LL-PTP, a pancreatic cancer-targeted nanocarrier, was synthesized via click chemistry, and the insoluble photosensitive drug zinc phthalocyanine (ZnPc) was physically encapsulated within LL-PTP to fabricate LL-PTP/ZnPc nanoparticles. The critical aggregation concentration (CAC) of LL-PTP was determined to be 52.97 μg/mL; the LL-PTP/ZnPc nanoparticles, formed by the physical encapsulation of ZnPc, appeared as a blue transparent solution; the ZnPc loading efficiency of these nanoparticles was (20.1 ± 1.4) %, with a hydrated particle size of (89.18 ± 0.21) nm; notably, the nanoparticles exhibited excellent storage stability and serum stability, which fully meet the stability requirements for injectable formulations in clinical applications; furthermore, the release rate of LL-PTP/ZnPc in tumor tissue was significantly higher (6.2-fold) than that in serum, which is significantly beneficial for the therapeutic effect of nanoparticles at the tumor site. To investigate the targeted uptake of LL-PTP/ZnPc, qualitative and quantitative analyses were performed using confocal laser scanning microscopy (CLSM) and flow cytometry, respectively, with the result that LL-PTP/ZnPc enhanced the uptake of nanoparticles by PANC-1 cells (a pancreatic cancer cell line) through Plectin-1-mediated endocytosis with an efficiency significantly superior to that of LL/ZnPc (non-targeted control nanoparticles) and free ZnPc. Intracellular reactive oxygen species (ROS) levels were detected using the DCFH-DA probe, with the finding that LL-PTP/ZnPc, upon light irradiation, induced a marked increase in intracellular ROS production—an effect that is conducive to achieving enhanced photodynamic therapy (PDT) efficacy against pancreatic cancer. In conclusion, this study successfully developed LL-PTP, a targeted nanocarrier for pancreatic cancer, and achieved efficient loading of ZnPc, which effectively improved the effect of PDT on pancreatic cancer.

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.

Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Neuropathic pain is frequently comorbidity with cognitive deficits. Neuralized1 (Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory. However, the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive. Herein, we found that lumbar 5 spinal nerve ligation (SNL) in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus. The Neurl1 expression in the hippocampal CA1 was decreased after SNL. And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2. Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment, and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL. Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity. The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1. Collectively, the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and, in turn, repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.
Animals ; Male ; Neuralgia/metabolism* ; Rats ; Down-Regulation/physiology* ; Ubiquitination/physiology* ; Neuronal Plasticity/physiology* ; Rats, Sprague-Dawley ; CA1 Region, Hippocampal/metabolism* ; Cognitive Dysfunction/metabolism* ; RNA-Binding Proteins/metabolism* ; Receptors, AMPA/metabolism*

OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
Stomach Neoplasms/pathology* ; Humans ; Prognosis ; Lysosomes/physiology* ; RNA-Seq ; Cell Death ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Cell Proliferation ; Single-Cell Gene Expression Analysis

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

Objective:To systematically review the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures.Methods:PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Database and VIP Database were systematically searched to collect literature on the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures from inception to June 30, 2024. The languages were limited to Chinese and English. Two researchers screened the literature according to the inclusion and exclusion criteria. Data extraction was performed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS), encompassing basic study characteristics, model development features, and model performance metrics. The predictors, validation methods, presentation formats, and predictive performance of the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures were evaluated. The prediction model risk-of-bias assessment tool (PROBAST) was employed to assess risk of bias and applicability of the included studies.Results:A total of 11 studies, comprising of 16 prediction models, were included, with a total sample size of 283-1 508 patients and a pulmonary infection incidence rate of 5.4%-16.25%. The independent predictive factors repeatedly included in the models were age, American Society of Anesthesiologists (ASA) scale, preoperative comorbidities, chronic obstructive pulmonary disease (COPD), preoperative albumin level, white blood cell count (WBC), and C-reactive protein (CRP) level. The models were internally validated in 7 studies and externally validated in 3. The models were visualized in the form of a nomogram in 7 studies and a web-based risk calculator in 1. Model prediction performance was analyzed: (1) In terms of the discrimination, 9 studies reported the area under the receiver operating characteristic curve (AUC), with the overall AUC range of 0.664-0.905. (2) In terms of the calibration, 5 studies had Hosmer-Lemeshow test, with the P-values all above 0.05; 2 studies reported the calibration plots, with the slopes close to 1 and the Brier scores of 0.016 and 0.112; 4 studies reported the sensitivity of the models of 73.91%-92.40% and specificity of 57.10%-92.41%. According to PROBAST, all 11 studies exhibited certain risk of bias while maintaining favorable applicability. Conclusions:Age, ASA scale, preoperative comorbidities, COPD, preoperative albumin level, WBC, and CRP level are found to be independent predictive factors repeatedly reported in the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures. The existing models demonstrate a robust overall prediction performance despite certain risks of bias.

Objective:To observe any effect of supplementing treatment according to the Early Start Denver model (ESDM) with repeated transcranial magnetic stimulation (rTMS) in the treatment of children with autism spectrum disorder (ASD).Methods:Sixty-seven children on the autism spectrum aged 2 or 3 years were randomly divided into a control group of 33 and an observation group of 34. Both groups were treated as specified by the ESDM for 24 weeks, but the observation group additionally received rTMS. At 12 and 24 weeks, both groups were evaluated using the Autism Behavior Checklist, the Childhood Autism Rating Scale (CARS), the revised version of the Repetitive Behavior Scale (RBS-R), Gesell Development Schedules, and the Autism Treatment Evaluation Checklist (ATEC).Results:The CARS, Gesell, RBS-R and ATEC results of both groups had improved significantly after 12 weeks, with further improvements observed another 12 weeks later, when the average Autism Behavior Checklist scores had also improved significantly. At that point the results of the observation group were significantly better than those of the control group, on average.Conclusions:Combining ESDM and rTMS can significantly relieve the main symptoms of autism and improve the comprehensive development of children on the autism spectrum 2 or 3 years old. Therefore, such combination is worthy of application in clinical practice.

Background: Diabetic nephropathy (DN) is the most common and serious complication of diabetes mellitus. Shionone (SH), an important triterpenoid compound in the root extract of Aster, might exert a protective effect in DN mice and high glucose cultivated glomerular podocytes. The current study aimed to unravel the underlying mechanism by which SH mitigates DN. We postulate that SH stimulates the expression of sestrin-2 (SESN2), a pivotal stress-inducible protein in the anti-inflammasome machinery. Methods: We utilized high-fat diet combined with streptozotocin (55 mg/kg intraperitoneal) for DN mice model, and high glucose (30 mM, 48 hours) cultured glomerular podocytes for DN cell model to evaluate the effect of SH. We also preformed experimentation on SESN2 deficiency models (SESN2 knockout mice and SESN2 siRNA in cells) to further prove our hypothesis. Results: The results demonstrated that SH effectively suppressed glomerular fibrosis, induced adenosine monophosphate-activated protein kinase (AMPK) phosphorylation, and inhibited NLR family pyrin domain containing 3 (NLRP3) activation. Furthermore, our findings revealed that SH exerted its anti-inflammatory effect through Sesn2-dependent nuclear factor erythroid 2-related factor 2 (Nrf2) nuclear translocation and subsequent activation of its downstream target heme oxygenase-1 (HO-1). Conclusion In summary, our findings suggest that SH serves as a promising therapeutic agent for the treatment of DN-related glomerular fibrosis. SH enhances the expression of SESN2, attenuates α-smooth muscle actin accumulation, and suppresses NLRP3-related inflammation through the Nrf2/HO-1 signaling pathway.