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.

Background: Disorders of the blood-brain barrier (BBB) arising from diabetes mellitus are closely related to diabetic encephalopathy. Previous research has suggested that neuron-glia antigen 2 (NG2)-glia plays a key role in maintaining the integrity of the BBB. However, the mechanism by which NG2-glia regulates the diabetic BBB remains unclear. Methods: Type 2 diabetes mellitus (T2DM) db/db mice and db/m mice were used. Evans-Blue BBB permeability tests and transmission electron microscopy techniques were applied. Tight junction proteins were assessed by immunofluorescence and transmission electron microscopy. NG2-glia number and signaling pathways were evaluated by immunofluorescence. Detection of matrix metalloproteinase-9 (MMP-9) in serum was performed using enzyme-linked immunosorbent assay (ELISA). Results: In T2DM db/db mice, BBB permeability in the hippocampus significantly increased from 16 weeks of age, and the structure of tight junction proteins changed. The number of NG2-glia in the hippocampus of db/db mice increased around microvessels from 12 weeks of age. Concurrently, the expression of MMP-9 increased in the hippocampus with no change in serum. Sixteen- week-old db/db mice showed activation of the Wnt/β-catenin signaling in hippocampal NG2-glia. Treatment with XAV-939 improved structural and functional changes in the hippocampal BBB and reduced MMP-9 secretion by hippocampal NG2-glia in db/db mice. It was also found that the upregulation of β-catenin protein in NG2-glia in the hippocampus of 16-week-old db/db mice was significantly alleviated by treatment with XAV-939. Conclusion The results indicate that NG2-glia can lead to structural and functional disruption of the diabetic BBB by activating Wnt/β-catenin signaling, upregulating MMP-9, and degrading tight junction proteins.

Objective To explore the prognostic value of serum growth hormone releasing peptide(Ghre-lin),angiopoietin like protein 4(ANGPTL4)combined with Geriatric Nutritional Risk Index(GNRI)in eld-erly patients with chronic pulmonary heart disease(CPHD).Methods From January 2019 to January 2022,a total of 98 elderly patients with CPHD admitted to the Hai'an People's Hospital were selected as the CPHD group,and another 98 healthy individuals who underwent physical examinations were selected as the control group.Enzyme linked immunosorbent assay(ELISA)was applied to determine the levels of serum Ghrelin and ANGPTL4.Multivariate Logistic regression was applied to analyze the influencing factors of prognosis in elderly patients with CPHD.Receiver operating characteristic(ROC)curve was plotted to analyze the predic-tive value of serum Ghrelin,ANGPTL4 combined with GNRI in the prognosis of CPHD.Results Compared with the control group,the level of serum Ghrelin and GNRI in the CPHD group were obviously reduced(P＜0.05),while the level of ANGPTL4 was obviously increased(P＜0.05).Compared with the survival group,the GNRI and Ghrelin level in the death group were obviously reduced(P＜0.05),while the level of AN-GPTL4 was obviously increased(P＜0.05).Multivariate Logistic regression results showed that ANGPTL4 was a risk factor affecting the prognosis of elderly CPHD patients(P＜0.05),while Ghrelin and GNRI were protective factors affecting the prognosis of elderly CPHD patients(P＜0.05).ROC curve results showed that the combination of serum Ghrelin,ANGPTL4,and GNRI had the largest area under the curve(AUC)in predicting the prognosis of elderly CPHD patients,which was better than that of the individual predictions of serum Ghrelin,ANGPTL4,and GNRI(P＜0.05).Conclusion The level of serum Ghrelin decreases and the level of ANGPTL4 increases in elderly patients with CPHD.The combination of the two with GNRI could better predict the prognosis of CPHD patients and has high predictive value.

Objective To analyze the relationship between clinical drug utilization and the risk of nosocomial infections among hospitalized patients,and provide evidence for the prevention and control of nosocomial infections.Methods This study adopted a retrospective case-control design.The case group included 209 patients with nosocomial infection reported from January 2023 to December 2023 in a tertiary hospital.The control group included 209 patients without nosocomial infection during the same period.The patients in the control group were selected by stratified sampling based on Charlson Comorbidity Index(CCI).Results Univariate analysis showed that proton pump inhibitors,antacids,immunosuppressants and prior antimicrobial combination therapy increased the risk of nosocomial infection(P＜0.05).Multivariate log-binomial regression analysis showed that proton pump inhibitors,immunosuppressive drugs,and prior antimicrobial combination therapy were correlated with nosocomial infection.The corresponding relative risk(RR)was 1.31(95％CI:1.07-1.60),1.40(95％CI:1.02-1.91),and 1.66(95％CI:1.01-2.74),respectively.Further analysis indicated that the patients with nosocomial infection had longer time in use of proton pump inhibitors and prior antimicrobial combination therapy than the patients in the control group(Z=-6.331,P＜0.001;Z=-2.667,P=0.008).The trend Chi-square test showed that there was a dose-response relationship for proton pump inhibitors(x2=73.869,P＜0.001),immunosuppressive drugs(x2=16.530,P＜0.001),and prior antimicrobial combination therapy(x2=35.107,P＜0.001).Conclusions The use of immunosuppressants,proton pump inhibitors and antimicrobial combination therapy increases the risk of nosocomial infections in hospitalized patients.The prolonged use of these drugs will further increase the risk of nosocomial infection.

Homeostasis and energy and substance metabolism reprogramming shape various tumor microenvironment to sustain cancer stemness, self-plasticity and treatment resistance. Aiming at them, a lipid-based pharmaceutical loaded with CaO₂ and glucose oxidase (GOx) (LipoCaO₂/GOx, LCG) has been obtained to disrupt calcium homeostasis and interfere with glycometabolism. The loaded GOx can decompose glucose into H₂O₂ and gluconic acid, thus competing with anaerobic glycolysis to hamper lactic acid (LA) secretion. The obtained gluconic acid further deprives CaO₂ to produce H₂O₂ and release Ca²⁺, disrupting Ca²⁺ homeostasis, which synergizes with GOx-mediated glycometabolism interference to deplete glutathione (GSH) and yield reactive oxygen species (ROS). Systematical experiments reveal that these sequential multifaceted events unlocked by Ca²⁺ homeostasis disruption and glycometabolism interference, ROS production and LA inhibition, successfully enhance cancer immunogenic deaths of breast cancer cells, hamper regulatory T cells (Tregs) infiltration and promote CD8⁺ T recruitment, which receives a considerably-inhibited outcome against breast cancer progression. Collectively, this calcium homeostasis disruption glycometabolism interference strategy effectively combines ion interference therapy with starvation therapy to eventually evoke an effective anti-tumor immune environment, which represents in the field of biomedical research.

Gastrointestinal (GI) cancers are a leading cause of cancer morbidity and mortality worldwide. Despite advances in treatment, cancer relapse remains a significant challenge, necessitating novel therapeutic strategies. In this study, we engineered nanobody-based chimeric antigen receptor (CAR) natural killer (NK) cells targeting cadherin 17 (CDH17) for the treatment of GI tumors. In addition, to enhance the efficacy of CAR-NK cells, we also incorporated CV1, a CD47-SIRPα axis inhibitor, to evaluate the anti-tumor effect of this combination. We found that CDH17-CAR-NK cells effectively eliminated GI cancers cells in a CDH17-dependent manner. CDH17-CAR-NK cells also exhibit potent in vivo anti-tumor effects in cancer cell-derived xenograft and patient-derived xenograft mouse models. Additionally, the anti-tumor activity of CDH17-CAR-NK cells is synergistically enhanced by CD47-signal regulatory protein α (SIRPα) axis inhibitor CV1, likely through augmented macrophages activation and an increase in M1-phenotype macrophages in the tumor microenvironment. Collectively, our findings suggest that CDH17-targeting CAR-NK cells are a promising strategy for GI cancers. The combination of CDH17-CAR-NK cells with CV1 emerges as a potential combinatorial approach to overcome the limitations of CAR-NK therapy. Further investigations are warranted to speed up the clinical translation of these findings.

OBJECTIVES: We propose a multi-scale jaw cyst segmentation model, AConvLSTM U-Net, which is based on bidirectional dense connections and attention mechanisms to achieve accurate automatic segmentation of mandibular cyst images. METHODS: A dataset consisting of 2592 jaw cyst images was used. AConvLSTM U-Net designs a MBC on the encoding path to enhance feature extraction capabilities. A DPD was used to connect the encoder and decoder, and a bidirectional ConvLSTM was introduced in the jump connection to obtain rich semantic information. A decoding block based on scSE was then used on the decoding path to enhance the focus on important information. Finally, a DS was designed, and the model was optimized by integrating a joint loss function to further improve the segmentation accuracy. RESULTS: The experiment with AConvLSTM U-Net for jaw cyst lesion segmentation showed a MCC of 93.8443%, a DSC of 93.9067%, and a JSC of 88.5133%, outperforming all the other comparison segmentation models. CONCLUSIONS The proposed algorithm shows a high accuracy and robustness on the jaw cyst dataset, demonstrating its superior performance over many existing methods for automatic segmentation of jaw cyst images and its potential to assist clinical diagnosis.
Humans ; Jaw Cysts/diagnostic imaging* ; Algorithms ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer

OBJECTIVES: To investigate the mechanism by which transforming growth factor‑β (TGF‑β) regulates major histocompatibility complex class I (MHC-I) expression in hepatocellular carcinoma (HCC) cells and its role in immune evasion of HCC. METHODS: HCC cells treated with TGF‑β alone or in combination with SB-431542 (a TGF-β type I receptor inhibitor) were examined for changes in MHC-I expression using RT-qPCR and Western blotting. A RNA interference experiment was used to explore the role of miR-23a-3p/IRF1 signaling in TGF‑β‑mediated regulation of MHC-I. HCC cells with different treatments were co-cultured with human peripheral blood mononuclear cells (PBMCs), and the changes in HCC cell proliferation was assessed using CCK-8 and colony formation assays. T-cell cytotoxicity in the co-culture systems was assessed with lactate dehydrogenase (LDH) release and JC-1 mitochondrial membrane potential assays, and T-cell activation was evaluated by flow cytometric analysis of CD69 cells and ELISA for TNF-α secretion. RESULTS: TGF‑β treatment significantly suppressed MHC-I expression in HCC cells and reduced T-cell activation, leading to increased tumor cell proliferation and decreased HCC cell death in the co-culture systems. Mechanistically, TGF-β upregulated miR-23a-3p, which directly targeted IRF1 to inhibit MHC-I transcription. Overexpression of miR-23a-3p phenocopied TGF‑β‑induced suppression of IRF1 and MHC-I. CONCLUSIONS We reveal a novel immune escape mechanism of HCC, in which TGF‑β attenuates T cell-mediated antitumor immunity by suppressing MHC-I expression through the miR-23a-3p/IRF1 signaling axis.
Humans ; MicroRNAs/genetics* ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Interferon Regulatory Factor-1/metabolism* ; Transforming Growth Factor beta/metabolism* ; Signal Transduction ; Histocompatibility Antigens Class I/metabolism* ; Cell Line, Tumor ; Tumor Escape ; Coculture Techniques

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.