Lung cancer is one of the most lethal tumors in the world with a 5-year overall survival rate of less than 20%, mainly including lung adenocarcinoma (LUAD). Tumor microenvironment (TME) has become a new research focus in the treatment of lung cancer. The TME is heterogeneous in composition and consists of cellular components, growth factors, proteases, and extracellular matrix. The various cellular components exert a different role in apoptosis, metastasis, or proliferation of lung cancer cells through different pathways, thus contributing to the treatment of adenocarcinoma and potentially facilitating novel therapeutic methods. This review summarizes the research progress on different cellular components with cell-cell interactions in the TME of LUAD, along with their corresponding drug candidates, suggesting that targeting cellular components in the TME of LUAD holds great promise for future theraputic development.
Humans ; Tumor Microenvironment/drug effects* ; Adenocarcinoma of Lung/drug therapy* ; Lung Neoplasms/pathology* ; Adenocarcinoma/metabolism* ; Animals ; Apoptosis/physiology*

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

OBJECTIVES: To investigate the mechanism by which Guijianyu ameliorates podocyte injury in a mouse model of diabetic kidney disease (DKD) induced by advanced glycation endproducts (AGEs). METHODS: Sixty db/db mouse models of DKD were randomized equally into 5 groups for treatment with saline, Guijianyu extract at 3 doses or irbesartan for 12 weeks, and the changes in renal pathology and structure were observed using transmission electron microscopy, and the expressions of related genes and key proteins were detected using RT-qPCR and immunohistochemistry. In cultured MPC-5 cells incubated with 50 mg/L AGEs-BSA for 24 h, the effect of different concentrations of Guijianyu extract on cell viability was examined with CCK-8 assay; Western blotting was performed to detect the protein expressions of RAGE, VEGFA, TNF-α, NF-κB(p65), IL-6 and caspase-3, and the mRNA expressions of RAGE, NF-κB (p65), VEGFA and IL-6 were detected with RT-qPCR. RESULTS: In mouse models of DKD, treatment with high-dose Guijianyu extract significantly reduced renal expressions of RAGE, VEGFA, NF-κB(p65), and IL-6 proteins and the mRNA expressions of RAGE, NF-κB, and IL-6. In MPC-5 cells, exposure to AGEs significantly reduced cell viability and increased the protein expressions of RAGE, NF‑κB (p65), VEGFA, TNF-α, IL-6 and caspase-3 (P<0.05) and mRNA expressions of RAGE, NF-κB (p65), VEGFA, and IL-6. Treatment with Guijianyu extract obviously improved cell viability and reduced the expressions of RAGE, NF-κB(p65), VEGFA, TNF-α, IL-6, and caspase-3. Furthermore, Guijianyu extract effectively reversed RAGE agonist-induced elevation of protein expressions of RAGE, VEGFA, TNF-α, IL-6, and caspase-3 and mRNA expressions of RAGE, NF-κB (p65), IL-6, and VEGFA in MPC-5 cells. CONCLUSIONS Guijianyu extract ameliorates AGEs-induced mouse renal podocyte injury in DKD by inhibiting the activation of AGEs-RAGE signaling pathway and reducing the expressions of pro-inflammatory cytokines and vascular endothelial growth factors.
Animals ; Glycation End Products, Advanced ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Signal Transduction/drug effects* ; Podocytes/pathology* ; Diabetic Nephropathies/drug therapy* ; Receptor for Advanced Glycation End Products ; Vascular Endothelial Growth Factor A/metabolism* ; Interleukin-6/metabolism* ; Male

More and more studies suggest that targeting neuronal antibodies,particularly anti-Hu antibody,can cause various degrees of damage to central nervous system and/or enteric nervous system and affect the occurrence,progression,and prognosis of related diseases.Therefore,elucidating the mechanisms of anti-Hu antibody-induced nervous system injury and its relevance to diseases is of significant importance for individualized diagnosis and treatment.This article reviewed the associations between anti-Hu antibody and paraneoplastic gastrointestinal dysmotility,chronic intestinal pseudo-obstruction,and irritable bowel syndrome,as well as the mechanisms of neuronal damage caused by anti-Hu antibody.

OBJECTIVE: To investigate the variations in the expression of voltage-gated sodium (Na_v) channel subunits during development of rat cerebellar Purkinje neurons and their correlation with maturation of electrophysiological characteristics of the neurons. METHODS: We observed the changes in the expression levels of Na_V1.1, 1.2, 1.3 and 1.6 during the development of Purkinje neurons using immunohistochemistry in neonatal (5-7 days after birth), juvenile (12-14 days), adolescent (21-24 days), and adult (42-60 days) SD rats. Using whole-cell patch-clamp technique, we recorded the spontaneous electrical activity of the neurons in ex vivo brain slices of rats of different ages to analyze the changes of electrophysiological characteristics of these neurons during development. RESULTS: The expression of Na_V subunits in rat cerebellar Purkinje neurons showed significant variations during development. Na_V1.1 subunit was highly expressed throughout the developmental stages and increased progressively with age (P < 0.05). Na_V1.2 expression was not detected in the neurons in any of the developmental stages (P > 0.05). The expression level of Na_V1.3 decreased with development and became undetectable after adolescence (P < 0.05). Na_V1.6 expression was not detected during infancy, but increased with further development (P < 0.05). Na_V1.1 and Na_V1.3 were mainly expressed in the early stages of development. With the maturation of the rats, Na_V1.3 expression disappeared and Na_V1.6 expression increased in the neurons. Na_V1.1 and Na_V1.6 were mainly expressed after adolescence. The total Na_V protein level increased gradually with development (P < 0.05) and tended to stabilize after adolescence. The spontaneous frequency and excitability of the Purkinje neurons increased gradually with development and reached the mature levels in adolescence. The developmental expression of Na_V subunits was positively correlated with discharge frequency (r=0.9942, P < 0.05) and negatively correlated with the excitatory threshold of the neurons (r=0.9891, P < 0.05). CONCLUSION The changes in the expression levels of Na_V subunits are correlated with the maturation of high frequency electrophysiological properties of the neurons, suggesting thatmature Na_V subunit expressions is the basis of maturation of electrophysiological characteristics of the neurons.
Rats ; Animals ; Purkinje Cells/physiology* ; Rats, Sprague-Dawley ; Neurons ; Brain ; Sodium/metabolism*

Objective:To analyze the relationship between free androgen index and insulin function in obese young men aged from 20 to 35.Methods:A total of 82 young obese men in Obesity Clinic from February to October 2019 were enrolled in the study. The subjects were divided into 3 subgroups according to free androgen index level tertiles. The blood glucose and insulin levels were tested after oral glucose tolerance test. Homeostasis model assessment for insulin resistance (HOMA-IR), homeostasis model assessment for β cell function (HOMA-β), insulin secretion index, and insulin sensitivity index (Matsuda index) were used to evaluate β cell function in oder to analyze the relationship between free androgen index and insulin function.Results:In young obese men, participants with higher free androgen index levels exhibited less waist circumference, lower body mass index, 1 h postprandial insulin, 2 h postprandial insulin, HOMA-IR level but with a higher total testosterone, sex hormone binding globulin, and Matsuda index level (all P<0.05). There was a negative correlation between the free androgen index and the HOMA-IR ( r=-0.386, P=0.016), and the correlation tended to a linear trend after adjustment for age, sex, body mass index, and waist circumference ( Ptrend=0.034). Free testosterone was positively correlated with Matsuda index ( r=0.280, P=0.004), but the correlation disappeared after adjustment ( Ptrend=0.623). The results of further regression analysis showed that the level of free testosterone index decreased by 14.1% ( OR=0.869, 95% CI0.767-0.984, P=0.028) for each increase of HOMA-IR after adjustment. Conclusion:The level of free testosterone index is a predictor of insulin resistance in obese young men, but the association between this parameter and insulin sensitivity may be caused by obesity.

Predicting the protein binding rate of drugs in plasma is helpful to us in understanding the pharmacokinetic characteristics of drugs, with much value of reference for early research on drug discovery. In this study, plasma protein binding rate information of 2 452 clinical drugs were collected.Two pieces of software, Molecular Operating Environment (MOE) and Mordred, were used to calculate molecular descriptors, which were used as input features of the model.Extreme gradient boosting (XGBoost) algorithm and random forest (RF) algorithm were then used to build a machine learning model.The results showed that, compared with MOE, the prediction performance of the constructed model was better using the molecular descriptor calculated by Mordred as the input of the model.The prediction performance results of the model constructed using the XGBoost algorithm and the RF algorithm were similar, and the R2 of the optimal model were both 0.715.According to the research results, it can be concluded that the drug plasma protein binding rate is closely related to some physical and chemical properties of the drug molecule, such as water solubility, octanol/water partition coefficient and conjugated double bonds.Using these parameters to predict the plasma protein binding rate of drugs has the advantages of convenience and efficiency, which can provide reference for related pharmacokinetic studies.