Portal vein thrombosis (PVT) frequently occurs in patients with porto-sinusoidal vascular disease (PSVD), but its clinical characteristics and outcomes remain poorly understood. This study aimed to investigate the clinical features and outcomes of PVT in PSVD.

A total of 169 patients with PSVD confirmed by hepatic histology were included. PVT was diagnosed using contrast-enhanced magnetic resonance imaging or computed tomography. Demographic, clinical, and laboratory data, portal hypertension-related complications, comorbidities, and mortality were collected and compared between patients with and without PVT. The primary outcomes were baseline clinical characteristics and liver-transplantation-free mortality; the secondary outcome was the dynamic changes of PVT during follow-up.

At baseline, 45 (26.6%) PSVD patients had PVT. Compared to those without PVT, patients with PVT had significantly higher rates of esophageal variceal bleeding (62.2% vs. 29.0%), ascites (73.3% vs. 35.5%), antithrombin III deficiency (78.1% vs. 38.4%) (all p < 0.001), and a history of hematological disorders (11.1% vs. 0.8%, p = 0.005). After a median follow-up of 40.1 (23.4–62.3) months, liver-transplantation-free mortality rates were 7.9% (3/38) and 1.8% (2/112) in patients with and without PVT, respectively (log-rank p = 0.110). Among 41 patients followed for a median of 17.1 (7.4–39.3) months, PVT resolved in 9.1% (1/11) of those with baseline PVT and developed in 13.3% (4/30) of those without PVT at baseline. The one- and two-year cumulative incidence rates of PVT were 3.3% and 6.7%, respectively.

PSVD patients with PVT experience more portal hypertension-related complications, complex coagulation profiles, hematological disorders, and a higher risk of death compared to those without PVT.

Digital pathology (DP) is transitioning from an adjunct technology to an enterprise diagnostic platform in the United States. Despite accelerating clinical adoption, many laboratories face persistent barriers, including high capital and operating costs, workflow disruption, interoperability challenges, and a complex regulatory and reimbursement environment. This narrative review proposes a practical lifecycle framework for implementing and sustaining DP programs, with an emphasis on defining and operationalizing institutional artificial intelligence (AI) readiness for safe and sustainable adoption.

We performed a targeted narrative review informed by searches of PubMed/MEDLINE and Google Scholar for English-language publications from January 1, 2014 through December 31, 2025. Core search concepts included DP, whole slide imaging, image management/viewing systems, laboratory information system integration, validation, reimbursement, U.S. Food and Drug Administration clearance, Clinical Laboratory Improvement Amendments oversight, College of American Pathologists accreditation, interoperability standards, cybersecurity, and AI. We supplemented database searches with reference screening and review of primary guidance and public databases from regulatory and professional organizations in the United States. We prioritized peer-reviewed literature and used web-based regulatory sources when they represented the authoritative primary reference. We also incorporated our professional experience and knowledge in DP and AI.

Key implementation domains span foundational infrastructure (scanners, storage/networking, and integrated image management platforms), workflow redesign across pre-analytic, analytic, and post-analytic phases, validation and quality management, regulatory compliance and accreditation, cost capture, interoperability strategy, cybersecurity and access control, education and change management, and long-term governance. We also describe an institution-level AI readiness model that can be assessed across data quality, integration, validation, monitoring, governance, and workforce capabilities to support safe clinical AI deployment.

Successful DP implementation requires a lifecycle approach that couples technical build-out with workflow redesign and institutional governance. Early planning for compliance, interoperability, reimbursement strategy, and AI readiness can reduce implementation risk and position laboratories for sustained clinical and computational innovation.

The history of screening for cervical cancer is rich with implementing cutting-edge ideas and technologies. From the very first “Pap smear” to the semi-automated and computerized systems of today, the way we screen for cervical cancer has changed dramatically in the past 75 years. With the advent of new techniques and more advanced machine learning algorithms, we sought to understand the current and future applications of artificial intelligence in clinical pathology around cervical cancer screening, diagnosis, and treatment.

A structured narrative review was conducted to examine the historical evolution and contemporary advances in cervical cancer screening, diagnosis, excisional management, and artificial intelligence applications. Peer-reviewed articles, consensus guidelines, and global policy documents published between January 1990 and March 2025 were identified through targeted searches of PubMed and review of reference lists from relevant publications. Search terms included combinations of “cervical cancer screening,” “Papanicolaou test,” “liquid-based cytology,” “HPV testing,” “colposcopy,” “loop electrosurgical excision procedure,” “digital pathology,” “deep learning,” and “artificial intelligence.” Emphasis was placed on multi-center validation studies, systematic reviews, regulatory and implementation guidance, and global health frameworks. Publications lacking methodological transparency or direct relevance to clinical or translational practice were excluded.

Through a review of the literature, we describe how innovations in conventional and liquid-based cytology, human papillomavirus testing, and organized screening programs established the current prevention framework. Building on this foundation, recent studies demonstrate promising performance of deep learning algorithms applied to conventionally prepared cervical cytology slides, with systems capable of binary normal versus abnormal classification as well as more granular diagnostic categorization. Artificial intelligence-assisted colposcopy and computer-vision approaches have also shown improved diagnostic concordance, workflow efficiency, and potential to expand screening capacity in resource-limited environments.

There has been much work done in the past several years surrounding the implementation of deep learning algorithms in regard to cervical cancer screening. The work in this field shows promise in enhancing diagnostic accuracy, streamlining diagnostic workflow, and decreasing turnaround times from specimen collection to rendering a diagnosis. However, there are still many technical, legal, and ethical questions that must be answered prior to widespread adoption of these algorithms for patient care.

Pediatric low-grade gliomas (pLGGs) exhibit distinct biological and clinical characteristics compared to adult gliomas, and their treatment strategies differ substantially from those used in adults. Since the release of the 2016 World Health Organization Classification of Tumors of the Central Nervous System and its subsequent updates, significant advances have been made in understanding the diagnosis and management of pLGGs. Therefore, updated guidelines tailored to current clinical practice are needed. In this document, we present the consensus guidelines for the diagnosis and management of pLGGs in China. The recommendations were developed through a comprehensive review of relevant domestic and international guidelines and literature, combined with expert consensus meetings and external peer review to ensure rigorous validation. The guideline integrates the levels of evidence from published studies, expert consensus, and practical clinical considerations. All recommendations were reviewed and approved by a multidisciplinary panel of experts from the Pediatric Neurosurgery Group. This guideline is intended to serve as guidance for healthcare professionals involved in pediatric neuro-oncology, as well as for patients, caregivers, and other healthcare providers participating in the management of pLGGs.

COVID-19 has resulted in significant long-term sequelae in convalescent patients, impacting overall quality of life. Traditional Chinese medicine (TCM) has shown promise in managing post-COVID-19 symptoms through syndrome differentiation. This study aimed to evaluate the efficacy and safety of TCM in COVID-19 convalescent patients in a real-world setting.

This prospective, real-world study will be conducted at Guangdong Provincial Hospital of Traditional Chinese Medicine. A total of 528 COVID-19 convalescent patients will be recruited and divided into two groups: a control group receiving routine Western medical treatment and an intervention group receiving additional TCM treatment based on syndrome differentiation. Patients will be assessed for three major TCM syndromes: Lung-Spleen Qi Deficiency, Qi-Yin Deficiency, and Cold Phlegm Obstructing the Lung, with corresponding TCM prescriptions administered accordingly. The primary outcome measure will be the improvement in clinical symptom scores based on a TCM symptom scoring system. Secondary outcomes will include changes in laboratory tests, imaging studies, heart function classification, and quality of life scores. Safety will be assessed through liver and kidney function tests and adverse event monitoring.

The study is expected to demonstrate that TCM treatment, based on syndrome differentiation, can significantly improve clinical symptoms and overall health in COVID-19 convalescent patients compared to routine Western medical treatment. These findings will provide evidence for integrating TCM into post-acute COVID-19 care.

This study will contribute to the evidence supporting TCM as an effective treatment for post-COVID-19 syndrome, enhancing patient outcomes and informing comprehensive recovery strategies.

Acute-on-chronic liver failure (ACLF) is a life-threatening syndrome characterized by systemic inflammation and immune dysregulation, in which macrophages play a key role in organ injury. This study aimed to investigate the role and mechanism of pyruvate dehydrogenase kinase 4 (PDK4) in ACLF to identify therapeutic targets that modulate macrophage function and mitigate ACLF progression.

Single-cell RNA sequencing data from healthy and ACLF liver tissues were analyzed from the Sequence Read Archive database. Transcriptomic data of peripheral blood mononuclear cells from ACLF patients (GSE168048) were also examined. In vitro experiments assessed PDK4 expression and macrophage polarization, and conditioned-medium studies evaluated effects on LO2 hepatocytes. In vivo validation was performed in ACLF mouse models treated with a PDK4 inhibitor.

Single-cell analysis revealed a predominance of M1-polarized hepatic macrophages in ACLF with marked upregulation of PDK4. Peripheral blood mononuclear cell transcriptomics showed that higher PDK4 expression correlated with 28-day mortality. In vitro, PDK4 expression increased in M1 macrophages; PDK4 inhibition attenuated M1 polarization and reduced cytotoxic effects on LO2 cells. In vivo, pharmacologic inhibition of PDK4 suppressed M1 polarization in macrophages, alleviated liver inflammation, and reduced tissue injury. Mechanistically, PDK4 promoted M1 polarization via activation of signal transducer and activator of transcription 1 signaling.

PDK4 is a key pro-inflammatory regulator in ACLF by promoting M1 macrophage polarization. Targeting PDK4 may be a promising strategy to attenuate inflammation and improve clinical outcomes in ACLF.

Chimeric antigen receptor (CAR)-T cell therapy faces significant challenges in treating solid tumors, including immune evasion, suppressive tumor microenvironments, and on-target/off-tumor toxicity, which limit its clinical efficacy. Although it has revolutionized treatment for hematological malignancies, these obstacles hinder its broader application in solid tumors. Nanotechnology offers innovative strategies to address these limitations through enhanced delivery, localization, and control. This review summarizes recent advances in nanotechnology-assisted CAR-T cell therapies for gynecologic cancers, with a particular focus on messenger RNA (mRNA)-based delivery systems, lipid nanoparticles, hydrogels, and external activation techniques such as photothermal and acoustogenetic modulation. The integration of nanotechnology, especially mRNA-based delivery systems, holds transformative potential for overcoming these barriers. mRNA enables transient, non-integrating expression of CARs, meaning the genetic modifications are temporary. This improves safety and allows flexible control over treatment intensity, while rational sequence optimization (e.g., codon usage, guanine-cytosine content, secondary structure) enhances mRNA stability and protein translation efficiency. Lipid nanoparticles, the leading delivery platform, can be engineered for cell-type specificity and tissue targeting through modulation of their components and surface functionalization. Recent innovations, including siloxane-modified lipid nanoparticles, injectable hydrogels, and photothermal or acoustogenetic activation strategies, enable precise spatiotemporal control of CAR-T cell function in vivo. In ovarian cancer, preclinical studies targeting nfP2X7 and employing multifunctional nanoparticles have demonstrated synergistic efficacy and tumor-specific delivery. This review highlights how nanotechnology platforms can be integrated with CAR-T cell therapies to enhance safety, precision, and therapeutic outcomes in ovarian cancer.

Regular exercise is fundamental for people with metabolic dysfunction-associated steatotic liver disease (MASLD), yet exercise maintenance is generally poor. This generative co-design process aimed to embed the voices and opinions of people with lived experience of MASLD and their care stakeholders to (i) frame barriers and enablers to exercise maintenance and (ii) highlight priorities for exercise-focused research agendas in MASLD.

A generative co-design framework was applied. Two virtual co-design sessions were undertaken: Session 1 – Framing the issue, where initial discovery was conducted with people with lived experience of MASLD; and Session 2 – Generative design and sharing ideas with lived experience partners and healthcare stakeholders. Sessions were audio-recorded and transcribed, and key determinants and considerations were discerned by two independent researchers.

Lived experience partners (n = 5, 53 ± 16 years, 40% male) ranked five equally important barriers to exercise maintenance: musculoskeletal and pain issues, lack of access to exercise equipment/facilities, cost, competing priorities, and low energy levels, which influenced core positive and negative determinants. Alongside lived experience partners, healthcare stakeholders (hepatologists [n = 3], exercise professionals [n = 3], 67% male) identified three core needs with eight considerations. Some disconnects in priorities were observed. Lived experience partners emphasized affordability, accessibility, and considerations for comorbidities, while healthcare partners advocated for research on natural history, prevention, behavior change, cost-effectiveness, and health system change.

This co-design methodology highlights unique consumer-informed research questions. Exercise interventions and their associated implementation trials will benefit from being co-designed with both people with MASLD and care stakeholders.

Colorectal cancer histopathological grading relies on the accurate segmentation of glandular structures. Current deep learning–based methods depend heavily on large-scale pixel-level annotations that are labor-intensive and not amenable to clinical practice. Weakly supervised semantic segmentation offers a promising alternative; yet, existing class activation map–based weakly supervised semantic segmentation approaches often produce incomplete, low-quality pseudo-masks that overemphasize discriminative regions and fail to provide reliable supervision for unannotated glandular structures, limiting their suitability for dense histopathology segmentation under sparse supervision. We propose a novel weakly supervised teacher–student framework that leverages sparse pathologists’ annotations and an Exponential Moving Average–stabilized teacher network to generate refined pseudo-masks.

Our framework integrates confidence-based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum-guided refinement, enabling the student network to progressively delineate and accurately segment unannotated glandular regions. We validated our framework on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center, consisting of 60 hematoxylin and eosin-stained whole-slide images from independent patients with varying degrees of gland differentiation, as well as on public benchmarks including the Gland Segmentation dataset (derived from stage T3–T4 colorectal adenocarcinomas), TCGA-COAD, TCGA-READ, and SPIDER.

The proposed framework achieved strong performance on the institutional dataset despite limited annotations. On the Gland Segmentation dataset, it demonstrated competitive performance compared to both weakly and fully supervised approaches, achieving a mean Intersection over Union of 80.10% ± 1.52 and a mean Dice coefficient of 89.10% ± 2.10. Moreover, cross-cohort evaluations showed robust generalization on TCGA-COAD and TCGA-READ without requiring additional annotations, while reduced performance on SPIDER reflected pronounced domain shift.

Our framework provides an annotation-efficient and generalizable paradigm for accurate gland segmentation in colorectal histopathology, offering a practical pathway toward significantly reducing annotation burdens while preserving high segmentation fidelity.

In the past decade, immune checkpoint inhibitors (ICIs) have dramatically changed cancer treatment, significantly improving outcomes for patients with various malignancies. Nonetheless, their widespread application has resulted in a rise in immune-related adverse events due to excessive immune activation, including immune-mediated hepatotoxicity (IMH). IMH can cause serious complications and even death, underscoring the need for early prediction and intervention. This review outlines the current understanding of risk factors and predictive biomarkers for IMH in cancer patients undergoing ICI therapy, with risk factors divided into patient-associated, tumor-associated, and agent-associated categories. Higher IMH risk is related to female sex, younger age, extreme BMI, Asian ethnicity, and chronic liver disease. Cancer type, prior ICI treatment, dual ICI combination therapy, and the concurrent use of chemotherapy, targeted agents, or other hepatotoxic drugs (e.g., acetaminophen, statins) also increase the risk of IMH. Potential predictive biomarkers encompass circulating blood cells, serum proteins, autoantibodies, cytokines, gene profiles, and the gut microbiome. Despite promising findings, the predictive value of these biomarkers remains inconsistent, and no definitive biomarker has been established for routine clinical use. Large-scale prospective studies are essential to verify the predictive value of these biomarkers and facilitate their integration into clinical practice, thereby providing deeper insights into the early identification and individualized management of IMH during ICI therapy.