Background Nuclear medicine is one of the medical specialties that uses small amounts of radioactive substances to diagnose and treat various conditions. Despite its increasing role in modern healthcare, there is limited public understanding of nuclear medicine. Objectives This study aims to analyze patients' perceived knowledge, perceptions, trust, willingness, and acceptance of nuclear medicine imaging in Saudi Arabia. Methods A cross-sectional questionnaire-based design was adopted. Participants (n = 1349) included adults aged 18 years and above who were attending outpatient imaging or nuclear medicine services at nine hospitals in Saudi Arabia. An online questionnaire comprising 41 substantive Likert-scale items and four demographic questions (45 questions in total) was used to collect the data. Composite scores were calculated for each domain. Descriptive statistics and inferential analyses (ANOVA, t-tests, Pearson correlation analysis) were applied, with significance set at p < 0.05. Results Moderate perceived knowledge of nuclear medicine was observed among the participants, with domain means around the scale midpoint (mean ≈ 3.0). Positive perceptions, trust, willingness, and acceptance were observed, with mean scores ranging from approximately 3.4 to 3.8. Education level was statistically associated with knowledge, trust, willingness, and acceptance (all p < 0.001), with large omnibus effect sizes (partial η2 = 0.293–0.496), while gender and age showed no significant differences. Perceived knowledge was moderately correlated with trust (r = 0.476), willingness (r = 0.408), and acceptance (r = 0.371). Conclusion Positive attitudes towards nuclear medicine imaging could coexist with incomplete knowledge among patients. Education level was consistently associated with knowledge, trust, willingness, and acceptance in this cross-sectional, unadjusted analysis; however, causal or independent-predictor interpretations are not warranted. Clear communication and targeted patient education remain central to strengthening informed decision-making and sustaining patient trust.
Background Bone formation requires coordinated communication between vascular and osteogenic compartments. Although simvastatin has been reported to stimulate both vascular growth and osteoblast activity, the molecular process by which these responses are integrated during bone anabolism remains incompletely defined. This study explored whether VEGF serves as a functional link between simvastatin-induced vascular remodeling and osteogenic activation. Methods Sixty male Sprague–Dawley rats were randomly assigned into five groups: normal control, vehicle control, intraosseous simvastatin administration (0.5 mg), intraperitoneal bevacizumab administration, and combined intraosseous simvastatin plus intraperitoneal bevacizumab treatment. Skeletal mass, trabecular architecture, dynamic bone formation, and local vascular networks were evaluated using DXA, μCT, histomorphometry, and Microfil® perfusion. Circulating osteogenic and angiogenic factors were measured by ELISA, and protein-level changes were assessed by Western blotting. In parallel, MC3T3-E1 osteoblast-lineage cells were used to examine alkaline phosphatase activity, BMP-2 production, BMP-2 secretion, and endoplasmic reticulum localization. Results Intraosseous simvastatin substantially improved bone mineral density, trabecular microstructure, and mineral apposition, together with increased serum VEGF and osteocalcin levels. These skeletal effects were accompanied by expansion of intraosseous and periosteal vascular networks. VEGF blockade largely attenuated these simvastatin-associated anabolic and vascular responses. In MC3T3-E1 cells, bevacizumab reduced osteoblast ALP activity, was associated with altered BMP-2 processing and reduced extracellular release, and increased the overlap between BMP-2 and the ER marker calnexin. These findings support a VEGF-dependent contribution to BMP-2 processing and secretion, while not establishing VEGF as the sole regulator of this pathway. Conclusions Simvastatin promotes bone formation by coordinating vascular expansion with osteoblast functional maturation through a VEGF-dependent component. The data are consistent with a role for VEGF signaling in BMP-2 processing and secretion, linking angiogenic activation to osteogenic output while requiring further studies to define the direct molecular mechanism.
Background and aim Traditional Chinese medicine has received attention as a potential method for treating STC. This investigation aimed to identify the target for therapy and the mechanism of Tongyou Decoction in STC by employing a rat model induced with a compound diphenoxylate suspension. Methods Body weight, stool frequency, fecal water content, and intestinal transit were recorded, while colonic histology, gastrointestinal hormones, inflammatory cytokines, and ICC/ANO1/PDGFRα-SK3 pathway components were assessed using standard histological and molecular techniques. Results Tongyou Decoction dose-dependently improved intestinal transit and stool parameters, attenuated colonic inflammation, normalized hormone profiles, and reversed dysregulated expression of PDGFRα, C-kit, SK3, and ANO1. Conclusion Tongyou Decoction improved intestinal transit, reduced colonic inflammation, and favorably modulated gastrointestinal hormones and ICC/ANO1/PDGFRα-SK3 pathway components in diphenoxylate-induced STC rats. These findings suggest that Tongyou Decoction may have potential as an adjunct therapy for slow transit constipation, but further studies are required to confirm its efficacy and safety in humans.
Objective This study investigated whether Erzhi Pill (EZP) ameliorates D-galactose (D-gal)-induced cognitive dysfunction by restoring hippocampal energy metabolism and preserving synaptic integrity, along with exploring upstream signaling mechanisms. Methods EZP efficacy was evaluated in D-gal mice through behavioral tests (MWM and Y-maze) and hippocampal histology (H&E and Nissl staining). Untargeted metabolomics was used to identify EZP-responsive metabolic pathways. For pathway analyses, we examined synaptic proteins (PSD95, SYN), Tau phosphorylation, glycogen, lactate, ATP metabolism (including GS, GP, HK activity), and PI3K/AKT/GSK3β signaling by western blotting. GPR30 involvement was further probed using the selective antagonist G15. Results EZP markedly improved cognitive function, attenuated hippocampal neuronal damage, normalized neuronal morphology, and increased Nissl body counts in D-gal mice. Untargeted metabolomics revealed that EZP reversed key metabolites in glycolysis and the TCA cycle. GPR30 expression was also elevated by EZP. G15 co-administration largely attenuated these beneficial effects, as evidenced by deteriorated behavioral performance, aggravated neuronal damage, reduced synaptic protein expression, and enhanced Tau hyperphosphorylation. EZP replenished hippocampal glycogen, lactate, and ATP, restored GS and GP expression, without affecting HK activity. At the signaling level, EZP promoted PI3K and AKT phosphorylation and reduced GSK3β phosphorylation; these effects were reversed by G15. Conclusion Overall, these data provide correlative evidence that GPR30 signaling is permissive for EZP's effects on hippocampal energy metabolism and synaptic integrity, with GSK3β phosphorylation as a noted downstream event in this process.
Background Insomnia is a common and burdensome sleep disorder, especially in cancer patients undergoing radiotherapy. Although Chaihu Jia Longgu Muli Decoction (CLMD) has been implicated in neuroinflammatory regulation, its role in radiation-induced sleep disruption remains unknown. This study investigates the neuroprotective effects of CLMD against radiation-induced neuronal injury and its molecular basis. Methods SH-SY5Y cells were exposed to 4 Gy X-ray irradiation to establish an in vitro radiation injury model. Cellular functions (viability and apoptosis), oxidative stress markers (ROS, MDA, GSH, TAS), inflammatory cytokines (IL-1β, IL-6, TNF-α, IL-10), and NF-κB/ERK signaling pathway proteins were assessed using standard biochemical and molecular biology techniques. Result X-ray radiation markedly elevated the relative levels of p-p65/p65 and p-ERK/ERK (P < 0.001 for 4 Gy). This irradiation increased reactive oxygen species and malondialdehyde levels alongside decreased glutathione and total antioxidant status (all P < 0.001). At the same time, there was an increase in the levels of IL-1β, IL-6, and TNF-α, while IL-10 showed a decrease (all P < 0.001). CLMD treatment (25, 50, and 75 mg/L) suppressed radiation-induced NF-κB (p-p65) and ERK MAPK activation, reduced oxidative stress and inflammatory responses, restored cell viability, and inhibited apoptosis in a dose-dependent manner (all P < 0.001). The protective effects of the highest CLMD dose (75 mg/L) were comparable to those of the positive control daridorexant (10 μM). Conclusion CLMD mitigates neuronal injury induced by radiation through the inhibition of the NF-κB (p-p65) and ERK MAPK signaling pathway, leading to a reduction in oxidative stress and inflammatory responses. These findings providing mechanistic support for its potential role in managing insomnia and other neuropsychiatric disorders of radiotherapy, while acknowledging that behavioral sleep outcomes were not directly assessed in this study. The application of CLMD in clinical practice will require further validation through animal studies and clinical trials.
Background Mixing oxidative damage, sterile immunological responses, and controlled cell-death pathways causes myocardial ischemia-reperfusion injury (MIRI). Although MIRI is related with macrophage diversity and ferroptotic damage in cardiomyocytes, it is unknown how inflammatory macrophage states cause ferroptosis-associated cardiac harm. Our study investigated the relationship between TRPV4-induced macrophage activation, USP47/Wnt/β-catenin activity, and cardiomyocyte damage caused by macrophages. Methods A public single-cell RNA-sequencing dataset was reanalysed to characterise MIRI macrophages and cardiomyocytes. Experimental clinical samples and RAW264.7 macrophage and HL-1 cardiomyocyte tests supported these results. TRPV4 was repressed with shRNA and Wnt/β-catenin and JAK2/STAT3 signalling were pharmacologically inhibited. Flow cytometry, cytokine tests, and protein markers assessed macrophage polarisation. LPS/IFN-γ-polarized macrophages’ conditioned media was given to HL-1 cells, with or without N-acetylcysteine or ferrostatin-1. Both biochemical and protein indicators were used to evaluate cardiomyocyte damage and ferroptosis. Results In MIRI, single-cell data indicated substantial macrophage rearrangement, including growth of a Trpv4-enriched subgroup with proinflammatory transcriptional state, whereas multiple cardiomyocyte subsets had greater ferroptosis-related signals. In RAW264.7 cells, TRPV4 knockdown reduced F4/80+CD86+ fraction, iNOS, and proinflammatory cytokines, while increasing IL-10 and TGF-β. It also restored USP47 and phospho-β-catenin. Wnt/β-catenin signalling inhibition partially reduced the anti-inflammatory effects of TRPV4 silencing, suggesting its functional involvement. NAC and Fer-1 partly corrected cardiomyocyte damage, lipid-peroxidation- and iron-associated indices, GPX4 and SLC7A11 changes, and ACSL4 elevation in M1-like macrophage-conditioned media. AG490 decreased LPS-induced inflammatory polarisation and JAK2/STAT3 activation. Conclusions TRPV4-linked inflammatory activation of macrophages, along with reduced USP47/Wnt/β-catenin signalling, promotes paracrine, ferroptosis-associated cardiomyocyte injury during MIRI. JAK2/STAT3 is better viewed as a parallel inflammatory amplifier rather than a downstream element of the TRPV4 pathway. This multicellular model needs mechanistic and in vivo testing before immunomodulatory and anti-ferroptotic treatments may be developed.
Objective Based on transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE), this study aims to evaluate the primary echocardiographic features, hemodynamic profiles, and comparative diagnostic utility of imaging in patients with artificial mechanical valve thrombosis. Methods A retrospective analysis was performed on the TTE and TEE characteristics of 48 valves of artificial MVT, including the echoes of valve frames and leaflets, the leaflet excursion and coaptation, color Doppler jet width and morphology, and the characteristics of blood flow spectra. All these data were verified and compared with surgical findings. Results Among the 48 occluded artificial mechanical valves, there were 24 located in the aortic valve, 22 in the mitral valve, and 2 in the tricuspid valve. Meanwhile, 28 presented with stenosis, 6 with regurgitation, and 14 in both stenosis and regurgitation. Among them, 6 cases were intermittent occluded valves. TEE demonstrated a significantly higher structural detection rate for thrombi and abnormal leaflet restriction compared to TTE (95.8% vs. 68.8%, P < 0.01). TTE, however, retained 100% sensitivity for identifying secondary hemodynamic modifications via continuous-wave spectral Doppler analysis. Spectral abnormalities could clearly indicate mechanical valve occlusion, which was consistent with the findings of TEE. Finally, the intraoperative observations were consistent with the results of echocardiography. Conclusion TTE combined with TEE demonstrated high detection rates for mechanical valve occlusion, with TEE showing superior structural visualization. These findings suggest that the combined approach is useful for diagnostic evaluation, but external validation in larger, multi-center cohorts is warranted.
Objective To compare the predicted pharmacological profiles and potential therapeutic mechanisms of two traditional Chinese medicine prescriptions, Longgu Muli Jiawei Xiaoyao Pill (XYWLM) and Zhenzhumu Longchi Jiawei Qinghao Biejia Decoction (BHQHZM), used in the severity- and pattern-informed management of menopause-associated sleep disorders (MASD). Methods Network pharmacology was used to generate hypotheses regarding formula-associated targets and pathways. Experimental assessment was subsequently performed in estrogen-deprived BV2 microglial cells with or without lipopolysaccharide (LPS) stimulation using ELISA, immunofluorescence, Western blotting, and qRT-PCR. Results The two formulas shared multiple MASD-associated targets but showed different network tendencies: XYWLM was more closely associated with TNF-centered signaling, whereas BHQHZM was linked to IL-6 and AGE-RAGE/MAPK signaling. In the estrogen-deprived/LPS-stimulated BV2 model, both formulas reduced inflammatory cytokine release, COX-2 and Iba1 fluorescence, NF-κB/MAPK-related protein expression, and pro-inflammatory gene transcription. The magnitude of reduction varied by endpoint, supporting partially overlapping rather than uniformly superior effects. Conclusion XYWLM and BHQHZM showed similar anti-inflammatory activity and neuroprotective effect, with different predicted pathway tendencies in a microglial cell model. These findings provide preliminary mechanistic support for their differentiated clinical use. However, further comparative animal studies and clinical investigations are required to determine their specific therapeutic effects and clinical relevance.
Objective The present study was undertaken to explore the effect of isobavachalcone (IBC) targeting Arachidonate 5-Lipoxygenase (ALOX5) to regulate ferroptosis in improving dexamethasone-induced osteoporosis. Materials and methods A total of sixty C57BL/6 J mice were allocated into six groups: normal control group (NC group, normal saline), osteoporosis model group (OP group, dexamethasone), positive control group (PC group, dexamethasone + alendronate sodium), low-dose isobavachalcone group (LD-IBC group, 10 mg/kg/d isobavachalcone), middle-dose isobavachalcone group (MD-IBC group, 20 mg/kg/d isobavachalcone), high-dose isobavachalcone group (HD-IBC group, 50 mg/kg/d isobavachalcone). The serum specimens were collected for the quantification of biochemical markers of bone metabolism. Femoral tissues were collected to compute the femur index and to evaluate parameters. Biomechanical integrity was assessed by employing a three-point bending assay. H&E staining was used to observe bone tissue morphology, Tartrate-Resistant Acid Phosphatase (TRAP) staining was employed to assess osteoclast activity, and Western blotting was performed to detect the protein expression of ALOX5, Keap1, Nrf2, SLC7A11, and GPX4 in the left femur. Results When contrasted with the NC group, the OP group exhibited significantly reduced serum levels of alkaline phosphatase, osteocalcin, and osteoprotegerin, alongside marked elevations in C-terminal telopeptide of type I collagen, tartrate-resistant acid phosphatase 5 b, and soluble receptor activator of nuclear factor-κB ligand. Moreover, the OP group showed decreased femur index, BMD, BMC, maximum load, maximum stress, and bending elastic modulus, with increased TRAP-positive staining area. Femoral tissue analysis revealed downregulation of ALOX5, Nrf2, SLC7A11, and GPX4, while Keap1 was significantly upregulated (P < 0.05). Conclusion Isobavachalcone can improve dexamethasone-induced osteoporosis by targeting ALOX5 and inhibiting ferroptosis-related pathways, providing a new experimental basis for its prevention and treatment.
Naturally occurring radioactive materials (NORMs) associated with natural gas production may pose radiological risks to workers and the environment if present at elevated levels. This study presents the first comprehensive radiological assessment of NORMs using representative samples of soils (n = 27), produced water (n = 15), wastewater (n = 6), treated water (n = 3) and gas condensates (n = 9) collected from the Mnazi Bay, Msimbati, and Madimba Natural Gas Processing Plant, Tanzania. Collected samples were analyzed by using high-purity germanium (HPGe) detector. Soil samples exhibited mean activity concentrations of 13.12 Bq kg−1 for 226Ra, 14.50 Bq kg−1 for 232Th, and 816.3 Bq kg−1 for 40K, while the corresponding mean absorbed dose rate, annual effective dose (AED), and excess lifetime cancer risk (ELCR) were 48.85 nGy h−1, 0.01368 mSv y−1, and 2.74 × 10−5, respectively. Mean radium equivalent activity (Raeq) in soils was 96.70 Bq kg−1, which is well below the recommended limit of 370 Bq kg−1, while the mean annual gonadal dose equivalent (AGDE) (357.44 μSv y−1) exceeded the reference value of 300 μSv y−1. In produced water, wastewater, and gas condensates, radionuclide activities and hazard indices were several orders of magnitude below internationally accepted safety limits. Wastewater treatment significantly reduced radionuclide concentrations and corresponding radiological hazards. Comparison with published data from other oil and gas producing countries indicated generally lower radionuclide concentrations in the Tanzanian facilities. These findings provide baseline data for environmental radiation monitoring and support the development of national guidelines for NORM management in Tanzania's expanding natural gas industry.
Background The study evaluated an integrated teaching model combining LBL, PBL, CBL, and AI assisted image analysis. Methods A total of 160 vascular surgery trainees, including interns and standardized training residents, were randomized into two groups. Post-intervention outcomes were compared using baseline-adjusted analyses. The 80 observation students received AI-assisted integrated teaching, and 80 controls received traditional LBL teaching. We compared OSCE scores, diagnostic accuracy, and satisfaction. The primary outcome was the post-intervention OSCE score. Results After baseline adjustment, baseline characteristics were comparable between groups. The intervention was associated with higher OSCE scores (86.74 ± 6.19 vs. 71.39 ± 7.06), higher diagnostic accuracy (87.59 ± 4.33 vs. 73.68 ± 5.88), shorter image interpretation time, and improved satisfaction, with a large effect size (Cohen's d = 2.32). Conclusion The AI-assisted integrated teaching model combining LBL, PBL and CBL improves imaging analysis teaching quality, which provides a promising strategy for intelligent medical education, although broader validation is still needed.
A problem of great interest in survey sampling is estimating finite population distribution functions (CDFs), which provide information about the overall population beyond a selected summary measure. In this study, we propose a new dual-auxiliary estimator for the finite population distribution function under simple random sampling without replacement (SRSWOR). The proposed estimator uses two sources of auxiliary information (the known distribution function of an auxiliary variable and its rank information) in a flexible ratio–difference framework. The goal of the proposed estimator is to improve the precision and effectiveness of CDF estimation, especially when the studied variable has auxiliary characteristics. Using first-order approximation, the bias and mean squared error (MSE) of the proposed estimator are computed, and the optimum values of the constants of the estimator are analytically derived. The theoretical results show that the efficiency of the proposed estimator depends on the multiple correlation between the study distribution function and the combined auxiliary information. The performance of the proposed estimator is tested on five real-world datasets from sustainable artistic design, radiation biology, and accounting applications. The proposed estimator is compared with available estimators, including ratio-type, product-type, regression-type, exponential-type, and difference-type estimators, in terms of MSE and PRE. The empirical results for all populations considered consistently showed that the proposed estimator has lower MSE and higher PRE. These improvements are especially significant in populations with strong relationships between the study variable and the auxiliary information.
Objective This study aimed to investigate the value of machine learning models based on pre-treatment radiomics for predicting response to concurrent chemoradiotherapy (CCRT) in locally advanced cervical cancer (LACC) patients. Response was defined as a tumor regression rate reaching 90% before brachytherapy. Methods This retrospective exploratory study analyzed 50 patients with locally advanced cervical cancer who underwent concurrent chemoradiotherapy. Radiomic features were extracted from primary lesions using pre-treatment contrast-enhanced T1-and T2-weighted imaging sequences. Tumor contours were delineated on T2-weighted magnetic resonance imaging images using the Monaco treatment planning system. Patients were classified according to whether the tumor regression rate (TRR) reached 90% before brachytherapy after concurrent chemoradiotherapy-high (TRR ≥ 90%)/low (TRR < 90%) regression groups. Machine learning models were constructed, and their performance was evaluated using the area under the curve (AUC) and confusion matrices. Results We constructed the model based on contrast-enhanced T1-weighted imaging (T1WI) images. The results showed that among the models based on pre-treatment contrast-enhanced T1WI images, the random forest (RF) model achieved the highest AUC of 0.760, outperforming the Tree (0.637), support vector machine (0.713), and Logit (0.624) models. The confusion matrix for the RF model showed an accuracy of 71.3%, a precision of 70.4%, and a recall of 76.7%. Furthermore, we evaluated the performance of the model on T2WI. Among the models based on pre-treatment T2WI images, the AUC of single models (SVM, 0.845; RF, 0.865; gradient boosting, 0.863; LR, 0.813). The advanced fusion model demonstrated the best overall performance with a test set AUC of 0.862 and cross-validation AUC of 0.863. Conclusion Machine learning based on pre-treatment radiomics may help predict whether the TRR will reach ≥90% after CCRT in patients with LACC. It shows preliminary potential as an auxiliary tool that could assist in informing treatment decisions and might help avoid unnecessary dose escalations.
Background Diabetic foot ulcer (DFUs) is a serious complication related to diabetes, which is caused by long-term inflammation, increased blood sugar levels and wound healing disorders caused by fibroblast dysfunction. The dysfunction of fibroblasts in DFU wounds, especially the loss of function to mediate the construction of neovascular networks, is considered to be the key to the failure of DFU healing. In-depth exploration of the molecular mechanism of fibroblast dysfunction in a high-sugar environment is very important for the treatment of DFU. Methods We integrate both single-cell and bulk transcriptomic data for bioinformatics and machine learning analyses; functional validation was performed in HFF-1 fibroblasts exposed to high-glucose conditions, with KAT2A gain- and loss-of-function experiments and DFO treatment used to assess ferroptosis-related changes. Results Single-cell analysis revealed a significant rise in a specific subgroup of fibroblasts exhibiting high levels of KAT2A expression within DFUs. This increase showed a positive relationship with genes linked to ferroptosis and an inverse relationship with those associated with angiogenesis. Cell communication assays showed impaired pro-angiogenic fibroblast-endothelial signaling in DFUs. Elevated glucose concentrations caused a dose-dependent increase in KAT2A levels, inducing ferroptosis, which was characterized by the accumulation of Fe2+, depletion of glutathione (GSH), heightened malondialdehyde (MDA) levels, and decreased expression of Glutathione peroxidase 4 (GPX4) and Solute carrier family 7 member 11 (SLC7A11). The enhanced expression of KAT2A exacerbated the ferroptosis phenotype and led to increased levels of Tfrc and Hmox1, while DFO treatment partially alleviated these effects. Conclusion The study suggests that the KAT2A–Tfrc/Hmox1–ferroptosis pathway may play an important role in diabetic fibroblast dysfunction and impaired wound repair. This mechanism leads to ferroptosis and impaired angiogenesis, which ultimately hinders the healing process of DFUs. This finding provides new avenues for targeted therapies centered on KAT2A and iron chelation.
Polymer-based composites containing high-atomic-number fillers have attracted considerable interest as potential lead-free radiation shielding materials. However, direct comparisons of different polymer matrices under identical computational conditions remain limited, making it difficult to isolate the influence of the polymer matrix on shielding performance. In this study, the gamma-ray shielding characteristics of TiO2-reinforced poly(methyl methacrylate) (PMMA) and epoxy composites containing 0–50 wt% TiO2 were systematically evaluated using the Geant4 Monte Carlo simulation toolkit over a photon energy range of 0.0595–1.333 MeV. Composite densities were determined using the rule of mixtures, and key shielding parameters, including the mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), half-value layer (HVL), mean free path (MFP), effective atomic number (Zeff), and radiation protection efficiency (RPE), were calculated. The simulation methodology was verified through comparison with the XCOM database, showing excellent agreement with relative deviations below 2%. The results demonstrate that increasing TiO2 concentration enhances the shielding performance of both polymer systems by increasing the attenuation coefficients and Zeff while reducing the HVL and MFP. Among the investigated compositions, the epoxy/TiO2 composites consistently exhibited superior shielding performance compared with the corresponding PMMA/TiO2 composites, primarily due to their higher density. The improvement was most pronounced at low photon energies, whereas higher photon energies required greater material thicknesses to achieve comparable attenuation, as reflected by increased HVL values. The findings provide a systematic computational assessment of the influence of the polymer matrix on gamma-ray shielding performance under identical simulation conditions and offer theoretical guidance for the design of polymer-based radiation shielding materials. Nevertheless, the present work is limited to computational evaluation, and further experimental studies, including mechanical, thermal, durability, processing, and biocompatibility assessments, are required before practical implementation of these composites can be established.
This paper introduces a new flexible version of the Shanker distribution, called the induced Shanker (ISH) distribution. In this work, the induced Shanker model is studied with respect to its statistical properties, which include moments, moment generating function, quantile function as well as Rényi entropy, Tsallis entropy and order statistics. We apply and compare three rigorous methods for parameter estimation, including maximum likelihood estimation (MLE), maximum product of spacings (MPS) and Bayesian estimation (BE). Monte Carlo simulations are conducted to assess the performance of these estimation methods for various sample sizes. The induced Shanker distribution is illustrated using two real data sets taken from the environmental engineering and Chemotherapy fields. The results show that the induced Shanker distribution fits both datasets better than other models and thus provides useful insights for researchers from different fields.
Background Hepatocellular carcinoma (HCC) is highly heterogeneous, yet extracellular matrix (ECM)-centered subtyping and its potential clinical and biological implications remain insufficiently defined. Methods TCGA-LIHC (n = 371) and GSE14520 (n = 225) served as discovery and independent replication cohorts, respectively. ECM-related transcriptional states were identified by cohort-specific consensus clustering. Single-cell RNA sequencing data from 10 HCC patients were used to characterize the cellular context of CXCL8. Histological assessment, IL-8 immunohistochemistry, and tissue CXCL8 RT-qPCR were performed in 76 HCC specimens from an independent FUSCC cohort. Radiological features were retrospectively reviewed in a limited subset (n = 11). Selected cellular stress- and DNA damage response-related programs were assessed by enrichment analysis. Results ECM-remodeled tumors exhibited distinct stromal-immune features, including fibrotic ECM activation, cancer-associated fibroblast programs, chemokine activity, TGF-β signaling, hypoxia, and macrophage-enriched inflammation. Cross-cohort analysis identified CXCL8 as a candidate gene associated with ECM-remodeled states, poor prognosis, and fibrotic-inflammatory microenvironmental remodeling. Single-cell RNA-seq analysis localized CXCL8 mainly to myeloid cells and an activated ECM-remodeling fibroblast subset. In the FUSCC cohort, higher collagen deposition was accompanied by higher IL-8 H-scores and CXCL8 mRNA expression, both positively correlated with collagen-positive area. Exploratory radiological review suggested associations between the ECM-high/CXCL8-high state and selected adverse imaging features. Exploratory differences in selected cellular stress- and DNA damage response-related programs were observed, without treatment-response endpoints. Conclusion These findings support a CXCL8-enriched fibrotic-inflammatory phenotype in HCC with tissue-level associations between collagen deposition and IL-8/CXCL8 expression, and provide an exploratory, hypothesis-generating link between molecular stratification and radiological features.
Objective In12-fold accelerated multi-coil brain MRI, joint reconstruction of multi-channel data faces a severely underdetermined problem due to high-density k-space undersampling, leading to significant aliasing artifacts and noise amplification. This study proposes an optimized reconstruction framework that jointly models coil sensitivity encoding, Wave-CAIPI three-dimensional aliasing dispersion, and sparse regularization to obtain stable solutions under highly underdetermined conditions. Methods Based on the sparsity-regularized Wave-SNMs framework, we propose an optimized reconstruction scheme for highly accelerated brain MRI with multi-coil arrays. By analyzing the statistical properties of k-space data under the complex Gaussian distribution assumption and incorporating the Wave-CAIPI sampling strategy, we investigate the statistical distribution characteristics of highly undersampled MRI data and the sparsity properties of Wave-CAIPI sampling. Accordingly, an L1-regularized Wave-SNMs model is constructed, which uses sparsity constraints to optimize multi-coil image reconstruction. Results Under the 12-fold accelerated MULTIPLEX scanning mode on a 3 T scanner (Siemens Prisma) with a 32-channel head coil using T1-weighted MP-RAGE and multi-echo GRE sequences, the proposed method achieves high consistency with fully sampled reference scans in quantitative assessments of T1, T2*, R2*, and magnetic susceptibility parameters, while effectively suppressing aliasing artifacts and noise amplification. Conclusions Combining statistical distribution modeling with L1 sparsity-regularized optimization effectively improves the accuracy and robustness of high-fold accelerated MRI reconstruction, providing a stable and interpretable reconstruction framework for multi-coil highly undersampled data.
Objective In this in-vitro study, by using H9C2 cardiomyocytes subjected to oxygen–glucose deprivation/reoxygenation (OGD/R), we evaluated the cardioprotective effects of matrine across graded concentrations and explored underlying mechanisms related to ferroptosis and BTF3/EPHB2 signaling. Materials and methods H9C2 cardiomyocytes were stochastically allocated into five groups: Control group (routine culture), OGD/R group (myocardial ischemia-reperfusion injury model), OGD/R + L-MAT group (30 μM MAT), OGD/R + M-MAT group (60 μM MAT), and OGD/R + H-MAT group (100 μM MAT). Cellular proliferative capacity was measured by the Cell Counting Kit-8 (CCK-8) assay. Oxidative stress markers (ROS, LDH, MDA, GSH) and ferroptosis-related proteins (ACSL4, FDX1, GPX4, FTL), as well as BTF3, EPHB2, p38, and p p38, were assessed by fluorescence imaging, ELISA, and Western blotting. Results Relative to the Control group, the OGD/R group exhibited a marked reduction in cellular proliferative capacity, a marked surge in both the rate of apoptotic cell death and the intracellular accumulation of ROS, coupled with significantly heightened concentrations of LDH and MDA alongside a discernible depletion of GSH content. Concurrently, the relative protein expression levels of ACSL4, FDX1, FTL, BTF3, EPHB2, and p-p38 were observed to be robustly upregulated, whereas the protein level of GPX4 exhibited a noticeable downregulation (all P < 0.05). Conversely, when contrasted with the OGD/R group, the OGD/R + L-MAT, OGD/R + M-MAT, and OGD/R + H-MAT groups displayed a distinct augmentation in cellular proliferative activity, a prominent reduction in the apoptotic index and intracellular ROS generation, accompanied by decreased concentrations of LDH and MDA as well as a restored elevation of GSH levels. Moreover, the relative protein abundances of ACSL4, FDX1, FTL, BTF3, EPHB2, and p-p38 were significantly diminished, while that of GPX4 was prominently increased (all P < 0.05). Conclusion MAT markedly attenuates OGD/R induced damage in H9C2 cardiomyocytes; these effects are associated with reduced oxidative stress, apoptosis, and ferroptosis-related protein changes and with downregulation of BTF3/EPHB2 signaling, suggesting, but not definitively proving, that this pathway may contribute to MAT's cardioprotective actions.
The increasing use of ionizing radiation in diagnostic imaging, radiotherapy, nuclear medicine, and interventional procedures has intensified the need for lightweight, effective, and clinically reliable shielding materials. This systematic and critical review evaluates the role of artificial intelligence (AI) in the design, prediction, optimization, and validation of radiation-shielding materials for medical applications. Following the PRISMA 2020 framework, 149 publications published between January 2016 and March 2026 were included from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. The evidence was synthesized according to material type, radiation field, dataset characteristics, input descriptors, target properties, AI technique, validation strategy, uncertainty analysis, and experimental maturity. Artificial neural networks, support vector regression, random forests, gradient-boosting methods, and evolutionary algorithms were most frequently applied to attenuation prediction, composition optimization, virtual screening, and Monte Carlo surrogate modeling. The strongest evidence supports interpolation of photon-shielding parameters within defined datasets, whereas external validation, uncertainty quantification, reproducibility, and prospective experimental confirmation remain limited. Many studies also insufficiently distinguish property prediction from genuine materials discovery. AI surrogates can accelerate radiation-transport calculations but should complement rather than replace validated Monte Carlo methods. Clinical translation requires standardized attenuation testing, durability assessment, manufacturing control, toxicological evaluation, and regulatory validation. Future progress depends on representative datasets, grouped and external validation, physics-informed modeling, explainability, and experimentally verified AI-guided material development.
