Iodine-131 intervention in hyperthyroidism with hepatic insufficiency: Metabolomic evaluation.

Hyperthyroidism, often accompanied by hepatic insufficiency (HI), poses significant clinical challenges, highlighting the necessity for identifying optimal treatment strategies and early diagnostic biomarkers to improve patient outcomes. This study aimed to determine the optimal iodine-131 (131I) intervention dose for alleviating hyperthyroidism with HI and to identify serum metabolic biomarkers for early diagnosis using UPLC-Q/TOF-MS technology. A mouse model for early 131I intervention was established to monitor changes in physiological response, body weight, fur condition, thyroid, and liver function. Metabolite identification was achieved through UPLC-Q/TOF-MS and further analyzed via MetaboAnalyst. Six biomarkers were identified and subjected to ROC analysis. Early intervention with 80 µCi 131I per gram of thyroid tissue effectively controlled hyperthyroidism and improved liver function. Metabolomics analysis uncovered 63 differentially abundant metabolites, six of which (L-kynurenine, Taurochenodesoxycholic acid, Glycocholic acid, Phytosphingosine, Tryptamine, and Betaine) were identified as early warning biomarkers. Post-intervention, these biomarkers progressively returned to normal levels. This study demonstrates the efficacy of UPLC-Q/TOF-MS in identifying metabolic biomarkers for early diagnosis of hyperthyroidism with HI and highlights the therapeutic potential of early 131I intervention in normalizing these biomarkers.

Effects of Several Auxiliary Acceptors and Anchoring Groups on Charge Transfer and Photophysical Properties of D-A-π-A Type DSSCs: A DFT Study.

In this paper, we performed theoretical studies on the twelve D-A-π-A type organic dyes (G-1 ~ G-3, M-1 ~ M-3, J-1 ~ J-3, and S-1 ~ S-3) with 9-phenylcarbazole as the electron donor in anticipation of the application of these dyes in dye-sensitized solar cells (DSSCs). DFT and TD-DFT methods are applied to investigate in detail the molecular geometries, frontier molecular orbitals (FMOs), absorption spectra, charge density difference (CDD), and transition density matrix (TDM) of several dyes. The results show that the M-series (M-1 ~ M-3) dyes have the largest dihedral angles between the electron donor and the auxiliary acceptor and also has the largest energy gaps in HOMO-LUMO orbitals, which greatly reduces the charge transfer efficiency. Finally, the UV-Vis absorption spectra inferred that the anchoring groups modified with o-nitrobenzoic acid (G-3, M-3, J-3, S-3) can red-shift the absorption peaks of the dyes, which results in higher light-harvesting efficiency and improves the power conversion efficiency of DSSCs. Overall, all of these dyes contribute to the improvement of photovoltaic power conversion efficiency and have potential for application in DSSCs devices.

Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.

Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance.

Objective. Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range dependency for volumetric dose prediction due to the loss of spatial dose features and the inherent locality of CPs. The purpose of this work is to construct a deep hybrid network by combining the self-attention mechanism-based Transformer with modified U-Net for predicting measurement-guided volumetric dose (MDose) of prePSQA.Approach. The enrolled 307 cancer patients underwent VMAT were randomly divided into 246 and 61 cases for training and testing the model. The input data included computed tomography images, radiotherapy dose images exported from the treatment planning system, as well as the MDose distribution from the verification system. The output was the predicted high-quality voxel-wise prePSQA dose distribution.Main results: qualitative and quantitative experimental results show that the proposed prediction method could achieve comparable or better performance on MDose prediction over other approaches in terms of spatial dose distribution, dose-volume histogram metrics, gamma passing rates, mean absolute error, root mean square error, and structural similarity.Significance. The preliminary results on multiple cancer sites show that our approach can be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

Comparison of the predictive value of anthropometric indicators for the risk of benign prostatic hyperplasia in southern China.

This study aimed to compare the predictive value of six selected anthropometric indicators for benign prostatic hyperplasia (BPH). Males over 50 years of age who underwent health examinations at the Health Management Center of the Second Xiangya Hospital, Central South University (Changsha, China) from June to December 2020 were enrolled in this study. The characteristic data were collected, including basic anthropometric indices, lipid parameters, six anthropometric indicators, prostate-specific antigen, and total prostate volume. The odds ratios (ORs) with 95% confidence intervals (95% CIs) for all anthropometric parameters and BPH were calculated using binary logistic regression. To assess the diagnostic capability of each indicator for BPH and identify the appropriate cutoff values, receiver operating characteristic (ROC) curves and the related areas under the curves (AUCs) were utilized. All six indicators had diagnostic value for BPH (all P ≤ 0.001). The visceral adiposity index (VAI; AUC: 0.797, 95% CI: 0.759-0.834) had the highest AUC and therefore the highest diagnostic value. This was followed by the cardiometabolic index (CMI; AUC: 0.792, 95% CI: 0.753-0.831), lipid accumulation product (LAP; AUC: 0.766, 95% CI: 0.723-0.809), waist-to-hip ratio (WHR; AUC: 0.660, 95% CI: 0.609-0.712), waist-to-height ratio (WHtR; AUC: 0.639, 95% CI: 0.587-0.691), and body mass index (BMI; AUC: 0.592, 95% CI: 0.540-0.643). The sensitivity of CMI was the highest (92.1%), and WHtR had the highest specificity of 94.1%. CMI consistently showed the highest OR in the binary logistic regression analysis. BMI, WHtR, WHR, VAI, CMI, and LAP all influence the occurrence of BPH in middle-aged and older men (all P ≤ 0.001), and CMI is the best predictor of BPH.

Hsa_circ_0023990 Promotes Tumor Growth and Glycolysis in Dedifferentiated TC via Targeting miR-485-5p/FOXM1 Axis.

BACKGROUND: During the transformation to dedifferentiated thyroid cancer (TC) types, the ability of papillary thyroid carcinomas (PTCs) to concentrate radioactive iodine might be lost, raising difficulty for the current therapy. circRNAs were proved to be implicated in the progression of various cancers. In this study, we aimed to investigate the functional role and mechanism of hsa_circ_0023990 in dedifferentiated TC. METHODS: The expression pattern of genes were detected using quantitative PCR or western blot assays. Cell proliferation was determined by CCK8, colony formation, EdU, and cell-cycle assays. Glycolysis was assessed using glucose uptake and lactate production assays. Luciferase reporter assay was performed to examine the interactions between miR-485-5p and hsa_circ_0023990 or FOXM1. Xenograft assay was allowed for observation of tumor growth in vivo. RESULTS: Hsa_circ_0023990 and FOXM1 were upregulated in dedifferentiated TC tissues and cell lines. The higher level of hsa_circ_0023900 could stimulate the proliferation and glycolysis of dedifferentiated TC cells via positively regulating FOXM1. Mechanistically, miR-485-5p was demonstrated to interact with hsa_circ_0023990 and FOXM1 and involved in the regulation of has_circ_0023990 and FOXM1 in TC biological processes. CONCLUSION: Our results discovered the functional network of hsa_circ_0023990 in dedifferentiated TC development by facilitating cell proliferation and glycolysis via miR-485-5p/FOXM1 axis, implying that hsa_circ_0023990 might be a potential therapeutic target for the dedifferentiated TC treatment.

Moderating hypoxia and promoting immunogenic photodynamic therapy by HER-2 nanobody conjugate nanoparticles for ovarian cancer treatment.

Photodynamic therapy (PDT) and immunotherapy have been often adopted for ovarian cancer therapy, yet their application is limited by the high recurrence rate and toxic side effects. Intriguingly, nanoparticles contribute to enhancing the performance of PDT. Here, we investigated the synthesis of HER-2-Nanobody (Nb)-conjugated human serum albumin (HSA) incorporated with chlorin (Ce6) and catalase (CAT) (Nb@HCC), and analyzed the synergic effect of Nb@HCC-mediated PDT and immunotherapy for SK-OV-3 tumors. The Ce6 and CAT were incorporated into HSA to construct the HCC nanoparticles. HER-2-Nanobody was the purified bacterial crude extract, and conjugated with HCC to prepare Nb@HCC via heterodisulfide. The effects of Nb@HCC with near infrared ray (NIR) irradiation on moderating hypoxia and hypoxia inducible factor-1α(HIF-1α) expression were evaluated in the SK-OV-3 cells and tumor tissues. A SK-OV-3 tumor-bearing model was developed, where the synergistic effect of Nb@HCC-mediated PDT and anti-CTLA-4 therapy was investigated. Nb@HCC with a 660 nm laser irradiation could induce massive reactive oxygen species and trigger apoptosis in SK-OV-3 cells. Nb@HCC and PDT promoted danger-associated molecular patterns (DAMPs), which indicated immunogenic cell death and maturation of dendritic cells in the SK-OV-3 cells. Irradiated by NIR, Nb@HCC alleviated the hypoxia and decreased the expression of HIF-1α. The Nb@HCC-mediated PDT and anti-CTLA-4 therapy synergically inhibited the progression of distant tumor, and induced T cell infiltration. Biosafety tests suggested that Nb@HCC would not cause damage to the major organs with less toxicity and side effects. To conclude, a combination of Nb@HCC-mediated PDT and anti-CTLA-4 therapy could inhibit the progression of distant tumor to attain remarkable therapeutic outcomes.

Targeted nanobody complex enhanced photodynamic therapy for lung cancer by overcoming tumor microenvironment.

BACKGROUND: To investigate the efficacy of a PLGA-based nanobody complex in photodynamic therapy (PDT) and NIR-II imaging in A549 tumor hypoxic model. METHOD: IR1048-MZ was firstly synthesized by conjugating a nitro imidazole group to IR1048. IR1048-MZ and Cat were then encapsulated in PLGA-SH solution. Anti-EGFR-Nanobody was also expressed and purified, and finally Anti-EGFR-Nanobody@PLGA-IR1048MZ-Cat (Nb@IC-NPs) nanobody complex was obtained based on the formation of desulfide bond between PLGA-SH and Anti-EGFR-Nanobody. Size distribution and morphology were characterized by TEM and DLS. Spectrum of Nb@IC-NPs towards NTR was measured by UV and fluorescence, while the particle's selective response was studied using fluorescence. The uptake of Nb@IC-NPs in A549 cells was observed by flow cytometry and CLSM. In the meantime, its' catalytic ability that decomposes H2O2 both extra-and intra-cellular was observed by fluorescence and CLSM. In vitro photodynamic toxicity of Nb@IC-NPs was examined by MTT, Live/Dead Cell Staining, Flow Cytometry and Apoptosis Assay. Tumor-bearing model was constructed to observe a semi-quantitative fluorescent distribution and the possibility of NIR-II fluorescence/photoacoustic (PA) imaging. Effect of Nb@IC-NPs on enhancing A549 tumor hypoxia and expression profile of HIF-1α was investigated in the presence of NIR. An A549 tumor metastasis model was also constructed to confirm the complex' potential to destroy primary tumor, inhibit lung metastasis, and prolong mice' survival. Lastly, impact of Nb@IC-NPs on mice' main organs and blood indices was observed. RESULTS: Nb@IC-NPs was successfully fabricated with good homogeneity. The fluorescent absorbance of Nb@IC-NPs showed a linear relationship with the concentration of NTR, and a higher concentration of NTR corresponded to a stronger photoacoustic signal. In addition, Nb@IC-NPs showed a stable selectivity toward NTR. Our results also suggested a high efficient uptake of Nb@IC-NPs in A549 cells, which was more efficient than IC-NPs and IR1048-MZ alone. In vitro assays confirmed the effects of Nb@IC-NPs on catalytic O2 generation even in hypoxic cells. The cell viability was upregulated with the nanocomplex at the absence of the laser, whereas it was dramatically declined with laser treatment that excited at 980 nm. Nb@IC-NPs achieved tumor hypoxia NIR-II/PA imaging through assisting A549 gathering. When NIR was applied, Nb@IC-NPs can significantly relieve A549 cellular/tumor hypoxia by generating more reactive oxygen species (ROS), which in turn helps lower the expression level of HIF-1α. In summary, Nb@IC-NPs based PDT can efficiently decimate A549 primary tumor, inhibit metastatic lung cancer, and prolong the lifespan of the mice under tolerable dosage. At last, in vivo toxicity tests of the nanocomplex showed its biosafety to the main organs and normal blood indices values. CONCLUSION: Nb@IC-NPs improves tumor hypoxia through catalytic reaction and lowers the expression level of HIF-1α. It achieves tumor PA imaging through intensified NIR-II fluorescence signal that caused by response of the complex to the lesion's nitroreductase (NTR). Nb@IC-NPs based PDT can efficiently kill A549 primary tumor, inhibit a lung metastasis, as well as prolong mice' survival cycle.