OBJECTIVE To explore a model for constructing a platform for medical institution formulation and provide insights for promoting their development. METHODS By systematically reviewing the development status and challenges of medical institution preparations in China， and based on the theory of industry-university-research collaborative innovation， the organizational structure， collaborative processes， and safeguard mechanisms of the platform were designed. RESULTS & CONCLUSIONS Medical institution formulations in China mainly faced challenges such as weak research and development （R&D） capacity， uneven quality standards， and blocked transformation pathways. This study established a full-chain， whole- industry collaborative innovation network covering the government， medical institutions， universities/research institutes， pharmaceutical enterprises， and the market， forming a new “government-industry-university-research-application” five-in-one platform model for medical institution formulations. By establishing mechanisms such as multi-entity collaborative cooperation， full- chain intellectual property management， contribution-based benefit distribution， staged risk-sharing， and third-party evaluation， the model clarified the responsibilities and collaborative pathways of all parties. The new model highlights the whole-process transformation of clinical experience-based prescriptions， enabling precise alignment between clinical needs and technological R&D， as well as between preparation achievements and industrial transformation. While breaking down the barriers of traditional platform construction， it effectively achieves optimal resource allocation and complementary advantages， addresses problems emerging in the development of medical institution preparations， and provides reference value for the formulation of relevant systems.

Radiotherapy is a crucial treatment modality for head and neck tumors. However, while effectively killing tumor cells, it significantly disrupts the homeostasis of the oral microecology, which is closely associated with various complications such as radiation-induced oral mucositis. Literature review indicates that as radiotherapy doses accumulate and treatment durations extend, the richness and diversity of the oral microbiota show a declining trend, with the genus Streptococcus decreasing most markedly. In contrast, radiotherapy selectively promotes the proliferation of bacterial phyla such as Proteobacteria and Bacteroidetes, which are rich in opportunistic pathogens. Mechanistically, radiotherapy activates the nuclear factor-kappa B pathway, triggering chronic inflammation and oxidative stress, damaging the epithelial barrier, suppressing local immunity, and causing damage to organs such as the salivary glands. It can also induce systemic diseases via the oral-gut axis, forming a multi-level, interconnected pathogenic network. In terms of interventions, treatment strategies including probiotics and prebiotics have shown promising efficacy against side effects such as radiation-induced oral mucositis. Saliva-based oral microbiota transplantation is an emerging strategy that is expected to become widely utilized for restoring oral microecological balance. Existing interventions provide preliminary pathways for clinical practice, but this field still faces several key scientific questions. The association between oral microecology and systemic diseases remains largely correlative, lacking causal evidence. Furthermore, critical parameters for oral microbiota transplantation, such as donor screening criteria, transplantation protocols, and long-term safety, are not yet well-defined. Therefore, future research should focus on conducting large-scale clinical trials to establish standardized protocols and safety evaluation systems for oral microecological interventions, and explore combined treatment therapies such as probiotics, prebiotics, and microbiota transplantation to advance the development of personalized precision modulation. These will enable more effective management of radiotherapy-induced oral microecological dysbiosis and improve treatment outcomes and quality of life for patients with head and neck tumors.

Objective To investigate the relationship of skin failure (SF) with nutritional status in elderly critically ill patients and analyze the predictive efficiency of nutritional status on SF. Methods A total of 340 elderly critically ill patients admitted to the hospital from January 2020 to January 2025 were selected as research subjects. According to whether skin failure occurred, the above patients were classified into skin failure group and non-skin failure group. The nutritional status indicators [serum albumin (ALB), prealbumin (PA), total protein (TP), hemoglobin (Hb), body mass index (BMI), nutritional risk score (NRS 2002)] were compared between both groups. Multivariate analysis was performed on statistically significant indicators, and ROC curve was applied to analyze the predictive value. Results Among the 340 elderly critically ill patients, 142 cases (41.76%) developed skin failure. The ALB, PA, TP, Hb and BMI in the skin failure group were lower than those in the non-skin failure group (P<0.05) while the NRS2002 score was higher (P<0.05). After logistic multivariate analysis, ALB, PA and Hb were independent influencing factors of skin failure (P<0.05). ROC curve analysis revealed that the predictive value of ALB (AUC=0.850, 95%CI: 0.808-0.888, Z=-3.707, P<0.001) was better than that of PA (AUC=0.770, 95%CI: 0.717-0.816, Z=-3.100, P=0.002) or Hb (AUC=0.773, 95%CI: 0.722-0.819, Z=-2.556, P=0.011). Conclusion The occurrence of skin failure in elderly critically ill patients is closely related to nutritional status. ALB, PA and Hb are independent risk factors of SF, and ALB has the best predictive efficiency on SF.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. As a critical component of surgical repair, reconstruction of the right ventricular outflow tract (RVOT) serves as a primary determinant of postoperative hemodynamics and long-term prognosis. With advancements in surgical techniques, early postoperative survival rates for TOF have significantly improved, shifting clinical focus toward addressing long-term complications such as right ventricular dysfunction and reduced exercise tolerance. However, challenges persist regarding optimal timing of surgery in relation to adverse long-term outcomes, balancing RVOT reconstruction strategies, and selecting appropriate repair materials for RVOT reconstruction, with a lack of unified high-level evidence-based guidelines. To address these issues and standardize the application of radical corrective surgery for TOF to better serve patients, the National Society for Congenital Heart Diseases has systematically reviewed existing high-level evidence. Consequently, we have registered and formulated the "Protocol for Chinese expert consensus on right ventricular outflow tract reconstruction in tetralogy of Fallot (2026 edition)", which details the methodology and development process of this upcoming consensus.

Urethral stricture is a common and complex disease in urology, which seriously affects the quality of life of patients and poses challenges to clinical treatment. Urethral reconstructive surgery has evolved from palliative approaches such as dilation and internal urethrotomy to systematic reconstruction based on flaps and mucosal grafts. With the introduction of oral mucosa and its histological compatibility, autologous mucosa has become the mainstay urethral substitute, while flaps remain indispensable in extensive and complex cases due to their robust vascularity. Advances in various fields of study have had a significant impact on the philosophy of surgery. The new approach has placed a higher value on anatomical structure and functional preservation alongside patency rates. This shift in focus has led to an increased importance of non-discontinuous anastomosis. Particularly in the repair of urethral injuries following pelvic fractures, Chinese scholars have utilized extensive case series to pioneer innovations in pediatric cases, complex obstructions, and combined fistula repair, thereby enriching global expertise. Concurrently, there is a growing interest in the exploration of tissue engineering and novel biomaterials for urethral repair. These approaches, ranging from the submucosal matrix of the small intestine to biomimetic degradable scaffolds, cellloaded engineered mucosa, and 3D-printed materials, are progressively entering clinical validation phases. In summary, urethral reconstruction is undergoing a shift from “reconstructing anatomy” to “balancing function.” Driven by precision medicine and regenerative medicine, it is expected to achieve more optimal individualized treatments in the future.

Ribosome is an intracellular ribonucleoprotein particle that serves as the site of protein biosynthesis. Ribosomal dysfunction caused by mutations in genes encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RBFs) can lead to a spectrum of diseases, collectively known as ribosomopathy. Phase separation is a thermodynamic process that produces multiple phases from a homogeneous mixture. The formation of membraneless organelles and intracellular structures, including ribosomes and nucleoli, cannot occur without the involvement of phase separation. Here, ribosome structure, biogenesis, and their relationship with ribosomopathy are systematically reviewed. The tissue specificity of ribosomopathy and the role of phase separation in ribosomopathy are particularly discussed, which may offer some clues for understanding the mechanisms of ribosomopathy. Then, some new ideas for the prevention, diagnosis, and treatment of ribosomopathy are provided.
Humans ; Ribosomes/physiology* ; Ribosomal Proteins/metabolism* ; Mutation ; Animals ; Cell Nucleolus/metabolism* ; Protein Biosynthesis ; Phase Separation

Fifty whitespotted bamboo sharks (Chiloscyllium playgiosum) of both sexes were used to establish a large capacity variable domain of the new antigen receptor (VNAR) library with a total capacity of over 10⁹ colony-forming units (CFU). It was applied to screen VNARs against human serum albumin (HSA) and human transcription factor EB (TFEB), respectively. Meanwhile, VNAR libraries specific to HSA and TFEB with capacities above 10⁸ CFU were obtained following conventional immunization. These two approaches were systematically studied in terms of VNAR yield and composition. By comparing the VNAR sequences obtained from naïve and antigen-immunized libraries, we found that the complementary-determining region 3 (CDR3) of the former differs in composition from that of the latter. It shares a higher degree of homology with the naïve library. Meanwhile, the binding efficiency assessed by ELISA is also different between the naïve and antigen-immunized libraries. The binding of VNARs from the TFEB-immunized library appeared to surpass that observed with the naïve libraries, whereas the performance of VNARs from the HSA-immunized library indicated that both the immunized and naïve libraries for HSA had positive binding responses in polyclonal and monoclonal ELISA. The results are useful to develop novel diagnostic and therapeutic products based on shark VNARs.

Sonodynamic therapy (SDT) can potentially induce immunogenic cell death in tumor cells, leading to the release of ATP, and facilitating the initiation of an immune response. Nevertheless, the enzymes CD39 and CD73 can swiftly convert ATP into immunosuppressive adenosine (ADO), resulting in an immunosuppressive tumor microenvironment (TME). This study introduced a nanomedicine (QD/POM1@NP@M) engineered to reprogram TME by modulating the CD39/CD73/ADO pathway. The nanomedicine encapsulated sonosensitizers silver sulfide quantum dots, and the CD39 inhibitor POM1, while also incorporating homologous tumor cell membranes to enhance targeting capabilities. This integrated approach, on the one hand, stimulates the release of ATP via SDT, thereby initiating the immune response. In addition, it reduced the accumulation of ADO by inhibiting CD39 activity, which ameliorated the immunosuppressive TME. Upon administration, the nanomedicine demonstrated substantial anti-tumor efficacy by facilitating the infiltration of anti-tumor immune cells, while reducing the immunosuppressive cells. This modulation effectively transformed the TME from an immunologically "cold" state to a "hot" state. Furthermore, combined with the checkpoint inhibitor α-PDL1, the nanomedicine augmented systemic anti-tumor immunity and promoted the establishment of long-term immune memory. This study provides an innovative strategy for combining non-invasive SDT and ATP-driven immunotherapy, offering new ideas for future cancer treatment.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.