Diabetic cardiomyopathy (DCM), a diabetes-specific cardiovascular complication, is pathologically characterized by cardiomyocyte apoptosis, oxidative stress, inflammatory responses, and myocardial fibrosis, distinguishing it from other cardiac disorders, such as hypertension and coronary artery disease. Challenges in early diagnosis, coupled with the limited efficacy and adverse effects of current treatments, have made DCM a significant contributor to heart failure and mortality in patients with diabetes. Natural products, recognized for their diverse sources, structural variety, and multitarget therapeutic potential, have shown promise in preventing and treating DCM. Drawing on advances over the past five years, this review systematically summarizes the pharmacological effects and molecular mechanisms of natural products (e.g., flavonoids, terpenoids, phenylpropanoids, alkaloids, and polysaccharides) in the treatment of DCM, with the aim of providing a theoretical foundation for further research and drug development.

Normalization can standardize and improve machine learning (ML) performance on omics data. However, it is unclear whether normalization is associated with overfitting (i.e., worse cross-dataset performance than intra-dataset performance). Therefore, we aimed to examine associations of normalization and regularization with overfitting of ML on omics data.

Using three paired transcriptomic and clinical datasets (lung adenocarcinoma: the Cancer Genome Atlas (TCGA)/Oncology Singapore; melanoma: TCGA/Dana-Farber Cancer Institute; glioblastoma: TCGA/Clinical Proteomic Tumor Analysis Consortium), we applied ANOVA-based gene selection methods, six normalization methods, and six ML models to classify cancer patients’ deaths. Balanced accuracy (BA) and area under the curve (AUC) in intra- and cross-dataset settings were compared using inferential analyses.

Normalization consistently improved intra-dataset performance (median BA/AUC changes: 0.035–0.214/0.115–0.279) on all data, particularly with Z_Raw, but decreased or slightly increased cross-dataset performance (median BA/AUC changes: −0.029–0.079/0.029–0.064). Least Absolute Shrinkage and Selection Operator (LASSO) model without normalization consistently outperformed most of the ML models in cross-dataset testing across cancer types. ML models on all and molecular-alone data showed similar best performances.

Normalization increases ML’s intra-dataset performance and overfitting in three paired cancer transcriptomic and clinical datasets. Regularized models such as LASSO appear to mitigate overfitting and achieve robust cross-dataset performance. Therefore, cross-dataset evaluation and regularized models are recommended to assess and reduce overfitting, while normalization should be used cautiously. Adding clinical data seems to have little impact on ML models’ performance. However, future work on other diseases and datasets is warranted.

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.

Early and accurate prognostic assessment is crucial to avoid serious disease progression in patients with liver failure. Thyroid hormone is an important metabolic regulator involved in hepatic function. This review examines in detail the pathophysiological regulation of the hypothalamic-pituitary-thyroid axis in patients with liver failure and emphasizes the importance of thyroid profiling (thyroid-stimulating hormone, T3, and T4) in prognostic assessment and risk stratification. T3 can enhance liver regeneration. The clinical application of thyroid hormone replacement therapy in patients with acute-on-chronic liver failure complicated by non-thyroidal illness syndrome is controversial. This review aims to inform clinical practice regarding the relevance of TH level assessment in liver failure and to provide novel insights into the prognostic evaluation and comprehensive care of liver failure complicated by thyroid dysfunction.

Chronic stress-induced hypercortisolism causes diminished ovarian reserve (DOR), contributing to infertility and miscarriage. Androgen supplementation is an emerging therapeutic approach for DOR. The traditional Chinese herbal decoction modified Gengnianchun formula (MGNC) has shown clinical efficacy in treating DOR. This study aimed to compare the effectiveness of MGNC with that of androgens in a stress-induced DOR mouse model.

Sexually mature female C57 mice aged six weeks were randomly assigned to six groups (n = 10 per group, with 3 independent replicates per group), including the control, model, low-dose testosterone (LT), medium-dose testosterone (MT), high-dose testosterone (HT), and MGNC groups. This sample size and study design were determined based on preliminary experimental data. Chronic stress was induced in mice, except for the control group, by daily glucocorticoid injection, and the mice in the LT, MT, HT, and MGNC groups were treated at the same time with testosterone (low, medium, or high dose) or MGNC for six weeks. Body weight, estrous cycles, ovarian follicle counts, hormone profiles, including follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol, and testosterone, and in vitro preantral follicle growth rates (via MGNC-enriched or androgen-treated serum) were assessed.

All groups presented stable body weights. MGNC ameliorated estrous cycle irregularities caused by stress, while testosterone exacerbated the abnormality. Moreover, MGNC outperformed LT in improving primordial/primary/antral follicle counts and corpus luteum formation, while MT and HT did not improve ovarian follicle reserve. LT was associated with the highest serum estradiol level, but none of the testosterone doses reduced FSH levels or the FSH/LH ratio, whereas MGNC lowered FSH and the FSH/LH ratio. Additionally, MGNC-enriched serum significantly enhanced the in vitro follicular growth rate in corticosterone-supplemented culture medium, and this effect was superior to that observed with testosterone-pretreated serum.

MGNC demonstrates superior efficacy over androgen therapy in treating chronic stress-induced DOR in mice, supporting further investigations into its clinical potential and mechanisms.

Perioperative anaphylaxis is a rare, life-threatening, iatrogenic condition that predominantly arises following anesthesia. The unique context of this condition, characterized by the concurrent administration of multiple drugs, patient draping, and altered physiological states, presents significant diagnostic and therapeutic challenges, contributing to a higher mortality rate compared to anaphylaxis in other settings. This narrative review synthesizes the evidence to delineate the evolving etiology, pathophysiology, atypical clinical presentation, evidence-based immediate management, and strategic prevention of perioperative anaphylactic reactions. The primary causative agents include neuromuscular blocking agents, antibiotics, and latex, with emerging culprits such as chlorhexidine, dyes, and novel agents like remimazolam. Diagnosis is complicated by the paucity of cutaneous signs; thus, cardiovascular collapse combined with a low end-tidal carbon dioxide level has emerged as a useful supportive diagnostic indicator that requires integration with the clinical context. Immediate management prioritizes the prompt administration of epinephrine and aggressive fluid resuscitation. Subsequent allergological investigations, primarily via skin testing and serum tryptase/histamine measurement, are paramount for identifying the causative agent and preventing its recurrence. Prevention strategies emphasize meticulous history-taking, risk stratification, and the creation of latex-free environments. Future directions must focus on establishing global surveillance networks, exploring novel biomarkers and risk factors such as the circulating microbiome—a preliminary but promising area of research—and enhancing team preparedness through simulation training to improve patient safety outcomes.

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.

Artificial intelligence (AI) translation in genitourinary (GU) pathology has progressed unevenly across organs and tasks. This review addresses a central clinical question: which GU pathology AI applications are deployment-ready, which require further validation, and what frameworks can guide safe implementation? We synthesize evidence across GU organs and introduce pragmatic translation frameworks to guide deployment and prioritize translational research.

Narrative review integrating foundational literature with targeted 2023–2025 publications, emphasizing regulatory milestones, external validation, and prospective studies. Literature was identified through PubMed, Embase, and conference proceedings using structured search terms for AI, digital pathology, and GU organ-specific queries. For each organ/task, we mapped evidence strength, regulatory maturity, generalizability, workflow integration, safety, and feasibility to a Translational Readiness Index (TRI) rubric (0–30 scale).

Prostate biopsy AI demonstrates the strongest maturity (TRI 26/30), supported by U.S. Food and Drug Administration-cleared systems, multi-site validation, and prospective implementations showing efficiency gains and reduced ancillary testing. Bladder cytology shows moderate readiness (TRI 19/30), with commercial offerings supporting pilotable prescreening workflows aligned with the Paris System when paired with uncertainty-aware deferral. Bladder histology, renal neoplasia, and low-prevalence domains (testis, penis) remain emerging (TRI 6–15/30), constrained by label variability, rare subtype underrepresentation, and limited external validation.

The TRI rubric, SURE-Path safety bundle, and VALIDATED/ORCHESTRATE implementation pathway provide a practical template for evidence-based deployment in GU pathology. Clinically defensible translation requires matching intended use to validation evidence, with explicit safeguards for emerging applications.

Comparative data on sequential transarterial chemoembolization (TACE) after stereotactic body radiation therapy (SBRT) in recurrent hepatocellular carcinoma (HCC) remain limited. This study aimed to evaluate the efficacy of this combination.

We retrospectively reviewed 152 patients with recurrent HCC who met predefined eligibility criteria; 109 received SBRT alone and 43 received SBRT plus TACE. To minimize selection bias, a 2:1 propensity score matching was performed, resulting in 68 patients in the SBRT-alone group and 36 in the SBRT plus TACE group for the final comparative analysis. Overall survival, progression-free survival, and local control were assessed using the Kaplan-Meier method.

The SBRT plus TACE group was associated with numerically higher survival rates, although this difference did not reach statistical significance. The cumulative one-, three-, and five-year overall survival rates were 91.2%, 76.3%, and 61.8% for SBRT alone, compared to 100.0%, 86.1%, and 77.5% for the combination therapy ( p = 0.069). The corresponding progression-free survival rates were 73.1%, 51.1%, and 32.3% versus 88.9%, 58.1%, and 52.3% ( p = 0.091). No acute grade ≥3 toxicities were observed in either group.

In this exploratory analysis of recurrent HCC, the combination of SBRT and TACE demonstrated a favorable trend toward improved survival compared with SBRT alone, without an increase in severe toxicity. While these findings did not reach statistical significance, they establish the safety profile of the combined approach and provide preliminary evidence supporting its potential therapeutic role. This hypothesis-generating study justifies and informs the design of larger, prospective trials to definitively evaluate the efficacy of this regimen.

Micro- and nanoplastics (MNPs) are pervasive environmental contaminants with growing recognition as potential contributors to human disease. Widespread human exposure occurs primarily through ingestion of contaminated food and water, and MNPs have been detected in multiple human tissues, including the gastrointestinal tract. Experimental evidence provides a plausible biological basis for disease associations, including impairment of intestinal barrier integrity, activation of mucosal immune pathways, and alteration of gut microbial communities caused by MNP exposure. Although human data remain limited, early studies demonstrate MNP detection in stool and suggest potential correlations with inflammatory biomarkers such as fecal calprotectin. These findings, together with mechanistic data from in vitro and animal models, raise concern that MNP exposure represents a paradigm shift in the pathogenesis or modulation of inflammatory bowel disease (IBD); however, methodological variability, small sample sizes, and contamination challenges currently limit definitive conclusions. The aim of this review is to evaluate the current understanding of MNP exposure and its impact on intestinal health, particularly in relation to IBD. We synthesize mechanistic and early clinical evidence linking MNPs to IBD and highlight critical research gaps. Future standardized exposure assessment, mechanistic validation in human systems, and longitudinal studies are essential to clarify causal relationships. Given the modifiable nature of environmental plastic exposure, advancing this field may offer new opportunities for IBD prevention and intervention.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by intermittent hypoxia and sleep fragmentation, which may contribute to lung cancer development and progression. This review synthesizes epidemiological evidence on the association between OSA and lung cancer incidence and mortality, highlighting inconsistencies due to study design, population differences, and confounding factors such as smoking and obesity. While some studies report an increased lung cancer risk, particularly with severe nocturnal hypoxemia, others suggest no significant association or a potential protective effect. Pathophysiologically, OSA promotes oncogenesis through hypoxia-inducible factor activation, tumor immune microenvironment remodeling, exosome-mediated signaling, nuclear factor κB pathway activation, and enhanced cancer stem cell properties. Continuous positive airway pressure therapy may mitigate these effects, with evidence suggesting reduced lung cancer incidence and improved prognosis in adherent patients. This review underscores the need for standardized studies using objective diagnostics and robust confounder adjustment to clarify the OSA–lung cancer link and optimize clinical management.

Accurate identification of invasive fungal pathogens is crucial for appropriate antifungal therapy. The Department of Clinical Laboratory at Indus Hospital & Health Network, Karachi, Pakistan, reported two cases of invasive fungal infections between 1st January and 31st March 2024 in which conventional identification methods and automated systems produced discordant results, highlighting critical diagnostic challenges.

Two invasive yeast isolates initially showing budding yeast cells without pseudohyphae on Gram stain were subjected to conventional identification using cornmeal-Tween 80 agar, chrome agar, and BiGGY agar, followed by automated identification using the VITEK 2 ID-YST system and confirmatory API 20C AUX testing. Both isolates demonstrated typical soft, wrinkled, cream-colored colonies on Sabouraud dextrose agar, which on chrome agar appeared as dry, blue colonies and on BiGGY agar as dry, brown colonies. Characteristic arthroconidia and blastoconidia formation on cornmeal-Tween 80 agar were observed, consistent with Trichosporon species. However, the VITEK 2 ID-YST system identified both isolates as Cryptococcus laurentii with good confidence levels. India ink staining was negative for both isolates. Confirmatory API 20C AUX testing correctly identified both isolates as Trichosporon asahii (identification profile 3740734).

This discordance between automated and conventional methods underscores the continued importance of conventional identification techniques and highlights potential limitations of automated systems for certain uncommon yeasts. Laboratories should maintain proficiency in conventional methods and consider confirmatory testing when automated results conflict with morphological findings. The clinical implications of misidentification include inappropriate antifungal selection, given the different susceptibility patterns between these species.

Helicobacter pylori infection represents a significant modifiable risk factor in the pathogenesis of gastric cancer. Nevertheless, conventional antibiotic treatments have increasingly proven inadequate due to challenges such as antibiotic resistance, microbial dysbiosis, and mucosal damage. In response to these issues, this review introduces an innovative intervention strategy based on the “nanotechnology-based 3R” approach (Remove H. pylori, Remodel the microenvironment, Repair the gastrointestinal tract), which aims to offer a comprehensive solution for managing H. pylori infection. This strategy comprises three principal components. Firstly, the utilization of pH/light/magnetic multi-responsive nanomaterials facilitates the precise eradication of the pathogen and its biofilm. Secondly, to address bacterial immune evasion, these nanomaterials are engineered to target and neutralize virulence factors such as VacA, thereby contributing to the reversal of the local immunosuppressive environment. Thirdly, the utilization of nanomaterials presents a promising approach for the concurrent repair of the mucosal barrier and the maintenance of intestinal microbiome homeostasis. Finally, this paper provides a comprehensive analysis of the specific mechanisms employed by typical nanomaterials, including metal-organic frameworks, charge-reversal nanoparticles, nanozymes, and antimicrobial peptide crystals. These mechanisms involve targeted microbial eradication, activation of autophagy, and the upregulation of tight junction proteins. Furthermore, the study delves into the critical roles played by multimodal external field stimulation and material–host interaction network analysis, which are essential for future clinical translation. Ultimately, this review suggests a potential roadmap for system-precision intervention that transcends the conventional “sterilization first” paradigm. Nonetheless, the current evidence regarding the efficacy and safety of this approach is predominantly derived from cell and mouse models. Therefore, its clinical applicability requires validation through studies involving large animal models and prospective clinical trials.

Breast cancer (BCA) is one of the most common cancers worldwide, with a high rate of mortality and morbidity in women. This review focuses on the applications of nanotechnology, nanomaterials, and nanoparticles (NPs) in BCA, encompassing diagnosis and therapy. Nanotechnologies, nanocarriers, and nano-encapsulations versus their corresponding counterparts for BCA diagnosis and therapy have been discussed. Various drug formulations into different nanocarriers (lipid NPs, nanoemulsions, polymeric NPs, and metal-based NPs) enhanced their bioavailability and therapeutic efficacy, overcoming the limitations of conventional formulations. Additionally, clinical specialists have achieved improved outcomes in the detection and monitoring of BCA at various stages using nanotechnology, ultimately leading to an improved quality of life for patients.

Chronic pancreatitis is an inflammatory disease and is difficult to manage despite advancements in medical science. This study examined the effect of water/ethanol extracts of Justicia carnea leaves on oxidative stress and glucagon expression in a mouse model of chronic pancreatitis induced by trinitrobenzenesulfonic acid (TNBS).

Twenty-five male Swiss albino mice were randomized and treated intrarectally with vehicle (the control group) or TNBS. Some TNBS-treated mice were treated orally with 200 mg/kg or 400 mg/kg J. carnea extracts, or with the positive control, 500 mg/kg sulfasalazine, every other day on three occasions. Oxidative stress markers and pancreatic glucagon expression were assessed.

Compared with the healthy control mice, treatment with TNBS significantly decreased the levels of pancreatic glutathione (0.89 µmol/g tissue vs. 7.16 µmol/g tissue in the control) and glutathione peroxidase activity, but significantly increased the levels of α-amylase and lipase activities, lipid peroxidation, total antioxidant capacity, and nitric oxide, as well as serum C-reactive protein (P < 0.05 for all), accompanied by severe inflammation and reduced glucagon expression in the pancreatic tissues. The toxic effects of TNBS were significantly mitigated by treatment with J. carnea extracts.

These findings provide evidence that treatment with J. carnea extracts inhibited oxidative stress and preserved glucagon expression in the pancreatic tissues of mice.

Extrahepatic cancers have been recognized as a significant outcome of metabolic dysfunction–associated steatotic liver disease (MASLD), which involves five cardiometabolic risk factors, including hypertension, and is associated with the tumorigenesis of several cancers or with anti-cancer treatment. We aimed to investigate the association between hypertension, liver fibrosis, and extrahepatic cancers in the MASLD population.

This multicenter cross-sectional study was based on a MASLD population from hospital-based databases across 11 centers nationwide in China, according to MASLD diagnostic criteria identified using keywords and ICD-10 codes. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between risk factors and extrahepatic cancers.

A total of 103,652 individuals with MASLD were identified, among whom 6,605 were diagnosed with extrahepatic cancers. The primary outcome revealed that hypertension was significantly associated with extrahepatic cancers (OR 1.14, 95% CI: 1.08–1.21), and its combination with hyperglycemia further increased this association (OR 1.36, 95% CI: 1.22–1.51). Risk factors for extrahepatic cancers included being over 40 years of age and female sex. Conversely, certain metabolism-based treatments were found to have potentially protective effects, including angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, fibrates, GLP-1 receptor agonists, and thiazolidinediones. After adjusting for confounding factors, the fibrosis-4 (FIB-4) score was associated with extrahepatic cancers. In the hypertension subgroup, FIB-4 scores of 1.30–2.66, 2.67–3.47, and ≥ 3.48 were associated with extrahepatic cancers in individuals aged 35–64 years, consistent with findings in those aged ≥ 65 years of age with FIB-4 ≥ 2.

Hypertension combined with liver fibrosis is associated with extrahepatic cancers in patients with MASLD.

Phenylethanoid glycosides (PhGs) are water-soluble natural compounds widely distributed in the plant kingdom, attracting significant attention from medicinal chemists due to their promising potential in pharmaceutical applications. PhGs exhibit a broad range of activities, including neuroprotective, hepatoprotective, anti-inflammatory, antioxidant, and immunomodulatory effects. This review aims to update the hepatoprotective effects of total PhG extracts and individual PhG compounds, as well as the underlying mechanisms. Additionally, we describe the structural characteristics, representative PhG compounds, and their structure–activity relationships. In brief, total PhG extracts can exert synergistic protection by reducing serum alanine aminotransferase/aspartate aminotransferase levels, suppressing oxidative stress, and attenuating inflammatory responses. Representative PhGs, including acteoside (verbascoside), echinacoside, forsythoside A (also known as forsythiaside A), and cistanoside A, protect against liver injury through modulation of the Nrf2/HO-1, NF-κB, MAPK, and TGF-β/Smad pathways, thereby regulating oxidative stress, inflammation, apoptosis, fibrosis, and lipid metabolism. Structurally, PhGs consist of a phenylethyl alcohol core, cinnamoyl residues, and glycosyl moieties. Structure–activity relationship analyses indicate that caffeoyl substitution, multiple phenolic hydroxyl groups, and optimal glycosylation patterns are key determinants of hepatoprotective efficacy.