Objective To investigate the correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. Methods The data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. Results A total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. Conclusion Male, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.

Deficiencies remain in the early identification and screening method for type 2 diabetes mellitus(T2DM).Relying solely on blood glucose indicators as diagnostic criteria fails to capture the systematic evolution of glucose metabolism destabilization and does not allow for the identification of the critical transition period preceding the onset of T2DM.In the complex system of the human body,structural and state variables correspond to the traditional Chinese medicine concepts of"zang"and"xiang."These variables determine the landscape of the systemic state changes over time.The pathogenesis of T2DM is characterized by a shift from compensatory insulin secretion to β-cell dysfunction,driven by negative-positive feedback dynamics,ultimately resulting in a marked increase in blood glucose levels.A critical transition exists between glycemic homeostasis and the establishment of T2DM disease homeostasis.Using theoretical approaches such as critical slowing and dynamic network markers in dynamical systems theory,various clinical case data-including four-diagnosis information,multiple biological samples,and histological analysis method-can be leveraged to identify the critical transition key stage from pre-disease to disease of T2DM,facilitating early intervention.This paper aims to develop a dynamic model describing the transition from"glucose homeostasis-glycemic state of instability-steady state of T2DM"by analyzing the mechanism of the complex human system and the dynamic characteristics underlying T2DM onset.This framework aims to enhance early identification method.Establishing this holistic approach offers a novel perspective for the prevention and treatment of T2DM.

Deficiencies remain in the early identification and screening method for type 2 diabetes mellitus(T2DM).Relying solely on blood glucose indicators as diagnostic criteria fails to capture the systematic evolution of glucose metabolism destabilization and does not allow for the identification of the critical transition period preceding the onset of T2DM.In the complex system of the human body,structural and state variables correspond to the traditional Chinese medicine concepts of"zang"and"xiang."These variables determine the landscape of the systemic state changes over time.The pathogenesis of T2DM is characterized by a shift from compensatory insulin secretion to β-cell dysfunction,driven by negative-positive feedback dynamics,ultimately resulting in a marked increase in blood glucose levels.A critical transition exists between glycemic homeostasis and the establishment of T2DM disease homeostasis.Using theoretical approaches such as critical slowing and dynamic network markers in dynamical systems theory,various clinical case data-including four-diagnosis information,multiple biological samples,and histological analysis method-can be leveraged to identify the critical transition key stage from pre-disease to disease of T2DM,facilitating early intervention.This paper aims to develop a dynamic model describing the transition from"glucose homeostasis-glycemic state of instability-steady state of T2DM"by analyzing the mechanism of the complex human system and the dynamic characteristics underlying T2DM onset.This framework aims to enhance early identification method.Establishing this holistic approach offers a novel perspective for the prevention and treatment of T2DM.

The "pre-disease" theory of traditional Chinese medicine focuses on the dynamic and continuous evolution from health to disease, and emphasizes early identification and intervention in the complex and gradual process of evolution from health to disease. The "pre-disease" theory and complexity science share the same perspective on health and disease from the standpoint of features of the dynamic evolution and holism, i. e., life is considered as a complex system with ongoing dynamic changes, which exhibit the nonlinear features of " homeostasis-destabilization-phase transition-another homeostasis". In this paper, from the perspective of nonlinear dynamics in complexity science, we explain the scientific connotation of the evolution law of "pre-disease"-disease based on the theory of critical slowing down in traditional Chinese medicine. Based on the theory of critical slowing down and the dynamic network biomarker method generated by its development, combined with the macro signs of comprehensive analysis of data gained by four diagnostic method and the micro features including transcriptomics and the microbiomics, this paper proposes to integrate macro and micro multi-hierarchy information to construct a "pre-disease" critical slowing down identification model with the characteristics of traditional Chinese medicine diagnosis and treatment, which provides a new perspective and method for the early warning of complex diseases.