Despite the emergence of new approaches in acute myeloid leukemia (AML) treatment in recent years, the overall prognosis remains poor. Particularly for elderly patients and relapsed/refractory cases, the five-year survival rate consistently remains below 30%. While traditional chemotherapy regimens can rapidly suppress tumor burden and alleviate clinical symptoms, they suffer from limitations such as insufficient targeting, prominent toxic side effects, and a tendency to induce drug resistance. Immunotherapy offers a novel therapeutic pathway for AML due to its advantages of precise targeting, long-lasting antitumor effects, and a controllable safety profile. However, single-agent immunotherapy demonstrates limited clinical response rates in AML and struggles to achieve complete tumor cell clearance. In this context, combination regimens of chemotherapy and immunotherapy are increasingly becoming the focus of research. This review aims to summarize the rationale and advances in the combination of immune checkpoint inhibitors, chimeric antigen receptor T-cell therapy, antibody-drug conjugates, bispecific antibodies, and cancer vaccines with chemotherapy for the treatment of AML. We have detailed the preclinical research and clinical trial progress of each combined regimen, analyzed the core challenges—including off-target toxicity, high tumor heterogeneity, and limited efficacy in specific AML subtypes—and further propose targeted solutions and future development directions, such as exploring novel specific antigens, developing multi-targeted drugs, and formulating precision individualized treatment plans. The clinical application of such combined strategies is attracting increasing attention. In conclusion, chemo-immunotherapy combinations represent a highly promising therapeutic paradigm for AML, harnessing the synergy of chemotherapy-mediated immune microenvironment remodeling and the specific antitumor activity of immunotherapies to overcome single-agent limitations and deliver meaningful survival benefits.

Liver fibrosis is a pivotal and reversible stage in the progression of chronic liver disease. However, the mechanisms underlying fibrosis reversal remain unclear, and effective diagnostic biomarkers are lacking in clinical practice. In this study, we aimed to elucidate the role and molecular mechanisms of glutathione S-transferase Mu 3 (GSTM3) in liver fibrosis reversal, and preliminarily determine whether GSTM3 can serve as a novel biomarker for liver fibrosis reversal.

Carbon tetrachloride-induced mouse models of liver fibrosis and spontaneous reversal were established. Proteomic analysis was used to identify proteins shared between liver tissue and serum that were continuously downregulated during fibrosis reversal. The expression of GSTM3 was evaluated in the livers of mice undergoing fibrosis reversal and in clinical samples from patients with liver fibrosis. Hepatic stellate cells (HSCs) were transfected with Gstm3-silencing RNA or an overexpression plasmid to assess the effects on fibrosis markers. RNA sequencing analyses were performed, and the underlying molecular mechanisms were investigated.

Proteomic analysis revealed significantly decreased GSTM3 levels in both hepatic tissue and serum in mice undergoing fibrosis reversal, and its expression was negatively correlated with the extent of reversal. GSTM3 levels were markedly increased in the hepatic tissue and serum of patients with liver fibrosis. GSTM3 expression was upregulated in transforming growth factor-β-stimulated HSCs. GSTM3 knockdown inhibited the expression of fibrosis markers, such as collagen type I α1 and tissue inhibitor of metalloproteinase 1, whereas its overexpression promoted their expression. Mechanistic studies indicated that GSTM3 knockdown activated peroxisome proliferator-activated receptor γ (PPARγ) signaling and downregulated its downstream targets, cluster of differentiation 36 and fatty acid-binding protein 4, thereby suppressing HSC activation.

GSTM3 knockdown promotes liver fibrosis reversal via PPARγ signaling-mediated inhibition of HSC activation. Therefore, GSTM3 is a promising therapeutic target and diagnostic biomarker for liver fibrosis.

PVT is a harmful event in cirrhosis, and the prevention and treatment of PVT are important in the management of cirrhosis and portal hypertension. The study aimed to observe the efficacy of Danggui Buxue Decoction (DBD) on portal vein thrombosis (PVT) in cirrhosis and to elucidate the related mechanism using a modified animal model.

A model of PVT in cirrhosis was established by partial portal vein ligation and intraperitoneal injection of CCl4 in rats, which showed obvious PVT with intra- and extravenous thrombosis as well as liver cirrhosis. Rats were randomly assigned into four groups and received intragastric administration of DBD (12 g/kg/day) or rivaroxaban (20 mg/kg/day) for 6 weeks.

DBD attenuated collagen deposition and reduced thrombus formation in model livers, increased portal vein blood flow, expanded the portal vein diameter, and reduced prothrombin time and international normalized ratio in the model rats. In addition, DBD reduced hepatic von Willebrand factor and plasminogen activator inhibitor-1 expression and increased hepatic fibrin degradation product content in the liver tissues of model rats.

We modified a model of cirrhotic PVT in rats and found that DBD had a good effect on PVT and liver fibrosis, with the mechanisms related to the enhancement of portal vein blood flow and the protection against endothelial cell injury.

Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a predominant cause of chronic liver disease, underscoring the demand for accessible, non-invasive diagnostic tools. Tongue diagnosis in Traditional Chinese Medicine provides a distinctive perspective on systemic health, though it remains largely subjective. This study aimed to develop an interpretable multimodal deep learning model for MAFLD screening by integrating quantitative tongue image features with routine clinical data.

From 904 screened candidates, 477 subjects (157 healthy, 320 MAFLD) were included and randomly allocated to training, validation, and test sets in an 8:1:1 ratio. All participants underwent standardized tongue imaging (International Commission on Illumination L*a*b color features) and comprehensive clinical evaluation. We constructed a dual-stream deep learning model, combining a ConvNeXt-Tiny network for tongue images and a multilayer perceptron for clinical variables. Feature fusion was achieved via a Dynamic Affine Feature Transformation module, and the model was trained using weighted cross-entropy loss.

MAFLD patients showed significant metabolic abnormalities compared to healthy controls. A progressive decrease in tongue yellowness (b* value) was observed with advancing fibrosis. On an independent test set (n = 48), the multimodal model achieved 97.92% accuracy, Quadratic Weighted Kappa of 0.9538, and 96.88% sensitivity, and 100% specificity, outperforming single-modality and serological models. Interpretability analyses confirmed the model’s focus on clinically relevant tongue regions and key metabolic drivers.

We developed an accurate and interpretable multimodal model that synergizes tongue image features with metabolic indicators for MAFLD screening. This approach presents a promising, low-cost tool potentially well-suited for resource-limited settings.

Infectious diarrhea is a gastrointestinal illness that results in around 1.7 billion cases and 525,000 deaths annually, particularly among children under five, according to the World Health Organization. While some Cameroonian medicinal plants show promise for treating diarrhea, many plants are used without established scientific evidence of their efficacy. These plants include Tithonia diversifolia (T. diversifolia) and Solanum torvum (S. torvum), which are traditionally used to treat diarrheal symptoms. This study sought to investigate the anti-Shigella activity of leaf extracts from T. diversifolia and S. torvum.

Extracts from T. diversifolia and S. torvum were obtained by successive maceration in solvents of increasing polarity, including hexane, dichloromethane, ethyl acetate, methanol, and water. The as-prepared extracts (10) were evaluated for antibacterial activity against selected Shigella species using an in vitro experiment. The mode of action of the bioactive extracts was determined in Shigella through growth kinetic analysis.

Hexane extract from S. torvum (St-HEX-F) and dichloromethane extract from T. diversifolia (Td-DCM-F) inhibited the growth of Shigella flexneri NR-518 and Shigella boydii NR-521 with minimum inhibitory concentration (MIC) values of 500 and 1,000 µg/mL, respectively. Shigella flexneri and Shigella boydii were the most sensitive strains, whereas Shigella sonnei was the most resistant strain. Bacterial growth kinetics revealed that St-HEX-F and Td-DCM-F are bacteriostatic at MIC and bactericidal at 2×MIC and 4×MIC.

Extracts from T. diversifolia and S. torvum possess anti-Shigella activity and could be used as a potential source of active ingredients for developing new treatments against diarrhea caused by multidrug-resistant Shigella.

Infections are frequent and lethal complications of acute-on-chronic liver failure (ACLF). Reliable biomarkers to distinguish fungal from bacterial infections remain limited. Given the central role of immune dysfunction in ACLF, we aimed to evaluate the diagnostic value of serum cytokines in differentiating invasive pulmonary aspergillosis (IPA) from bacterial pneumonia (BP) in HBV-associated ACLF.

This retrospective case-control study enrolled ACLF patients admitted to the Tongji Hospital, between 2018 and 2022. Patients were categorized into IPA, BP, and non-infection groups. The BP and non-infection groups were propensity score-matched to the IPA cases. Serum cytokines levels (IL-1β, sIL-2R, IL-6, IL-8, IL-10, TNF-α) and clinical data were collected, with the diagnostic performance of these cytokines as biomarkers assessed via ROC curves.

A total of 32 IPA, 96 BP, and 96 non-infection patients were enrolled, with balanced baseline characteristics. Compared with the non-infection group, the IPA group had higher sIL-2R (1,606.00 vs. 1,211.50 U/mL, P = 0.019) and IL-6 (69.03 vs. 15.98 pg/mL, P < 0.001) levels, but lower IL-8 levels (62.20 vs. 132.00 pg/mL, P = 0.025). The BP group showed elevated sIL-2R (1,792.00 U/mL), IL-6 (49.42 pg/mL), IL-10 (13.40 pg/mL) levels compared to the non-infection group (all P < 0.001). Also, IL-8 was lower in the IPA group than in the BP group (62.20 vs. 176.00 pg/mL, P < 0.001) and its assessment could best distinguish IPA from BP (AUC = 0.743, cut-off = 76.60 pg/mL; sensitivity = 66.7%, specificity = 82.1%).

Serum IL-8 exhibited superior diagnostic value for IPA in patients with HBV-ACLF and could effectively discriminate Aspergillus infections from bacterial infections.

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.

Amaranth is conventionally consumed as a significant source of nutrients and bioactive compounds and is a potential alternate crop. The present study aimed to validate the folklore and ethnomedicinal claims regarding the utilization of foliar tissues of the pseudocereal Amaranthus hypochondriacus L. for their pharmacological propensities, primarily focusing on bioactive polyphenolic compounds and associated anti-degenerative properties, in view of the scarce evidence available on the same.

Reverse-phase high-performance liquid chromatography coupled with a photodiode array assay of nineteen significant bioactive polyphenolic compounds, along with their in vitro antioxidant-based pharmacological properties (superoxide and hydroxyl radical scavenging properties, metal-chelating and reducing properties, radical scavenging properties, anti-lipid peroxidation and protein coagulation properties, and α-glucosidase and α-amylase inhibitory activities), were assessed and compared for foliar extracts of ten promising experimental accessions of Amaranthus hypochondriacus, grown in two different seasons (summer and winter).

The results exhibited germplasm-specific variations in the pharmacological potential of foliar tissues of the experimental amaranths, which can be substantiated by data showing a close correlation between the abundance of bioactive polyphenolic compounds (naringin, myricetin, naringenin, apigenin, rutin, catechin, quercetin) and in vitro antioxidant (2,2-diphenyl-1-picrylhydrazyl, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) assay, hydroxyl radical scavenging, reducing, and metal-chelating) properties, as well as anti-diabetic (inhibition of α-glucosidase and α-amylase activities) and anti-inflammatory (anti-lipid peroxidation) attributes. Accessions IC107144 and IC47434 stood out as the most promising medicinal crops based on overall in vitro anti-degenerative properties and the bioavailability of polyphenolic compounds.

Overall, the results validated the traditional ethnomedicinal claim regarding the utilization of foliar tissues of the underutilized pseudocereal Amaranthus hypochondriacus L., and identified lead germplasms (IC107144 and IC47434) as low-cost natural sources of bioactive compounds, potentially promoting their pharmacological utilization.

The long-term clinical outcomes of patients with hepatitis B virus (HBV)-related cirrhosis receiving nucleos(t)ide analog (NA) therapy according to virological response patterns remain inadequately defined. This study aimed to investigate the association between virological response patterns and clinical outcomes in a large, long-term, real-world cohort.

This retrospective–prospective cohort study enrolled patients with HBV-related cirrhosis receiving NA therapy from 2009 to 2019. According to the serum HBV DNA levels during the initial two years of antiviral treatment, patients were categorized as having a complete (CVR) or partial virological response (PVR). Patients with CVR were further stratified according to their dynamic HBV DNA changes during follow-up into maintained virological response (MVR) or virological breakthrough (VBT) patterns. The primary clinical outcomes included hepatocellular carcinoma (HCC), acute-on-chronic liver failure, and liver-related death. Secondary endpoints included recompensation and progression to decompensation. Cox proportional hazards regression was used to assess the association between virological response patterns and clinical endpoints.

In total, 1,869 patients were enrolled. During a median follow-up of seven years, the MVR, VBT, and PVR rates were 65.4%, 26.5%, and 8.1%, respectively. The cumulative serum hepatitis B surface antigen (HBsAg) clearance rate was 9.8%. Moreover, 34.9% of patients with HBsAg < 100 IU/mL at baseline experienced HBsAg clearance. Compared with patients with VBT and PVR, those with MVR had a lower five- and ten-year cumulative incidence of HCC in both the compensated (five-year: 10.1% vs. 17.0%; ten-year: 14.2% vs. 33.6%; P < 0.001) and decompensated cirrhosis subgroups (five-year: 19.5% vs. 36.7%; ten-year: 25.7% vs. 49.7%; P < 0.001). Similarly, patients with MVR also had a lower cumulative incidence of liver-related death. Additionally, a higher hepatic recompensation rate was observed in patients with MVR than in those with VBT (34.1% vs. 22.5%, P < 0.001). Importantly, patients achieving HBsAg clearance and undetectable serum HBV DNA levels (“functional cure” during ongoing NA therapy) had the lowest five- and ten-year cumulative incidence of HCC (3.9% and 8.7%, respectively).

Patients with long-term MVR exhibited a lower incidence of HCC and liver-related death in both compensated and decompensated HBV-related cirrhosis subgroups, especially those achieving “functional cure.” However, more than 30% of patients experienced PVR or VBT during long-term NA antiviral therapy. These findings highlight the importance of long-term, rigorous monitoring after initial CVR to optimize outcomes and support clinical decision-making.

Colorectal polyp detection from endoscopic images is critical for the early diagnosis of colorectal cancer. However, traditional deep learning methods often suffer from limited generalization when deployed across datasets containing different polyp morphologies. This work aimed to investigate whether vision-language foundation models can facilitate zero-shot generalization across multiple polyp datasets without target-domain fine-tuning.

We introduced a zero-shot colorectal polyp detection framework based on Contrastive Language-Image Pretraining (CLIP) to improve cross-dataset detection performance. Key innovations include: (1) a background patch contrastive loss using pseudo-normal tissue patches to teach the model to distinguish normal mucosa from polyps; (2) attribute-enhanced text prompts that incorporate domain-specific descriptors of polyp appearance, improving the model’s semantic generalization to novel polyp morphologies; and (3) an enhanced CLIP visual adapter with per-layer adaptive feature fusion and generalized mean pooling to capture multi-scale features for better polyp localization. During training, we use one annotated colorectal polyp dataset (e.g., CVC-ColonDB) to learn patch-level image-text correspondence. The model is then evaluated in a zero-shot manner on different polyp datasets (CVC-ClinicDB, Kvasir-SEG, and CVC-300), where we evaluate pixel-level anomaly detection performance.

The framework demonstrated robust zero-shot generalization on unseen test cohorts. Without any dataset-specific fine-tuning, the model achieved a mean pixel-level AUROC of 0.94 and a mean average precision of 0.81 across the 12 leave-one-dataset-out zero-shot transfer settings. In the CVC-ColonDB-source benchmark, the model achieved a mean Dice coefficient of 0.84 across CVC-ClinicDB, Kvasir-SEG, and CVC-300. This high level of performance was consistent across datasets with distinct visual characteristics, underscoring the ability of the model to detect diverse polyp morphologies that it had not been explicitly trained to recognize.

Our findings demonstrate that an anomaly-aware vision-language model significantly improves cross-dataset polyp detection generalization without requiring normal images for training. This multimodal strategy may facilitate the robust deployment of artificial intelligence-based colorectal screening systems by enabling reliable detection of diverse polyp morphologies across different clinical settings. Extension to non-polyp colorectal pathologies (e.g., ulcerative colitis and colorectal tumors) remains an important direction for future work, pending the availability of pixel-level annotated datasets for these lesion categories.