Efficient Light-Based Bioprinting via Rutin Nanoparticle Photoinhibitor for Advanced Biomedical Applications.

Digital light processing (DLP) bioprinting, known for its high resolution and speed, enables the precise spatial arrangement of biomaterials and has become integral to advancing tissue engineering and regenerative medicine. Nevertheless, inherent light scattering presents significant challenges to the fidelity of the manufactured structures. Herein, we introduce a photoinhibition strategy based on Rutin nanoparticles (Rnps), attenuating the scattering effect through concurrent photoabsorption and free radical reaction. Compared to the widely utilized biocompatible photoabsorber tartrazine (Tar), Rnps-infused bioink enhanced printing speed (1.9×), interlayer homogeneity (58% less overexposure), resolution (38.3% improvement), and print tolerance (3× high-precision range) to minimize trial-and-error. The biocompatible and antioxidative Rnps significantly improved cytocompatibility and exhibited resistance to oxidative stress-induced damage in printed constructs, as demonstrated with human induced pluripotent stem cell-derived endothelial cells (hiPSC-ECs). The related properties of Rnps facilitate the facile fabrication of multimaterial, heterogeneous, and cell-laden biomimetic constructs with intricate structures. The developed photoinhibitor, with its profound adaptability, promises wide biomedical applications tailored to specific biological requirements.

Fukushima Daiichi Nuclear Power Plant Accident: Understanding Formation Mechanism of Radioactive Particles through Sr and Pu Quantities.

The Fukushima Daiichi Nuclear Power Plant accident released considerable radionuclides into the environment. Radioactive particles, composed mainly of SiO2, emerged as distinctive features, revealing insights into the accident's dynamics. While studies extensively focused on high-volatile radionuclides like Cs, investigations into low-volatile nuclides such as 90Sr and Pu remain limited. Understanding their abundance in radioactive particles is crucial for deciphering the accident's details, including reactor temperatures and injection processes. Here, we aimed to determine 90Sr and Pu amounts in radioactive particles and provide essential data for understanding the formation processes and conditions within the reactor during the accident. We employed radiochemical analysis on nine radioactive particles and determined the amounts of 90Sr and Pu in these particles. 90Sr and Pu quantification in radioactive particles showed that the 90Sr/137Cs radioactivity ratio (corrected to March 11, 2011) aligned with core temperature expectations. However, the 239+240Pu/137Cs activity ratio indicated nonvolatile Pu introduction, possibly through fuel fragments. Analyzing 90Sr and Pu enhances our understanding of the Fukushima Daiichi accident. Deviations in 239+240Pu/137Cs activity ratios underscore nonvolatile processes, emphasizing the accident's complexity. Future research should expand this data set for a more comprehensive understanding of the accident's nuances.

MIST: Multi-instance selective transformer for histopathological subtype prediction.

Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate histopathological subtype prediction is a challenging task due to (1) instance-level discrimination of histopathological images, (2) low inter-class and large intra-class variances among histopathological images in their shape and chromatin texture, and (3) heterogeneous feature distribution over different images. In this paper, we formulate subtype prediction as fine-grained representation learning and propose a novel multi-instance selective transformer (MIST) framework, effectively achieving accurate histopathological subtype prediction. The proposed MIST designs an effective selective self-attention mechanism with multi-instance learning (MIL) and vision transformer (ViT) to adaptive identify informative instances for fine-grained representation. Innovatively, the MIST entrusts each instance with different contributions to the bag representation based on its interactions with instances and bags. Specifically, a SiT module with selective multi-head self-attention (S-MSA) is well-designed to identify the representative instances by modeling the instance-to-instance interactions. On the contrary, a MIFD module with the information bottleneck is proposed to learn the discriminative fine-grained representation for histopathological images by modeling instance-to-bag interactions with the selected instances. Substantial experiments on five clinical benchmarks demonstrate that the MIST achieves accurate histopathological subtype prediction and obtains state-of-the-art performance with an accuracy of 0.936. The MIST shows great potential to handle fine-grained medical image analysis, such as histopathological subtype prediction in clinical applications.

Spoof surface plasmons spawning pencil-beam antennas of subwavelength profile.

Slot-array antennas based on metallic waveguides have been widely used to generate pencil-beams, attracting attention due to their design simplicity and compact size. However, current slot-array antennas possess wavelength-scale profiles, which do not align optimally with the low-profile requisites of contemporary integrated communication and radar systems. Here, we propose a low-profile slot-array antenna designed specifically for the pencil-beam generation. Constructed with the two-dimensional-array (2D-array) slots situated on a sub-wavelength domino plasmon waveguide, the pencil-beam is generated with a peak gain of up to 21.6 dBi. Moreover, the generated pencil-beam allows for a wide scanning range of over 73.6° by adjusting the operating frequency from 45 to 65 GHz. Our research shows great potential for enhancing millimeter-wave radar capabilities and advancing communication systems.

Highly Efficient Organic Solar Cells with the Highly Crystalline Third Component as a Morphology Regulator.

The morphology of the active layer is crucial for highly efficient organic solar cells (OSCs), which can be regulated by selecting a rational third component. In this work, the highly crystalline nonfullerene acceptor BTP-eC9 is selected as the morphology regulator in OSCs with PM6:BTP-BO-4Cl as the main system. The addition of BTP-eC9 can prolong the nucleation and crystallization progress of acceptor and donor molecules, thereby enhancing the order of molecular arrangement. Meanwhile, the nucleation and crystallization time of the donor is earlier than that of the acceptors after introducing BTP-eC9, which is beneficial for obtaining a better vertical structural phase separation. The exciton dissociation, charge transport, and charge collection are promoted effectively by the optimized morphology of the active layer, which improves the short-circuit current density and filling factor. After introducing BTP-eC9, the power conversion efficiencies (PCEs) of the ternary OSCs are improved from 17.31% to 18.15%. The PCE is further improved to 18.39% by introducing gold nanopyramid (Au NBPs) into the hole transport layer to improve photon utilization efficiency. This work indicates that the morphology can be optimized by selecting a highly crystalline third component to regulate the nucleation and crystallization progress of the acceptor and donor molecules.

Prognostic models for mucinous and non-specific adeno cholangiocarcinoma: a population-based retrospective study.

Background: Clinically, the diagnosis and treatment of cholangiocarcinoma are generally different according to the location of occurrence, and the studies rarely consider the differences between different pathological types. Cholangiocarcinomas in large- and middle-sized intrahepatic bile ducts are mostly mucinous, while in small sized bile duct are not; mucinous extrahepatic cholangiocarcinomas are also more common than mucinous intrahepatic cholangiocarcinoma. However, it is unclear whether these pathological type differences are related to the prognosis. Methods: Data of total 22509 patients was analyzed from Surveillance, Epidemiology, and End Results program database out of which 22299 patients were diagnosed with common adeno cholangiocarcinoma while 210 were diagnosed with mucinous cholangiocarcinoma. Based on the propensity score matching (PSM) analysis, between these two groups' clinical, demographic, and therapeutic features were contrasted. The data were analyzed using Cox and LASSO regression analysis and Kaplan-Meier survival curves. Ultimately, overall survival (OS) and cancer specific survival (CSS) related prognostic models were established and validated in test and external datasets and nomograms were created to forecast these patients' prognosis. Results: There was no difference in prognosis between mucinous cholangiocarcinoma and adeno cholangiocarcinoma. Therefore, we constructed prognostic model and nomogram that can be used for mucinous and adeno cholangiocarcinoma at the same time. By comparing the 9 independent key characteristics i.e. Age, tumor size, the number of primary tumors, AJCC stage, Grade, lymph node status, metastasis, surgery and chemotherapy, risk scores were calculated for each individual. By integrating these two pathological types in OS and CSS prognostic models, effective prognosis prediction results could be achieved in multiple datasets (OS: AUC 0.70-0.87; CSS: AUC 0.74-0.89). Conclusion: Age, tumor size, the number of primary tumors, AJCC stage, Grade, lymph node status, metastasis, surgery and chemotherapy are the independent prognostic factors in OS or CSS of the patients with mucinous and ordinary cholangiocarcinoma. Nomogram that can be used for mucinous and adeno cholangiocarcinoma at the same time is of significance in clinical practice and management of cholangiocarcinoma.

Enhanced Surface Accessibility of SARS-CoV-2 Omicron Spike Protein Due to an Altered Glycosylation Profile.

SARS-CoV-2 spike (S) proteins undergo extensive glycosylation, aiding in proper folding, enhancing stability, and evading host immune surveillance. In this study, we used mass spectrometric analysis to elucidate the N-glycosylation characteristics and disulfide bonding of recombinant spike proteins derived from the SARS-CoV-2 Omicron variant (B.1.1.529) in comparison with the D614G spike variant. Furthermore, we conducted microsecond-long molecular dynamics simulations on spike proteins to resolve how the different N-glycans impact spike conformational sampling in the two variants. Our findings reveal that the Omicron spike protein maintains an overall resemblance to the D614G spike variant in terms of site-specific glycan processing and disulfide bond formation. Nonetheless, alterations in glycans were observed at certain N-glycosylation sites. These changes, in synergy with mutations within the Omicron spike protein, result in increased surface accessibility of the macromolecule, including the ectodomain, receptor-binding domain, and N-terminal domain. Additionally, mutagenesis and pull-down assays reveal the role of glycosylation of a specific sequon (N149); furthermore, the correlation of MD simulation and HDX-MS identified several high-dynamic areas of the spike proteins. These insights contribute to our understanding of the interplay between structure and function, thereby advancing effective vaccination and therapeutic strategies.

Pancreatic mucinous adenocarcinoma has different clinical characteristics and better prognosis compared to non-specific PDAC: A retrospective observational study.

Background: Pancreatic mucinous adenocarcinoma (PMAC) is a rare malignant tumour, and there is limited understanding of its epidemiology and prognosis. Initially, PMAC was considered a metastatic manifestation of other cancers; however, instances of non-metastatic PMAC have been documented through monitoring, epidemiological studies, and data from the Surveillance, Epidemiology, and End Results (SEER) database. Therefore, it is crucial to investigate the epidemiological characteristics of PMAC and discern the prognostic differences between PMAC and the more prevalent pancreatic ductal adenocarcinoma (PDAC). Methods: The study used data from the SEER database from 2000 to 2018 to identify patients diagnosed with PMAC or PDAC. To ensure comparable demographic characteristics between PDAC and PMAC, propensity score matching was employed. Kaplan-Meier analysis was used to analyse overall survival (OS) and cancer-specific survival (CSS). Univariate and multivariate Cox regression analyses were used to determine independent risk factors influencing OS and CSS. Additionally, the construction and validation of risk-scoring models for OS and CSS were achieved through the least absolute shrinkage and selection operator-Cox regression technique. Results: The SEER database included 84,857 patients with PDAC and 3345 patients with PMAC. Notably, significant distinctions were observed in the distribution of tumour sites, diagnosis time, use of radiotherapy and chemotherapy, tumour size, grading, and staging between the two groups. The prognosis exhibited notable improvement among married individuals, those receiving acceptable chemotherapy, and those with focal PMAC (p < 0.05). Conversely, patients with elevated log odds of positive lymph node scores or higher pathological grades in the pancreatic tail exhibited a more unfavourable prognosis (p < 0.05). The risk-scoring models for OS or CSS based on prognostic factors indicated a significantly lower prognosis for high-risk patients compared to their low-risk counterparts (area under the curve OS: 0.81-0.82, CSS: 0.80-0.82). Conclusion: PMAC exhibits distinct clinical characteristics compared to non-specific PDAC. Leveraging these features and pathological classifications allows for accurate prognostication of PMAC or PDAC.

Surufatinib combined with photodynamic therapy induces ferroptosis to inhibit cholangiocarcinoma in vitro and in tumor models.

The curative effect of single therapy for advanced cholangiocarcinoma (CCA) is poor, thus investigating combined treatment strategies holds promise for improving prognosis. Surufatinib (SUR) is a novel multikinase inhibitor that has been confirmed to prolong survival of patients with advanced CCA. Photodynamic therapy (PDT) can also ablate advanced CCA and relieve biliary obstruction. In this study, we explored the anti-CCA effect of SUR combined with PDT, and explored the underlying mechanism. We found that SUR could effectively inhibit the abilities of proliferation, migration and metastasis in CCA cells (HUCCT-1, RBE). The ability of SUR to inhibit CCA was also confirmed by the HUCCT-1 cell xenograft model in Balb/c nude mice and CCA patient-derived organoids. SUR combined with PDT can significantly enhance the inhibitory effect on CCA, and can be alleviated by two ferroptosis inhibitors (Ferrostatin-1, Deferoxamine). By detecting the level of reactive oxygen species, lipid peroxides, malondialdehyde and glutathione, we further confirmed that SUR combined with PDT can inhibit CCA cells by inducing ferroptosis. Glutathione peroxidase 4 (GPX4) belongs to the glutathione peroxidase family and is mainly responsible for the metabolism of intracellular hydrogen peroxide. GPX4 inhibits ferroptosis by reducing cytotoxic lipid peroxides (L-OOH) to the corresponding alcohols (L-OH). Acyl-CoA synthetase long-chain family member 4 (ACSL4) is a member of the long-chain fatty acid coenzyme a synthetase family and is mainly involved in the biosynthesis and catabolism of fatty acids. ACSL4 induces ferroptosis by promoting the accumulation of lipid peroxides. Both SUR and PDT can induce ferroptosis by promoting ACSL4 and inhibiting GPX4. The regulation effect is found to be more significant in combined treatment group. In conclusion, SUR combined with PDT exerted an anti-CCA effect by inducing ferroptosis. Combination therapy provides a new idea for the clinical treatment of CCA.

Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.

OBJECTIVE: Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist 90 (SCL-90) designed to minimize patient response burden. Utilizing machine learning algorithms, the study sought to construct classification models capable of distinguishing between depression and anxiety. METHODS: The study included 4262 individuals currently experiencing depression alone (n = 2998), anxiety alone (n = 716), or both depression and anxiety (n = 548). Counterfactual diagnosis was used to construct a causal network on the dataset. Employing a causal network, the SCL-90 was simplified. Items that have causality with only depression, only anxiety and both depression and anxiety were selected, and these streamlined items served as input features for four distinct machine learning algorithms, facilitating the creation of classification models for distinguishing depression and anxiety. RESULTS: Cross-validation demonstrated the performance of the classification models with the following metrics: (1) K-nearest neighbors (AUC = 0.924, Acc = 92.81 %); (2) support vector machine (AUC = 0.937, Acc = 94.38 %); (3) random forest (AUC = 0.918, Acc = 94.38 %); and (4) adaptive boosting (AUC = 0.882, Acc = 94.38 %). Notably, the support vector machine excelled, with the highest AUC and superior accuracy. CONCLUSION: Incorporating the simplified SCL-90 and machine learning presents a promising, efficient, and cost-effective tool for the precise identification of depression and anxiety.

A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT.

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor's deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

Pyroptotic macrophages promote proliferation and chemotherapy resistance of peripheral T-cell lymphoma via TLR4 signaling pathway.

Peripheral T-cell lymphoma (PTCL) is a highly aggressive type of non-Hodgkin's lymphoma with a poor prognosis. Pyroptosis is a newly discovered procedural cell death mode, which has been implicated to occur in both tumor cells and immune cells. However, the occurrence and effect of pyroptosis on PTCL remain unclear. Here, we found that pyroptosis occurred in interstitial macrophages of PTCL rather than in tumor cells. In clinical specimens, macrophage pyroptosis was associated with a poor prognosis of PTCL. In vitro experiments and gene sequencing results showed that pyroptotic macrophages could upregulate the expression of TLR4 through secreting inflammatory cytokines IL-18. Upregulated TLR4 activated its downstream NF-κB anti-apoptotic signaling pathway, thus leading to malignant proliferation and chemotherapy resistance of tumor cells. Moreover, the expression of factors such as XIAP in the NF-κB anti-apoptotic pathway was downregulated after the knockdown of TLR4, and the malignant promotion effect of pyroptotic macrophages on PTCL cells was also reversed. Our findings revealed the mechanism of pyroptotic macrophages promoting the malignant biological behavior of PTCL and elucidated the key role of TLR4 in this process. In-depth analysis of this mechanism will contribute to understanding the regulatory effect of PTCL by the tumor microenvironment and providing new ideas for the clinical treatment of PTCL.

Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. / åŒºåˆ†èƒ¶è´¨æ¯ç»†èƒžç˜¤å’Œå•å‘æ€§è„‘è½¬ç§»ç˜¤çš„å¤šæ¨¡æ€èžåˆæ·±åº¦å­¦ä¹ æ¨¡åž‹çš„å¼€å‘å’ŒéªŒè¯.

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Preparation of pH-sensitive porous polylactic acid-based medical dressing with self-pumping function.

Excessive exudation from the wound site and the difficulty of determining the state of wound healing can make medical management more difficult and, in extreme cases, lead to wound deterioration. In this study, we fabricated a pH-sensitive colorimetric chronic wound dressing with self-pumping function using electrostatic spinning technology. It consisted of three layers: a polylactic acid-curcumin (PCPLLA) hydrophobic layer, a hydrolyzed polyacrylonitrile (HPAN) transfer layer, and a polyacrylonitrile-purple kale anthocyanin (PAN-PCA) hydrophilic layer. The results showed that the preparation of porous PLLA fiber membrane loaded with 0.2 % Cur was achieved by adjusting the spinning-related parameters, which could ensure that the composite dressing had sufficient anti-inflammatory, antibacterial and antioxidant properties. The HPAN membrane treated with alkali for 30 min had significantly enhanced liquid wetting ability, and the unidirectional transport of liquid could be achieved by simple combination with the 20 um PCPLLA fiber membrane. In addition, the 4 % loaded PCA showed more obvious color difference than the colorimetric membrane. In vivo and ex vivo experiments have demonstrated the potential of multifunctional dressings for the treatment of chronic wounds.

Improving therapeutic outcomes in heart failure with reduced nonvalvular ejection fraction: A clinical study of heart failure education intervention.

OBJECTIVE: The current study delves into the impact of heart failure education intervention on improving therapeutic outcomes for heart failure (HF) patients with reduced nonvalvular ejection fraction. METHODS: There involved a total of 60 HF patients with non-valvular ejection fraction reduction who met the inclusion requirements. Patients enrolled were randomly distributed into an observation group and a control group. The observation group received heart failure education intervention, while the control group received conventional intervention. The therapeutic effect, changes in physical indicators, cardiac function indicators, coagulation function, self-management scale scores, and the incidence of adverse cardiovascular events were meticulously evaluated. RESULTS: The total effective proportion in the observation group was 96.67%, which was significantly higher than the control group's proportion of 76.67% (p < .05). After treatment, several parameters in the observation group showed significant improvements compared to the control group: hs-CRP, IL-6, LVEDV value, LVESV value, PT value, APTT value, and TT value were all evidently lower in the observation group. Additionally, the cardiac index, LVEF value, and heart failure self-management scale fraction were significantly higher in the observation group compared to the control group (p < .05). Furthermore, the incidence of adverse cardiovascular events in the observation group was only 6.67%, which was significantly lower than the control group's incidence of 20.00% (p < .05). CONCLUSION: Heart failure education intervention demonstrates effectiveness in improving the therapeutic outcomes for HF patients and reduced nonvalvular ejection fraction. Additionally, it enhances patients' self-management abilities. Given these positive results, it is highly recommended to promote and implement HF education intervention in clinical practice.

Oxidized cellulose-filled double thermo/pH-sensitive hydrogel for local chemo-photothermal therapy in breast cancer.

Lumpectomy plus radiation is a treatment option offering better survival than conventional mastectomy for patients with early-stage breast cancer. However, successive radioactive therapy remains tedious and unsafe with severe adverse reactions and secondary injury. Herein, a composite hydrogel with pH- and photothermal double-sensitive activity is developed via physical crosslinking. The composite hydrogel incorporated with tempo-oxidized cellulose nanofiber (TOCN), polyvinyl alcohol (PVA) and a polydopamine (PDA) coating for photothermal therapy (PTT) triggered in situ release of doxorubicin (DOX) drug was utilized to optimize postoperative strategies of malignant tumors inhibition. The incorporation of TOCN significantly affects the performance of composite hydrogels. The best-performing TOCN/PVA7 was selected for drug loading and polydopamine coating by rational design. In vitro studies have demonstrated that the composite hydrogel exhibited high NIR photothermal conversion efficiency, benign cytotoxicity to L929 cells, pH-dependent release profiles, and strong MCF-7 cell inhibitory effects. Then the TOCN/PVA7-PDA@DOX hydrogel is implanted into the tumor resection cavity for local in vivo chemo-photothermal synergistical therapy to ablate residue tumor tissues. Overall, this work suggests that such a chemo-photothermal hydrogel delivery system has great potential as a promising tool for the postsurgical management of breast cancer.

AURKA emerges as a vulnerable target for KEAP1-deficient non-small cell lung cancer by activation of asparagine synthesis.

AURKA is an established target for cancer therapy; however, the efficacy of its inhibitors in clinical trials is hindered by differential response rates across different tumor subtypes. In this study, we demonstrate AURKA regulates amino acid synthesis, rendering it a vulnerable target in KEAP1-deficient non-small cell lung cancer (NSCLC). Through CRISPR metabolic screens, we identified that KEAP1-knockdown cells showed the highest sensitivity to the AURKA inhibitor MLN8237. Subsequent investigations confirmed that KEAP1 deficiency heightens the susceptibility of NSCLC cells to AURKA inhibition both in vitro and in vivo, with the response depending on NRF2 activation. Mechanistically, AURKA interacts with the eIF2α kinase GCN2 and maintains its phosphorylation to regulate eIF2α-ATF4-mediated amino acid biosynthesis. AURKA inhibition restrains the expression of asparagine synthetase (ASNS), making KEAP1-deficient NSCLC cells vulnerable to AURKA inhibitors, in which ASNS is highly expressed. Our study unveils the pivotal role of AURKA in amino acid metabolism and identifies a specific metabolic indication for AURKA inhibitors. These findings also provide a novel clinical therapeutic target for KEAP1-mutant/deficient NSCLC, which is characterized by resistance to radiotherapy, chemotherapy, and targeted therapy.

Microbial bile acid metabolite ameliorates mycophenolate mofetil-induced gastrointestinal toxicity through vitamin D3 receptor.

Mycophenolate mofetil (MMF) is one of the most used immunosuppressive drugs in organ transplantation, but frequent gastrointestinal (GI) side effects through unknown mechanisms limit its clinical use. Gut microbiota and its metabolites were recently reported to play a vital role in MMF-induced GI toxicity, but the specific mechanism of how they interact with the human body is still unclear. Here, we found that secondary bile acids (BAs), as bacterial metabolites, were significantly reduced by MMF administration in the gut of mice. Microbiome data and fecal microbiota transfer model supported a microbiota-dependent effect on the reduction of secondary BAs. Supplementation of the secondary BA lithocholic acid alleviated MMF-induced weight loss, colonic inflammation, and oxidative phosphorylation damage. Genetic deletion of the vitamin D3 receptor (VDR), which serves as a primary colonic BA receptor, in colonic epithelial cells (VDRΔIEC) abolished the therapeutic effect of lithocholic acid on MMF-induced GI toxicity. Impressively, we discovered that paricalcitol, a Food and Drug Administration-approved VDR agonist that has been used in clinics for years, could effectively alleviate MMF-induced GI toxicity. Our study reveals a previously unrecognized mechanism of gut microbiota, BAs, and VDR signaling in MMF-induced GI side effects, offering potential therapeutic strategies for clinics.

Effects of In Vitro Fermentation of Polysialic Acid and Sialic Acid on Gut Microbial Community Composition and Metabolites in Healthy Humans.

The influence of polysialic acid (PSA) and sialic acid (SA) on the gut microbial community composition and metabolites in healthy humans was investigated using a bionic gastrointestinal reactor. The results indicated that PSA and SA significantly changed the gut microbiota and metabolites to different degrees. PSA can increase the relative abundances of Faecalibacterium and Allisonella, whereas SA can increase those of Bifidobacterium and Megamonas. Both can significantly increase the content of short-chain fatty acids. The results of metabolome analysis showed that PSA can upregulate ergosterol peroxide and gallic acid and downregulate the harmful metabolite N-acetylputrescine. SA can upregulate 4-pyridoxic acid and lipoic acid. PSA and SA affect gut microbiota and metabolites in different ways and have positive effects on human health. These results will provide a reference for the further development of PSA- and SA-related functional foods and health products.