This article systematically reviews the characteristics and trends of the writing, editing, publication and promotion of physiology textbooks in China from the late 19th century to the present, focusing on the introduction, development and innovation of Chinese physiology textbooks. The development of physiology textbooks in China is divided into four main stages: the introduction and initial development of physiology textbooks from the late 19th century to 1925; the localization and diversification of textbooks from 1926 to 1949, after the establishment of the Chinese Physiological Society; the exploratory phase of textbook construction after the founding of the People's Republic of China from 1949 to 1976; the formation and innovation of the textbook development process from 1977 to the present, following the restoration of the college entrance examination. For each phase, the article not only records the historical development of physiology textbooks, but also analyzes the evolution of their content, writing styles and the interaction with the social and political contexts. The article summarizes the characteristics and experiences of all these four phases. Special attention is given to the comprehensive statistical analysis of physiology textbooks published since the restoration of the college entrance examination and Economic Reform and Opening-up in 1977, revealing the changes in the number, publication trends and academic features of textbooks during this period. Finally, the article presets the future development of physiology textbooks in China, proposing that textbook writing should integrate aspects such as ideological and political education, medical humanities, basic and clinical medicine, health education, scientific research and international exchange and collaboration. The article also advocates for the application of new technologies and methods, such as artificial intelligence, virtual teaching models and knowledge graphs, to support "personalized learning". This research provides a systematic reference for the study of the history of medical education and offers theoretical support for the future innovation of physiology textbook in China.
Humans ; China ; History, 19th Century ; History, 20th Century ; History, 21st Century ; Physiology/education* ; Textbooks as Topic/history*

Nutritional support therapy is one of the extremely important treatment methods for patients in the intensive care unit. Timely and effective nutritional support regimens can improve patients' immune function, reduce complications, and optimize clinical outcomes. Energy expenditure is influenced by multiple factors, including patients' baseline characteristics (such as physical condition, gender, age) and dynamic changes in indicators (such as body temperature, nutritional support regimens, and therapeutic interventions). The currently recognized "gold standard" for accurately assessing energy metabolism in clinical practice is the indirect calorimetry system, also known as the metabolic cart. This device monitors carbon dioxide production and oxygen consumption in real time and uses specific algorithms to estimate the metabolic proportions of the three major nutrients (carbohydrates, fats, and proteins) in energy expenditure. An appropriate nutrient ratio helps maintain the balance between supply and demand in the body's nutritional metabolism. In the management of critically ill patients, the application of the metabolic cart enables personalized nutritional therapy, avoiding over- or under-supply of energy and optimizing the use of medical resources. Furthermore, with real-time, quantitative data support from the energy metabolism monitoring system, clinicians can develop more precise nutritional intervention strategies, thereby improving patient prognosis. This article provides a systematic review of the technical features of the metabolic cart and its application value in various critical care scenarios, aiming to offer a reference for indirect calorimetry in clinical practice.
Humans ; Critical Illness/therapy* ; Nutritional Support ; Energy Metabolism ; Calorimetry, Indirect

Peyronie's disease (PD) is a disorder characterized by fibrous plaque formation in the penile tissue that leads to curvature and complications in advanced stages. In this study, we aimed to compare four injectable induction agents for the establishment of a robust rat model of PD: transforming growth factor-β1 (TGF-β1), fibrin, sodium tetradecyl sulfate (STS) combined with TGF-β1, and polidocanol (POL) combined with TGF-β1. The results showed that injection of TGF-β1 or fibrin into the tunica albuginea induced pathological endpoints without causing penile curvature. The STS + TGF-β1 combination resulted in both histological and morphological alterations, but with a high incidence of localized necrosis that led to animal death. The POL + TGF-β1 combination produced pathological changes and curvature comparable to STS + TGF-β1 and led to fewer complications. In conclusion, fibrin, STS + TGF-β1, and POL + TGF-β1 all induced PD with a certain degree of penile curvature and histological fibrosis in rats. The POL + TGF-β1 combination offered comparatively greater safety and clinical relevance and may have the greatest potential for PD research using model rats.
Animals ; Male ; Penile Induration/drug therapy* ; Rats ; Transforming Growth Factor beta1/metabolism* ; Disease Models, Animal ; Fibrin ; Penis/drug effects* ; Polidocanol/administration & dosage* ; Rats, Sprague-Dawley ; Polyethylene Glycols/administration & dosage* ; Injections

OBJECTIVES: To investigate the clinical features and gene mutation characteristics of combined oxidative phosphorylation deficiency type 7 (COXPD7) caused by mutations in the C12orf65 gene, and to enhance the awareness of this disease. METHODS: A child diagnosed with COXPD7 in the Department of Neurology, Children's Hospital Affiliated to Zhengzhou University in 2021 was included, along with 10 patients reported in the literature. All subjects were analyzed for their genotypes and clinical phenotypes. RESULTS: A total of 11 patients with COXPD7 were included, comprising 1 reported in this study and 10 from the literature. Among the 11 patients, 9 had homozygous mutations in the C12orf65 gene, while 2 had compound heterozygous mutations, which were identified as frameshift or nonsense mutations. The age of onset ranged from 1 day to 2 years, and clinical manifestations included optic nerve atrophy and delays in intellectual and motor development. Eight patients exhibited external ophthalmoplegia, and five patients displayed spastic paralysis. Cranial magnetic resonance imaging revealed optic nerve atrophy in all 11 patients, abnormal brainstem signals in 10 patients, and a lactate peak on brainstem magnetic resonance spectroscopy scans in 3 patients. CONCLUSIONS COXPD7 associated with the C12orf65 gene results from homozygous or compound heterozygous mutations, with primary clinical manifestations of optic nerve atrophy and delays in intellectual and motor development. Some patients may also present with spastic paralysis or external ophthalmoplegia. Cranial imaging reveals symmetrical abnormal signals in bilateral basal ganglia and brainstem, and a lactate peak is observed on brainstem magnetic resonance spectroscopy scans.
Child, Preschool ; Female ; Humans ; Infant ; Male ; Mitochondrial Diseases/genetics* ; Mitochondrial Proteins/genetics* ; Mutation ; Oxidative Phosphorylation ; Infant, Newborn

BACKGROUND: Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease (CAD) remains incomplete. The aim of this study was to investigate the relationships between postdischarge cancers and all-cause mortality and cardiovascular mortality in CAD patients. METHODS: In this retrospective cohort study, 25% of CAD patients without prior cancer history who underwent coronary artery angiography between January 1, 2011 and December 31, 2015, were randomly enrolled using SPSS 26.0. Patients were monitored for the incidence of postdischarge cancer, which was defined as cancer diagnosed after the index hospitalization, survival status and cause of death. Cox regression analysis was used to explore the association between postdischarge cancer and all-cause mortality and cardiovascular mortality in CAD patients. RESULTS: A total of 4085 patients were included in the final analysis. During a median follow-up period of 8 years, 174 patients (4.3%) developed postdischarge cancer, and 343 patients (8.4%) died. A total of 173 patients died from cardiovascular diseases. Postdischarge cancer was associated with increased all-cause mortality risk (HR = 2.653, 95% CI: 1.727-4.076, P < 0.001) and cardiovascular mortality risk (HR = 2.756, 95% CI: 1.470-5.167, P = 0.002). Postdischarge lung cancer (HR = 5.497, 95% CI: 2.922-10.343, P < 0.001) and gastrointestinal cancer (HR = 1.984, 95% CI: 1.049-3.750, P = 0.035) were associated with all-cause mortality in CAD patients. Postdischarge lung cancer was significantly associated with cardiovascular death in CAD patients (HR = 4.979, 95% CI: 2.114-11.728, P < 0.001), and cardiovascular death was not significantly correlated with gastrointestinal cancer or other types of cancer. CONCLUSIONS Postdischarge cancer was associated with all-cause mortality and cardiovascular mortality in CAD patients. Compared with other cancers, postdischarge lung cancer had a more significant effect on all-cause mortality and cardiovascular mortality in CAD patients.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.