The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Central venous pressure(CVP)is a commonly used hemodynamic index in clinical practice,and the accuracy of its measurement results is influenced by the measurement technical specifications.Based on research evidence and clinical practice,this article interprets the main content of the group standard of CVP measurement technology,and proposes suggestions for its use.It aims to guide clinical nursing staff to correctly understand the principles and methods of CVP measurement,and promote the standardization of nursing practice and the accuracy of measurement results.

Dysfunction of the diaphragm is a common problem in mechanically ventilated patients and is closely related to various adverse outcomes.This review summarizes the evaluation methods of diaphragm function,the association between diaphragm dysfunction and adverse clinical outcomes in critically ill mechanically ventilated patients,and the protective effect of diaphragm function on adverse outcomes related to mechanical ventilation.The aim is to provide references for medical staff to optimize evaluation techniques,develop personalized nursing plans,and improve patients' quality of life and health outcomes.

Objective:To explore the current status and influencing factors of fatigue in patients with Cushing syndrome in China, so as to provide a basis for clinical interventions.Methods:Convenience sampling was used to select 260 inpatients with Cushing syndrome who met the inclusion and exclusion criteria in 9 ClassⅢ Grade A hospitals within the 7 geographic subregions of China from February to April 2023 for the study. General Information Questionnaire, the Chinese version of the Multidimensional Fatigue Inventory-20, Barthel Index, and Self-Rating Depression Scale were used for the survey.Results:A total of 260 questionnaires were distributed and 241 valid questionnaires were recovered, with an effective recovery rate of 92.7% (241/260). The MFI-20 score of 241 patients with Cushing syndrome was 58.00 (46.00, 64.00). 65.1% (157/241) of patients with Cushing syndrome suffered from fatigue, and 73.2% (115/157) of patients with fatigue exhibited multidimensional fatigue. Univariate analysis showed that there were statistically significant differences in fatigue scores among patients with Cushing syndrome with different cultural levels, recurrence frequency, activity of daily living, hypertension, and depression ( P<0.05). Multiple linear regression analysis showed that depression was a risk factor for fatigue in patients with Cushing syndrome, and the difference was statistically significant ( P<0.05) . Conclusions:Fatigue in patients with Cushing syndrome needs attention. Medical and nursing staff should pay close attention to the psychological status of patients with Cushing syndrome, encourage them to actively cope, alleviate their depression, in order to improve their fatigue.

Objective:To explore the current status and influencing factors of fatigue in patients with Cushing syndrome in China, so as to provide a basis for clinical interventions.Methods:Convenience sampling was used to select 260 inpatients with Cushing syndrome who met the inclusion and exclusion criteria in 9 ClassⅢ Grade A hospitals within the 7 geographic subregions of China from February to April 2023 for the study. General Information Questionnaire, the Chinese version of the Multidimensional Fatigue Inventory-20, Barthel Index, and Self-Rating Depression Scale were used for the survey.Results:A total of 260 questionnaires were distributed and 241 valid questionnaires were recovered, with an effective recovery rate of 92.7% (241/260). The MFI-20 score of 241 patients with Cushing syndrome was 58.00 (46.00, 64.00). 65.1% (157/241) of patients with Cushing syndrome suffered from fatigue, and 73.2% (115/157) of patients with fatigue exhibited multidimensional fatigue. Univariate analysis showed that there were statistically significant differences in fatigue scores among patients with Cushing syndrome with different cultural levels, recurrence frequency, activity of daily living, hypertension, and depression ( P<0.05). Multiple linear regression analysis showed that depression was a risk factor for fatigue in patients with Cushing syndrome, and the difference was statistically significant ( P<0.05) . Conclusions:Fatigue in patients with Cushing syndrome needs attention. Medical and nursing staff should pay close attention to the psychological status of patients with Cushing syndrome, encourage them to actively cope, alleviate their depression, in order to improve their fatigue.

Objective:To explore the latent profile characteristics of cancer-related fatigue (CRF) in breast cancer patients during the rehabilitation phase and to analyze the influencing factors of different latent profiles.Methods:A convenience sampling method was used to recruit 190 breast cancer patients in the rehabilitation phase from the follow-up cohort at Peking Union Medical College Hospital between March and May 2024. Data was collected using a general information questionnaire, the Cancer Fatigue Scale, the Self-Rating Anxiety Scale, the Self-Rating Depression Scale, the Pittsburgh Sleep Quality Index and the Visual Analogue Scale. Logistic regression analysis was conducted to identify the influencing factors of CRF latent profiles. A total of 190 questionnaires were distributed, and 183 valid responses were collected, yielding an effective response rate of 96.32% (183/190) .Results:Three latent profiles of CRF were identified: mild fatigue, moderate fatigue, and severe fatigue. Menopausal status, depressive symptoms, and pain scores were significant influencing factors of the CRF latent profiles ( P<0.05) . Conclusions:Cancer-related fatigue in breast cancer patients during the rehabilitation phase exhibits distinct latent profile characteristics. Early identification of profile types and associated influencing factors may provide a scientific basis for healthcare professionals to develop targeted clinical interventions to help patients alleviate fatigue.

Objective:To explore the current status and research trends of cancer-related cognitive impairment (CRCI) worldwide, and to provide a reference for clinical nursing practice.Methods:Literature related to CRCI nursing was retrieved from the Web of Science Core Collection and China National Knowledge Infrastructure, with the search period from database inception to December 31, 2024. CiteSpace software was used to conduct bibliometric analysis of the included studies.Results:A total of 463 English and 43 Chinese articles were included. The overall number of publications showed an upward trend, with some degree of collaboration among institutions and authors. Research hotspots focused on influencing factors of CRCI, construction of risk prediction models, symptom management, and nursing interventions.Conclusions:The volume of CRCI nursing research has increased annually. Foreign countries started earlier in this field, while China is still in the initial stage. There is a need to strengthen international cooperation, multi-institutional collaboration, and interdisciplinary research to improve research quality and provide guidance for CRCI nursing research development in China.

Central venous pressure(CVP)is a commonly used hemodynamic index in clinical practice,and the accuracy of its measurement results is influenced by the measurement technical specifications.Based on research evidence and clinical practice,this article interprets the main content of the group standard of CVP measurement technology,and proposes suggestions for its use.It aims to guide clinical nursing staff to correctly understand the principles and methods of CVP measurement,and promote the standardization of nursing practice and the accuracy of measurement results.