Identification of alternative protein targets of glutamate-ureido-lysine associated with PSMA tracer uptake in prostate cancer cells.

Prostate-specific membrane antigen (PSMA) is highly overexpressed in most prostate cancers and is clinically visualized using PSMA-specific probes incorporating glutamate-ureido-lysine (GUL). PSMA is effectively absent from certain high-mortality, treatment-resistant subsets of prostate cancers, such as neuroendocrine prostate cancer (NEPC); however, GUL-based PSMA tracers are still reported to have the potential to identify NEPC metastatic tumors. These probes may bind unknown proteins associated with PSMA-suppressed cancers. We have identified the up-regulation of PSMA-like aminopeptidase NAALADaseL and the metabotropic glutamate receptors (mGluRs) in PSMA-suppressed prostate cancers and find that their expression levels inversely correlate with PSMA expression and are associated with GUL-based radiotracer uptake. Furthermore, we identify that NAALADaseL and mGluR expression correlates with a unique cell cycle signature. This provides an opportunity for the future study of the biology of NEPC and potential therapeutic directions. Computationally predicting that GUL-based probes bind well to these targets, we designed and synthesized a fluorescent PSMA tracer to investigate these proteins in vitro, where it shows excellent affinity for PSMA, NAALADaseL, and specific mGluRs associated with poor prognosis.

Clonal origin and genomic diversity in Lynch syndrome-associated endometrial cancer with multiple synchronous tumors: Identification of the pathogenicity of MLH1 p.L582H.

Lynch syndrome-associated endometrial cancer patients often present multiple synchronous tumors and this assessment can affect treatment strategies. We present a case of a 27-year-old woman with tumors in the uterine corpus, cervix, and ovaries who was diagnosed with endometrial cancer and exhibited cervical invasion and ovarian metastasis. Her family history suggested Lynch syndrome, and genetic testing identified a variant of uncertain significance, MLH1 p.L582H. We conducted immunohistochemical staining, microsatellite instability analysis, and Sanger sequencing for Lynch syndrome-associated cancers in three generations of the family and identified consistent MLH1 loss. Whole-exome sequencing for the corpus, cervical, and ovarian tumors of the proband identified a copy-neutral loss of heterozygosity (LOH) occurring at the MLH1 position in all tumors. This indicated that the germline variant and the copy-neutral LOH led to biallelic loss of MLH1 and was the cause of cancer initiation. All tumors shared a portion of somatic mutations with high mutant allele frequencies, suggesting a common clonal origin. There were no mutations shared only between the cervix and ovary samples. The profiles of mutant allele frequencies shared between the corpus and cervix or ovary indicated that two different subclones originating from the corpus independently metastasized to the cervix or ovary. Additionally, all tumors presented unique mutations in endometrial cancer-associated genes such as ARID1A and PIK3CA. In conclusion, we demonstrated clonal origin and genomic diversity in a Lynch syndrome-associated endometrial cancer, suggesting the importance of evaluating multiple sites in Lynch syndrome patients with synchronous tumors.

Extraction of local structure differences in silica based on unsupervised learning.

Silica exhibits a rich phase diagram with numerous stable structures existing at different temperature and pressure conditions, including its glassy form. In large-scale atomistic simulations, due to the small energy difference, several phases may coexist. While, in terms of long-range order, there are clear differences between these phases, their short- or medium-range structural properties are similar for many phases, thus making it difficult to detect the structural differences. In this study, a methodology based on unsupervised learning is proposed to detect the differences in local structures between eight phases of silica, using atomic models prepared by molecular dynamics (MD) simulations. A combination of two-step locality preserving projections (TS-LPP) and locally averaged atomic fingerprints (LAAF) descriptor was employed to find a low-dimensional space in which the differences among all the phases can be detected. From the distance between each structure in the found low-dimensional space, the similarity between the structures can be discussed and subtle local changes in the structures can be detected. Using the obtained low-dimensional space, the ß-α transition in quartz at a low temperature was analyzed, as well as the structural evolution during the melt-quench process starting from α-quartz. The proper differentiation and ease of visualization make the present methodology promising for improving the analysis of the structure and properties of glasses, where subtle differences in structure appear due to differences in the temperature and pressure conditions at which they were synthesized.

Trends in endometrial carcinoma: experience of a single institute for four decades.

OBJECTIVE: To determine the current prognosis of endometrial carcinoma in Japan by analyzing long-term trends in endometrial carcinoma at our hospital. METHODS: We divided 1463 patients with endometrial carcinoma who visited our hospital between 1984 and 2022 into group 1984-1991, group 1992-1999, group 2000-2006, group 2007-2014 and group 2015-2022. Trends were determined using the Jonckheere-Terpstra and Cochran-Armitage tests. Data were analyzed using Cox regression analysis. RESULTS: When group 2015-2022 was used as a reference in the univariate analysis, the hazard ratios for the other groups were <1. In particular, the hazard ratio for group 2007-2014 was 0.65 (95% confidence interval, 0.47-0.90, P = 0.009), suggesting that the prognosis of group 2015-2022 was worse than that of group 2007-2014 and seemed to be the worst among all prognoses. In multivariate analysis, the hazard ratios for each group were 1.38, 1.42, 1.88, 1.16 and 1, respectively; the group with the worst prognosis changed from group 2015-2022 to group 2000-2006 (hazard ratio, 1.88; 95% confidence interval, 1.27-2.78, P = 0.001). Age and the rate of non-endometrioid carcinoma exhibited significantly increasing trends (P < 0.001 and P < 0.001, respectively), as did the rates of serous and mixed carcinomas (P = 0.001 and 0.024, respectively). The rates of non-endometrioid carcinoma, serous carcinoma and mixed carcinoma were 19.0%, 5.5% and 3.1% in group 2007-2014 and 28.2%, 10.8% and 4.6% in group 2015-2022, respectively. CONCLUSIONS: The increasing rates of non-endometrioid carcinoma-especially serous and mixed carcinoma-may be associated with the worsening prognosis of endometrial carcinoma at our institution. Careful monitoring is needed to confirm whether this phenomenon is observed throughout Japan.

Construction of mouse cochlin mutants with different GAG-binding specificities and their use for immunohistochemistry.

Glycosaminoglycan (GAG) is a polysaccharide present on the cell surface as an extracellular matrix component, and is composed of repeating disaccharide units consisting of an amino sugar and uronic acid except in the case of the keratan sulfate. Sulfated GAGs, such as heparan sulfate, heparin, and chondroitin sulfate mediate signal transduction of growth factors, and their functions vary with the type and degree of sulfated modification. We have previously identified human and mouse cochlins as proteins that bind to sulfated GAGs. Here, we prepared a recombinant cochlin fused to human IgG-Fc or Protein A at the C-terminus as a detection and purification tag and investigated the ligand specificity of cochlin. We found that cochlin can be used as a specific probe for highly sulfated heparan sulfate and chondroitin sulfate E. We then used mutant analysis to identify the mechanism by which cochlin recognizes GAGs and developed a GAG detection system using cochlin. Interestingly, a mutant lacking the vWA2 domain bound to various types of GAGs. The N-terminal amino acid residues of cochlin contributed to its binding to heparin. Pathological specimens from human myocarditis patients were stained with a cochlin-Fc mutant. The results showed that both tryptase-positive and tryptase-negative mast cells were stained with this mutant. The identification of detailed modification patterns of GAGs is an important method to elucidate the molecular mechanisms of various diseases. The method developed for evaluating the expression of highly sulfated GAGs will help understand the biological and pathological importance of sulfated GAGs in the future.

Cell-type-specific, multicolor labeling of endogenous proteins with split fluorescent protein tags in Drosophila.

The impact of the Drosophila experimental system on studies of modern biology cannot be understated. The ability to tag endogenously expressed proteins is essential to maximize the use of this model organism. Here, we describe a method for labeling endogenous proteins with self-complementing split fluorescent proteins (split FPs) in a cell-type-specific manner in Drosophila A short fragment of an FP coding sequence is inserted into a specific genomic locus while the remainder of the FP is expressed using an available GAL4 driver line. In consequence, complementation fluorescence allows examination of protein localization in particular cells. Besides, when inserting tandem repeats of the short FP fragment at the same genomic locus, we can substantially enhance the fluorescence signal. The enhanced signal is of great value in live-cell imaging at the subcellular level. We can also accomplish a multicolor labeling system with orthogonal split FPs. However, other orthogonal split FPs do not function for in vivo imaging besides split GFP. Through protein engineering and in vivo functional studies, we report a red split FP that we can use for duplexed visualization of endogenous proteins in intricate Drosophila tissues. Using the two orthogonal split FP systems, we have simultaneously imaged proteins that reside in distinct subsynaptic compartments. Our approach allows us to study the proximity between and localization of multiple proteins endogenously expressed in essentially any cell type in Drosophila.

Spatial genomic diversity associated with APOBEC mutagenesis in squamous cell carcinoma arising from ovarian teratoma.

Although the gross and microscopic features of squamous cell carcinoma arising from ovarian mature cystic teratoma (MCT-SCC) vary from case to case, the spatial spreading of genomic alterations within the tumor remains unclear. To clarify the spatial genomic diversity in MCT-SCCs, we performed whole-exome sequencing by collecting 16 samples from histologically different parts of two MCT-SCCs. Both cases showed histological diversity within the tumors (case 1: nonkeratinizing and keratinizing SCC and case 2: nonkeratinizing SCC and anaplastic carcinoma) and had different somatic mutation profiles by histological findings. Mutation signature analysis revealed a significantly enriched apolipoprotein B mRNA editing enzyme catalytic subunit (APOBEC) signature at all sites. Intriguingly, the spread of genomic alterations within the tumor and the clonal evolution patterns from nonmalignant epithelium to cancer sites differed between cases. TP53 mutation and copy number alterations were widespread at all sites, including the nonmalignant epithelium, in case 1. Keratinizing and nonkeratinizing SCCs were differentiated by the occurrence of unique somatic mutations from a common ancestral clone. In contrast, the nonmalignant epithelium showed almost no somatic mutations in case 2. TP53 mutation and the copy number alteration similarities were observed only in nonkeratinizing SCC samples. Nonkeratinizing SCC and anaplastic carcinoma shared almost no somatic mutations, suggesting that each locally and independently arose in the MCT. We demonstrated that two MCT-SCCs with different histologic findings were highly heterogeneous tumors with clearly different clones associated with APOBEC-mediated mutagenesis, suggesting the importance of evaluating intratumor histological and genetic heterogeneity among multiple sites of MCT-SCC.

Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces.

The atomic descriptors used in machine learning to predict forces are often high dimensional. In general, by retrieving a significant amount of structural information from these descriptors, accurate force predictions can be achieved. On the other hand, to acquire higher robustness for transferability without overfitting, sufficient reduction of descriptors should be necessary. In this study, we propose a method to automatically determine hyperparameters in the atomic descriptors, aiming to obtain accurate machine learning forces while using a small number of descriptors. Our method focuses on identifying an appropriate threshold cut-off for the variance value of the descriptor components. To demonstrate the effectiveness of our method, we apply it to crystalline, liquid, and amorphous structures in SiO2, SiGe, and Si systems. By using both conventional two-body descriptors and our introduced split-type three-body descriptors, we demonstrate that our method can provide machine learning forces that enable efficient and robust molecular dynamics simulations.

Comparison of survival rates between incident hemodialysis patients and peritoneal dialysis patients: a 5-year prospective cohort study with propensity score matching.

BACKGROUND: The effect of dialytic modality at the start of renal replacement therapy on prognosis is controversial. METHODS: This multicenter, prospective cohort study included patients undergoing incident hemodialysis (HD) (n = 646) and peritoneal dialysis (PD) (n = 72). We excluded patients who lacked complete data for 3 months. One-to-one propensity score (PS) matching was performed before between-group comparison of survival rates (Kaplan-Meier method and log-rank test) and identification of factors affecting prognosis (Cox proportional-hazards regression analysis). RESULTS: We enrolled 621 and 71 patients undergoing HD and PD, respectively (overall mean ± standard deviation age: 74 ± 13 years); 20% had cardiovascular disease (CVD). The median follow-up period was 41 (interquartile range 24-66) months. Following PS matching, we analyzed 65 patients undergoing HD and PD each. The 5-year overall survival rates did not differ between the groups (P = 0.97). The PD group exhibited a better CVD-related survival rate (P = 0.03). PD yielded adjusted hazard ratios for all-cause and CVD-related mortality of 0.99 (95% confidence interval [CI] 0.49-1.99, P = 0.97) and 3.92 (95% CI 1.05-14.7, P = 0.04), respectively. Age (P < 0.001) and the use of a central venous catheter (CVC) at dialytic initiation (P = 0.02) were independent risks for all-cause mortality; whereas, only the use of a CVC (P = 0.01) was an independent risk for CVD-related mortality. CONCLUSION: Although no differences were observed in overall survival, CVD-related survival may be better with dialytic initiation with PD than with HD.

Mapping stress inside living cells by atomic force microscopy in response to environmental stimuli.

The response of cells to environmental stimuli, under either physiological or pathological conditions, plays a key role in determining cell fate toward either adaptive survival or controlled death. The efficiency of such a feedback mechanism is closely related to the most challenging human diseases, including cancer. Since cellular responses are implemented through physical forces exerted on intracellular components, more detailed knowledge of force distribution through modern imaging techniques is needed to ensure a mechanistic understanding of these forces. In this work, we mapped these intracellular forces at a whole-cell scale and with submicron resolution to correlate intracellular force distribution to the cytoskeletal structures. Furthermore, we visualized dynamic mechanical responses of the cells adapting to environmental modulations in situ. Such task was achieved by using an informatics-assisted atomic force microscope (AFM) indentation technique where a key step was Markov-chain Monte Carlo optimization to search for both the models used to fit indentation force-displacement curves and probe geometry descriptors. We demonstrated force dynamics within cytoskeleton, as well as nucleoskeleton in living cells which were subjected to mechanical state modulation: myosin motor inhibition, micro-compression stimulation and geometrical confinement manipulation. Our results highlight the alteration in the intracellular prestress to attenuate environmental stimuli; to involve in cellular survival against mechanical signal-initiated death during cancer growth and metastasis; and to initiate cell migration.

A retrospective study of Pipelle endometrial biopsy for ovarian, fallopian tube, and peritoneal cancers.

OBJECTIVES: Although the Pipelle endometrial biopsy is widely performed as a practical and minimally invasive test for endometrial disease(s), its effectiveness in ovarian cancer has not been explored. The aim of the present study was to evaluate the results of Pipelle endometrial biopsy for ovarian, fallopian tube, and peritoneal cancers. METHODS: A pre-treatment Pipelle-endometrial biopsy was performed in 90 patients with ovarian, fallopian tube, or peritoneal cancers between January 2014 and November 2021. We retrospectively analysed the association between the results of Pipelle endometrial biopsy and clinicopathological data. Moreover, we evaluated their impact on the following treatment in advanced cases initially treated with chemotherapy. RESULTS: The sensitivity and false-negative rates for Pipelle endometrial biopsy were 25/90 (27.8%) and 65/90 (72.2%) in all patients, respectively, and 23/56 (41.0%) and 33/56 (58.9%) in cases with advanced disease (stages III and IV), respectively. Pipelle-positive endometrial biopsy-positive (Pipelle-positive) was not observed in 29 patients with clinical stage I disease, and Pipelle-positive patients exhibited significantly more high-grade serous carcinomas, and positive peritoneal, endometrial, and cervical cytologies than Pipelle-endometrial biopsy-negative cases. Surgical pathology was confirmed in 23 Pipelle-positive patients, and 17/23 (74.0%) had the same diagnosis as that for Pipelle endometrial biopsy. Conversely, 6/23 (26.0%) patients exhibited a minor diagnostic discrepancy between Pipelle endometrial biopsy and surgical pathology. Nineteen of the 38 (50.0%) patients initially treated with chemotherapy were identified as Pipelle-positive, contributing to a prompt histological diagnosis and pre-treatment tumour sampling. Companion diagnostic tests were performed using Pipelle endometrial biopsy samples from 4 inoperable patients. CONCLUSION: Although the positive rate of Pipelle endometrial biopsy in ovarian, fallopian tube, and peritoneal cancers is low, Pipelle endometrial biopsy may enable prompt histological diagnosis and initiation of chemotherapy while collecting tumour tissue for genetic testing in some cases with advanced disease.

The effectiveness of pre-treatment Pipelle endometrial biopsy for ovarian, fallopian tube, and peritoneal cancers remains unclear. This study demonstrated that Pipelle endometrial biopsy may enable prompt histological diagnosis and initiation of chemotherapy while collecting tumour tissue for genetic testing in some cases with advanced disease. This was a single-centre, retrospective study; as such, the effectiveness of Pipelle endometrial biopsy should be evaluated in larger prospective studies, including comparisons with other tumour sampling methods.

Black-Box Optimization for Automated Discovery.

In chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as black-box optimization. For instance, the trial-and-error process of synthesizing various molecules for better material properties can be regarded as optimizing a black-box function describing the relation between a chemical formula and its properties. Various black-box optimization algorithms have been developed in the machine learning and statistics communities. Recently, a number of researchers have reported successful applications of such algorithms to chemistry. They include the design of photofunctional molecules and medical drugs, optimization of thermal emission materials and high Li-ion conductive solid electrolytes, and discovery of a new phase in inorganic thin films for solar cells.There are a wide variety of algorithms available for black-box optimization, such as Bayesian optimization, reinforcement learning, and active learning. Practitioners need to select an appropriate algorithm or, in some cases, develop novel algorithms to meet their demands. It is also necessary to determine how to best combine machine learning techniques with quantum mechanics- and molecular mechanics-based simulations, and experiments. In this Account, we give an overview of recent studies regarding automated discovery, design, and optimization based on black-box optimization. The Account covers the following algorithms: Bayesian optimization to optimize the chemical or physical properties, an optimization method using a quantum annealer, best-arm identification, gray-box optimization, and reinforcement learning. In addition, we introduce active learning and boundless objective-free exploration, which may not fall into the category of black-box optimization.Data quality and quantity are key for the success of these automated discovery techniques. As laboratory automation and robotics are put forward, automated discovery algorithms would be able to match human performance at least in some domains in the near future.

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation.

A case report of atypical anti-glomerular basement membrane disease.

BACKGROUND: Anti-glomerular basement membrane (anti-GBM) disease is characterized by crescentic necrotizing glomerulonephritis, with linear deposits of immunoglobulin G (IgG) in the GBM. Classic anti-GBM disease is clinically associated with rapidly progressive glomerulonephritis with or without pulmonary hemorrhage. Some patients have a better renal prognosis and milder symptoms than those with classic anti-GBM disease, which is termed atypical anti-GBM disease. CASE PRESENTATION: A 43-year-old Japanese woman was admitted to our hospital complaining of hematuria that had persisted for more than one month. Serological examination revealed negativity for anti-nuclear, anti-neutrophilic cytoplasmic, and anti-GBM antibodies. However, renal biopsy showed cellular crescents. Immunofluorescence revealed strong diffuse linear capillary loop staining for IgG. An indirect immunofluorescence antibody method was performed by applying the patient serum to normal kidney tissue to confirm the presence of autoantibodies binding to the GBM. Using this method, anti-GBM antibodies were detected. The patient was treated with high-dose steroids, cyclophosphamide, and plasma exchange. Aggressive treatment resolved proteinuria and hematuria and improved renal function. CONCLUSIONS: Renal biopsy is crucial in the diagnosis of anti-GBM disease, especially when serological tests are negative. Accurately identifying the presence of anti-GBM disease is important to initiate optimal treatment.

Differences in isolated bacteria between perforated and non-perforated appendicitis: an analysis of 680 consecutive appendicectomies in a single institution.

PURPOSE: Escherichia coli and Bacteroides species are the most frequently detected species in ascites in perforated appendicitis and are generally sensitive to non-empiric cephalosporins like cefazolin or cefmetazole. However, monotherapy with such antibiotics is mostly insufficient for perforated appendicitis. To investigate this issue, this study aimed to compare bacterial floras in ascites culture between perforated and non-perforated appendicitis. METHODS: Ascites culture results in perforated and non-perforated appendicitis cases were analyzed using a departmental database. The duration of symptoms before surgery, pre-surgical white blood cell count, C-reactive protein value, postsurgical length of stay, length of antibiotic treatment, and the rate of using second-line antibiotics or complications were also compared. RESULTS: A total of 608 and 72 cases of non-perforated and perforated appendicitis were included. Escherichia coli and Bacteroides species were the dominant bacteria in both conditions. However, the total proportions of Pseudomonas aeruginosa, Streptococcus anginosus group, and Enterococcus group were significantly higher in perforated appendicitis than in non-perforated appendicitis. CONCLUSION: Pseudomonas aeruginosa, Streptococcus anginosus group, and Enterococcus group have better susceptibility to penicillin-based empiric antibiotics than cephalosporins. The abundance of these bacteria might explain why non-empiric cephalosporins are not effective in perforated appendicitis and the superiority of penicillin-based empiric antibiotics.

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.

Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.

Topological alternation from structurally adaptable to mechanically stable crosslinked polymer.

Stimuli-responsive polymers with complicated but controllable shape-morphing behaviors are critically desirable in several engineering fields. Among the various shape-morphing materials, cross-linked polymers with exchangeable bonds in dynamic network topology can undergo on-demand geometric change via solid-state plasticity while maintaining the advantageous properties of cross-linked polymers. However, these dynamic polymers are susceptible to creep deformation that results in their dimensional instability, a highly undesirable drawback that limits their service longevity and applications. Inspired by the natural ice strategy, which realizes creep reduction using crystal structure transformation, we evaluate a dynamic cross-linked polymer with tunable creep behavior through topological alternation. This alternation mechanism uses the thermally triggered disulfide-ene reaction to convert the network topology - from dynamic to static - in a polymerized bulk material. Thus, such a dynamic polymer can exhibit topological rearrangement for thermal plasticity at 130°C to resemble typical dynamic cross-linked polymers. Following the topological alternation at 180°C, the formation of a static topology reduces creep deformation by more than 85% in the same polymer. Owing to temperature-dependent selectivity, our cross-linked polymer exhibits a shape-morphing ability while enhancing its creep resistance for dimensional stability and service longevity after sequentially topological alternation. Our design enriches the design of dynamic covalent polymers, which potentially expands their utility in fabricating geometrically sophisticated multifunctional devices.

Ultrasmall Downconverting Nanoparticle for Enhanced Cerenkov Imaging.

Cerenkov imaging provides an opportunity to expand the application of approved radiotracers and therapeutic agents by utilizing them for optical approaches, which opens new avenues for nuclear imaging. The dominating Cerenkov radiation is in the UV/blue region, where it is readily absorbed by human tissue, reducing its utility in vivo. To solve this problem, we propose a strategy to shift Cerenkov light to the more penetrative red-light region through the use of a fluorescent down-conversion technique, based upon europium oxide nanoparticles. We synthesized square-shape ultrasmall Eu2O3 nanoparticles, functionalized with polyethylene glycol and chelate-free radiolabeled for intravenous injection into mice to visualize the lymph node and tumor. By adding trimethylamine N-oxide during the synthesis, we significantly increased the brightness of the particle and synthesized the (to-date) smallest radiolabeled europium-based nanoparticle. These features allow for the exploration of Eu2O3 nanoparticles as a preclinical cancer diagnosis platform with multimodal imaging capability.

Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus.

Cellular mechanical properties are potential cancer biomarkers used for objective cytology to replace the current subjective method relying on cytomorphology. However, heterogeneity among intra/intercellular mechanics and the interplay between cytoskeletal prestress and elastic modulus obscured the difference detectable between malignant and benign cells. In this work, we collected high density nanoscale prestress and elastic modulus data from a single cell by AFM indentation to generate a cellular mechanome. Such high dimensional mechanome data was used to train a malignancy classifier through machine learning. The classifier was tested on 340 single cells of various origins, malignancy, and degrees of similarity in morphology and elastic modulus. The classifier showed instrument-independent robustness and classification accuracy of 89% with an AUC-ROC value of 93%. A signal-to-noise ratio 8 times that of the human-cytologist-based morphological method was also demonstrated, in differentiating precancerous hyperplasia cells from normal cells derived from the same lung cancer patient.

Biological significance of KRAS mutant allele expression in ovarian endometriosis.

KRAS is the most frequently mutated in ovarian endometriosis. However, it is unclear whether the KRAS mutant allele's mRNA is expressed and plays a biological role in ovarian endometriosis. Here, we performed mutation-specific RNA in situ hybridization to evaluate mutant allele expression of KRAS p.G12V, the most frequently detected mutation in ovarian endometriosis in our previous study, in formalin-fixed paraffin-embedded tissue (FFPE) samples of ovarian endometriosis, cancer cell lines, and ovarian cancers. First, we verified that mutant or wild-type allele of KRAS were expressed in all 5 cancer cell lines and 9 ovarian cancer cases corresponding to the mutation status. Next, we applied this assay to 26 ovarian endometriosis cases, and observed mutant allele expression of KRAS p.G12V in 10 cases. Mutant or wild-type allele of KRAS were expressed in line with mutation status in 12 available endometriosis cases for which KRAS gene sequence was determined. Comparison of clinical features between ovarian endometriosis with KRAS p.G12V mutant allele expression and with KRAS wild-type showed that KRAS p.G12V mutant allele expression was significantly associated with inflammation in ovarian endometriosis. Finally, we assessed the spatial distribution of KRAS mutant allele expression in 5 endometriosis cases by performing multiregional sampling. Intratumor heterogeneity of KRAS mutant allele expression was observed in two endometriosis cases, whereas the spatial distribution of KRAS p.G12V mutation signals were diffuse and homogenous in ovarian cancer. In conclusion, evaluation of oncogene mutant expression will be useful for clarifying the biological significance of oncogene mutations in benign tumors.