repair offers promising therapeutic potential. Of particular interest is the condensation of progenitor cells, an essential event in organogenesis that has inspired clinically effective cell aggregation approaches in dental regeneration. However, the precise cellular coordination mechanisms during condensation and regeneration remain elusive. Here, taking the tooth as a model organ, we employed single-cell RNA sequencing to dissect the cellular composition and heterogeneity of human dental follicle and dental papilla, revealing a distinct Platelet-derived growth factor receptor alpha (PDGFRA) mesenchymal stem/stromal cell (MSC) population with remarkable odontogenic potential. Interestingly, a reciprocal paracrine interaction between PDGFRA⁺ dental follicle stem cells (DFSCs) and CD31⁺ Endomucin⁺ endothelial cells (ECs) was mediated by Vascular endothelial growth factor A (VEGFA) and Platelet-derived growth factor subunit BB (PDGFBB). This crosstalk not only maintains the functionality of PDGFRA⁺ DFSCs but also drives specialized angiogenesis. In vivo periodontal bone regeneration experiments further reveal that communication between PDGFRA⁺ DFSC aggregates and recipient ECs is essential for effective angiogenic-osteogenic coupling and rapid tissue repair. Collectively, our results unravel the importance of MSC-EC crosstalk mediated by the VEGFA and PDGFBB-PDGFRA reciprocal signaling in orchestrating angiogenesis and osteogenesis. These findings not only establish a framework for deciphering and promoting periodontal bone regeneration in potential clinical applications but also offer insights for future therapeutic strategies in dental or broader regenerative medicine.
Receptor, Platelet-Derived Growth Factor alpha/metabolism* ; Humans ; Neovascularization, Physiologic/physiology* ; Dental Sac/cytology* ; Single-Cell Analysis ; Transcriptome ; Mesenchymal Stem Cells/metabolism* ; Bone Regeneration ; Animals ; Dental Papilla/cytology* ; Periodontium/physiology* ; Stem Cells/metabolism* ; Regeneration ; Angiogenesis

To summarize the applications of data-intelligence technology in diagnosing lysosomal storage disease(LSD), analyze their opportunities and challenges in clinical practice as well as their development trends, and provide insights and recommendations for advancing digitally driven auxiliary diagnostic technologies.

The exploration and pilot studies of applying blockchain to drug supply chain show great potential in promoting information sharing, collaboration competence among the actors, regulatory efficiency, and etc. In the future, with the help of blockchain, the optimization of the entire supply chain for orphan drugs is expected to be realized. However, there is no such exploration in China at present. This paper systematically sorts out the whole process of supply chain for orphan drugs and the existing problems of the chain. The article concludes that at present, blockchain is mainly used in the " circulation" and " use" of the drug supply chain. It helps to improve the traceability of drugs, to cope with the problem of counterfeit drugs, to enable actors of the drug supply chain to form a collaborative network in optimizing resource allocation, and to improve the operation and supervision efficiency of the supply chain. In the future, the application faces challenges such as high costs in system conversion, lack of personnel awareness, and incomplete supporting systems. Based on the three dimensions of technology, practice, and research, this paper also looks into the future and suggests for the future use of blockchain in the supply chain of orphan drugs by constructing a practice model, the so called DI-GIVE (Digital, Intelligence, Government′s supervision, Innovation, Views of variety, Evaluation-based) hoping to innovate the supply chain of orphan drugs and to ensure the drug use for the patients with rare diseases in China.

To summarize the current status in the application of digital and intelligent technologies in the field of pharmacovigilance and to provide reference to the selection and development of methods for pharmacovigilance of rare diseases.

Our study aims at systematically summarizing and evaluating the applications of digital intelligence technologies in the field of rare disease medical care insurance now and in the future and at constructing a conceptual framework for the digital powered mechanism for the medical care insurance for rare diseases. By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. The finding of our study shows that medical care insurance mechanism of rare disease in China faces significant challenges in drug accessbility and the funding sustainability. Meanwhile, our study shows that the digital intelligence technologies have broad potential in applications-in financing, accessbility, payment, and supervision. Specifically, dynamic simulation models and big data analysis can make precise prediction of the demand for funding of medical care insurance. The machine learning algorithms improve the dynamic evaluation of drug safety and cost-effectiveness. The personalized payment models enhance the efficiency in identifying the cohort with high expenditure so as to alleviate fund expenditure pressures. The intelligent monitoring technologies can accurately detect the abnormal behaviors in funds of medical care insurance. These technologies provide systematic and scientific solutions for improving the medical care mechanism for rare diseases. Even though further investigation is needed, the digital intelligence technologies have shown remarkable potential in enhancing the flexibility, efficiency, and sustainability of the medical care insurance system and a promising future in meeting the needs of patients with rare diseases.

In recent years, the rapid development of digital intelligence has provided a new path for rare disease data sharing and injected new power into the progress of research of rare diseases. This research is aimed at summarizing and consolidating relevant literatures on data sharing driven by digital intelligence (DI) in China and abroad, and constructing a local theoretical framework of DI-driven data sharing for rare diseases based on the status of rare diseases in China. Searching PubMed, EMbase, Cochrane, CNKI, Wanfang, and VIP database, we obtain a total of 214 representative literatures. Through literature review, we find that DI technologies have played important roles in different aspects of rare disease data sharing. China, the United States, and Europe have formed their own DI-driven data sharing systems for rare disease. From the theory of " Information Commons", we analyze the gap between China′s current situation and the goal of a " Rare Disease Data Commons". Based on the analysis, we put forward the idea of framework of " DI-STARS". China should develop the Data Sharing system making DI as the core of the system. Meanwhile, China should strengthen the data standardization system, create an innovation-encouraging environment, and build a bridge between different platforms. Using the DI-STARS theory, China will be able to build the " Rare Disease Data Commons" so that the diagnosis and treatment of rare diseases will be enhanced in China to meet the patients′ needs.

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic pulmonary infection that commonly occurs in immunocompromised children. We report a case of infantile leukemia complicated by PJP and review the relevant literature. A summary and analysis of 10 infantile leukemia patients with PJP infection (9 cases reported in the literature and 1 case from our center) showed that PJP mostly occurred in the early stages of chemotherapy (80%, 8/10). The main clinical manifestations were dyspnea (100%, 10/10) and hypoxemia (50%, 5/10), while pulmonary imaging findings lacked specificity. In most cases (50%, 5/10), diagnosis was established by identifying pathogens in bronchoalveolar lavage fluid under microscopy. In our case, diagnosis was confirmed using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid. Treatment with intravenous sulfamethoxazole complex was administered in 8 patients, all of whom eventually recovered. PJP may occur in the early stages of chemotherapy for infantile leukemia, thus early prevention is necessary. tNGS facilitates early diagnosis of PJP, and sulfamethoxazole complex remains an effective therapeutic option.
Humans ; Infant ; Bronchoalveolar Lavage Fluid/microbiology* ; Immunocompromised Host ; Leukemia/complications* ; Pneumocystis carinii/isolation & purification* ; Pneumonia, Pneumocystis/diagnosis* ; Trimethoprim, Sulfamethoxazole Drug Combination/therapeutic use*