High Labeling Research Articles

Development of a Novel 99mTc-Labeled Folate Derivative Containing Phenyl Isonitrile to Target Folate Receptor with Reduced Renal Uptake.

The folate receptor has attracted much attention in the field of radiolabeled imaging agents due to the significant difference in its expression levels between tumor cells and most normal cells. However, the development of folate-based imaging agents has been limited by their high uptake in the kidney. In this study, to reduce the high renal uptake of radiolabeled folate-based tracers, a phenyl-isonitrile folate derivative (CNMBFA) was designed and labeled with technetium-99m. The complex obtained via the one-step kit labeling method had a high labeling yield (>95%) and high in vitro stability and hydrophilicity (log D7.4 = -1.72 ± 0.13). The results of the in vitro cell uptake and blocking studies and competitive binding experiments revealed that the [[99mTc]Tc-(CNMBFA)6]+ complex was specific for the folate receptor. Biodistribution and inhibition studies in KB tumor-bearing mice revealed moderate uptake and significant inhibition of the complex in tumors, whereas the renal uptake of [[99mTc]Tc-(CNMBFA)6]+ was significantly lower than that of previously reported tracers. Micro-SPECT/CT images further supported its ability to target the folate receptor for tumor imaging. Taken together, these results indicate that [[99mTc]Tc-(CNMBFA)6]+ is a potential tumor imaging agent that has good tumor-targeting properties with minimal radiation damage to the kidney.

Self‐supervised vessel trajectory segmentation via learning spatio‐temporal semantics

AbstractThe study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods.

ExoSloNano: Multi-Modal Nanogold Tags for identification of Macromolecules in Live Cells & Cryo-Electron Tomograms.

In situ cryo-Electron Microscopy (cryo-EM) enables the direct interrogation of structure-function relationships by resolving macromolecular structures in their native cellular environment. Tremendous progress in sample preparation, imaging and data processing over the past decade has contributed to the identification and determination of large biomolecular complexes. However, the majority of proteins are of a size that still eludes identification in cellular cryo-EM data, and most proteins exist in low copy numbers. Therefore, novel tools are needed for cryo-EM to identify the vast majority of macromolecules across multiple size scales (from microns to nanometers). Here, we introduce and validate novel nanogold probes that enable the detection of specific proteins using cryo-ET (cryo-Electron Tomography) and resin-embedded correlated light and electron microscopy (CLEM). We demonstrate that these nanogold probes can be introduced into live cells, in a manner that preserves intact molecular networks and cell viability. We use this system to identify both cytoplasmic and nuclear proteins by room temperature EM, and resolve associated structures by cryo-ET. We further employ gold particles of different sizes to enable future multiplexed labeling and structural analysis. By providing high efficiency protein labeling in live cells and molecular specificity within cryo-ET tomograms, we establish a broadly enabling tool that significantly expands the proteome available to electron microscopy.

Indoor occupancy monitoring using environmental feature fusion and semi-supervised machine learning models

Smart buildings optimize energy consumption and occupant comfort through Heating, Ventilation and Air Conditioning, and lighting management. Nevertheless, large venues require data fusion techniques to improve analysis and forecasting. This study aims to evaluate the effectiveness of using different feature fusion techniques, environmental sensors, and semi-supervised learning to estimate indoor occupancy in a 230 m2 office. Using five Internet of Things devices measuring air temperature, relative humidity, and barometric pressure, data was collected for 99 days with 6800 entries (on average) and only 14% labeled. Eight feature selection methods were evaluated along with three supervised and two semi-supervised classification methods. Results indicate that the Chi-squared-based approach for feature fusion outperformed others. Similarly, the semi-supervised Self-Training model achieved better performance than the supervised methods. This research shows that combining semi-supervised learning and data fusion allows for estimating the occupancy level in large indoor spaces with high accuracy and low labeling costs. Highlights This study pioneers in exploring semi-supervised learning and distinct feature fusion methods for estimating indoor occupancy levels in a 230 m 2 open office using only Internet of Things (IoT) environmental sensors (air temperature, relative humidity, and barometric pressure). A comprehensive comparison of statistical methods, feature selection, and dimensionality reduction techniques are conducted to determine their ability to generate robust feature fusion sets. The feature fusion selected through the Chi-squared test stood out with a high accuracy F1-score (average of 0.95) and an average accuracy of 0.99. The Self-Training model reached the best performance from semi-supervised learning, with an average F1-Score of 0.90 and an average accuracy of 0.97, based on a dataset with a large proportion of unlabelled data (16,847 entries) and only 9367 labels. For supervised learning, Random Forest achieved a high accuracy (average of 0.98) and F1-score (average of 0.93) across various feature sets.

An unusual pattern of endometrial involvement: superficial spreading squamous cell carcinoma of the cervix.

Cervical squamous cell carcinoma (SCC) is the most common type of cervical carcinoma. Usually, the cancer metastasizes through lymphatic or hematogenous dissemination. However, it is uncommon for a superficial spreading of cervical cancer to reach the endometrium, fallopian tubes, and the ovaries. In the present study, we report 15 cases of superficial spreading SCC and discuss the possible mechanism involved. We collected 15 samples diagnosed by histopathology after surgery. Immunostaining, which included P16, P63, CD138, CD34, D2-40, and Ki-67, were performed for all samples. All patients were postmenopausal or perimenopausal women. The commonest clinical presentation was vaginal bleeding in 66.67%. All patients were infected with HPV 16. The endometrium was replaced by high-grade squamous intraepithelial lesion (HSIL), which involved the endometrial gland, even squeezing into the myometrium and forming SCC. Bilateral fallopian tubes and ovaries involvement was in 1/15. A total of 10/15 (66.67%) of the women had disease of stage 1B or less. All SCCs were moderately or poorly differentiated. Immunohistochemistry revealed that the tumor cells were positive for P63 and P16, with a high Ki-67 labeling index. There was CD138 positive expression in varying degrees, which was strongly and diffusely expressed in 6/15 (40.00%). Superficial spread of cervical cancer towards the endometrium is a rare but cognizable phenomenon, and a guideline for the management of these cases has not been established. Our present findings suggest that multiple factors may interact with each other simultaneously, contributing to this rare disease.

4-plex quantitative glycoproteomics using glycan/protein-stable isotope labeling in cell culture

Alterations in glycoprotein abundance and glycan structures are closely linked to numerous diseases. The quantitative exploration of glycoproteomics is pivotal for biomarker discovery, but comprehensive analysis within biological samples remains challenging due to low abundance, complexity, and lack of universal technology. We developed a multiplex glycoproteomic approach using an LC-ESI-MS platform for direct comparison of glycoproteomic quantitation. Glycopeptides were isotopically labeled during cell culture, achieving high labeling efficiency (≥ 95 %) for both glycans and peptides. Quantitation was validated by mixing the same cell line in a 1:1:1:1 ratio, with mathematical correction applied to deconvolute the ratios. This method proved reliable and was applied to a comparative glycoproteomic study of three breast cancer cell lines (HTB22, MDA-MB-231, MDA-MB-231BR) and one brain cancer cell line (CRL-1620), quantifying glycopeptides from three replicates. The expression of glycopeptides was relatively quantified, and up/down-regulation between cell lines was investigated. This approach provided insights into glycosylation microheterogeneity, crucial for breast cancer brain metastasis research. Benefits include eliminating fluctuations from nano electrospray ionization and reducing analysis time, enabling up to 4-plex profiling in a single injection. Metabolic labeling introduced mass differences at the MS1 level, ensuring increased sensitivity and higher resolution for accurate quantitation. SignificanceAlternations in glycoprotein abundance, changes in glycosylation levels, and variations in glycan structures are closely linked to numerous diseases. The quantitative exploration of glycoproteomics has emerged as a popular area of research for biomarker discovery. However, conducting a comprehensive quantitative analysis of the glycoproteome within biological samples remains challenging due to low abundance, inherent complexities, and the absence of universal quantitative technology. Here, we developed a multiplex glycoproteomic approach using an LC-ESI-MS platform to facilitate direct comparison of glycoproteomic quantitation and enhance throughput. This approach offers benefits such as eliminating quantitative fluctuations arising from nano electrospray ionization (ESI) and reducing analysis time, enabling up to 4-plex glycoproteomic profiling in a single injection. Glycopeptides were stable isotopic labeled during cell culture procedure, attaching to monosaccharides, amino acids, or both. We achieved a high labeling efficiency (≥ 95 %) for both glycans and peptides. Quantitation validation was tested on glycopeptides by mixing the same cell line with 1:1:1:1 ratio. Due to the overlapped isotopes, a mathematical correction was applied to deconvolute the ratio of 4-plex glycopeptides. This method demonstrated quantitative reliability and was successfully applied to a comparative glycoproteomic study of three breast cancer cells (HTB22, MDA-MB-231, and MDA-MB-231BR) and one brain cancer cell (CRL-1620), identifying a total of 264 glycopeptides from three replicates. The expression of glycopeptides among these four cells was relatively quantified and up/down-regulation between two cell lines was investigated. The exploration of glycosylation microheterogeneity through glycopeptide quantification may offer valuable insights for further investigation into breast cancer brain metastasis.Conclusion: The primary advantage of our presented work lies in the multiplexing offered by combining two established labeling techniques, SILAC and IDAWG, both of which have been effectively used and widely cited in the scientific community. This combination enhances the applicability and accuracy of our method, as demonstrated by the extensive citations and successful use of these techniques independently. We believe that this multiplexing approach significantly advances the field, despite the method's current limitation to cell systems.

AOCBLS: A novel active and online learning system for ECG arrhythmia classification with less labeled samples

Electrocardiogram (ECG) is a pivotal determinant of cardiac arrhythmia. In practice, ECG data are often acquired as continuous unlabeled chunks with high labeling costs and severe class imbalances. This poses formidable challenges to existing offline learning methods. To address these issues, we propose a novel model called active online class-specific broad learning system (AOCBLS) to learn streaming imbalanced ECG and classify arrhythmias. First, a two-stage active learning strategy combining diversity and uncertainty queries was designed to select the most valuable beats from the current chunk for further labeling and learning, thereby reducing labeling costs. Subsequently, a stable training weight update rule was developed to address the dynamic imbalances in the streaming data. Finally, an incremental learning algorithm was proposed to update network weights online using selected beats, eliminating the need for retraining from scratch. Thus, the AOCBLS can effectively learn online from imbalanced data streams with less label information. Intra-patient experiments on the benchmark MIT-BIH arrhythmia database showed our model achieved an overall accuracy of 99.10% and a G-mean of 96.35% in identifying the four AAMI-recommended beat types, with only 10% additional annotation, outperforming advanced methods at lower labeling cost. Additionally, patient-specific experimental results on the benchmark database and two other databases, INCARTDB and SVDB, suggest the potential of AOCBLS for personalized ECG monitoring on portable devices.

Surface Coloring and Plasmonic Information Encryption at 50000 dpi Enabled by Direct Femtosecond Laser Printing.

Femtosecond (fs) laser pulses drive matter into a highly nonequilibrium state, allowing precise sculpturing of irradiated surface sites with sophisticated nanomorphologies. Here, we used fs-laser patterning to create diverse plasmonic morphologies on the top Au layer of the metal-insulator-metal sandwich. Mutual action of laser-driven thermomechanical effects and ultrafast solid-to-liquid transition allows control of the morphology resulting in pronounced surface reflectivity modulation, i.e., in a structural color effect. This enables template-free high-resolution color printing at a superior lateral resolution up to 50000 dots per inch and facile tunability of the color tone and saturation. Moreover, precise control over the orientation of the printed nanostructures within subwavelength lattices allows modulation of their local plasmonic response encrypting the optical information within the colorful images. The hidden information can be unveiled using a facile cross-polarized optical visualization scheme, rendering the proposed method with extra modalities combining high resolution information encryption, coloring, and security labeling.

Successful outcome achieved with adjuvant chemotherapy with irinotecan plus cisplatin in rectal neuroendocrine carcinoma: a case report

BackgroundRectal neuroendocrine carcinomas (NECs) are rare and associated with poorer prognoses compared to conventional adenocarcinomas. The efficacy of adjuvant chemotherapy for resectable rectal NECs remains uncertain. Herein, we present a case of rectal NEC successfully treated with postoperative chemotherapy using irinotecan plus cisplatin.Case presentationA 48-year-old woman with a history of endometrial cancer presented with an intramural rectal tumour detected on follow-up imaging. Colonoscopy revealed a 30 mm submucosal tumour, and laparoscopic low anterior resection was performed. Histopathological examination showed poorly differentiated atypical cells with solid growth patterns. Metastasis from the uterine cancer was ruled out due to histological differences between the primary uterine tumour and the rectal lesion, as well as the absence of hormone receptor immunohistochemical expression. Further immunohistochemical analysis revealed diffuse CD56 positivity, a high mitotic rate (> 20/10 high power fields) and a Ki-67 labelling index exceeding 70%. Based on these findings, a diagnosis of rectal NEC, T3N0M0, Stage IIB (UICC 8th edition), was established. Given the aggressive nature of the tumour evidenced by a high Ki-67 labelling index, adjuvant chemotherapy comprising six cycles of irinotecan plus cisplatin was administered to mitigate the risk of recurrence. At the 3-year follow-up, the patient was free of disease recurrence.ConclusionThis case highlights the importance of multidisciplinary surgical interventions followed by adjuvant chemotherapy in managing rectal NECs.

Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins invivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

A high Ki-67 labeling index and high thyroglobulin doubling rate are significant predictors of excision-site recurrence of papillary thyroid carcinoma following airway resection for locally curative surgery.

Papillary thyroid carcinoma (PTC) occasionally invades the trachea and requires airway resection. Tracheal excision site recurrence (ESR) is a serious problem. We investigated predictors of ESR in patients with PTC who underwent airway resection for locally curative surgery. We enrolled 149 patients with PTC who underwent airway resection (median age at the initial surgery: 67years), including partial-thickness resection (n=73) or full-thickness resection (n=76), for grossly curative surgery. The median postoperative follow-up period was 93months. To date, 11 patients (6.7%) underwent ESR: 6 underwent full-thickness resection and 5 underwent partial-thickness resection. The time to ESR ranged from 14 to 113months (median: 57months) after the initial surgery. None of the 11 ESR patients underwent adjuvant external beam radiotherapy (EBRT) and none of the 4 airway resection patients who underwent EBRT developed ESR. The 5- and 10-year ESR rates were 4.3% and 11.3%, respectively. In the multivariate analysis (forward-backward stepwise selection method), a Ki-67 labeling index (LI) ≥5% (p=0.048) and the thyroglobulin doubling rate (Tg-DR) >0.33/year (p=0.009) (for Tg-antibody negative cases) were independent predictors of ESR. Nine of the 11 patients underwent ESR resection and only one developed a second recurrence. A high Ki-67 LI was a static predictor, and high Tg-DR was a dynamic predictor, of ESR in patients with PTC following airway resection. In such patients, careful postoperative monitoring for ESR is necessary and adjuvant therapies, such as EBRT, may be considered.

Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.

Engineering Vehicle Detection Based on Improved YOLOv6

Engineering vehicles play a vital role in supporting construction projects. However, due to their substantial size, heavy tonnage, and significant blind spots while in motion, they present a potential threat to road maintenance, pedestrian safety, and the well-being of other vehicles. Hence, monitoring engineering vehicles holds considerable importance. This paper introduces an engineering vehicle detection model based on improved YOLOv6. First, a Swin Transformer is employed for feature extraction, capturing comprehensive image features to improve the detection capability of incomplete objects. Subsequently, the SimMIM self-supervised training paradigm is implemented to address challenges related to insufficient data and high labeling costs. Experimental results demonstrate the model’s superior performance, with a mAP50:95 value of 88.5% and mAP50 value of 95.9% on the dataset of four types of engineering vehicles, surpassing existing mainstream models and proving its effectiveness in engineering vehicle detection.

A sound event detection support system for smart home based on “two-to-one” teacher–student learning

Sound event detection (SED) is a core technology in smart home projects that rely on detected sound events to trigger specific actions. SED systems face two major challenges: high labeling costs and complex acoustic environments. To reduce labeling costs, some semi-supervised systems extract both global and local features for classification. However, these methods treat global and local features equally, not accounting for their varying importance when recognizing different types of sound events. Furthermore, to address complex acoustic environments, some studies use multitask learning frameworks to introduce SED-related tasks as auxiliaries to improve detection performance. However, these methods fail to align tasks within the framework, leading to conflicting outputs that may limit system performance. To address these issues, in this paper we propose a “two-to-one” teacher-student learning based semi-supervised SED system. This system employs a gating mechanism to selectively enhance global and local features, improving adaptability to different types of sound events, and incorporates a cross-task alignment module to interact SED with related tasks, reducing the risk of performance degradation caused by conflicting outputs. Experimental results on two datasets demonstrate that our system achieves the best performance in all metrics, with EB-F1 scores of 48.1 % and 64.7 %, representing improvements of 15.3 % and 10.6 % over the baseline ConformerSED system, respectively. Our work offers an effective SED solution for smart home projects by providing a semi-supervised SED system that performs well while reducing labeling costs.

Prognostic factors of papillary and follicular carcinomas based on pre-, intra-, and post-operative findings.

Papillary and follicular thyroid carcinomas (PTC and FTC) are prominent malignancies that originate from thyroid follicular cells. PTC is usually diagnosed via preoperative cytology, and large tumor size, clinical node metastasis, and distant metastasis constitute preoperative prognostic factors. Gross extrathyroidal and extranodal tumor extensions have a significant prognostic impact, are evaluated intraoperatively, and are useful for determining the extent of surgery. Aggressive variants, such as tall cell and hobnail variants, a high Ki-67 labeling index (LI), and somatic gene mutations are prognostic factors in postoperative pathological and molecular examinations. In contrast, FTC is generally diagnosed based on postoperative pathology. Large tumor size and M factors have prognostic value; however, the findings of pathological examinations are very important. FTCs are classified as minimally invasive, encapsulated angioinvasive, and widely invasive FTCs. Widely invasive FTC with vascular invasion (VI) and encapsulated angioinvasive FTCs with extensive VI have a poor prognosis, whereas widely invasive FTC without VI has an excellent prognosis, which is similar to that of minimally invasive FTC. This indicates that VI is a considerably more important prognostic marker than capsular invasion. For postoperative follow-up, dynamic markers such as the thyroglobulin-doubling rate (DR), metastatic tumor volume-DR, and change in the neutrophil-to-lymphocyte ratio are important and are useful for evaluating the effectiveness of treatments, such as radioactive iodine therapy and molecular targeted therapy, for recurrent lesions. For clinicians, it is important to accurately evaluate prognostic markers of PTC and FTC in the pre-, intra-operative, and post-operative phases.

Real-world Comparison of P53 Immunohistochemistry and TP53 Mutation Analysis Using Next-generation Sequencing.

TP53 mutation in breast cancer (BC) is associated with chemoresistance, endocrine therapy resistance, and late recurrence, resulting in poor prognosis. Nuclear accumulation of p53 in immunohistochemistry (IHC) is a surrogate marker of TP53 mutation. This study analyzed the frequency, type, and distribution of TP53 mutations in BCs and assessed the efficacy of p53 IHC as a surrogate marker of TP53 mutation. We collected data from 112 BC cases, including the results of p53 IHC and next-generation sequencing (NGS). Over-expression of p53 IHC was observed in 36 patients (32.1%), complete absence in 19 patients (17.0%), aberrant cytoplasmic staining in 1 patient (0.9%), and wild-type in 56 (50.0%) patients. The concordance rate between TP53 mutation and p53 IHC was 88.4% in all BCs, 89.9% in luminal BCs, and 86.0% in triple-negative BCs (TNBC). TNBC, abnormal p53 IHC pattern, p53 IHC over-expression, neoadjuvant chemotherapy (NAC) history, TP53 mutation, and high pre-treatment ki-67 labeling index (≥50%) were significantly associated with worse distant metastasis-free survival (DMFS) and overall survival (OS) (p<0.05). Pre-NAC clinical stage III was associated with worse DMFS but not OS. Multivariate analysis showed that NAC history, TNBC, and p53 IHC over-expression were independent predictors of worse DMFS. An abnormal p53 IHC pattern and NAC history were independent predictors of worse OS. P53 IHC is a valid surrogate marker of TP53 mutation in BC. Accumulation of abnormal p53 alone, regardless of TP53 mutation, was associated with worse DMFS and can be used as an easily accessible biomarker to predict chemoresistance.

Long-Term Survival Outcomes and Risk Factors for Axillary and Locoregional Recurrence in Japanese Patients with Sentinel Node-Positive Breast Cancer Treated in Accordance with the ACOSOG Z0011 Strategy.

In 2018, we reported the results of a study to assess the feasibility of applying the ACOSOG Z0011 criteria to Japanese patients with early-stage breast cancer (median follow-up, 3 years). Their results over the longer term can now be presented. Risk factors for axillary and locoregional recurrence in Z0011-eligible patients are unknown. Long-term survival outcomes were investigated by analyzing data from patients enrolled in the feasibility study. Data from the feasibility study patients, and from patients eligible for the Z0011 strategy after its introduction into clinical practice, were subjected to multivariate logistic regression analysis to identify risk factors for axillary and locoregional recurrence. Regarding long-term outcomes for the feasibility study patients (n = 189), distant disease-free survival rates at 5 and 7 years were 90.4 ± 2.1% and 85.9 ± 2.6%, respectively, and overall survival rates at 5 and 7 years were 97.3 ± 1.2% and 95.3 ± 1.7%, respectively. Analysis of data from these patients plus the 93 who received Z0011 in clinical practice (total, n = 282) identified the following independent risk factors for axillary recurrence: absence of high axillary tangential irradiation (OR, 5.87 [95% CI, 1.09-31.35], p = 0.04) and number of positive sentinel lymph nodes (OR, 4.65 [95% CI, 1.11-19.48], p = 0.04). Only high Ki67 labeling index (OR, 5.92 [95% CI, 1.31-26.70], p = 0.02) was identified as an independent risk factor for locoregional recurrence. Long-term survival outcome results of the feasibility study show that the Z0011 strategy can be used to treat Japanese patients with early-stage breast cancer. Our findings regarding risk factors suggest that high axillary tangent irradiation is necessary for the prevention of axillary recurrence and that irradiation, including of the regional lymph nodes, should be considered, especially in patients with high Ki67 index values.

Diffusion–based virtual MR elastography for predicting recurrence of solitary hepatocellular carcinoma after hepatectomy

BackgroundTo explore the capability of diffusion-based virtual MR elastography (vMRE) in the preoperative prediction of recurrence in hepatocellular carcinoma (HCC) and to investigate the underlying relevant histopathological characteristics.MethodsBetween August 2015 and December 2016, patients underwent preoperative MRI examination with a dedicated DWI sequence (b-values: 200,1500 s/mm2) were recruited. The ADC values and diffusion-based virtual shear modulus (μdiff) of HCCs were calculated and MR morphological features were also analyzed. The Cox proportional hazards model was used to identify the risk factors associated with tumor recurrence. A preoperative radiologic model and postoperative model including pathological features were built to predict tumor recurrence after hepatectomy.ResultsA total of 87 patients with solitary surgically confirmed HCCs were included in this study. Thirty-five patients (40.2%) were found to have tumor recurrence after hepatectomy. The preoperative model included higher μdiff and corona enhancement, while the postoperative model included higher μdiff, microvascular invasion, and histologic tumor grade. These factors were identified as significant prognostic factors for recurrence-free survival (RFS) (all p < 0.05). The HCC patients with μdiff values > 2.325 kPa showed poorer 5-year RFS after hepatectomy than patients with μdiff values ≤ 2.325 kPa (p < 0.001). Moreover, the higher μdiff values was correlated with the expression of CK19 (3.95 ± 2.37 vs. 3.15 ± 1.77, p = 0.017) and high Ki-67 labeling index (4.22 ± 1.63 vs. 2.72 ± 2.12, p = 0.001).ConclusionsThe μdiff values related to the expression of CK19 and Ki-67 labeling index potentially predict RFS after hepatectomy in HCC patients.

Development of a SnO2-based 44Ti/44Sc generator for medical applications

Towards application of 44Sc for diagnostic nuclear medicine, a 44Ti/44Sc generator based on an inorganic resin has been evaluated. Unlike other radionuclide generators used for medical applications, the long-term retention of the parent 44Ti is vital due to its long half life. Herein, tin dioxide (SnO2), a robust inorganic-based resin, has been synthesized and used as the stationary phase for a 44Ti/44Sc generator. The sorption behavior of 44Ti/44Sc was tested on SnO2 with varying acids, concentrations, and times. Preliminary batch study results showed >88 % 44Ti retention to the resin at lower acid concentrations (0.05 M HNO3 and 0.05 M HCl). A pilot generator was evaluated for a year, demonstrating 85.3 ± 2.8 % 44Sc elution yields and 0.71 ± 0.14 % 44Ti breakthrough in 5 M HNO3. Based on capacity studies, a 7.4 MBq (200 µCi) upscaled generator system was constructed for further evaluation of the SnO2 resin stability and the efficacy of the eluted 44Sc for radiolabeling. 44Sc could be regularly eluted from this generator in 5 M HNO3 with an overall average radiochemical yield 84.7 ± 9.5 %. Post-elution processing of the 44Sc with DGA-normal resin removed all 44Ti present and allowed for high 44Sc-DOTA labeling yields of 94.2 ± 0.5 %. Overall, SnO2 has been shown to be a viable material for a 44Ti/44Sc generator.

Surface-Functionalized Halo-Tag Gold Nanoprobes for Live-Cell Long-Term Super-Resolution Imaging of Endoplasmic Reticulum Dynamics.

Super-resolution fluorescence microscopy has emerged as a powerful tool for studying endoplasmic reticulum (ER) dynamics in living cells. However, the lack of high-brightness, high-photostability, and stable labeling probes makes long-term super-resolution imaging of the ER still challenging. Herein, we reported a surface-functionalized Halo-tag gold nanofluorescent probe (GNP-Atto565-fR8-CA) that exhibits excellent brightness, photostability, and biocompatibility. GNP-Atto565-fR8-CA can simultaneously load multiple Atto565 dye molecules, significantly improving its brightness. Modifying the cell-penetrating peptide fR8 enables GNP-Atto565-fR8-CA to be efficiently delivered into the cytoplasm, overcoming the challenge of their easy entrapment in vesicles. Fluorescent labeling of ER proteins via Halo tags enables high specificity and stable labeling of GNP-Atto565-fR8-CA to the ER. The SIM super-resolution imaging results showed that GNP-Atto565-fR8-CA can track and observe the long-term dynamic process of the ER, and can also be used for long-term super-resolution imaging of the dynamic interactions between the ER and other organelles. This work offers a practical tool to study live-cell ER ultrastructure and dynamics.