Electrical and fluorescence in situ monitoring of tumor microenvironment-based pH-responsive polymer dot coated surface.

A boronate-ester structure forming a pH-responsive polymer dot (Plu-PD) coated biosensor between carbonized-sp2 rich dopamine-alginate [PD(Alg)] and boronic acid-grafted Pluronic (BA-Pluronic) was developed for the electrochemical and fluorescence detection of cancer cells. The reduced fluorescence (FL) resulting from fluorescence resonance energy transfer (FRET) mediated by π-π interactions within Plu-PD was successfully reinvigorated through the specific cleavage of the boronate-ester bond, triggered by the acidic conditions prevailing in the cancer microenvironment. The anomalous variations in extracellular pH levels observed in cancer (pH ∼6.8), as opposed to the normal cellular pH range of approximately 7.4, serve as robust indicators for discerning cancer cells from their healthy counterparts. Moreover, the Plu-PD coated surface demonstrated remarkable adaptability in modulating its surface structure, concurrently exhibiting tunable electroconductivity under reduced pH conditions, thereby imparting selective responsiveness to cancer cells. The pH-modulated conductivity change was validated by a reduction in resistance from 211 ± 9.7 kΩ at pH 7.4 to 73.9 ± 9.4 kΩ and 61.5 ± 11.5 kΩ at pH 6.8 and 6.0, respectively. The controllable electrochemical characteristics were corroborated through in vitro treatment of cancer cells (HeLa, B16F10, and SNU-C2A) via LED experiments and wireless output analysis. In contrast, identical treatments yielded a limited response in normal cell line (CHO-K1). Notably, the Plu-PD coated surface can be seamlessly integrated with a wireless system to facilitate real-time monitoring of the sensing performance in the presence of cancer and normal cells, enabling rapid and accurate cancer diagnosis using a smartphone.

Unusual Imaging Findings of Epithelioid Hemangioma: Case Report of Single Intramedullary Sclerotic Bone Lesion.

Epithelioid hemangioma (EH) is an uncommon, benign vascular tumor of mesenchymal origin. It mainly presents as a tumor with a lytic appearance and septations. However, no case reports have documented the predominantly sclerotic appearance of EH. Here, we present the imaging findings, including X-ray, CT, MRI, and histopathological findings of a 24-year-old female with EH.

Radiographic Analysis of Scoliosis Using Convolutional Neural Network in Clinical Practice.

Purpose: To assess the reliability and accuracy of an automated Cobb angle measurement (ACAM) using a convolutional neural network (CNN) for scoliosis evaluation and to compare measurement times. Materials and Methods: ACAM was applied to spine radiographs in 411 patients suspected of scoliosis. Observer 1 (consensus of two musculoskeletal radiologists) and observer 2 (a radiology resident) measured Cobb angle (CA). CA measurements were categorized using observer 1's measurements as the reference standard. Inter-observer reliability and correlation were assessed using intraclass correlation coefficient (ICC) and Spearman's rank correlation coefficient, respectively. Accuracy and measurement time of ACAM and observers were evaluated. Results: ACAM demonstrated excellent reliability and very high correlation with observer 1 (ICC = 0.976, Spearman's rank correlation = 0.948), with a mean CA difference of 1.1. Overall accuracy was high (88.2%), particularly in mild (92.2%) and moderate (96%) scoliosis. Accuracy was lower in spinal asymmetry (77.1%) and higher in severe scoliosis (95%), although the CA was lower compared to the observers. ACAM significantly reduced measurement time by nearly half compared to the observers (p < 0.001). Conclusion: ACAM using CNN enhances CA measurement for assessing mild or moderate scoliosis, despite limitations in spinal asymmetry or severe scoliosis. Nonetheless, it substantially decreases measurement time.

Ruptured venous aneurysm associated with a dural arteriovenous fistula: Two case reports.

BACKGROUND: In general, venous aneurysm associated with dural arteriovenous fistula (dAVF) is considered to be developed under long standing venous hypertension and manifested as venous ectasia of draining vein itself. However, discrete saccular shaped venous aneurysm without angiographic evidence of venous hypertension arising from the draining vein, like cerebral arterial aneurysm, is quite rare and its pathomechanism remains unclear in patients with dAVF. CASE SUMMARY: In this report, we present two cases of ruptured saccular venous aneurysms associated with dAVF without venous hypertension or venous ectasia. In both cases, significant curve or stenosis is observed in draining vein, which is located in just distal portion of the venous aneurysms. These aneurysms were successfully treated with a transarterial embolization. Underlying mechanism of venous aneurysms in these cases is discussed. CONCLUSION: Although there is little doubt that hemodynamic stress has a critical role in the development of venous aneurysms in patients with dAVF, preceding venous hypertension or venous ectasia is not necessary for development and enlargement of venous aneurysms. Considering the significant risk of rupture, a careful review of draining vein features including tortuosity or stenosis is needed, especially in venous aneurysms without evidence of venous hypertension.

Acute Transient Cataract Associated With 5-Fluorouracil.

This case report discusses a diagnosis of transient cataract in the setting of 5-fluorouracil injection to treat a fibrotic trabeculectomy bleb.

Novel FRET-Based Biosensors for Real-Time Monitoring of Estrogen Receptor Dimerization and Translocation Dynamics in Living Cells.

Estrogen receptors (ERs), comprising ER α and ER ß, are crucial for regulating cell growth and differentiation via homo- and hetero-dimer formation. However, accurately detecting ER dimerization with precise spatiotemporal resolution remains a significant challenge. In this study, fluorescence resonance energy transfer-based biosensors to monitor ER dynamics in real-time, are developed and optimized. This approach involves comprehensive structural analysis, linker comparison, and the selection of optimal fluorescent protein pairs, resulting in three distinct biosensors capable of detecting all ER homo- and hetero-dimerizations within the nucleus. These biosensors are utilized to reveal interactions between ER α/ß and calmodulin during dimer formation. Furthermore, by leveraging the ligand-binding domain (LBD) of ER ß, ER ßß LBD biosensor is designed for real-time analysis of ER ßß homodimerization in the cytoplasm, enhancing the ability to screen ER dimerization-related drugs. Additionally, we developed a novel ER ßß translocation biosensor, which enables real-time observation of ER ßß translocation to the nucleus-a capability previously unavailable, is developed. This spatiotemporal analysis demonstrates the relevance of ER translocation in response to drug binding efficacy and extracellular matrix changes. Our biosensors offertransformative tools for studying ER dynamics, providing valuable insights for drug screening and the investigation of ER-related cellular processes.

The Influence of Solvent Choice on the Extraction of Bioactive Compounds from Asteraceae: A Comparative Review.

While the potential of Asteraceae plants as herbal remedies has been globally recognized, their widespread application in the food, cosmetic, and pharmaceutical industries requires a deeper understanding of how extraction methods influence bioactive compound yields and functionalities. Previous research has primarily focused on the physiological activities or chemical compositions of individual Asteraceae species, often overlooking the critical role of solvent selection in optimizing extraction. Additionally, the remarkable physiological activities observed in these plants have spurred a growing number of clinical trials, aiming to validate their efficacy and safety for potential therapeutic and commercial applications. This work aims to bridge these knowledge gaps by providing an integrated analysis of extraction techniques, the diverse range of bioactive compounds present in Asteraceae, and the influence of solvent choice on isolating these valuable substances. By elucidating the interplay between extraction methods, solvent properties, and bioactivity, we underscore the promising potential of Asteraceae plants and highlight the importance of continued research, including clinical trials, to fully unlock their potential in the food, cosmetic, and pharmaceutical sectors.

A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (IHCC) is highly aggressive primary hepatic malignancy with an increasing incidence. OBJECTIVE: This study aimed to develop machine learning-based radiomic clustering using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for predicting recurrence-free survival (RFS) and overall survival (OS) in IHCC. METHODS: We retrospectively reviewed pretreatment F-18 FDG PET/CT scans of 60 IHCC patients who underwent surgery without neoadjuvant treatment between January 2008 and July 2020. Radiomic features such as first order, shape, and gray level were extracted from the scans of 52 patients and analyzed using unsupervised hierarchical clustering. RESULTS: Of the 60 patients, 36 experienced recurrence and 31 died during follow-up. Eight patients with a negative FDG uptake were classified as Group 0. The unsupervised hierarchical clustering analysis divided the total cohort into three clusters (Group 1: n = 27; Group 2: n = 23; Group 3: n = 2). The Kaplan-Meier curves showed significant differences in RFS and OS among the clusters (p < 0.0001). Multivariate analyses showed that the PET radiomics grouping was an independent prognostic factor for RFS (hazard ratio (HR) = 3.03, p = 0.001) and OS (HR = 2.39, p = 0.030). Oxidative phosphorylation was significantly activated in Group 1, and the KRAS, P53, and WNT ß-catenin pathways were enriched in Group 2. CONCLUSIONS: This study demonstrated that machine learning-based PET radiomics clustering can preoperatively predict prognosis and provide valuable information complementing the genomic profiling of IHCC.

Latent factors affecting safer injection practices that can reduce infections and how education can improve them.

BACKGROUND: The incidence of healthcare-associated infections, particularly injection-related infections, can increase patient comorbidities even in countries with adequate medical resources. Although there are clear guidelines for injection practices to prevent infections, their application in clinical settings is insufficient. Therefore, the objective of this study was to identify factors affecting injection practices associated with reduced infections by conducting surveys targeting practicing healthcare providers involved in administering injections at each healthcare organization and performing data analysis. METHODS: We administered a survey to healthcare providers responsible for injection practices at each healthcare organization that included items related to infection-safe injection practice guidelines. All survey questions were reviewed by an expert panel of infectious disease and infection control nurses. Survey contents were subjected to exploratory factor analysis (EFA), confirmatory factor analysis, and multivariable robust regression tests to determine the impact of each factor and their correlations. RESULTS: A total of 842 questionnaires were analyzed. Each questionnaire was classified into four factors: reuse and contamination, compliance with aseptic technique, exchange of infusion set, and use of multidose vials. Nurses with higher careers showed more compliance. Education within one year and awareness of each item of the questionnaire had positive associations with proper injection practice. CONCLUSIONS: Education is thought to be the most important factor in good injection practices that could reduce infections. Relevant knowledge through timely training is expected to have a positive impact on performance and compliance related to safe injections.

Genome-wide methylation profiling reveals extracellular vesicle DNA as an ex vivo surrogate of cancer cell-derived DNA.

Extracellular vesicle-derived DNA (evDNA) encapsulates the complete genome and mutational status of cells; however, whether cancer cell-derived evDNA mirrors the epigenetic features of parental genomic DNA remains uncertain. This study aimed to assess and compare the DNA methylation patterns of evDNA from cancer cell lines and primary cancer tissues with those of the nuclear genomic DNA. We isolated evDNA secreted by two cancer cell lines (HCT116 and MDA-MB-231) from various subcellular compartments, including the nucleus and cytoplasm. Additionally, we obtained evDNA and nuclear DNA (nDNA) from the primary cancer tissues of colon cancer patients. We conducted a comprehensive genome-wide DNA methylation analysis using the Infinium Methylation EPIC BeadChip, examining > 850,000 CpG sites. Remarkable similarities were observed between evDNA and nDNA methylation patterns in cancer cell lines and patients. This concordance extended to clinical cancer tissue samples, showcasing the potential utility of evDNA methylation patterns in deducing cellular origin within heterogeneous populations through methylation-based deconvolution. The observed concordance underscores the potential of evDNA as a noninvasive surrogate marker for discerning tissue origin, particularly in cancer tissues, offering a promising future for cancer diagnostics. This finding enhances our understanding of cellular origins and would help develop innovative diagnostic and therapeutic strategies for cancer.

Accelerating Additive-Assisted Defect Passivation via the Structural Isomer Effect for Highly Efficient and Stable Perovskite Solar Cells.

Defects in hybrid perovskites have hindered the development of highly efficient and stable hybrid perovskite solar cells (PSCs). Therefore, researchers have used additives to passivate defects in hybrid perovskites; however, detailed studies on multifunctional group additives with structural isomers are limited. In this letter, we report the improved defect passivation ability of additives through the structural isomer effect and enhanced photovoltaic performance using this effect. l-Alanine methyl ester hydrochloride (l-AMECl) and its structural isomer, ß-AMECl, were used to understand the influence of structural variations in functional groups. The structural isomer ß-AMECl effectively reduced the trap density in the hybrid perovskite, thereby enhancing the photovoltaic parameters. Consequently, we achieved a power conversion efficiency of 24.25% with ß-AMECl, which is the best result among PSCs using additives. Additionally, the PSCs with ß-AMECl maintained an initial efficiency of 94% over 2500 h at 25 °C and 25% relative humidity, showing improved long-term stability.

Development of a digital multidomain lifestyle intervention for mild cognitive impairment: A pilot study on the feasibility and efficacy of cognitive training.

Background: The heterogeneous etiology of mild cognitive impairment (MCI) presents significant challenges in monitoring its progression and impeding its advancement toward dementia. Digital multidomain lifestyle interventions have shown promise as potential solutions for their ability to treat MCI. Objective: This study is the first phase in a series of evaluations aimed at assessing various components of Silvia-Rx, which was originally designed as a digital multidomain lifestyle intervention. Specifically, this study focused on a 60-session core cognitive training program to evaluate its feasibility and efficacy in addressing cognitive decline in individuals with MCI. Methods: Individuals aged 60 to 80 years diagnosed with MCI were enrolled to participate in a 60-session tablet-based cognitive regimen of Silvia-Rx. Feasibility was assessed through adherence and retention rates, while the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) scale was utilized to measure cognitive function as the primary indicator of efficacy. Results: The mean age of the participants was 71.11 years, and 13 (68.42%) were women. Only one withdrawal occurred, resulting in a 95% retention rate (19 participants) post-intervention. The completion rate was excellent at 100%, indicating that the 60-session core cognitive program in Silvia-Rx was well tolerated by older participants with MCI. Regarding efficacy, there was a statistically significant improvement in cognitive function among MCI participants after the intervention, as evidenced by changes in total ADAS-cog scores. Conclusions: Results demonstrated excellent adherence throughout the program and significant cognitive improvements after the intervention. This pilot study indicates that Silvia-Rx's digital cognitive program is feasible for people diagnosed with MCI, suggests potential for improving cognitive function, though further research with large sample size is needed to confirm these results.

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

Do extra-pulmonary triggers or autonomic neural activity affect rhythm control by anti-arrhythmic drugs in patients with post-ablation atrial fibrillation recurrence?

Background: The role of anti-arrhythmic drugs (AADs) in recurrent atrial fibrillation (AF) after catheter ablation (CA) is not fully understood. The aim of this study was to explore the effects of AADs in patients who recurred after AFCA depending on extra-pulmonary vein triggers (ExPVTs) and post-ablation heart rate variability (HRV) parameters. Methods: We analyzed 2,036 patients who underwent de-novo AFCA and 486 patients with post-AFCA recurrence who underwent rhythm control with AADs. We investigated the effects of ExPVTs and 3rd month HRV parameters on the post-AFCA recurrence and subsequent AAD responsiveness. Results: A total of 486 out of 2,036 patients developed clinical recurrence of AF and subsequently underwent rhythm control with AADs. 486 out of 310 patients (63.8%) remained free of second recurrence at 1-year. Post-AFCA recurrence was significantly higher in patients with ExPVT [Log-rank p < 0.001, HR 1.45 (1.16-1.83), p = 0.001] or higher 3rd month root mean square of the differences between successive RR intervals (rMSSD) [Log-rank p < 0.001, HR 1.36 (1.11-1.65), p = 0.003] than their counterparts. Patients with ExPVTs during the de-novo procedure had significantly higher 3rd month rMSSD (15.0 [11.0-23.0] vs. 17.0 [11.0-28.0], p = 0.022). Patients with high 3rd month rMSSD had higher rate of ExPVTs during the repeat procedure (n = 160, 41.0% vs. 22.2%, p = 0.019). Among patients with recurrent AF after AFCA, post-AAD recurrence did not differ depending on the presence of ExPVT [Log-rank p = 0.455, HR 1.12 (0.78-1.69), p = 0.436] or 3rd month rMSSD [Log-rank p = 0.457, HR 1.16 (0.87-1.55), p = 0.300]. Post-AAD recurrence did not differ between class IC and III AADs (p for interaction = 0.311). Conclusions: ExPVT and post-procedural high rMSSD are independent risk factors for post-AFCA recurrence but not for AAD response in patients with recurrent AF. AADs may suppress ExPVTs and modulate cardiac autonomic activity after post-AFCA recurrence.

The toxic effects of exposure to fibrous and fragmented microplastic in juvenile rockfish based on two omics approach.

Although the hazards of environmental microplastics (MPs) are well known, it is unclear which of their characteristics have the greatest effects on organism. We investigated the toxic effects of oral administration according to physical properties, including the shape of fragmented polyethylene terephthalate (PET) (FrPET) and fibrous PET (FiPET) MPs. After 72 h of exposure, apoptosis and phagocytic activity varied significantly among juvenile rockfish (Sebastes schlegeli) exposed to both FrPET and FiPET. The levels of immune-related genes and hepatic metabolic activity also increased after exposure to both shapes of MPs, but the variation in responses was greater in fish exposed to FiPET compared with those exposed to FrPET. The transcriptomic and metabolomics analysis results indicated that the maintenance and homeostasis of immune system was affected by oral exposure to FrPET and FiPET. The amino acid metabolic processes were identified in rockfish exposed to FrPET, but the notch signaling pathway were evident in the FiPET exposure group. Metabolomics analysis revealed that oral ingestion of MP fibers led to a stronger inflammatory response and greater oxidative stress in juvenile rockfish. These results can be used to understand environmentally dominant MP toxic effects such as type, size, shapes, as well as to prioritize ecotoxicological management.

Accelerated muscle mass estimation from CT images through transfer learning.

BACKGROUND: The cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a certain device often does not work with images from a different device. METHODS: In this study, we propose an efficient learning strategy for deep learning models in medical image segmentation. We aim to overcome the difficulties of segmentation in CT images by training a VNet segmentation model which enables rapid labeling of organs in CT images with the model obtained by transfer learning using a small number of manually labeled images, called SEED images. We established a process for generating SEED images and conducting transfer learning a model. We evaluate the performance of various segmentation models such as vanilla UNet, UNETR, Swin-UNETR and VNet. Furthermore, assuming a scenario that a model is repeatedly trained with CT images collected from multiple devices, in which is catastrophic forgetting often occurs, we examine if the performance of our model degrades. RESULTS: We show that transfer learning can train a model that does a good job of segmenting muscles with a small number of images. In addition, it was confirmed that VNet shows better performance when comparing the performance of existing semi-automated segmentation tools and other deep learning networks to muscle and liver segmentation tasks. Additionally, we confirmed that VNet is the most robust model to deal with catastrophic forgetting problems. CONCLUSION: In the 2D CT image segmentation task, we confirmed that the CNN-based network shows better performance than the existing semi-automatic segmentation tool or latest transformer-based networks.

3D Scaffold-Based Culture System Enhances Preclinical Evaluation of Natural Killer Cell Therapy in A549 Lung Cancer Cells.

Cell-based immunotherapies have emerged as promising cancer treatment modalities, demonstrating remarkable clinical efficacy. As interest in applying immune cell-based therapies to solid tumors has gained momentum, experimental models that enable long-term monitoring and mimic clinical administration are increasingly necessary. This study explores the potential of scaffold-based cell culture technologies, specifically three-dimensional (3D) extracellular matrix (ECM)-like frameworks, as promising solutions. These frameworks facilitate unhindered immune cell growth and enable continuous cancer cell culture. The three-dimensional (3D) cell culture model was developed using tailored scaffolds for natural killer (NK) cell culture. Within this framework, A549 lung cancer cells were cocultured with NK cells, allowing real-time monitoring for up to 28 days. The expression of critical markers associated with anticancer drug resistance and epithelial-mesenchymal transition (EMT) was evaluated in cancer cells within this 3D culture context. Compared to conventional 2D monolayer cultures, this 3D scaffold-based culture revealed that solid tumor cells, specifically A549 cells, exhibited heightened resistance to anticancer drugs. Additionally, the 3D culture environment upregulated the expression of EMT markers namely vimentin, N-cadherin, and fibronectin, while NK and zEGFR-CAR-NK cells displayed anticancer effects. In the two-dimensional (2D) coculture, only zEGFR-CAR-NK cells exhibited such effects in the 3D coculture system, highlighting an intriguing inconsistency with the 2D culture model, further confirmed by in vivo experiments. This in vitro 3D cell culture model reliably predicts outcomes in NK immunotherapy experiments. Thus, it represents a valuable tool for investigating drug resistance mechanisms and assessing the efficacy of immune cell-based therapies. By bridging the gap between in vitro and in vivo investigations, this model effectively translates potential treatments into animal models and facilitates rigorous preclinical evaluations.

Inability to Remove Locking Screws from the Femoral Neck System due to Stripping of the Screwdriver within the Locking Screw Head.

OBJECTIVES: The purposes of this study were to investigate the frequency of screwdriver stripping in the head of the locking screw that attaches to the side plate to the femur shaft among the patients who underwent implant removal after Femoral Neck System (FNS) for femoral neck fracture, to determine the risk factors for locking head screw stripping in FNS treatment of femoral neck fracture, and to suggest a surgical tip that removes FNS, which is difficult to remove due to screw stripping. METHODS: Design: Retrospective cohort study. SETTING: Eight Urban tertiary referral academic hospitals. PATIENT SELECTION CRITERIA: Included were patients with OTA/AO 31-B1, 31-B2, and 31-B3 femoral neck fractures who underwent surgical fixation with FNS from Nov 2019 to Feb 2023. OUTCOME MEASURES AND COMPARISONS: The frequency of locking head screw stripping of FNS during the implant removal was evaluated. RESULTS: Among the 47 patients (18 (38%) men and 29 (62%) women) who met the inclusion criteria with average age of 59.2 years (range, 28 to 94 years), 13 (27.7%) experienced screwdriver stripping in the head of the distal locking screw during FNS removal surgery. A higher BMI showed a borderline significant association with the stripping in the adjusted model (OR = 1.233; 95% CI: 0.988-1.539; p = 0.064). No other variables showed significant association with the stripped locking head screw (p>0.05). CONCLUSIONS: Stripping of the screwdriver within the head of the distal locking screw occurred in over one quarter of cases. While a higher BMI demonstrated a borderline significant association, none of the other variables examined showed a statistically significant relationship with the stripped locking head screw. LEVELS OF EVIDENCE: Level III (retrospective cohort study).