Haining Zhou

Background: Low peak expiratory flow (PEF) may precede the first self-report of recognized lung disease, but cross-cohort consistency and incremental value are uncertain. Methods: We analysed Health and Retirement Study (HRS) discovery and prespecified person-disjoint replication samples and the English Longitudinal Study of Ageing (ELSA). The estimand linked baseline PEF to the first subsequently observed self-report of recognized lung disease, not biological onset. HRS used a prespecified nominal lower-decile z score and ELSA an empirical lower decile. Participant-clustered models adjusted for demographic, clinical and functional factors. Weighting and a stroke comparator were sensitivity analyses. Post-hoc ELSA benchmarking compared PEF with FEV1 and FVC using person-disjoint cross-validation and full-pipeline bootstrap sensitivity. Results: Models included 18,510 HRS discovery windows (320 reports), 11,424 replication windows (188), and 8,634 ELSA person-waves (95). Low PEF was associated with first report in discovery (odds ratio [OR] 3.36, 95% confidence interval [CI] 2.54–4.45), replication (3.14, 2.19–4.49), and ELSA (3.77, 2.36–6.01). Weighted estimates were similar; stroke associations were weaker or null. After adjustment for separate FEV1 and FVC indices, the continuous-PEF OR was 1.04 (0.90–1.20). In 1,584 fixed-horizon participants (95 events), AUCs were 0.650 for covariates, 0.684 with PEF, 0.745 with FEV1 and FVC, and 0.739 with all three. Full-pipeline bootstrap sensitivity found no clear incremental evidence beyond FEV1 and FVC. Across differing horizons, low-PEF rules identified fewer than one-third of reports in HRS replication and ELSA. Conclusions: Lower PEF was associated with a subsequent first observed lung-disease report; cohort-specific low-PEF rules identified fewer than one-third. In exploratory fixed out-of-fold predictions, PEF improved average precision and prediction error over covariates; no clear incremental evidence remained after FEV1 and FVC. Same-protocol collection does not establish a peak-flow-only pathway.

Radioligand therapy (RLT) delivers therapeutic radionuclides to tumor-associated targets through specific targeting ligands and has demonstrated substantial clinical value in malignancies such as prostate cancer and neuroendocrine tumors. Beyond its direct induction of radiation-mediated tumor cell damage, RLT has attracted increasing attention for its potential immunomodulatory effects. Current evidence suggests that RLT may create local immune conditions favorable for immunotherapy by promoting tumor antigen release, enhancing antigen presentation, and modulating the tumor immune microenvironment. Immunotherapy, particularly immune checkpoint inhibitors, can restore or enhance T cell-mediated antitumor immune responses. Accordingly, RLT and immunotherapy are mechanistically complementary and have shown synergistic antitumor activity in several preclinical models. However, clinical evidence supporting this combination remains preliminary, and whether preclinical synergy can be consistently translated into meaningful clinical benefit is still unclear. Following a “mechanism–evidence–translation” framework, this review summarizes the radioimmunological rationale, preclinical evidence, and clinical progress of RLT combined with immunotherapy. It also provides a descriptive overview of the available evidence and discusses key translational challenges, including dosimetry, treatment sequencing, patient selection, and long-term safety, with the aim of informing the optimization of future combination strategies and clinical trial design.

N6-methyladenosine (m6A) constitutes the most prevalent internal modification of eukaryotic mRNAs, conferring dynamic, sequence-independent regulation of gene expression through a reversible epitranscriptomic mechanism. Orchestrated by methyltransferase (“writers”), demethylases (“erasers”), and binding proteins (“readers”), m6A governs critical nodes of RNA metabolism, including splicing, nuclear export, transcript stability, and translational control. In this review, we systematically dissect how dysregulated m6A methylation fuels malignant transformation and progression by reinforcing core oncogenic hallmarks, sustained proliferation, metastasis, metabolic rewiring, and resistance to programmed cell death. Beyond cancer cell-intrinsic effects, m6A modification profoundly remodels the tumor immune microenvironment through coordinated regulation of immune cell function and immune checkpoint molecules, thereby facilitating immune evasion. We further discuss the context-dependent oncogenic or tumor-suppressive roles of specific m6A regulators and their impact on therapeutic resistance. Finally, we evaluate the translational potential of targeting the m6A axis, highlighting recent advances in small-molecule inhibitors and combination immunotherapies. Overcoming delivery challenges remains essential for integrating m6A-targeted strategies into clinical practice and advancing personalized oncology.

The gastrin-releasing peptide receptor (GRPR) is aberrantly expressed in several malignancies, including prostate cancer, breast cancer, lung cancer, and gastrointestinal stromal tumors, making it an important target for molecular imaging and targeted radionuclide therapy. In recent years, advances in radioligand design, radionuclide selection, and theranostic concepts have substantially promoted the development of GRPR-mediated oncologic applications in nuclear medicine. This review systematically summarizes recent progress in GRPR-targeted radioligands for tumor imaging and radioligand therapy, with particular emphasis on their biological rationale, representative tracers, clinical utility, and major limitations across different tumor types. Overall, GRPR-targeted imaging has demonstrated promising diagnostic value in prostate cancer, estrogen receptor-positive breast cancer, and selected gastrointestinal stromal tumors, and may complement existing imaging modalities. From a therapeutic perspective, the development of high-stability antagonists, metabolic protection strategies, and novel therapeutic radionuclides is driving GRPR-targeted radioligand therapy toward improved therapeutic index and greater translational potential. Nevertheless, several challenges remain, including receptor heterogeneity, physiologic uptake in normal tissues, optimization of pharmacokinetics, the complexity of dual-targeted constructs, and the lack of high-quality prospective clinical evidence. Future progress will likely depend on ligand engineering, dual-target strategies, individualized dosimetry, and the integration of imaging-based treatment assessment into a closed-loop precision theranostic framework.

Glutamine-fructose-6-phosphate amidotransferase 2 (GFPT2) is a rate-limiting enzyme of the hexosamine biosynthetic pathway, but its protein-level expression and functional relevance in lung adenocarcinoma (LUAD) remain unclear. GFPT2 protein abundance was evaluated in Clinical Proteomic Tumor Analysis Consortium (CPTAC) LUAD proteomics and validated by Western blotting in paired LUAD and adjacent tissues. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) transcriptomes were analyzed for differential expression and pathway enrichment, with survival assessed by Kaplan-Meier and Cox regression. Stable GFPT2 knockdown/overexpression in A549 cells was used to examine proliferation, migration, invasion, and transforming growth factor-β (TGF-β)/Smad2/3 signaling by Western blotting. GFPT2 protein was significantly upregulated in LUAD compared with adjacent tissues. TCGA-based analyses showed distinct transcriptional profiles between GFPT2 mRNA-high and GFPT2-low tumors, with enrichment of extracellular matrix organization, cell adhesion, and TGF-β-related signaling. High GFPT2 mRNA expression was associated with poorer overall survival and remained an independent adverse prognostic factor in multivariable Cox models. In A549 cells, GFPT2 overexpression was accompanied by enhanced proliferation, migration, and invasion, whereas GFPT2 knockdown showed the opposite effects. Consistently, GFPT2 upregulation was associated with increased TGF-β1 levels and Smad2/3 phosphorylation, suggesting a positive association with TGF-β/Smad signaling activity. GFPT2 is upregulated at the protein level in LUAD and is associates with aggressive cellular phenotypes and unfavorable prognosis. Integrated multi-omics analyses and cell-based experiments support a link between GFPT2 and TGF-β/Smad2/3 pathway activation, indicating GFPT2 as a potential prognostic biomarker and therapeutic target.

Classical heatstroke (CHS) is a life-threatening condition necessitating accurate prognostic tools for risk stratification. The potential of machine learning to predict clinical outcomes in CHS remains largely unexplored. Our objective was to develop and externally validate machine learning-based Cox model for predicting survival in hospitalized CHS patients. This retrospective multicenter study analyzed data from 538 CHS patients admitted to eight hospitals in western China between June 2022 and September 2023, with four institutions constituting the training cohort and four geographically distinct hospitals forming the external validation cohort. An innovative machine learning framework integrating nine algorithms with 54 combinatorial implementations was employed to identify robust prognostic features. Significant predictors from the optimal algorithm combination were incorporated into a multivariable Cox regression model, visualized as a clinical nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves, while survival differences were assessed with Kaplan-Meier analysis. The least absolute shrinkage and selection operator (Lasso) + supervised principal components (SuperPC) combination yielded the highest C-indexes (0.921 training; 0.811 validation). The nomogram achieved AUCs of 0.91, 0.85, and 0.86 for 10-, 20-, and 30-day survival in the training set, and 0.710, 0.80, and 0.80 in external validation. Calibration curves indicated strong agreement between predicted and observed 10-day survival probabilities, and Kaplan-Meier analyses confirmed significant survival stratification between risk groups. We developed and validated an accurate machine learning integration-based prognostic tool for CHS inpatients. This approach supports personalized clinical decision-making and offers new avenues for intelligent prognosis monitoring in critical care.

Sublobar resection is increasingly established as a standard of care for early-stage non-small cell lung cancer, yet predictors of delayed postoperative atrial fibrillation (POAF) in this specific population remain understudied. This retrospective cohort study utilized the MIMIC-IV database to investigate whether early postoperative coefficient of variation of heart rate (HR-CV) independently predicts delayed POAF (> 24 h) in 348 patients requiring intensive care after wedge resection or segmentectomy. Unlike traditional static risk factors, continuous monitoring data were analyzed to capture dynamic autonomic fluctuations. The incidence of delayed POAF was 16.7%. Multivariable logistic regression demonstrated that elevated HR-CV in the first 24 postoperative hours was significantly associated with delayed POAF (p = 0.003), whereas coefficient of variation of systolic blood pressure (SBP-CV) showed no significant association. Adding HR-CV to a clinical baseline model (age, hypertension, and heart failure) improved the Area Under the Curve (AUC) from 0.690 to 0.722. These findings suggest that autonomic dysregulation, rather than systemic hemodynamic instability, acts as a primary driver of arrhythmia in this cohort. We further developed a web-based risk calculator to facilitate the translation of these physiological signals into bedside risk stratification.