Clinical and Experimental Medicine最新文献

The programmed cell death protein 1 (PD‑1)/programmed death‑ligand 1 (PD‑L1) signaling axis is recognized as a central pathway maintaining immune suppression. Within the liver's inherently tolerogenic microenvironment, parenchymal, non‑parenchymal and immune cell populations are engaged in a dynamic regulatory network mediated by PD‑1/PD‑L1, which serves to preserve immune homeostasis and to balance innate and adaptive immune responses. Aberrant PD‑1/PD‑L1 signaling has been implicated across the disease continuum of many chronic liver disorders, spanning viral hepatitis, fibrosis, and hepatic malignancy. A systematic synthesis is presented of the regulatory roles and recent advances concerning the PD‑1/PD‑L1 axis in viral hepatitis, metabolic dysfunction‑associated fatty liver disease (MAFLD), autoimmune liver diseases and related conditions. Mechanisms regulating PD‑1/PD‑L1 expression and function in hepatocellular carcinoma (HCC) are comprehensively summarized, including tumor microenvironmental determinants, intracellular signaling cascades, post‑translational modifications and epigenetic control. A theoretical framework and novel perspectives are thereby provided for elucidating PD‑1/PD‑L1 dysregulation in chronic liver disease, for identifying candidate biomarkers, and for informing the development of precision immunotherapeutic strategies.

This study aims to describe the demographic, laboratory, and clinical characteristics of systemic sclerosis (SSc) patients in a Chinese national tertiary referral center, and to outline the treatment model. A retrospective observational study was conducted on adult SSc patients admitted to Peking Union Medical College Hospital between January 2012 and January 2022. Data were collected from electronic medical records, including demographics, clinical, laboratory parameters, and treatment details, and analyzed using SPSS. This study included 232 SSc patients, with 45.7% classified as diffuse cutaneous SSc (dcSSc) and 54.3% as limited cutaneous SSc (lcSSc). Anticentromere antibody and anti-topoisomerase I antibody were more common in lcSSc (33.3%) and dcSSc (36.2%), respectively. Excluding skin manifestations, the most common organ involvement was gastrointestinal, particularly esophageal involvement (56.5%), followed by diastolic dysfunction (53.2%), pericardial effusion (48.9%), pulmonary arterial hypertension (PAH) (44.8%), and lung fibrosis (20.1%). Combined immunosuppressive therapy was common, with 85.1% and 51.8% of patients receiving glucocorticoids and cyclophosphamide, respectively; medications for complication management, such as proton pump inhibitors (64.5%) for gastrointestinal involvement, were also frequently prescribed. Additionally, traditional Chinese medicine was incorporated into the regimen for almost one-quarter (23.7%) of patients. Multivariate analysis revealed that elevated N-terminal pro-brain natriuretic peptide and elevated C-reactive protein were significantly associated with PAH (OR = 12.359 and 3.239). This study delineates a clinical profile of Chinese SSc patients characterized by frequent multi-organ involvement and a common practice of combined immunosuppressive therapy. Further exploration is needed to develop more precise pharmacological strategies and to identify predictive factors for SSc-related complications.

To investigate the association between quantitative sialoscintigraphy parameters and rheumatoid factor (RF) seropositivity in patients with suspected autoimmune salivary gland dysfunction and to evaluate the diagnostic performance of functional imaging in relation to anti-Ro/SSA antibody (SSA) and anti-La/SSB antibody (SSB) profiles. We retrospectively reviewed the records of 100 patients who underwent sialoscintigraphy for sicca symptoms or suspected Sjögren's syndrome from April 2020 to May 2022. Quantitative parameters including time to maximum uptake(T_max)and time to onset of secretion(T_min)were extracted from time-activity curves. Serologic data for RF, SSA, and SSB antibodies were analyzed. Logistic regression and receiver operating characteristic (ROC) analyses were performed to assess associations and diagnostic performance. Of the 100 included patients, 22% were RF-positive, 52% were SSA positive, and 22% were SSB positive. Logistic regression analysis identified younger age (OR [odds ratio] = 0.955; 95% CI [confidence interval], 0.922-0.989;p = 0.009), prolonged submandibular gland T_min(OR = 1.309; 95% CI, 1.026-1.672;p= 0.031); and reduced T_maxin both the parotid (OR = 0.904; 95% CI, 0.820-0.997;p = 0.043) and submandibular glands (OR = 0.941; 95% CI, 0.891-0.993;p= 0.028) as significantly associated with RF seropositivity. Although group-wise comparisons showed no statistically significant differences, borderline trends were observed in submandibularT_maxand T_min.Taken together, these findings suggest that although the individual diagnostic performance of T_maxand T_minmay be limited, their combined assessment offers clinically meaningful insights into autoimmune-related salivary gland dysfunction. Quantitative sialoscintigraphy parameters, particularly prolonged submandibular T_minand reduced T_maxin submandibular and parotid glands, are associated with RF seropositivity and likely reflect early immune-mediated glandular dysfunction. Although their individual diagnostic performance is modest, these scintigraphic parameters may contribute valuable information when integrated with serologic markers and visual interpretation in assessing autoimmune salivary gland disease.

von Hippel-Lindau (VHL) syndrome is an autosomal dominant tumor susceptibility syndrome whose pathogenesis is closely associated with dysfunction of the ubiquitin-proteasome system (UPS). When core UPS components-E1, E2, E3 enzymes, and the proteasome-malfunction, the intracellular protein homeostasis network becomes severely disrupted, thereby driving tumorigenesis. This discovery also opens a novel perspective for addressing the therapeutic challenges of VHL syndrome. This review systematically analyzes the mechanisms of abnormally expressed enzymes within the UPS in VHL syndrome and thoroughly examines the progress in therapeutic strategies targeting various UPS components. It aims to provide a theoretical foundation for understanding the molecular mechanisms of this disease and developing precision treatment approaches.

Although most studies have reported that a high number of negative lymph nodes (NLNs) at surgery can be associated with improved overall survival (OS) in patients with breast cancer (BC), the effect size was reported differently in several studies, which may be due to the small sample size of the primary studies. This systematic review and meta-analysis aimed to investigate the association of a high number of NLNs removed during surgery with OS and recurrence-free survival (RFS) in BC patients who are candidates for axillary lymph node dissection. We searched the PubMed, Embase, Scopus, Google Scholar, and Web of Science databases, as well as study references, to identify related articles published from the beginning of 2000 to October 2024. Based on sensitivity analysis, the removal of ≥ 10 NLNs was defined as the high number of NLNs removed group, and the removal of < 10 NLNs was defined as the low number of NLNs removed group. The heterogeneity between studies was assessed using Cochran's Q and I2 tests. Publication bias was assessed using Egger's test. Ultimately, 14 studies encompassing 36,576 BC patients were included. A pooled estimate of 14 studies showed that a high number of NLN removed compared to a low number of NLN removed was significantly associated with improved 5-year OS (HR: 0.82, 95% CI: 0.74, 0.90), I2 = 93.8) and RFS rate (HR:0.76, CI: 0.765, 0.86), I2 = 86.3). A higher number of NLNs removed during surgery in BC patients who are candidates for axillary lymph node dissection appears to be associated with improved OS and RFS.

PANoptosis is a novel form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis; in this study, we combined the single-cell RNA sequencing (scRNA-seq) dataset GSE227136 with transcriptome data to elucidate its role in idiopathic pulmonary fibrosis (IPF) pathogenesis. PANoptosis-related genes were compiled from GeneCards and published literature. Consensus clustering was used to identify distinct PANoptosis-related clusters of IPF in the GEO dataset based on filtered PANoptosis-related differentially expressed genes (PRDEGs). Specific hub genes were identified using weighted gene co-expression network analysis (WGCNA) and two machine learning methodologies, which were used to develop predictive models. The inflammatory programmed cell death score (PANoptosis score, Ps) for each IPF patient was calculated based on nine PRDEGs, followed by analyses of these PRDEGs' expression differences and their ROC curves. PRDEG expression was confirmed in murine pulmonary tissues using quantitative real-time polymerase chain reaction (qRT-PCR). We successfully identified nine PRDEGs and two distinct PANoptosis-related clusters with these PRDEGs. Using WGCNA and machine learning approaches, we constructed a nomogram with robust predictive capacity for diagnosis of IPF. In addition, immune infiltration analysis among different molecular groups and single cell analysis revealed that increased PANoptosis activity was closely associated with immune activation. Finally, results from qRT-PCR showed a significant increase in the expression of MLKL and AIM2 in the lung tissue of the IPF animal model.

Hepatocellular carcinoma (HCC) has a poor prognosis, particularly with spinal metastases. Current prognostic scores (e.g., Revised Tokuhashi, New England Spinal Metastasis Score) lack integration of tumor microenvironment (TME)-based molecular subtypes, limiting their utility in precision medicine. This study evaluated the prognostic value of these subtypes and whether they enhance established scoring systems. In a single-center retrospective cohort of 117 HCC patients undergoing surgery for spinal metastases (2009-2024), patients were stratified into three TME subtypes: immune-inflamed (n = 39), immune-excluded (n = 53), and immune-desert (n = 25). Overall survival (OS) was analyzed using Kaplan-Meier and Cox regression. The discriminative ability of four prognostic scores was assessed with time-dependent ROC curves. Recursive partitioning analysis (RPA) integrated molecular subtypes with clinical scores to develop novel decision trees. Median OS for the cohort was 13.1 months. TME subtype was a powerful independent prognostic factor, with immune-inflamed, immune-excluded, and immune-desert subtypes showing median OS of 17.2, 12.1, and 8.8 months, respectively (P < 0.001). Multivariable analysis confirmed this association (e.g., immune-desert aHR = 9.52, P < 0.001). The Revised Tokuhashi score showed the highest baseline discriminative ability for 1-year survival (AUROC = 0.726). Integrating TME subtype and postoperative systemic therapy significantly improved predictive accuracy across all models (AUROCs > 0.92). RPA generated clinically actionable decision trees, defining three distinct prognostic groups. TME-based molecular subtypes are critical independent survival determinants in HCC with spinal metastases. Their integration with clinical scores using RPA produces highly accurate predictive models and practical decision aids, advocating for a biology-augmented approach to personalize patient management.

Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV database. Multivariate logistic regression and machine learning (ML, gradient-boosting (GBM) was optimized with LASSO regularization) were employed to identify 30-day mortality predictors, validated through SHAP interpretability, calibration curves, and decision curve analysis. Multi-organ dysfunction (AST, BUN, T-Bil), systemic inflammation (NLR, WBC) and APTT emerged as critical mortality determinants, and selected for model construction. GBM achieved superior discrimination (training AUC = 0.89; test AUC = 0.75), SHAP analysis, calibration curve and decision curve analysis (DCA) confirmed clinical utility, outperforming empirical intervention strategies. This study establishes a biomarker-driven ML framework for HL prognosis, integrating renal, hepatic, and inflammatory markers into actionable risk stratification. thereby providing a scientific basis for comprehensive HL management.

Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM) form a continuum of plasma cell disorders, with progression from MGUS to MM being difficult to predict. Current risk stratification models, largely based on clinical, laboratory, and cytogenetic markers, fail to capture the molecular complexity underlying disease progression, limiting their predictive accuracy. Recent advancements in multi-omics technologies, encompassing genomics, transcriptomics, proteomics, and metabolomics, have provided deeper insights into the molecular drivers of these conditions. The integration of artificial intelligence (AI) and machine learning (ML) further enhances this understanding, offering new avenues for dynamic, personalized risk prediction. AI-based approaches that incorporate multi-omics data have the potential to identify novel biomarkers and predict disease outcomes with greater precision. These advancements could revolutionize risk stratification by providing a more individualized and dynamic framework for patient monitoring and treatment. However, the clinical adoption of AI and multi-omics tools is fraught with challenges, including the integration of complex data types, the need for standardized protocols, and concerns surrounding data privacy and algorithmic bias. Furthermore, evolving regulatory frameworks must accommodate the continuous learning capabilities of AI systems. This article explores the current limitations of risk stratification models in MGUS and SMM and examines the potential of multi-omics and AI to improve predictive accuracy. It also discusses the technical, ethical, and regulatory hurdles that must be overcome to enable the clinical implementation of these technologies, offering a roadmap for their future integration into patient care.