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.

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.

The abnormal accumulation, structural change, and spread of tau protein in the brain are now considered important factors in the progression of Alzheimer’s disease (AD) and many other tau-related neurodegenerative diseases. Clinical and pathological studies have shown that the amount and distribution of abnormal tau protein are closely related to synaptic damage, neuronal loss, and cognitive decline. Therefore, tau protein has become an important target for disease-modifying therapy. However, tau is difficult to target because it has no fixed structure, lacks a clear active site, undergoes many post-translational modifications, and forms different abnormal structures in different diseases and disease stages. Therefore, the effective and selective removal of harmful tau species is both a major opportunity and a major challenge in drug discovery for neurodegenerative diseases. Proteolysis-targeting chimeras (PROTACs) offer a new strategy to deal with this problem. Unlike traditional inhibitors, PROTACs do not simply block protein function. Instead, they bring the target protein close to an E3 ubiquitin ligase, which leads to ubiquitination and degradation of the target protein by the proteasome. This event-driven mechanism may be especially useful for disease-related proteins such as tau protein, which are hard to inhibit with traditional small molecules. In recent years, tau protein-targeting PROTACs and related degradation strategies have shown promising effects in cell and animal models. These effects include reducing abnormal tau protein levels, lowering synaptic toxicity, regulating disease-related signaling pathways, and improving behavioral or cognitive outcomes. These studies support targeted tau protein degradation as a promising therapeutic approach, although many problems still need to be solved before clinical use. In this review, we summarize recent progress in the development of tau protein-targeting PROTACs and discuss the main factors that affect their design and activity. We focus on the choice of tau protein-binding ligands, their binding sites, and their ability to recognize abnormal tau protein rather than normal tau protein. We also discuss the selection of E3 ligase recruiters, linker length and structure, ternary complex formation, and structure-activity relationships that influence degradation potency and selectivity. In addition, we introduce new strategies beyond traditional proteasomal degradation, including the regulation of tau protein phosphorylation and autophagy-based clearance. These approaches may be more suitable for different tau species or different stages of disease. A major challenge for tau protein-targeting PROTACs is delivery to the central nervous system. Many PROTACs have high molecular mass, large polar surface area, flexible structures, and may be removed by efflux transporters. As a result, they often have poor ability to cross the blood-brain barrier and may not reach enough free drug levels in the brain. Therefore, the therapeutic potential of tau protein-targeting PROTACs depends not only on their ability to degrade tau protein in vitro, but also on whether they can achieve long-lasting, selective, and safe target engagement in important brain regions after practical administration. Future studies should focus on improving selectivity for abnormal tau protein, understanding E3 ligase function in neural cells, designing brain-penetrant molecules, developing better delivery methods, and testing these drugs in models that better reflect human disease. Overall, tau protein-targeting PROTACs are a promising and fast-developing strategy for treating tau-related diseases. However, their successful clinical translation will require progress in chemical biology, medicinal chemistry, neuroscience, drug delivery, and translational medicine.

Objective To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective To explore the effect of PCSK9 inhibitor combined with atorvastatin on carotid atherosclerosis and its anti-inflammatory effect in patients with hypertension complicated with type 2 diabetes mellitus.Methods A total of 100 patients with hypertension complicated with type 2 diabetes mellitus who were treated in Nanchang Third Hospital from October 2022 to August 2023 were selected as the research subjects.They were divided into the control group and the study group by the random number table method,with 50 cases in each group.Both groups of patients received conventional antihypertensive,hypoglycemic,and antiplatelet therapy.The control group took 20 mg of atorvastatin calcium tablets orally,once a night.On the basis of the control group,the study group was additionally given 150 mg of evolocumab injection(a PCSK9 inhibitor)by subcutaneous injection,once every two weeks.Both groups of patients were followed up for 24 weeks.The levels of blood lipids,blood glucose,inflammatory cytokines,carotid intima-media thickness(IMT),atherosclerotic plaque score and adverse reactions of the patients in the two groups before and after treatment were detected and compared.Results The levels of TC,TG and LDL-C in the study group after treatment were lower than those before treatment and those in the control group at the same period,and the differences were statistically significant(P＜0.05).The levels of IL-1,IL-6,TNF-α,hs-CRP,as well as the ca-rotid IMT and atherosclerotic plaque score in the study group after treatment were lower than those before treatment and those in the control group at the same period,and the differences were statistically significant(P＜O.05).During the treatment period,there was no significant difference in the occurrence of adverse reac-tions between the two groups(P＞0.05).Conclusion The combination of PCSK9 inhibitor and atorvastatin can effectively regulate the blood lipid levels of patients with hypertension complicated and type 2 diabetes mellitus,alleviate the inflammatory response,and improve the degree of carotid atherosclerosis in these pa-tients.

Objective:To investigate relationship between expression of ADGRG5 and clinical prognosis and immune response in pancreatic adenocarcinoma(PAAD).Methods:ADGRG5 expression in PAAD and normal tissues were compared by Wilcoxon rank sum test.Diagnostic value of ADGRG5 was evaluated by ROC curve in PAAD.Kaplan-Meier method and Cox regres-sion analysis were used to evaluate prognostic factors.Gene set enrichment analysis(GSEA)and immune infiltration analysis were applied to annotate biological function of ADGRG5.Results:ADGRG5 expression in PAAD was significantly higher than normal tissue(P=2.8e-32).ADGRG5 had significant diagnostic and prognostic ability for PAAD(AUC=0.866).High ADGRG5 expression predicted a good progress free interval(PFI)(P=0.01),and expression of ADGRG5 was independently associated with PFI(HR:0.656,95%CI:0.433～0.972,P=0.035).ADGRG5 expression was related to regulation of immunomodulatory pathway and function of some types of immune infiltrating cells.Conclusion:Increased ADGRG5 may be a potential biomarker for PAAD diagnosis and prognosis,which affects prognosis of PAAD patients and significantly correlated with immune infiltration.

Objective:We aimed to explore the effect of exercise on cardiac function and APJ system in old mice.Meth-ods:Male C57/BL6 mice were divided into young group(n=30),old control group(n=30),old continuous moderate-intensity training(CMIT)group(n=30),and old high-intensity interval training(HIIT)group(n=30).Mice in the CMIT group and HIIT group underwent 8-week exercise intervention,respectively.Mice in the young group and old con-trol group were placed on stationary treadmill simultaneously.The heart function parameters were measured,and the ex-pression of APJ/Apelin in myocardium and circulating Apelin/Elabela were compared among above-mentioned groups.Results:Compared yo mice in old control group,those in the CMIT group and HIIT group had significant higher left ven-tricular ejection fraction[(67.74±6.18)％,(70.69±7.43)％vs.(61.17±4.78)％]and left ventricular fractional short-ening[(33.07±4.29)％,(33.37±5.57)％vs.(28.05±3.61)％](P＜0.05 or＜0.01);and significant lower Tei index[(0.78±0.09),(0.78±0.12)vs.(0.88±0.08)]and left ventricular posterior wall thickness at diastole[(1.01±0.09)mm,(1.02±0.10)mm vs.(1.19±0.14)mm](P＜0.05all).Compared to mice in old control group,those in the HIIT group had significant higher mitral valve E/A ratio[(1.63±0.11)vs.(1.42±0.18),P=0.043].Compared to mice in old control group,those in the HIIT group had significant higher expressions of APJ and Apelin in myocardium,as well as the levels of Apelin[(1186.23±393.74)pg/ml vs.(413.17±80.81)pg/ml]and Elabela[(5.38±2.76)ng/ml vs.(1.57±0.60)ng/ml]in the serum(P＜0.05 or＜0.01).Conclusion:Long-term exercise initiated late in life could im-prove heart function of mice.The APJ-Apelin/Elabela system may be involved in the exercise-mediated improvement of cardiac aging.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Pheochromocytoma is a neuroendocrine tumor that produces catecholamines, leading to elevated blood pressure and metabolic changes in patients. It can result in serious complications affecting the heart, brain, kidneys, and blood vessels, potentially becoming a primary cause of death. Most pheochromocytoma patients present with atypical symptoms, making misdiagnosis or missed diagnosis common. This article reports a case of pheochromocytoma crisis misdiagnosed as severe viral myocarditis and includes a review of the relevant literature.