Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy worldwide, accounting for more than 90% of all oral cancers, and is characterized by high invasiveness and poor long-term prognosis. Its etiology is multifactorial, involving tobacco use, alcohol consumption, and human papillomavirus (HPV) infection. Oral leukoplakia and erythroplakia are the main precancerous lesions lesions, with oral leukoplakia being the most common. Both OSCC and premalignant lesions are closely associated with aberrant activation of multiple signaling pathways. Post-translational modifications (such as ubiquitination and deubiquitination) play key roles in regulating these pathways by controlling protein stability and activity. Growing evidence indicates that dysregulated ubiquitination/deubiquitination can mediate OSCC initiation and progression via aberrant activation of signaling pathways. The ubiquitination/deubiquitination process mainly involves E3 ligases (E3s) that catalyze substrate ubiquitination, deubiquitinating enzymes (DUBs) that remove ubiquitin chains, and the 26S proteasome complex that degrades ubiquitinated substrates. Abnormal expression or mutation of E3s and DUBs can lead to altered stability of critical tumor-related proteins, thereby driving OSCC initiation and progression. Therefore, understanding the aberrantly activated signaling pathways in OSCC and the ubiquitination/deubiquitination mechanisms within these pathways will help elucidate the molecular mechanisms and improve OSCC treatment by targeting relevant components. Here, we summarize four aberrantly activated signaling pathways in OSCC―the PI3K/AKT/mTOR pathway, Wnt/β-catenin pathway, Hippo pathway, and canonical NF-κB pathway―and systematically review the regulatory mechanisms of ubiquitination/deubiquitination within these pathways, along with potential drug targets. PI3K/AKT/mTOR pathway is aberrantly activated in approximately 70% of OSCC cases. It is modulated by E3s (e.g., FBXW7 and NEDD4) and DUBs (e.g., USP7 and USP10): FBXW7 and USP10 inhibit signaling, while NEDD4 and USP7 potentiate it. Aberrant activation of the Wnt/β‑catenin pathway leads to β‑catenin nuclear translocation and induction of cell proliferation. This pathway is modulated by E3s (e.g., c-Cbl and RNF43) and DUBs (e.g., USP9X and USP20): c-Cbl and RNF43 inhibit signaling, while USP9X and USP20 potentiate it. Hippo pathway inactivation permits YAP/TAZ to enter the nucleus and promotes cancer cell metastasis. This pathway is modulated by E3s (e.g., CRL4^DCAF1 and SIAH2) and DUBs (e.g., USP1 and USP21): CRL4^DCAF1 and SIAH2 inhibit signaling, while USP1 and USP21 potentiate it. Persistent activation of the canonical NF-κB pathway is associated with an inflammatory microenvironment and chemotherapy resistance. This pathway is modulated by E3s (e.g., TRAF6 and LUBAC) and DUBs (e.g., A20 and CYLD): A20 and CYLD inhibit signaling, while TRAF6 and LUBAC potentiate it. Targeting these E3s and DUBs provides directions for OSCC drug research. Small-molecule inhibitors such as YCH2823 (a USP7 inhibitor), GSK2643943A (a USP20 inhibitor), and HOIPIN-8 (a LUBAC inhibitor) have shown promising antitumor activity in preclinical models; PROTAC molecules, by binding to surface sites of target proteins and recruiting E3s, achieve targeted ubiquitination and degradation of proteins insensitive to small-molecule inhibitors, for example, PU7-1-mediated USP7 degradation, offering new strategies to overcome traditional drug limitations. Currently, NX-1607 (a Cbl-b inhibitor) has entered phase I clinical trials, with preliminary results confirming its safety and antitumor activity. Future research on aberrant E3s and DUBs in OSCC and the development of highly specific inhibitors will be of great significance for OSCC precision therapy.

With the advent of an aging society,Alzheimer's disease(AD)has gradually become a major ailment affecting the elderly.AD is a neurodegenerative disorder associated with cognitive impairments.In AD patients,brain network connections are disrupted,and their topological properties are also affected,leading to the disintegration of anatomical and functional connections.Anatomical connections can be tracked and evaluated using structural magnetic imaging(MRI)and diffusion tensor imaging(DTI),while functional connections are detected through functional MRI to assess their connectivity status.This review incorporates the findings of previous scholars and summarizes the current research of AD.It mainly discusses the imaging characteristics of large-scale brain network changes in AD patients,so as to provide researchers with scientific and objective imaging markers for AD prediction and early diagnosis,as well as future research.

Objective To construct quality evaluation indicators for perioperative nursing in heart transplantation,and to provide standard and professional quantitative bases for monitoring and management of perioperative nursing quality.Methods This study was conducted based on the frame work of the three-dimensional"structure-process-outcome"quality model,using literature review,Delphi method and analytic hierarchy to determine the content of the indicators,and the weight of each index.Results A total of 22 experts from 14 qualified heart transplantation hospitals were included,and a total of 2 rounds of consultations were conducted.The effective recovery rates of 2 rounds of expert consultation questionnaires were 100％.The authority coefficients were 0.817.The variation coefficients of each item ranged from 0.025～0.169 and 0.039～0.157.The Kendall harmony coefficients were 0.126 and 0.225(P＜0.001).The final evaluation indicators for perioperative nursing quality in heart transplantation included 3 first-level indicators,12 second-lever indicators and 59 third-level indicators.Conclusion The evaluation indicators of perioperative nursing quality in heart transplantation was scientific,comprehensive and specialized,which can provide references for the evaluation of perioperative nursing quality in heart transplantation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.