Esophageal cancer （EC） is a highly prevalent malignant tumor in China. The phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway， as one of the key oncogenic pathways， can promote the cell cycle progression， proliferation， migration， and invasion， induce chemoresistance， and inhibit apoptosis and autophagy of EC cells. Traditional Chinese medicine （TCM）， with the advantages of targeting multiple points with multiple components to delay cancer progression， can target the PI3K/Akt signaling pathway for EC treatment. This article preliminarily discusses the molecular mechanism and role of the PI3K/Akt signaling pathway in EC and elaborates on the specific targets and efficacy of TCM in treating EC through intervention in the PI3K/Akt signaling pathway in the past five years. TCM materials and extracts inhibiting the PI3K/Akt signaling pathway in EC include Borneolum， spore powder of Ganoderma lucidum without spore coat， extract of Celastrus orbiculatus， root extract of Taraxacum， and Bruceae Fructus oil emulsion. TCM active ingredients exerting the effect include flavonoids， terpenoids， saponins， phenols， polysaccharides， alkaloids， and other compounds. TCM compound prescriptions with such effect include Qige San， Huqi San， Xuanfu Daizhetang， Tongyoutang and its decomposed prescriptions， Liujunzi Tang， and Xishenzhi Formula. In addition， TCM injections such as Compound Kushen Injection and Kang'ai injection also inhibit the PI3K/Akt signaling pathway in EC. This paper summarizes the role of the PI3K/Akt signaling pathway in EC and the TCM interventions， aiming to provide reference for the research and clinical application of new drugs for EC.

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.

ObjectiveTo interpret the main contents of the Expert Consensus on Clinical Application of Yifei Zhike Capsules （hereinafter referred to as the Consensus）， analyze its evidence basis， and explore future research directions， thus providing a reference for rational drug use in clinical practice. MethodsThe core contents of the Consensus are summarized. Focusing on clinical issues， this paper summarizes the recommendations and consensus suggestions in the Consensus and explains them from aspects such as indications， key advantages， usage and dosage， combined medication， contraindications， precautions， and safety. ResultsThe Consensus clearly states that Yifei Zhike capsules can be used for acute or chronic bronchitis， pulmonary tuberculosis and other respiratory system diseases. It for the first time proposes that the applicable TCM syndrome of Yifei Zhike capsules is Qi-Yin deficiency together with combined phlegm and stasis. For different diseases， the Consensus provides specific usage instructions， dosage， and treatment course recommendations， supplementing the contents not covered in the drug instructions. In terms of combined medication， the Consensus emphasizes the principle of using this drug in combination with standardized chemotherapy regimens or conventional Western medicines and the applicability for the treatment of pulmonary tuberculosis and moderate to severe acute or chronic bronchitis. In terms of safety， the Consensus proposes methods for identifying common adverse reactions， drug contraindications， and precautions based on a review of the existing data and preliminarily builds a risk management framework. However， the evidence on which the Consensus is based are mostly early studies， with small sample sizes， which result in the low evidence grades （mainly C and D） of some recommendations. ConclusionThe Consensus provides specific guidance for the clinical use of Yifei Zhike capsules. However， due to the limited quality of the existing research evidence， more high-quality clinical studies are needed in the future to further verify its efficacy， safety， and explore its broader application prospects.

Objective To describe the significance of the pretreatment inflammatory indicators in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) after undergoing radical radiotherapy. Methods The data of 246 ESCC patients who underwent radical radiotherapy were retrospectively collected. Receiver operating characteristic (ROC) curves were drawn to determine the optimal cutoff values for platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII). The Kaplan-Meier method was used for survival analysis. We conducted univariate and multivariate analyses by using the Cox proportional risk regression model. Software R (version 4.2.0) was used to create the nomogram of prognostic factors. Results The results of the ROC curve analysis showed that the optimal cutoff values of PLR, NLR, and SII were 146.06, 2.67, and 493.97, respectively. The overall response rates were 77.6% and 64.5% in the low and high NLR groups, respectively (P<0.05). The results of the Kaplan-Meier survival analysis revealed that the prognosis of patients in the low PLR, NLR, and SII group was better than that of patients in the high PLR, NLR, and SII group (all P<0.05). The results of the multivariate Cox regression analysis showed that gender, treatment modalities, T stage, and NLR were independent factors affecting the overall survival (OS). In addition, T stage and NLR were independent factors affecting the progression-free survival (PFS) (all P<0.05). The nomogram models of OS and PFS prediction were established based on multivariate analysis. The C-index values were 0.703 and 0.668. The calibration curves showed excellent consistency between the predicted and observed OS and PFS. Conclusion The pretreatment values of PLR, NLR, and SII are correlated with the prognosis of patients with ESCC who underwent radical radiotherapy. Moreover, NLR is an independent factor affecting the OS and PFS of ESCC patients. The NLR-based nomogram model has a good predictive ability.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective: To investigate the role and mechanism of the heat-clearing and detoxifying drug Laggerae Herba in regulating the nuclear factor-erythroid 2-related factor-2(Nrf2)/solute carrier family 7 member 11 (SLC7A11)/glutathione peroxidase 4 (GPX4) signaling pathway to inhibit ferroptosis and improve chronic heart failure induced by transverse aortic arch constriction in mice. Methods: Twenty-four male ICR mice were divided into the sham (n=6) and transverse aortic arch constriction groups (n=18) according to the random number table method. The transverse aortic arch constriction group underwent transverse aortic constriction surgery to establish models. After modeling, the transverse aortic arch constriction group was further divided into the model, captopril, and Laggerae Herba groups according to the random number table method, with six mice per group. The captopril (15 mg/kg) and Laggerae Herba groups (1.95 g/kg) received the corresponding drugs by gavage, whereas the sham operation and model groups were administered the same volume of ultrapure water by gavage once a day for four consecutive weeks. After treatment, the cardiac function indexes of mice in each group were detected using ultrasound. The heart mass and tibia length were measured to calculate the ratio of heart weight to tibia length. Hematoxylin and eosin staining were used to observe the pathological changes in myocardial tissue. Masson staining was used to observe the degree of myocardial fibrosis. Wheat germ agglutinin staining was used to observe the degree of myocardial cell hypertrophy. Prussian blue staining was used to observe the iron deposition in myocardial tissue. An enzyme-linked immunosorbent assay was used to detect the amino-terminal pro-brain natriuretic peptide (NT-proBNP) and glutathione (GSH) contents in mice serum. Colorimetry was used to detect the malondialdehyde (MDA) content in mice serum. Western blotting was used to detect the Nrf2, GPX4, SLC7A11, and ferritin heavy chain 1 (FTH1) protein expressions in mice cardiac tissue. Results: Compared with the sham group, in the model group, the ejection fraction (EF) and fractional shortening (FS) of mice decreased, the left ventricular end-systolic volume (LVESV) and left ventricular end-systolic diameter (LVESD) increased, the left ventricular anterior wall end-systolic thickness (LVAWs) and left ventricular posterior wall end-systolic thickness (LVPWs) decreased, the ratio of heart weight to tibia length increased, the myocardial tissue morphology changed, myocardial fibrosis increased, the cross-sectional area of myocardial cells increased, iron deposition appeared in myocardial tissue, the serum NT-proBNP and MDA levels increased, the GSH level decreased, and Nrf2, GPX4, SLC7A11, and FTH1 protein expressions in cardiac tissue decreased (P<0.05). Compared with the model group, in the captopril and Laggerae Herba groups, the EF, FS, and LVAWs increased, the LVESV and LVESD decreased, the ratio of heart weight to tibia length decreased, the myocardial cells were arranged neatly, the degree of myocardial fibrosis decreased, the cross-sectional area of myocardial cells decreased, the serum NT-proBNP level decreased, and the GSH level increased. Compared with the model group, the LVPWs increased, the iron deposition in myocardial tissue decreased, the serum MDA level decreased, and Nrf2, GPX4, SLC7A11, and FTH1 protein expressions in cardiac tissue increased (P<0.05) in the Laggerae Herba group. Conclusion Laggerae Herba improves the cardiac function of mice with chronic heart failure caused by transverse aortic arch constriction, reduces the pathological remodeling of the heart, and reduces fibrosis. Its mechanism may be related to Nrf2/SLC7A11/GPX4 pathway-mediated ferroptosis.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective: To analyze the epidemiological characteristics and changing trends of non suicidal self injury (NSSI) behaviors among middle school students in Jiading District of Shanghai, from 2015 to 2023, so as to provide a basis for the development of NSSI prevention and control measures among students. Methods: Using a stratified cluster random sampling method, a total of five times for Shanghai Adolescent Health Risk Behavior Surveys were conducted for every two years in Jiading District of Shanghai from 2015 to 2023. A total of 5 231 middle school students from junior high schools and senior high schools were selected for questionnaire surveys. Intergroup comparisons were performed using the x 2 test or the χ 2 trend test, and the JointPoint 5.0 software was used to analyze the changing trends, with the annual percent change (APC) used for evaluation. A binary Logistic regression model was employed to analyze the related factors of NSSI behavior among middle school students. Results: In 2023, the reported NSSI rate among middle school students in Jiading District was 14.2%. The rate was significantly higher among junior high school students (17.1%) than that among senior high school students (11.1%), and higher among females (19.2%) than that among males (10.0%) ( χ 2=10.04, 23.21, both P <0.01). From 2015 to 2023, the overall reported NSSI rate showed an increasing trend, rising from 8.6% in 2015 to 14.2% in 2023 ( χ 2 trend =22.25), with an APC of 6.64% ( t =3.49), and the APC for girls was 9.79 % ( t =3.20) (all P <0.05). Among students reporting NSSI, the proportion experiencing ≥6 episodes increased from 10.8% in 2015 to 19.2% in 2023 ( χ 2 trend =6.57, P <0.05). Multivariate Logistic regression analysis indicated that girls, junior high school students, those with insomnia, depressive emotion and drinkers had higher risks of NSSI, compared to boys, senior high school students, those without insomnia, non depressive emotion students and non drinkers ( OR =1.71, 1.96, 3.44, 4.76, 1.77, all P < 0.05 ). Conclusions The reported rate of NSSI among middle school students in Jiading District of Shanghai, increased annually from 2015 to 2023, and the proportion of repeated NSSI also showed an upward trend. Early intervention measures targeting middle school students, especially junior high school students and females, should be implemented to prevent and control its occurrence and development.