Polycythemia vera (PV) and secondary polycythemia are clinical conditions characterized by erythrocytosis, but their biological basis and clinical course differ. Whether this difference is reflected in long-term kidney function is not well defined. We aimed to compare longitudinal estimated glomerular filtration rate (eGFR) trajectories and kidney function loss in patients with PV and secondary polycythemia.

In this retrospective, single-center cohort, adult patients with PV or secondary polycythemia were evaluated from baseline through 4 years. Repeated eGFR measurements were examined using a linear mixed-effects model; the group-time interaction was the primary inferential test, with adjustment for age, sex, diabetes mellitus, hypertension, coronary artery disease, and baseline chronic kidney disease.

The cohort included 1,616 patients: 258 with PV and 1,358 with secondary polycythemia. The global group-time interaction indicated different eGFR trajectories (p = 0.049). The adjusted PV-secondary polycythemia eGFR contrast was small at baseline but indicated lower eGFR in PV by the fourth year (- 3.86 mL/min/1.73 m²; 95% CI, - 6.39 to - 1.33; Holm-corrected p = 0.014). The fourth-year contrast remained in the same direction in most sensitivity analyses.

PV was associated with a late and modest eGFR separation compared with secondary polycythemia. These findings support consideration of kidney function monitoring in selected patients with PV and prospective assessment of factors influencing renal progression.

Chiglitazar was approved by the China National Medical Products Administration (NMPA) in 2021 for the treatment of patients with type 2 diabetes mellitus, and may also benefit from metabolic dysfunction-associated steatohepatitis (MASH).

To characterise the role of Chiglitazar in MASH, in vitro experiments examining monocyte activation and migration were conducted, alongside liver and stellate cell proliferation assays. Three distinct MASH animal models (db/db mouse model, MCD model, CCl4 model) for conducting in vivo studies were established.

The results indicated that Chiglitazar reduced MCP-1-induced THP-1 migration and down-regulated TNF-α and MCP-1 expression. It also blocked TGF-β-driven proliferation in dermal fibroblasts and hepatic stellate cells while suppressing α-SMA and CTGF expression. In vivo, Chiglitazar alleviates hepatic steatosis and inflammation in the MCD model, whilst improving hepatic inflammation and fibrosis in both the CCL4 and HFD+CCL4 models.

In the study, it has been demonstrated that chiglitazar suppresses monocyte activation and chemotaxis, thereby attenuating chronic inflammation-elicited vascular and tissue damage. It also blocks fibroblast proliferation and differentiation, curbing toxin- or metabolite- induced inflammation and fibrosis. Although CCl4, MCD, and HFD+CCl4 models were used, they only partially reproduce human MASH pathology. Future work should employ more physiologically faithful systems.

As the world's first pan-PPAR agonist approved for the treatment of Type 2 Diabetes Mellitus (T2DM), Chiglitazar has also demonstrated potential for treating MASH by modulating steatosis, inflammation, and fibrosis. This potential may be translatable into clinical treatments in the foreseeable future.

Diabetes mellitus is a metabolic disease that requires continuous glucose monitoring to achieve glycemic control. However, current glucose monitoring technologies have many limitations and are often associated with adherence issues. Therefore, in this review, we considered some recent developments in biosensors using microneedles due to their non-invasive character and interstitial fluid measurement for real-time monitoring. The review highlights the use of nanomaterials in nanotechnology, the microfabrication methods, enzyme immobilization methods, and surface modification methods that lead to increased sensitivity and biocompatibility of the sensors. The review also emphasized the advances in clinical studies and the artifacts related to microneedle sensors, such as biofouling and signal drift. Moreover, the review specifically targeted the use of emerging technologies such as non-enzymatic sensing, multiplex biomarker detection, AI-based analysis, health monitoring, and a closed-loop insulin release system. The current review includes more than 100 peer-reviewed articles. It provides a thorough analysis of how microneedle-based biosensors can be considered a new approach for glucose detection that improves the quality of patients' lives. Collectively, all the recent advancements in microneedle-based biosensors have made them a new emerging platform for next-generation personalized medicine, along with a digital healthcare system.

Diabetic kidney disease (DKD) is a major cause of chronic kidney diseases. Panoptosis has emerged as a key contributor to renal injury. However, the specific panoptosis-related genes (PRGs) that drive diabetic tubular injury remain unknown. TNFSF10, a known regulator of apoptosis and inflammation, has been linked to podocyte injury in DKD, but its role in diabetic tubular injury remains unknown. To address this, we integrated weighted gene co-expression network analysis, machine learning algorithms, and clinical data from the Nephroseq database to identify PRGs associated with DKD. The biological function of the identified hub gene was further characterized by integrating single-cell and single-nucleus RNA sequencing data with analytical frameworks for cellular communication, trajectory inference, and regulatory network. In this study, we identified TNFSF10 as a conserved and robust hub PRG for DKD. TNFSF10 expression was significantly upregulated in DKD kidneys and inversely correlated with glomerular filtration rate. TNFSF10^high kidneys exhibited elevated fibrosis and inflammation scores. scRNA/snRNA analysis revealed its predominant expression in injured proximal tubular cells. Importantly, TNFSF10⁺ proximal tubular cells showed transcriptomic signatures consistent with panoptosis activation and inflammatory signaling, along with enhanced ligand-receptor interactions with fibroblasts and immune cells. Consistently, TNFSF10 treatment induced cell death and increased the expression of multiple inflammatory genes in vitro. Collectively, our results suggest that TNFSF10 is associated with tubular injury and kidney fibroinflammation in DKD. These exploratory findings suggest it may represent a promising candidate for DKD, and further in vivo functional validation is required to confirm its diagnostic and therapeutic potential.

Diabetes mellitus (DM), a chronic metabolic disorder characterized by persistent hyperglycemia, poses a significant global health burden. Berberine (BBR), a natural isoquinoline alkaloid derived from medicinal plants such as Coptidis Rhizoma, has shown therapeutic promise for DM and its complications. Preclinical and clinical studies have revealed that BBR exerts multi-target effects, including remodeling the gut microbiome, promoting insulin secretion, ameliorating insulin resistance, and inhibiting hepatic gluconeogenesis. Its protective mechanisms against diabetic complications, such as nephropathy, cardiomyopathy, and retinopathy, involve modulating key pathways like AMPK/SIRT1, PI3K/Akt, and TGF-[Formula: see text]/Smad, which attenuate oxidative stress, inflammation, and fibrosis. Despite its low oral bioavailability, a wide array of formulation strategies, including polymer nanocarriers, lipid-based nanocarriers, mixed micelles, and absorption enhancer, have improved systemic exposure in preclinical models, although clinical translation remains limited. This review synthesizes evidence from the past decade spanning preclinical mechanisms, randomized controlled trials, pharmacokinetics and nanoformulation strategies, concluding that BBR is a promising candidate for complementary therapy, although the supporting clinical evidence remains limited. Further studies are warranted to clarify its mechanisms, and optimize its delivery. In particular, larger and longer-term trials, along with systematic pharmacokinetic-pharmacodynamic studies of BBR and its metabolites, are needed to confirm its efficacy and long-term safety.

Patients undergoing pancreatectomy for pancreatic cancer may experience gastrointestinal symptoms that impair health-related quality of life (HRQoL) before and after pancreatic surgery. The longitudinal course of these symptoms and their association with HRQoL remains unclear as large prospective studies are lacking.

Patients who underwent pancreatectomy for pancreatic cancer and filled out questionnaires of the Dutch Pancreatic Cancer Project were included in this cohort study. Gastrointestinal symptoms and HRQoL were assessed before surgery and 0-3, 3-6, 6-9, and 9-12 months after surgery. Multivariable generalized linear mixed models were applied to evaluate longitudinal changes in gastrointestinal symptoms and their association with HRQoL.

After pancreatectomy for pancreatic cancer, overall, 1009 patients were included and 2995 questionnaires were completed. Before surgery, gastrointestinal symptoms were present in 95% of patients; commonly flatulence, dry mouth, problems with taste, and altered bowel habits. Gastrointestinal symptom scores peaked at 0-3 months after surgery and generally returned to preoperative or lower levels at 9-12 months after surgery. Severe gastrointestinal symptoms (defined as 'quite a bit' or 'very much') decreased from 61% of patients before surgery to 52% at 9-12 months after surgery. Patients with two or more severe symptoms had moderate (10-20 points) to large (>20 points) clinically relevant lower scores for HRQoL.

Gastrointestinal symptoms in patients undergoing pancreatectomy for pancreatic cancer were highly prevalent and peaked within 3 months after surgery. Severe gastrointestinal symptoms affected more than half of all patients after surgery, which led to substantial declines in HRQoL.

To determine whether a CT radiomics-defined poor-prognosis phenotype in NSCLC is associated with reproducible transcriptomic programs and distinct transcription factor (TF) activity patterns.

This retrospective radiogenomic study used public datasets from The Cancer Imaging Archive. A prognostic radiomics model was developed from pretreatment CT in 402 patients from the NSCLC-Radiomics collection and evaluated in an independent cohort of 110 patients from the NSCLC Radiogenomics collection. Seven tumor-centered intra- and peritumoral regions were analyzed. Features were extracted from original, wavelet-, and Laplacian-of-Gaussian-filtered CT images. After preprocessing and training-cohort standardization, a LASSO-penalized Cox model was developed. The locked model retained eight features, all from the GTV+10-mm region, and was applied to the internal and external validation cohorts without refitting. The training-cohort median RadScore was used unchanged for Kaplan-Meier visualization. Matched transcriptomic data were analyzed using preranked gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), target-gene-based TF activity inference, TF co-activity networks, exploratory regulator prioritization, unsupervised TF-state clustering, and LINCS-based perturbational analysis.

The radiomics model achieved Harrell concordance indices of 0.785 (95% confidence interval [CI], 0.767-0.793), 0.819 (95% CI, 0.788-0.833), and 0.785 (95% CI, 0.752-0.802) in the training, internal validation, and external validation cohorts, respectively. The high-risk phenotype showed exploratory enrichment of proliferative and stress-adaptive programs, led by HALLMARK_E2F_TARGETS (normalized enrichment score [NES] = 1.696, false discovery rate [FDR] = 0.045), with additional enrichment of MYC targets and inflammatory response. HALLMARK_HYPOXIA showed a positive directional trend but did not meet the prespecified exploratory threshold. GSVA provided complementary sample-level evidence for MYC targets, hypoxia, E2F targets, DNA repair, and cell-cycle activity. Eleven of 13 evaluated TFs met the prespecified FDR threshold, including YAP1, MYC, HIF1A, E2F3, STAT3, KLF4, E2F1, and TP53. Integrated analysis prioritized HIF1A, STAT3, TP53, YAP1, and E2F1/E2F3 as candidate regulatory nodes. Unsupervised clustering of all 110 radiogenomic patients, performed independently of radiomics risk labels, identified three exploratory TF-defined states: Hypoxia_Metabolic (n = 69), Cell_Cycle_E2F (n = 31), and Hippo_Mechanosensing (n = 10). LINCS-based analysis generated regulatory-state-associated compound hypotheses.

A CT radiomics-defined poor-prognosis phenotype in NSCLC is associated with coherent transcriptomic programs and an interpretable TF regulatory architecture. This framework links routine imaging phenotypes to latent tumor-control programs and supports regulatory-state-informed therapeutic hypothesis generation.

Radiotherapy treatment plan comparisons often utilize structure doses, tumor control probabilities (TCPs), and normal tissue complication probabilities (NTCPs), but such metrics do not reflect the duration for which a patient suffers from a developed toxicity. Markov models allowing for the simulation of the patient's entire lifetime post-treatment and the determination of quality-adjusted life expectancy (QALE) have been developed but generally disregard important considerations such as additional treatments received and second primary malignancies (SPMs). Clinical validation also largely remains outstanding.

This study aims to expand and clinically validate a Markov model focusing on head-and-neck cancer patients.

A Markov model considering tumor control, healthy tissue toxicities, distant metastases, radiation-associated secondary cancer, and death from various causes was expanded to further include additional treatments received, SPMs, and the patient's smoking history. For each of 148 head-and-neck cancer patients, 437,000 simulations of the patient's entire lifetime post-treatment were performed and compared to the patient's clinically-documented outcomes (wherever available).

94.4% of simulations exhibited locoregional control, compared to 89.9% of patients in clinical reality. Simulated death with locoregional failure was 4.9%, compared to 5.4% in real life. Patients developed and died with distant metastases in 8.0% of simulations, while 10.8% of patients developed distant metastases in real life and 5.7% of patients passed away with distant disease. The simulated second primary cancer rate was 13.5%. Patients exhibited at least one healthy tissue toxicity in 43.8% of simulations, and simulated life expectancy and QALE were 76.5 ± 11.6 years and 74.3 ± 11.0 quality-adjusted life years (QALYs), respectively.

Agreement between simulation results and available clinically-documented outcomes was generally excellent, enabling the evaluation of prospective clinical outcomes and comparisons between different patient populations along with variable treatment combinations.

Clinical trial enrollment is essential to advancing cancer care, yet participation among patients with advanced cancer remains low. While patient-level barriers are frequently cited, the role of health care delivery factors-specifically physician- and practice-level variation-has been poorly quantified.

To quantify variation in clinical trial participation and identify associated delivery system factors among patients with advanced cancer.

This cohort study included adults with advanced or metastatic cancers diagnosed and treated between January 1, 2011, and December 31, 2024, by 1664 physicians across 107 trial-available oncology practices across the US. Data were obtained from the Flatiron Health Research Database. Analyses were conducted between January and April 2025.

Patient-level (eg, demographic characteristics, cancer type, and performance status), physician-level (eg, patient volume, subspecialization, and prior trial enrollment activity), and practice-level (eg, size, research engagement, and patient composition) factors.

The primary outcome was clinical trial participation, defined as receipt of a clinical study drug. Mixed-effects logistic regression models were used to decompose the proportion of variation attributable to patient-, physician-, and practice-level factors. Multivariable logistic regression was used to identify characteristics associated with trial participation.

Among the 205 743 patients with advanced or metastatic cancer (mean [SD] age at advanced cancer diagnosis, 67.5 [11.3]; 107 370 males [52.2%]), 10 785 (5.2%) participated in a clinical trial over the study period, increasing from 235 of 5964 (3.9%) in 2011 to 638 of 9336 (6.8%) in 2024. Participation remained highly concentrated: of 1664 physicians, 389 (23.4%) accounted for 9018 (83.6%) of all enrollments (10 785), while 21 of 107 practices (19.6%) accounted for 8686 (80.5%) of all enrolled patients. Additionally, 494 of 1664 physicians (29.7%) enrolled no patients. Physician-level (12.3%) and practice-level (15.1%) factors explained a greater share of variation in trial participation than observable patient-level characteristics (11.4%), with physician- and practice-level contributions increasing over time. Physician's subspecialization (treated 1-2 cancer groups: odds ratio [OR], 4.11 [95% CI, 2.53-6.68], P = .002; treated 3-4 cancer groups: OR, 2.38 [95% CI, 1.37-4.14], P < .001), physician's prior trial enrollment activity (enrolled patients in the 2 previous years: OR, 1.58; 95% CI, 1.43-1.76; P < .001), and practice's research engagement (OR, 2.68; 95% CI, 2.12-3.39; P < .001) were associated with higher odds of trial participation.

In this cohort study of patients with advanced cancer, clinical trial participation increased moderately but remained low over the study period, with physician- and practice-level factors consistently accounting for a substantial share of participation variation. Improving trial accrual requires interventions targeting structural factors in enrollment, including research infrastructure and physician engagement.

The Promoting Informed Decisions About Colorectal Cancer Screening in Older Adults (PRIMED) cluster randomized trial found that 2-hour online training combined with a pre-visit reminder for primary care physicians (PCPs) increased shared decision making (SDM) among patients aged 76-85 years who were due for colorectal cancer screening (CRC) screening discussions. However, the best way to translate findings from the clinical trial setting into routine primary care remains unclear.

To explore PCPs' experiences with the PRIMED SDM training and reminders, and to identify barriers, facilitators, and recommendations for implementation in routine primary care.

A qualitative study using semi-structured interviews was conducted. 59 PCPs from 35 primary care practices affiliated with five hospitals in Massachusetts and Maine who participated in the PRIMED trial were invited to be interviewed. Interviews were conducted between April and July 2021, transcribed verbatim, and analyzed by two independent researchers using a Framework Analysis-informed approach.

Of 59 PCPs, 43 participated. Themes included: (i) Training and Reminders Supported SDM; (ii) Real-World Constraints Challenged SDM Implementation, but PCPs Made Efforts to Adapt despite challenges such as time pressures, competing priorities, and the COVID-19 pandemic; and (iii) Scaling and Sustaining SDM Requires a System-Level Approach, such as automated reminders, patient decision aids, concise training, and interprofessional SDM.

Overall, PCPs perceived the SDM training and reminders as supporting a more nuanced and individualized approach to CRC screening conversations with older adults. To enable broader scale-up in primary care, these interventions should minimize burden on PCPs by streamlining integration into existing workflows.