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Background Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations. Methods Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient. Results A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94). Conclusions Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes.

Background Heterogeneity of hepatocellular carcinoma (HCC) presents significant challenges for therapeutic strategies and necessitates combinatorial treatment approaches to counteract suppressive behavior of tumor microenvironment and achieve improved outcomes. Here, we employed cytokines to induce memory-like behavior in natural killer (NK) cells, thereby enhancing their cytotoxicity against HCC. Additionally, we evaluated the potential benefits of combining sorafenib with this newly developed memory-like NK cell ( p NK) immunochemotherapy in a preclinical model. Methods HCC tumors were grown in SD rats using subcapsular implantation. Interleukin 12/18 cytokines were supplemented to NK cells to enhance cytotoxicity through memory activation. Tumors were diagnosed using MRI, and animals were randomly assigned to control, p NK immunotherapy, sorafenib chemotherapy, or combination therapy groups. NK cells were delivered locally via the gastrointestinal tract, while sorafenib was administered systemically. Therapeutic responses were monitored with weekly multi-parametric MRI scans over three weeks. Afterward, tumor tissues were harvested for histopathological analysis. Structural and functional changes in tumors were evaluated by analyzing MRI and histopathology data using ANOVA and pairwise T-test analyses. Results The tumors were allowed to grow for six days post-cell implantation before treatment commenced. At baseline, tumor diameter averaged 5.27 mm without significant difference between groups ( p  = 0.16). Both sorafenib and combination therapy imposed greater burden on tumor dimensions compared to immunotherapy alone in the first week. By the second week of treatment, combination therapy had markedly expanded its therapeutic efficacy, resulting in the most significant tumor regression observed (6.05 ± 1.99 vs. 13.99 ± 8.01 mm). Histological analysis demonstrated significantly improved cell destruction in the tumor microenvironment associated with combination treatment (63.79%). Interestingly, we observed fewer viable tumor regions in the sorafenib group (38.9%) compared to the immunotherapy group (45.6%). Notably, there was a significantly higher presence of NK cells in the tumor microenvironment with combination therapy (34.79%) compared to other groups (ranging from 2.21 to 26.50%). Although the tumor sizes in the monotherapy groups were similar, histological analysis revealed a stronger response in p NK cell immunotherapy group compared to the sorafenib group. Conclusions Experimental results indicated that combination therapy significantly enhanced treatment response, resulting in substantial tumor growth reduction in alignment with histological analysis.

Background: Assessing the efficacy of combination therapies in hepatocellular carcinoma (HCC) requires both accurate tumor delineation and biologically validated prediction of therapeutic response. Conventional MRI-based criteria, which rely primarily on tumor size, often fail to capture treatment efficacy due to tumor heterogeneity and pseudo-progression. This study aimed to develop and biologically validate a multi-task deep learning model that simultaneously segments HCC tumors and predicts treatment outcomes using clinically relevant multi-parametric MRI in a preclinical rat model. Methods: Orthotopic HCC tumors were induced in rats assigned to Control, Sorafenib, NK cell immunotherapy, and combination treatment groups. Multi-parametric MRI (T1w, T2w, and contrast enhanced MRI) scans were performed weekly. We developed a U-Net++ architecture incorporating a pre-trained EfficientNet-B0 encoder, enabling simultaneous segmentation and classification tasks. Model performance was evaluated through Dice coefficients and area under the receiver operator characteristic curve (AUROC) scores, and histological validation (H&E for viability, TUNEL for apoptosis) assessed biological correlations using linear regression analysis. Results: The multi-task model achieved precise tumor segmentation (Dice coefficient = 0.92, intersection over union (IoU) = 0.86) and reliably predicted therapeutic outcomes (AUROC = 0.97, accuracy = 85.0%). MRI-derived deep learning biomarkers correlated strongly with histological markers of tumor viability and apoptosis (root mean squared error (RMSE): viability = 0.1069, apoptosis = 0.013), demonstrating that the model captures biologically relevant imaging features associated with treatment-induced histological changes. Conclusions: This multi-task deep learning framework, validated against histology, demonstrates the feasibility of leveraging widely available clinical MRI sequences for non-invasive monitoring of therapeutic response in HCC. By linking imaging features with underlying tumor biology, the model highlights a translational pathway toward more clinically applicable strategies for evaluating treatment efficacy.

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and is often associated with temporary tumor hypoxia due to disrupted perfusion. C. novyi-NT is an attenuated, anaerobic bacterium engineered to selectively germinate and proliferate in hypoxic tumor regions, resulting in localized tumor cell lysis while sparing healthy, oxygenated tissue. The synergy between IRE-induced hypoxia and hypoxia-sensitive C. novyi-NT may enhance tumor destruction and stimulate systemic antitumor immunity. Furthermore, the integration of advanced imaging and artificial intelligence can support precise treatment planning and real-time monitoring. This integrated approach holds promise for improving outcomes in patients with CRLM, though further preclinical and clinical validation is needed.

* The combination of NaOH and nitrite was used to control harmful gas in sewers. * Hydrogen sulfide and methane in airspace were reduced by 96.01% and 91.49%. * Changes in sewage quality and greenhouse effect by chemical dosing were negligible. * The strong destructive effects on biofilm slowed down the recovery of harmful gases. * The cost of the method was only 3.92 × 10 −3 $/m 3. An innovative treatment method by the combination of NaOH and nitrite is proposed for controlling hydrogen sulfide and methane in gravity sewers and overcome the drawbacks of the conventional single chemical treatment. Four reactors simulating gravity sewers were set up to assess the effectiveness of the proposed method. Findings demonstrated hydrogen sulfide and methane reductions of about 96.01% and 91.49%, respectively, by the combined addition of NaOH and nitrite. The consumption of NaNO 2 decreased by 42.90%, and the consumption rate of NaOH also showed a downward trend. Compared with a single application of NaNO 2, the C/N ratio of wastewater was increased to about 0.61 mg COD/mg N. The greenhouse effect of intermediate N 2O and residual methane was about 48.80 gCO 2/m 3, which is far lower than that of methane without control (260 gCO 2/m 3). Biofilm was destroyed to prevent it from entering the sewage by the chemical additives, which reduced the biomass and inhibited the recovery of biofilm activity to prolong the control time. The sulfide production rate and sulfate reduction rate were reduced by 92.32% and 85.28%, respectively. Compared with conventional control methods, the cost of this new method was only 3.92 × 10 −3 $/m 3, which is potentially a cost-effective strategy for sulfide and methane control in gravity sewers.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality around the world. Despite advances in surgery, chemotherapy, and targeted therapies, the prognosis for patients with metastatic or advanced CRC remains poor. Immunotherapies comprising immune checkpoint inhibitors showed disappointing responses in metastatic CRC (mCRC). However, cellular immunotherapy, specifically using classical dendritic cells (cDCs), may hold unique promise in immune recognition for CRC antigens. cDCs are substantial players in immune recognition and are instrumental in orchestrating innate and adaptive immune responses by processing and presenting tumor antigens to effector cells. Natural killer T (NKT) cells are insufficiently studied but unique effector cells because of their ability to bridge innate and adaptive immune reactions and the crosstalk with dendritic cells in cancer. This review explores the therapeutic potential of using both cDCs and NKT cells as a synergistic therapy in CRC, focusing on their biological roles, strategies for harnessing their capabilities, clinical applications, and the challenges within the tumor microenvironment. Both cDCs and NKT cells can be used as a new effective approach for cell-based therapies in cancers to provide a new hope for CRC patients that are challenging to treat.

The presence of Alternaria toxins (ATs) in fruit purees may cause potential harm to the life and health of consumers. As time passes, ATs have become the key detection objects in this kind of food. Based on this, a novel and rapid method was established in this paper for the simultaneous detection of seven ATS (tenuazonic acid, alternariol, alternariol monomethyl ether, altenuene, tentoxin, altenusin, and altertoxin I) in mixed fruit purees using ultra-high performance liquid chromatography-tandem mass spectrometry. The sample was prepared using the modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) method to complete the extraction and clean-up steps in one procedure. In this QuEChERS method, sample was extracted with water and acetonitrile (1.5% formic acid), then salted out with NaCl, separated on an ACQUITY UPLC BEH C18 with gradient elution by using acetonitrile and 0.1% formic acid aqueous as eluent, and detected by UPLC-MS/MS under positive (ESI+) and negative (ESI−) electrospray ionization and MRM models. Results showed that the seven ATs exhibited a good linearity in the concentration range of 0.5–200 ng/mL with R2 > 0.9925, and the limits of detection (LODs) of the instrument were in the range of 0.18–0.53 μg/kg. The average recoveries ranged from 79.5% to 106.7%, with the relative standard deviations (RSDs) no more than 9.78% at spiked levels of 5, 10, and 20 μg/kg for seven ATs. The established method was applied to the determination and analysis of the seven ATs in 80 mixed fruit puree samples. The results showed that ATs were detected in 31 of the 80 samples, and the content of ATs ranged from 1.32 μg/kg to 54.89 μg/kg. Moreover, the content of TeA was the highest in the detected samples (23.32–54.89 μg/kg), while the detection rate of Ten (24/31 samples) was higher than the other ATs. Furthermore, the other five ATs had similar and lower levels of contamination. The method established in this paper is accurate, rapid, simple, sensitive, repeatable, and stable, and can be used for the practical determination of seven ATs in fruit puree or other similar samples. Moreover, this method could provide theory foundation for the establishment of limit standard of ATs and provide a reference for the development of similar detection standard methods in the future.

Hepatocellular carcinoma (HCC) is a major global health issue characterized by poor prognosis and complex tumor biology. One of the critical components of the HCC tumor microenvironment (TME) is tumor-associated macrophages (TAMs), which play a pivotal role in modulating tumor growth, immune evasion, and metastasis. Macrophages are divided into two major subtypes: pro-inflammatory M1 and anti-inflammatory M2, both of which may exist in TME with altered function and proportion. The anti-inflammatory M2 macrophages are further subdivided into four distinct immune suppressive subsets. TAMs are generally counted as M2-like macrophages with altered immune suppressive functions that exert a significant influence on both cancer progression and the ability of tumors to escape immune surveillance. Their involvement in modulating immune responses via different mechanisms at the local and systemic levels has made them a key target for therapeutic interventions seeking to enhance treatment outcomes. How TAMs’ depletion influences immune responses in cancer is the primary interest in cancer immunotherapies. The purpose of this review is to delve into the recent progress made in TAM-targeting therapies. We will explore the current theories, benefits, and challenges associated with TAMs’ depletion or inhibition. The manuscript concludes with future directions and potential implications for clinical practice.

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts and contribute to improving patient outcomes in pancreatic cancer management.

Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health issue due to its high mortality rate and resistance to standard treatments. Sorafenib, the first-line systemic therapy for unresectable HCC, shows limited effectiveness due to resistance and severe side effects. Recent studies suggest that combining sorafenib with immunotherapy, particularly natural killer (NK) cells, may improve treatment outcomes. Methods: This study examined the effectiveness of sorafenib combined with NK cells pretreated with interleukin-12 (IL-12) and interleukin-18 (IL-18) in a rat HCC model. Tumor progression and treatment outcomes were assessed using MRI and histological analysis. Results: The results show that combination therapy significantly reduced tumor growth, increased tumor cell density, and inhibited angiogenesis and fibrosis in the tumor microenvironment. The sorafenib- and IL-12/IL-18-pretreated NK cell combination enhanced tumor inhibition by overcoming drug resistance and modulating the immune response. Conclusions: This study suggests that this combination therapy could be a promising strategy for treating HCC, offering both direct antitumor effects and modification of the tumor microenvironment for better clinical outcomes.