Search Results (139)

Laser surface treatment (LST) has been employed on ductile cast iron (DCI) parts to obtain a good performance and a long service life. There is a need to understand the laser surface-treated microstructure and hardening ability of DCIs with different matrix structures to facilitate the scientific selection of DCI for specific applications. In this study, a Laserline-LDF3000 fiber-coupled semiconductor laser with a rectangular spot was used to harden the surface of ductile cast irons (DCIs) with different pearlite contents. The hardened surface layer having been solid state transformed (SST) and with or without being melted–solidified (MS) was obtained under various process parameters. The microstructure, hardened layer depth, hardness and hardening ability were analyzed and compared as functions of pearlite contents and laser processing parameters. The results show that the MS layers on the DCIs with varied pearlite contents have similar microstructures consisting of fine transformed ledeburite, martensite and residual austenite. The microstructure of the SST layer includes martensite, residual austenite and ferrite, whose contents vary with the pearlite content of DCI. In the pearlite DCI, martensite and residual austenite are found, while in ferrite DCI, there is only a small amount of martensite around the graphite nodule, with a large amount of unaltered ferrite remaining. There exists no significant difference in the hardness of MS layers among DCIs with different pearlite contents. Within the SST layer, the variation in the hardness value in the pearlite DCI is relatively small, but it gradually decreases along the depth in the ferrite DCI. In the transition region between the SST layer and the base metal (BM), there is a steep decrease in hardness in the pearlite DCI, but it decreases gently in the ferrite DCI. The depth of the hardened layer increases slightly with the increase in the pearlite content in the DCI; however, the effective hardened depth and the hardening ability increase significantly. When the pearlite content of DCI increases from 10% to 95%, its hardening ability increases by 1.1 times. Full article

Background/Objectives: Spread through air spaces (STAS) is defined as the spread of tumor cells into the parenchymal alveolar space beyond the margins of the main tumor, and it is associated with worse clinical outcomes in resected lung adenocarcinoma. This study aimed to evaluate the preoperative computed tomography (CT) findings of primary lung adenocarcinoma in surgically resected T1 cases and to compare CT findings with and without STAS. Methods: A total of 145 patients were included in this study. The following factors were evaluated on CT images: nodule type (pure ground-glass nodule [GGN], part-solid nodule, or solid nodule), margin (smooth or irregular), the presence of lobulation, spicula, cavity, calcification, central low attenuation, peripheral opacity (well-defined or ill-defined), air bronchogram, satellite lesions, pleural retraction, pulmonary emphysema, and interstitial pneumonia; CT values (maximum, minimum, and mean); volume (tumor and solid component); and diameter (tumor and solid component). CT criteria were compared between the presence and absence of STAS. Results: Lobulation and central low attenuation were significantly more frequent in patients with STAS (p < 0.05). The mean CT value, and the volume, rate, and diameter of the solid component were significantly larger in cases with STAS (p < 0.05). A multiple logistic regression analysis identified central low attenuation as an indicator of the presence of STAS (p < 0.001; odds ratio, 3.993; 95% confidence interval, 1.993–8.001). Conclusions: Quantitative and qualitative analyses are useful for differentiating between the presence and absence of STAS. Full article

Background: Optimization of pulmonary nodule detection across varied imaging protocols remains challenging. We evaluated four DL-CAD systems and manual reading with volume rendering (VR) for performance under varying radiation doses and reconstruction methods. VR refers to a post-processing technique that generates 3D images by assigning opacity and color to CT voxels based on Hounsfield units. Methods: An anthropomorphic phantom with 169 artificial nodules was scanned at three dose levels using two kernels and three reconstruction algorithms (1080 image sets). Performance metrics included sensitivity, specificity, volume error (AVE), and Lung-RADS classification accuracy. Results: DL-CAD systems demonstrated high sensitivity across dose levels and reconstruction settings, with three fully automatic DL-CAD systems (0.92–0.95) outperforming manual CT readings (0.72), particularly for sub-centimeter nodules. However, DL-CAD systems exhibited limitations in volume measurement and Lung-RADS classification accuracy, especially for part-solid nodules. VR-enhanced manual reading outperformed original CT interpretation in nodule detection, particularly benefiting less-experienced radiologists under suboptimal imaging conditions. Conclusions: These findings underscore the potential of DL-CAD for lung cancer screening and the clinical value of VR in low-dose settings, but they highlight the need for improved classification algorithms. Full article

Background and Objectives: Indeterminate thyroid nodules (Bethesda III and IV) are a common clinical entity that present a diagnostic challenge due to their intermediate risk of malignancy. This study aimed to evaluate the role of ultrasound in risk stratification and malignancy prediction to support clinical decision-making and reduce unnecessary surgical interventions. Materials and Methods: This retrospective multicenter cohort study included patients aged ≥18 years who underwent thyroid surgery between 2016 and 2022 at four centers in the Eastern Province of Saudi Arabia. Only nodules with indeterminate cytology (Bethesda III or IV) were included. Data collected included demographic characteristics, thyroid function, ultrasound features, cytology results, and histopathological findings. Results: A total of 679 patients with 733 nodules were reviewed. Of these, 206 patients with 223 indeterminate nodules were included (median age: 42 years; 88.3% female). The overall malignancy rate was 46.6%. Independent predictors of malignancy included solid hypoechoic composition (OR = 2.26, p = 0.012), microcalcifications (OR = 3.07, p = 0.002), lymph node involvement (OR = 2.43, p = 0.021), American Thyroid Association (ATA) intermediate to high suspicion category (OR = 1.9, p = 0.018), and Thyroid Imaging Reporting and Data Systems (TI-RADS) categories 4–5 (OR = 2.3, p = 0.003). Solid hypoechoic nodules showed 82.3% specificity and 63.0% positive predictive value (PPV); microcalcifications demonstrated 84.1% specificity and 68.4% PPV; lymph node involvement had 87.6% specificity and 68.9% PPV. The ATA and TI-RADS classifications showed higher sensitivity (63.5% and 68.0%, respectively), but lower specificity (53.1% and 52.8%, respectively). Conclusions: Ultrasound features, particularly solid hypoechoic composition, microcalcifications, and lymph node involvement, as well as ATA and TI-RADS classifications, were independent predictors of malignancy in indeterminate thyroid nodules. Although ATA and TI-RADS offered higher sensitivity, individual features demonstrated greater specificity and PPV. These findings support the use of ultrasound risk stratification to guide surgical decisions in high-risk cases and suggest that additional diagnostic evaluation may be appropriate for low-risk nodules. Full article

Background: A micropapillary pattern (MP) and solid pattern (SP) in lung adenocarcinoma (LUAD), a major subtype of non-small-cell lung cancer (NSCLC), are associated with a poor prognosis and necessitate accurate preoperative identification. This study aimed to develop and validate a predictive model combining clinical and radiomics features for differentiating a high-risk MP/SP in LUAD. Methods: This retrospective study analyzed 180 surgically confirmed NSCLC patients (Stages I–IIIA), randomly divided into training (70%, n = 126) and validation (30%, n = 54) cohorts. Three prediction models were constructed: (1) a clinical model based on independent clinical and CT morphological features (e.g., nodule size, lobulation, spiculation, pleural indentation, and vascular abnormalities), (2) a radiomics model utilizing LASSO-selected features extracted using 3D Slicer, and (3) a comprehensive model integrating both clinical and radiomics data. Results: The clinical model yielded AUCs of 0.7975 (training) and 0.8462 (validation). The radiomics model showed superior performance with AUCs of 0.8896 and 0.8901, respectively. The comprehensive model achieved the highest diagnostic accuracy, with training and validation AUCs of 0.9186 and 0.9396, respectively (DeLong test, p < 0.05). Decision curve analysis demonstrated the enhanced clinical utility of the combined approach. Conclusions: Integrating clinical and radiomics features significantly improves the preoperative identification of aggressive NSCLC patterns. The comprehensive model offers a promising tool for guiding surgical and adjuvant therapy decisions. Full article

Background: Chronic obstructive pulmonary disease (COPD) and lung cancer are the leading causes of death globally, which share common risk factors such as age and smoking exposure. In high-income countries, low-dose computed tomography (LDCT) lung cancer screening programs have decreased lung cancer mortality and facilitated the detection of emphysema, a key radiological indicator of COPD. This study aimed to assess the prevalence of emphysema during a pilot LDCT screening program for lung cancer in a middle-income country with a high smoking prevalence. Methods: A secondary analysis of the Lung Cancer Screening Database of the Autonomous Province of Vojvodina, Serbia, from 20 September 2020 to 30 May 2022. Persons aged 50–74 years, with a smoking history of ≥30 pack-years/or ≥20 pack-years with additional risks (chronic lung disease, prior pneumonia, malignancy other than lung cancer, family history of lung cancer, and professional exposure to carcinogens) were offered LDCT. Results: Of 1288 participants, mean age of 62.1 ± 6.7 years and 535 males (41.5%), 386 (30.0%) had emphysema. The majority of patients with emphysema (301/386, 78.0%) had no prior history of chronic lung diseases. Compared to the patients without emphysema, the patients with emphysema reported more shortness of breath (140/386, 36.3% vs. 276/902, 30.6%, p = 0.046), chronic cough (117/386, 30.3% vs. 209/902, 23.17% p = 0.007), purulent sputum expectoration (70/386, 18.1% vs. 95/902, 10.53%, p < 0.001), and weight loss (45/386, 11.7% vs. 63/902, 7.0%, p = 0.005). The patients with emphysema had more exposure to smoking (pack/years, 43.8 ± 18.8 vs. 39.3 ± 18.1, p < 0.001) and higher prevalence of solid or semisolid lung nodules (141/386, 36.5% vs. 278/902 30.8%, p = 0.04). Conclusions: Almost one-third of the patients who underwent the LDCT screening program in a middle-income country had emphysema that was commonly undiagnosed despite being associated with a significant symptom burden. Spirometry screening should be considered in high-risk populations. Full article

Background: Oncocytic adenomas (OAs) of the thyroid, previously referred to as Hürthle cell adenomas, are uncommon tumors, particularly in pediatric populations, where they represent a minority of thyroid nodules. Due to their rarity and the potential difficulty in distinguishing benign from malignant forms on cytology, these adenomas present unique diagnostic and management challenges. Here, we report a pediatric case of a large OA of the thyroid, managed with surgical resection following inconclusive fine-needle aspiration (FNA) results. Case Presentation: A 13-year-old girl presented with an enlarging thyroid nodule. An ultrasound examination showed a large (26 × 16 mm), solid, isoechoic nodule with a hypoechoic halo. The FNA findings were inconclusive, indicating a follicular neoplasm with oncocytic features, classified as Bethesda IV. The patient underwent a hemithyroidectomy, and a histopathological examination confirmed an encapsulated OA without evidence of capsular or vascular invasion. The postoperative recovery was uneventful, and follow-up assessments showed no recurrence. Conclusions: OAs in pediatric patients are rare and may pose diagnostic challenges. This case highlights the importance of a comprehensive approach, including surgical resection, for definitive diagnoses in cases where FNA results are inconclusive. Further studies are warranted to establish guidelines for the management of oncocytic thyroid neoplasms in pediatric patients, as well as to understand their clinical behavior in this population. Full article

Background/Objectives: Persistent pulmonary nodules are at higher risk of developing into lung cancers. Assessing their future cancer risk is essential for successful interception. We evaluated the performance of two risk prediction models for persistent nodules in hospital-based cohorts: the Brock model, based on clinical and radiological characteristics, and the Sybil model, a novel deep learning model for lung cancer risk prediction. Methods: Patients with persistent pulmonary nodules—defined as nodules detected on at least two computed tomography (CT) scans, three months apart, without evidence of shrinkage—were included in the retrospective (n = 130) and prospective (n = 301) cohorts. We analyzed the correlations between demographic factors, nodule characteristics, and Brock scores and assessed the performance of both models. We also built machine learning models to refine the risk assessment for our cohort. Results: In the retrospective cohort, Brock scores ranged from 0% to 85.82%. In the prospective cohort, 62 of 301 patients were diagnosed with lung cancer, displaying higher median Brock scores than those without lung cancer diagnosis (18.65% vs. 4.95%, p < 0.001). Family history, nodule size ≥10 mm, part-solid nodule types, and spiculation were associated with the risks of lung cancer. The Brock model had an AUC of 0.679, and Sybil’s AUC was 0.678. We tested five machine learning models, and the logistic regression model achieved the highest AUC at 0.729. Conclusions: For patients with persistent pulmonary nodules in real-world cancer hospital-based cohorts, both the Brock and Sybil models had values and limitations for lung cancer risk prediction. Optimizing predictive models in this population is crucial for improving early lung cancer detection and interception. Full article

Background/Objectives: Periodontitis and diabetes mellitus share a well-established bidirectional relationship, where hyperglycemia exacerbates periodontal inflammation, and periodontal disease further impairs glycemic control. Within the diabetic periodontal microenvironment, an imbalance between pro-inflammatory (M1) and anti-inflammatory (M2) macrophages promotes chronic inflammation, oxidative stress, delayed healing, and alveolar bone resorption. Resveratrol (RSV), a polyphenol with antioxidant, anti-inflammatory, and pro-osteogenic properties, holds potential to restore macrophage balance. However, its clinical application is limited by poor bioavailability and instability. This study aimed to develop and evaluate a novel RSV delivery system to overcome these limitations and promote periodontal tissue regeneration under diabetic conditions. Methods: A drug delivery system comprising RSV-loaded solid lipid nanoparticles embedded within a cross-linked hyaluronic acid hydrogel (RSV@CLgel) was formulated. The system was tested under hyperglycemic and inflammatory conditions for its effects on macrophage polarization, cytokine expression, oxidative stress, mitochondrial function, and osteoblast differentiation. Results: RSV@CLgel effectively suppressed pro-inflammatory cytokines (TNF-α, IL-1β, IL-6) while upregulating anti-inflammatory markers (IL-10, TGF-β). It significantly reduced oxidative stress by decreasing ROS and lipid peroxidation levels and improved mitochondrial function and antioxidant enzyme activity. Furthermore, RSV@CLgel enhanced osteoblast differentiation, as evidenced by increased ALP activity, calcium nodule formation, and upregulation of osteogenic genes (COL-I, RUNX2, OCN, OPN). It also inhibited RANKL-induced osteoclastogenesis, contributing to alveolar bone preservation. Conclusions: The RSV@CLgel delivery system presents a promising multifunctional strategy for the management of diabetic periodontitis. By modulating immune responses, reducing oxidative stress, and promoting periodontal tissue regeneration, RSV@CLgel addresses key pathological aspects of diabetes-associated periodontal disease. Full article

Phosphorus is an indispensable nutrient for nitrogen metabolism in soybeans. In this study, two P levels were established, 1 mg/L (low-P stress) and 31 mg/L (normal P, CK), by combining ¹⁵N labeling with real-time quantitative PCR and the UHPLC-MS/MS method, to analyze soybean nitrogen accumulation, ¹⁵N abundance, nodule nitrogen fixation accumulation, nodule nitrogen fixation rate, soluble protein content, the relative expression of phosphorus transporters, amino acid changes, and metabolic pathways. The impacts of phosphorus stress on soybean nitrogen metabolism were explored from the perspectives of nitrogen accumulation and protein metabolism. The results demonstrated that low-P stress promoted the absorption of fertilizer nitrogen by aboveground parts, roots, and nodules of soybeans. However, it significantly inhibited nitrogen accumulation (11.09–95.41%), nodule nitrogen fixation accumulation (21.54–96.21%), and nodule nitrogen fixation rate (2.95–37.75%). The soluble protein content in both leaves and nodules decreased remarkably, while the relative expression of GmPT7 was upregulated in leaves, roots, and nodules under low-P stress. A total of 70 amino acids exhibited alterations, among which 26 amino acids were involved in 37 metabolic pathways, playing a crucial role in regulating the effects of low-P stress on soybean nitrogen metabolism. This study identifies significant alterations in nitrogen accumulation, nodule nitrogen fixation, and the expression of phosphorus transporter genes, providing insights into the metabolic pathways involved in soybean’s adaptation to phosphorus deficiency. This research provides a solid theoretical foundation for further in-depth investigations into the physiological and molecular mechanisms of soybean response to low-P stress. Full article

Background/Objectives: Although immunotherapy is the primary treatment option for intermediate-stage hepatocellular carcinoma (HCC), its efficacy varies. This study aimed to identify non-invasive imaging biomarkers predictive of the immunoscore linked to dynamic contrast-enhanced computed tomography (CECT). Methods: We performed immunohistochemical staining with CD3⁺ and CD8⁺ antibodies and counted the positive cells in the invasive margin (IM) and central tumor (CT), converting them to an immunoscore of 0 to 4 points. We assessed the dynamic CECT findings obtained from 96 patients who underwent hepatectomy for HCC and evaluated the relationship between dynamic CECT findings and immunoscores. For validation, we assessed the treatment effects on 81 nodules using the Response Evaluation Criteria in Solid Tumors in another cohort of 41 patients who received combined immunotherapy with atezolizumab and bevacizumab (n = 27) and durvalumab and tremelizumab (n = 14). Results: HCCs with peritumoral enhancement in the arterial phase (p < 0.001) and rim APHE (p = 0.009) were associated with the immunoscore in univariate linear regression analysis and peritumoral enhancement in the arterial phase (p = 0.004) in multivariate linear regression analysis. The time to nodular progression in HCCs with peritumoral enhancement in the arterial phase was significantly longer than that in HCCs without this feature (p < 0.001). Conclusions: We identified HCCs with peritumoral enhancement in the arterial phase as a noninvasive imaging biomarker to predict immune-inflamed HCC with a high immunoscore tendency. These HCCs were most likely to respond to combined immunotherapy. Full article

Background/Objectives: Thyroid cancer ranks among the most prevalent endocrine neoplasms, with a significant rise in incidence observed in recent decades, particularly in papillary thyroid carcinoma (PTC). This increase is largely attributed to the enhanced detection of subclinical cancers through advanced imaging techniques and fine-needle aspiration biopsies. The present study aims to externally validate a predictive model previously developed by our group, designed to assess the risk of a thyroid nodule being malignant. Methods: By utilizing clinical, analytical, ultrasound, and histological data from patients treated at the Puerto Real University Hospital, this study seeks to evaluate the performance of the predictive model in a distinct dataset and perform a decision curve analysis to ascertain its clinical utility. Results: A total of 455 patients with thyroid nodular pathology were studied. Benign nodular pathology was diagnosed in 357 patients (78.46%), while 98 patients (21.54%) presented with a malignant tumor. The most frequent histological type of malignant tumor was papillary cancer (71.4%), followed by follicular cancer (6.1%). Malignant nodules were predominantly solid (95.9%), hypoechogenic (72.4%), with irregular or microlobed borders (36.7%), and associated with suspicious lymph nodes (24.5%). The decision curve analysis confirmed the model’s accuracy and its potential impact on clinical decision-making. Conclusions: The external validation of our predictive model demonstrates its robustness and generalizability across different populations and clinical settings. The integration of advanced diagnostic tools, such as AI and ML models, improves the accuracy in distinguishing between benign and malignant nodules, thereby optimizing treatment strategies and minimizing invasive procedures. This approach not only facilitates the early detection of cancer but also helps to avoid unnecessary surgeries and biopsies, ultimately reducing patient morbidity and healthcare costs. Full article

Background/Objectives: Accurate non-invasive tests to improve early detection and diagnosis of lung cancer are urgently needed. However, no regulatory-approved blood tests are available for this purpose. We aimed to improve pulmonary nodule classification to identify malignant nodules in a high-prevalence patient group. Methods: This study involved 806 participants with undiagnosed nodules larger than 5 mm, focusing on assessing nucleosome levels and histone modifications (H3.1 and H3K27Me3) in circulating blood. Nodules were classified as malignant or benign. For model development, the data were randomly divided into training (n = 483) and validation (n = 121) datasets. The model’s performance was then evaluated using a separate testing dataset (n = 202). Results: Among the patients, 755 (93.7%) had a tissue diagnosis. The overall malignancy rate was 80.4%. For all datasets, the areas under curves were as follows: training, 0.74; validation, 0.86; and test, 0.79 (accuracy range: 0.80–0.88). Sensitivity showed consistent results across all datasets (0.91, 0.95, and 0.93, respectively), whereas specificity ranged from 0.37 to 0.64. For smaller nodules (5–10 mm), the model recorded accuracy values of 0.76, 0.88, and 0.85. The sensitivity values of 0.91, 1.00, and 0.94 further highlight the robust diagnostic capability of the model. The performance of the model across the reporting and data system (RADS) categories demonstrated consistent accuracy. Conclusions: Our epigenetic biomarker panel detected non-small-cell lung cancer early in a high-risk patient group with high sensitivity and accuracy. The epigenetic biomarker model was particularly effective in identifying high-risk lung nodules, including small, part-solid, and non-solid nodules, and provided further evidence for validation. Full article

This study developed a novel domain-adaptive neural network framework, CNDAD—Net, for addressing the challenges of lung lesion detection in cross-domain medical image analysis. The proposed framework integrates domain adaptation techniques into a classical encoding–decoding structure to align feature distributions between source and target domains. Specifically, a “Generative Adversarial Network” GAN-based domain discriminator is utilized for the iterative refinement of feature representations to minimize cross-domain discrepancies and improve the generalization capability of the model. In addition, a novel Cross-Fusion Block (CFB) is proposed to implement multi-scale feature fusion that facilitates the deep integration of 2D, 3D, and domain-adapted features. The CFB achieves bidirectional feature flow across dimensions, thereby improving the model’s capability to detect diverse lesion morphologies while minimizing false positives and missed detections. For better detection, coarse-grained domain adaptation is implemented by MMD for further optimization. It integrates a module inspired by a CycleGAN for the process to generate high-resolution images on low-quality data. Using the Lung Nodule Analysis (LUNA16) dataset, the test was conducted and its experimental result was compared with that of previous standard methods such as Faster R-CNN and YOLO, yielding mAP 0.889, recall at 0.845 and the F1-score at 0.886. This work, with a novel CNDAD—Net model, lays down a solid and scalable framework for the precise detection of lung lesions, which is extremely critical for early diagnosis and treatment. The model has prospects and is capable of being extended in future to multimodal imaging data ad real-time diagnostic scenarios, and can help in further developing intelligent medical image analysis systems. Full article

The hybrid progeny (1-1) resulting from the cross between Caucasian clover and white clover initially demonstrated an inability to fix nitrogen naturally via spontaneous nodulation. However, following inoculation with specific rhizobia strains derived from the Trifolium genus, successful nodulation and nitrogen fixation were observed in the 1-1 progeny, resulting in enhanced biomass production and adaptability. To explore in greater depth the mechanisms driving nitrogen fixation in these hybrid progeny, the inoculation was carried out using the dominant rhizobia strain (No. 5), isolated from Mengnong Clover No. 1. Root samples were collected at 3, 6, and 9 days post inoculation for RNA sequencing. A total of 1755 differentially expressed unigenes were identified between the control and treatment groups. KEGG pathway analysis highlighted key pathways associated with nodule nitrogen fixation. In combination with Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Set Enrichment Analysis (GSEA), several differentially expressed genes were identified, suggesting their potential contribution to nitrogen fixation. Noteworthy among these, the gene TRINITY_DN7551_c0_g1 in the Phenylpropanoid biosynthesis pathway (MAP00940) emerged as a key candidate. This study offers valuable RNA-seq data, contributing significantly to the understanding of the molecular regulatory mechanisms underpinning nodule nitrogen fixation in legumes, thereby laying a solid foundation for future investigations into the hybrid progeny of Caucasian and white clover crosses. Full article