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Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.

Zn2SnO4 plates, particles and spheres are successfully prepared via a facile synthesis way by carefully adjusting the solvothermal conditions, which are further applied as photoanodes in dye-sensitized solar cells (DSSCs) to explore the relationships between the photoanode nanostructure and the photovoltaic performances. As a result, the DSSCs based on Zn2SnO4 spheres photoanode showcased the best power conversion efficiency (PCE, 4.85%), compared to Zn2SnO4 plates (3.80%) and particles (4.13%). It is found that Zn2SnO4 spheres exhibit the highest light-scattering abilities, as evidenced by ultraviolet–visible (UV–Vis) diffuse reflectance spectra. Additionally, investigations on dynamic electron transport and recombination properties via intensity-modulated photovoltage/photocurrent spectroscopy (IMVS/IMPS), and electrochemical impedance spectroscopy (EIS) measurements demonstrate that the Zn2SnO4 spheres-based DSSCs possess the fastest electron transport rate, the longest electron lifetime, the highest electron collection efficiency (ηcc), and the largest charge recombination resistance, compared with the Zn2SnO4 plates and particles, all of which are highly beneficial for the powder conversion efficiency enhancements.

Aims To explore whether perceived overqualification increases the risk of burnout and whether transformational leadership negatively moderates this relationship. Background Perceived overqualification might contribute to burnout and lead to poor experience of transformational leadership, and transformational leadership might be associated with burnout. However, these relationships have not yet been confirmed. Methods A multicentre cross-sectional study. A total of 321 nurses from intensive care units were recruited from six tertiary hospitals. Scale of Perceived OverQualification, Transformational Leadership Questionnaire and emotional exhaustion subscale of the Maslach Burnout Inventory—General Survey were employed to collect the data. Hierarchical multiple regression and bootstrap resampling were applied to analyse the data. Results Burnout was positively associated with perceived overqualification and negatively associated with transformational leadership (each p

Vitiligo is an acquired chronic depigmentation disorder that can have a negative impact on the quality of life (QoL). This is especially true for patients with non-white skin. Only few studies have investigated the QoL of Asian patients with vitiligo. We aimed to investigate the QoL in Taiwanese vitiligo patients and identify the factors that influence their QoL. The cross-sectional study recruited 100 vitiligo patients and 100 controls with general skin diseases in the Department of Dermatology of Changhua Christian Hospital. Data were obtained using a structured questionnaire for demographic information and modified Skindex-21 instruments. The QoL was not significantly different between vitiligo patients and controls. Among the vitiligo patients, adults exhibited deteriorated emotional levels and total QoL as compared with non-adults. Married females reported greater levels of emotional disturbance than the unmarried ones. A higher educational level and shorter history of disease were associated with greater emotional impacts. The patients with a generalized type of vitiligo suffered more in total QoL. After multivariate adjustment, the young adult patients aged 20–39 were associated with poorer total QoL. It is suggested that vitiligo patients who are aged between 20 and 39, are married females, are highly educated, have a shorter disease history, and suffer from the generalized type of this disease demonstrate more deterioration in their life quality compared with other vitiligo patients. Care providers should tailor the psychological counseling and treatment accordingly.

Background/Objective Breast biopsy and analysis of sentinel lymph nodes (SLNs) accurately predict tumor status in the affected basin and help in avoiding unnecessary axillary lymph node dissection, which is associated with remarkable morbidity risk. Blue dye and radioisotope are the most widely used mapping agents, but non-radioactive tracers of comparable accuracy warrant further investigation. This study aimed to investigate utilization of indocyanine green (ICG) fluorescence in sentinel node localization compared with blue dye and to assess the incremental value of ICG. Methods A total of 39 consecutive patients underwent sentinel lymph node biopsy (SLNB) (40 cases: 38 unilateral and 1 bilateral) with combined blue dye and ICG for localization. The obtained fluorescence images of the lymphatic system were investigated. Results All 84 lymph nodes removed in 40 procedures were identified by ICG, but only 37 were identified by blue dye. The ICG method identified an average of 2.1 SLNs in 39 of 40 cases with a detection rate of 97.5%, but only 0.93 SLN per case with blue dye. Subcutaneous lymphatic channel patterns were also detected by fluorescent imaging in 37 procedures, which all revealed lymphatic drainage toward the axilla except in one case with internal mammary pathway. Conclusion This study demonstrated the accuracy and safety of ICG for SLNB and its superiority to blue dye method in SLN localization. Therefore, ICG fluorescence method is safe and effective addition in breast clinical settings, wherein blue dye alone is used.

Elevation of naphthoquinones and estrogen quinones, which are reactive metabolites of naphthalene and estrogen, is thought to be an important indicator of naphthalene- and estrogen-induced carcinogenesis. We compared background levels of naphthalene and estrogen quinone-derived adducts in serum albumin (Alb) from 143 women with breast cancer and 119 healthy controls. Cysteinyl adducts of naphthoquinones, including 1,2-naphthoquinone (1,2-NPQ) and 1,4-naphthoquinone (1,4-NPQ), and estrogen quinones, including estrogen-2,3-quinones (E2-2,3-Q) and estrogen-3,4-quinones (E2-3,4-Q), were characterized after adduct cleavage. Levels of estrogen quinones and naphthoquinones were positively correlated in healthy controls, but not in breast cancer patients (p 2-3,4-Q were elevated by two- to ten-fold in cancer patients (p 2-3,4-Q-2-S-Alb to (E2-2,3-Q-4-S-Alb plus E2-3,4-Q-2-S-Alb) in patients and controls. These two groups were separable using albumin adducts of estrogen quinones and naphthoquinones, with 99.6% overall correct classification rate (overall accuracy). The findings of this study suggest that differences in the disposition of estrogen and naphthalene, and the subsequent elevation of cumulative E2-3,4-Q and 1,2-NPQ may serve as biomarkers of breast cancer risk.

Tissue samples are widely used for the study of metabolomics, lipidomics and proteomics due to their abundant useful biomolecular information. Modern analytical mass spectrometry has the ability to directly obtain the molecular information contained in various tissue samples (e.g., plant tissues, animal tissues, and clinical tissues, etc.) in real-time, with minimal or no sample pretreatment. Such features of analytical mass spectrometry promote the throughput of tissue analysis and extend the application of tissue analysis in multiple fields. This review summarizes the applications of mass spectrometry in direct analysis of plant tissues, animal tissues and clinical tissues, and the challenges and perspectives of mass spectrometry in direct analysis of tissue samples are discussed as well.

Background Vitiligo is an acquired disease that involves low pigment variation in the skin. The use of cultured melanocytes for treatment of recalcitrant vitiligo has become a well-established treatment modality. In vitro cultured melanocytes present an effective autologous transplantation treatment modality for vitiligo. The present study investigated the in vitro culture of epidermal melanocytes sampled from multiple body parts, as well as the differences in total propagation time. Methods Skin specimens were collected from 30 participants (14 males and 16 females) who supplied normal colored skin sections from various regions of their body, including the face, chest, abdomen, buttock, and extremities. Subsequently, all of these specimens were treated with an identical melanocyte purification and culturing process. Results Melanocytes from the face demonstrated the most rapid growth and longest total propagation time. Melanocytes from the buttock, abdomen, and extremities displayed similar results to one another, and melanocytes from the chest and back had the slowest growth and shortest total propagation time. Conclusion Selecting the most favorable site to obtain epidermal melanocytes will reduce the required quantity of skin and culturing time, and maximize the growth and total propagation time of melanocytes. Therefore, care should be exercised when selecting the region of skin when culturing epidermal melanocytes.

Tamoxifen is one of the most common hormone therapy drug for estrogen receptor (ER)-positive breast cancer. Tumor cells with drug resistance often cause recurrence and metastasis in cancer patients. Luteolin is a natural compound found from various types of vegetables and exhibit anticancer activity in different cancers. This study demonstrated that luteolin inhibits the proliferation and induces apoptosis of tamoxifen-resistant ER-positive breast cancer cells. Luteolin also causes cell cycle arrest at the G2/M phase and decreases mitochondrial membrane potential. Besides, luteolin reduces the levels of activated PI3K/AKT/mTOR signaling pathway. The combination treatment of luteolin and PI3K, AKT, or mTOR inhibitors synergistically increases apoptosis in tamoxifen-resistant ER-positive breast cancer cells. Ras gene family (K-Ras, H-Ras, and N-Ras), an activator of PI3K, was transcriptionally repressed by luteolin via induction of tumor suppressor mixed-lineage leukemia 3 (MLL3) expression. MLL3 increases the level of monomethylation of Histone 3 Lysine 4 on the enhancer and promoter region of Ras genes, thus causes repression of Ras expressions. Our finding implies that luteolin was a promising natural agent against tamoxifen resistance of breast cancer.

Traditional image contour tracking algorithm has low tracking accuracy. To solve this problem, an object contour tracking algorithm based on local significant edge features in complex background is proposed. In the algorithm, the projective invariant is firstly introduced, to construct the geometric information descriptor between the edge positions of the infrared image, and set up the histogram of the feature number of each target contour. The geometric similarity between the features is measured by the pasteurized coefficient, the edge features of the neighbourhood around the object contour are established, and the object contour with significant features in the edge of the image is searched. Combining Shape-context operator with edge feature, the feature description vector can be formed, and Euclidean distance is defined to track measurement function. Using this function, the selected object contour is tracked preliminarily. The random consistency checking algorithm is used to eliminate the false tracking feature points and obtain the best tracking value of the object contour, thus the infrared image’s object contour tracking is carried out in the complex background. Experimental simulation shows that the proposed algorithm has high tracking accuracy and effectively improves the quality of infrared image analysis.

Introduction: We studied the association of waiting time from diagnostic Acute Myeloid Leukemia (AML) tests to initial treatment and medical expenditures, length of hospitalization, and survival rates for new AML patients. Method: We extracted 3,976 new AML patients from Optum’s de-identified Clinformatics࣪ Data Mart Database, a data warehouse of adjudicated commercial and medicare advantage claims. Their waiting days from standard diagnostic testings to the initial AML-specific treatment as the risk factor. Medical expenditures occurring within 6 months, 1 year, and 2 years after the index AML diagnosis was measured for the patients’ financial wellbeing, along with the survival rate and the length of hospital stay. We compared these outcomes for patients falling in each waiting time category. Different types of expenditures, including inpatient and outpatient costs for patients with different waiting lengths were compared. Results: For patients with moderate lengths of waiting time, medical expenditure and mortality rate increases and length of hospital stay decreased with waiting time. Within 2 years after the index AML diagnosis, the average expenditures per day goes from $4,803 for patients waiting for 0-2 days to $7,489 for patients waiting for 1-2 weeks, while the survival rate goes from 42.8% to 28.1% and the average length of stay goes from 45.6 days to 35.0 days. The increment in medical expenditure is mostly accounted for by outpatient costs, but is off-set by inpatient costs. The patterns for medical expenditure and survival rate reverse as waiting time becomes severe (3-4 weeks). Similar reverse is not observed for length of stay. Conclusion: The waiting time between AML diagnostic tests and initial treatments is associated with higher medical expenditure and higher mortality rate for moderate waiters. Our findings suggest that reducing waiting time has strong potential in improving AML patients’ survival and financial sustainability. Medical expenditure, survival rate, and length of hospital stay within 2 years by waiting time Medical expenditure per day ($) 2-yr Survival rate (%) Length of hospital stay (days) 28d 2224 51.0 35.0 Citation Format: Kang Lin Hsieh, Bor-Sheng Ko, Xiaoqian Jiang, Teng-Jen Chang, Yu-Fen Wang. The costs of waiting to get treated for new AML patients: Medical expenditures, mortality rate, and length of hospital stay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1026.

Background: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks. Methods: From 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set. Findings: Promising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p

Objectives Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model’s performance. Methods Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification. Results High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties. Conclusions Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.

Melanoma is the cause of most deaths from skin cancer. The extracellular signal-regulated kinase 1/2 (ERK1/2) pathway has been reported to participate in progression of melanoma in fair skinned populations. ERK1/2 is found in both the cytoplasm and nucleus of cells, and phosphorylated ERK1/2 has been implicated in tumor progression. We investigated the relation between melanoma progression and expression of cytoplasmic and nuclear phosphorylated ERK1/2. We examined 34 surgically resected melanomas and investigated their clinicopathologic characteristics. We found immunostaining of phosphorylated ERK1/2 in all melanomas and faint staining in benign nevi. We found expression of cytoplasmic phosphorylated ERK1/2 in most melanomas; however, nuclear phosphorylated ERK1/2 expression was found in only five melanomas. Expression of cytoplasmic phosphorylated ERK1/2 was related to the tumor stage in melanoma. Nine of 10 cases of distant metastasis were positive for cytoplasmic phosphorylated ERK1/2. Our findings suggest that phosphorylated ERK1/2 expression is relevant to clinical pathology and that in melanoma patients, phosphorylated ERK1/2 expression is found in both the cytoplasm and nucleus. Our findings suggest that cytoplasmic phosphorylated ERK1/2 participates in progression of melanoma and that it could be a useful target for clinical treatment of melanoma.

Acute leukemia often comes with life-threatening prognosis outcome and remains a critical clinical issue today. The implementation of measurable residual disease (MRD) using flow cytometry (FC) is highly effective but the interpretation is time-consuming and suffers from physician idiosyncrasy. Recent machine learning algorithms have been proposed to automatically classify acute leukemia samples with and without MRD to address this clinical need. However, most prior works either validate only on a small data cohort or focus on one specific type of leukemia which lacks generalization. In this work, we propose a transfer learning approach in performing automatic MRD classification that takes advantage of a large scale acute myeloid leukemia (AML) database to facilitate better learning on a small cohort of acute lymphoblastic leukemia (ALL). Specifically, we develop a knowledge-reserved distilled AML pre-trained network with ALL complementary learning to enhance the ALL MRD classification. Our framework achieves 84.5% averaged AUC which shows its transferability across acute leukemia, and our further analysis reveals that younger and elder ALL patient samples benefit more from using the pre-trained AML model.

Background The aim of the current study was to report a single-institution experience using breast-conserving surgery after neoadjuvant chemotherapy (NACT), focusing on the association between microscopic resection margin status and locoregional recurrence (LRR). Methods Our institutional prospectively maintained database was reviewed to identify patients who were treated with NACT between January 2008 and April 2018. Results Among the main partial mastectomy specimens available for analysis (n = 161), 28 had margins 2 mm. LRR occurred in 16 patients (9.9%) and distant metastases were detected in 27 (16.8%) patients. There was no significant difference in the LRR between the > 2 mm margin group with a 60-month cumulative survival of 85.2% compared with 76.2% for the ≤2 mm group (P = 0.335) in the Kaplan-Meier analysis. When we stratified patients by margin widths of ≥1 mm or P = 0.161) with a 60-month cumulative LRR-free survival of 84.9% compared with 69.5% for the P = 0.150). Conclusions In the absence of multiple scattered microscopic tumour foci, a negative margin of no ink on tumour maybe sufficient for stage I–III invasive breast cancer treated with NACT and breast-conserving surgery.

Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients’ FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.

The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. However, the challenges of accurate and consistent forecasting lie in the high variability of the disease outcomes and the complexity of the multiple clinical measurements available over the course of the treatment. In order to capture the multi-dimensional and longitudinal aspect of these comprehensive clinical parameters, we utilize an attention-based bi-directional long shortterm memory (Att-BLSTM) network to predict AML patient's survival and relapse. Specifically, we gather a 10-year worth of real patient's clinical data including blood test, medication, HSCT status, and gene mutation information. Our proposed Att-BLSTM framework achieves 77.1% and 67.3% AUC in tasks of predicting the next 2-year mortality and disease relapse with these comprehensive clinical parameters, and our further analysis demonstrates that a next 0 to 3 months prediction performs equally well, i.e., 74.8% and 67% AUC for mortality and relapse respectively.

BACKGROUND Hypoxic-ischemic brain injury in the perinatal period is a main cause of perinatal mortality and neurologic complications in neonates and children. Recent studies have focused on the neuroprotective effect of anesthetic drugs. The volatile anesthetic isoflurane has been shown to exert neuroprotective effects in cerebral ischemia. Mangiferin is a natural polyphenol with various pharmacological properties, including antioxidant and ant-tumor effects. This study aimed to determine whether mangiferin potentiates the neuroprotective effects of isoflurane and also if mangiferin when administered alone exerts neuroprotective effects following hypoxic-ischemic brain injury. MATERIAL AND METHODS Sprague-Dawley rats were subjected to cerebral hypoxic ischemia on postnatal day 10 (P10). Mangiferin (50, 100, or 200 mg/kg b.w.) was intragastrically administered from P3 to P12 and 1 h prior to insult on the day of ischemic induction. At 3 h after hypoxia-ischemia (HI) insult, separate groups of rat pups were exposed to isoflurane (1.5%) for 6 h. Following 48 h of HI, the rats were sacrificed and brain tissues were used for analysis. RESULTS Mangiferin treatment attenuated neuronal apoptosis and reduced cerebral infarct volume. The expression of cleaved caspase-3 and apoptotic cascade proteins were regulated. The levels of reactive oxygen species (ROS) and malondialdehyde were reduced by mangiferin and/or isoflurane exposure. The levels of antioxidant glutathione were considerably raised under HI injury, which was modulated by mangiferin and isoflurane exposure. The PI3K/Akt signaling pathway, which was downregulated following HI insult, was activated by mangiferin and/or isoflurane. CONCLUSIONS This study reveals the potent neuroprotective efficacy of mangiferin against HI-induced brain injury via effectively modulating apoptotic pathways, ROS levels, and PI3K/Akt cascades while potentiating protective effects of isoflurane.

Three-dimensional hierarchical SnO2 microspheres (∼0.35–1.60 μm) comprised of 5–10 nm SnO2 nanoparticles are successfully obtained through a facile hydrothermal reaction pathway, which are detailly characterized by X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM) and Transmission electron microscopy (TEM). These samples are then used in photoanodes in dye-sensitized solar cells (DSSCs), whose photovoltaic performances are discussed with photocurrent-voltage (J-V) curve. And the highest power conversion efficiency of 4.21% is achieved as the result of hierarchical SnO2 microspheres (200 °C 12 h) film (∼15.9 μm in thick) photoanode, and the corresponding short-circuit current density (Jsc) is 9.38 mA cm−2, open-circuit voltage (Voc) is 692 mV, and the fill factor (FF) is 0.59, respectively. Based on the time-dependent SnO2 products, we propose the evolution mechanism of SnO2 microspheres.

Background and purposeEngorged medullary vein (EMV) in patients with intracranial dural arteriovenous fistula (DAVF) suggests venous congestion. The aim of this study is to investigate its prevalence, pattern, and correlation with clinical findings.Materials and methodsCT angiography (CTA) raw data of DAVF were used for multiplanar reconstruction and then analyzed for the presence and pattern of EMV, which is defined as a dilated vein in the cerebral white matter. Patients with EMV were divided into two groups: regional and extensive. Regional type is defined as EMV limited to one cerebral hemisphere or cerebellum without evidence of subcortical calcification. Extensive type is defined as EMV involvement of more than one cerebral hemisphere or both the cerebrum and cerebellum. Descriptive analysis of clinical information, DAVF characteristics, and other imaging findings was conducted. Clinical information, including demographic data, clinical presentation, and hemorrhage, were correlated with both types of EMV.ResultsAmong 192 eligible patients with DAVF, 71 (37%) had EMV. Patients with EMV were older (63 years vs 56 years, P=0.02), with DAVF more often at the transverse and sigmoid sinus (PConclusionsEMV in patients with DAVF is associated with an aggressive manifestation. Regional type EMV is associated with a higher risk of a hemorrhagic presentation.

Introduction Identification of gene mutation status prior treatment has improved our capability in risk stratifying acute myeloid leukemia (AML) patients greatly, but it is really a labor-exhausting work to identify these mutations. In this this study, we hypothesize immunophenotyping could predict gene mutation and we aim to develop machine learning algorithms that could predict the AML gene mutation status with the immunophenotype by clinical flow cytometry. Method Retrospective clinical data of patients with AML, including demographic (age & gender), molecular genetics, cytogenetics as well as flow cytometry (FC) data at National Taiwan University Hospital were collected. A total of 529 newly diagnosed de novo AML from 2009 to 2019 enrolled this study. The median age at diagnosis was 58 years (Table 1). In total, 428 NPM1, 415 FLT3, 331 CEBPA and 338 RUNX1 gene testing results and a total of 529 initial diagnostic FC data from these patients were used in developing the gene mutation prediction models. Each FC data sample contained 100,000 cells acquired on FACSCantoII machine 6 fluorescent channels with multiple fluorescent markers. The markers measured are listed in Table 2. There were 19 combinations of markers and fluorescent channels which were served as feature inputs of the machine learning framework. Our proposed machine learning framework can be divided as a phenotype representation learning paradigm and a classification model. To derive the phenotype representation, we trained a multivariate Gaussian Mixture Model (GMM) on the 19-dimension FC data to capture the training data distribution and characteristics in a probabilistic unsupervised manner. Then, a Fisher-scoring method was used to vectorize each sample as a high dimensional representation via differential computation in terms of the learned GMM parameters. This Fisher vectorization method transformed samples to a high dimensional feature space as phenotype vectors. We performed analysis of variance (ANOVA)-based feature selection on these representations which were finally fed into the support vector machine (SVM) classifier. To alleviate the negative effects of imbalance classes in gene mutation identification tasks, we applied synthetic minority oversampling technique (SMOTE) algorithm which augmented the minority class by interpolating samples near support vectors. We train independent SVM models to detect the occurrences of the four gene mutation. The algorithm is evaluated by randomly divided 5-fold cross validation which separates 80% data for training and 20% for testing. Results This gene mutation rate of this cohort for NPM1, FLT3, CEBPA and RUNX1 were 22.2% (95/428), 25.1% (104/415), 20.2% (67/331), and 13.6% (46/338), respectively. The average accuracies (ACC) of the prediction model performance for NPM1, FLT3, CEBPA and RUNX1 were 82.6%, 76.3%, 84.2% and 84.1%, respectively, whereas the area under the ROC curve (AUC) were 77.9%, 63.4%, 80.7% and 67.7%, respectively (Table. 3). Conclusions We demonstrated the potential of the correlation of recurrent AML gene mutation status with immunophenotype of AML through our preliminary gene mutation prediction model. Further study with larger cohorts followed by external validation are needed to further evaluate the feasibility of using machine learning based algorithm as one of triage tools to support physicians in aggressive AML clinical decision before receiving molecular genetic reports. Disclosures Ko: Roche: Honoraria.

3D hierarchical Zn2SnO4 microspheres show great potential as alternative photoanodes for dye-sensitized solar cells (DSSCs). Herein, hierarchical Zn2SnO4 microspheres with different sizes are fabricated via a one-step hydrothermal method, and a plausible formation process for Zn2SnO4 microspheres is proposed. The DSSCS devices based on 1.20 μm Zn2SnO4 microspheres show the best power conversion efficiency (PCE) of 4.00%. Further, TiCl4 treatment of a Zn2SnO4 microsphere photoanode results in an improved PCE up to 4.72%.

Aloe-emodin (AE) is derived from Aloe vera and rhubarb (Rheum palmatum) and exhibits anticancer activities via multiple regulatory mechanisms in various cancers. AE can also enhance the anticancer efficacy of cisplatin, doxorubicin, docetaxel, and 5-fluorouracil; however, its effects remain poorly characterized. MCF-7, MDA-MB-231, MDA-MB-468, BT-474, and HCC-1954 breast cancer cell lines were treated with the indicated conditions of AE, and cell viability assays were performed. The expression levels of signaling proteins were determined by western blot analysis, intracellular reactive oxygen species (ROS), cell cycle distributions, and rates of apoptosis as estimated by flow cytometry. In comparison with other cells, MCF-7 cells were more sensitive to AE treatment; AE enhanced the cytotoxicity of 9[Formula: see text][Formula: see text]g/ml tamoxifen by reducing EGFR, ER[Formula: see text], Ras, ERK, c-Myc, and mTOR protein expression and blocking PI3K and mTOR activation. Finally, although co-treatment of AE with tamoxifen increased intracellular ROS, there were no effects on cell cycle progression. Besides facilitating tamoxifen-induced cell death, AE also enhanced the antiproliferative activity of tamoxifen by blocking Ras/ERK and PI3K/mTOR pathways in breast cancer cells, thus demonstrating the chemosensitizing potential of AE.

Hydroxyl group, as one of the main impurities of CVD synthetic silica glass, affects glass structure and performance. IR absorption spectra of CVD synthetic silica glass prepared with different carrier gases, H2 and O2, shows different hydroxyl group distribution, it indicates that carrier gas has an important influence on hydroxyl group distribution. Combined the MAS-NMR testing results with the distribution results, the function of homogenization process has been discussed. The results indicate that: hydroxyl group distribution presents a parabolic figure when H2 is the carrier gas, while when O2 is the carrier gas, the distribution presents an inverse parabolic figure; During homogenization process, different structures of hydroxyl group acting with each other, leading to an average distribution of hydroxyl group.

Introduction Current risk stratification of Acute Lymphoblastic Leukemia (ALL) approach has significantly improve the outcome for pediatric and adolescent ALL. However, the outcome for adult ALL remains poor. With the emerging technologies that could drastically increase the parameters and scales of clinical data for treatment strategy formulation and more new therapeutic agents approved for ALL treatment, a better integrated and more systematic risk stratification method is needed for ALL. In our study, we propose to use artificial intelligence to develop an integral outcome prediction models that include all clinical exam and treatment history, aiming to further improve current risk stratification strategies. Method Retrospective clinical data of patients with ALL, including demographic (age & gender), laboratory results including complete blood count (CBC), white blood count (WBC), molecular genetics, cytogenetics, flow cytometry (FC) and treatment history at National Taiwan University Hospital were collected. A total of 513 newly diagnosed ALL from 2005 to 2017 were enrolled this study. The median age at diagnosis was 20 years and the median follow up duration was 49.1 months, 78.9% of them achieved complete remission (CR), 27.5% had relapsed, 31.6% had received HSCT (Table 1). In total, 243,464 CBC & WBC records, 32 types of anti-neoplastic medications in L01, L03 and L04 ATC code category with a total of 20,043 medication records from these 513 patients were utilized in developing the ALL outcome prediction model. A vectorized representation that captures the static-dynamic clinical aspects of an ALL patient can be learned directly from the collected data. The representation includes both static personal attributes (demographic, and genetic) and time-varying progression of patient's clinical assessment across time (laboratory results, HSCT, and medication). The time-dependent representation was derived from training a deep network architecture of bi-directional long short-term memory network (BLSTM). By taking 7 days as a time step, the BLSTM took the input of Fisher-vector encoded time series of a patient's CBC and WBC, medication, and HSCT records separately. The last output layer, which summarized the relapse/mortality risk exhibited in the measurements of patient's clinical conditions over time, of each separately-learned BLSTM was used as the encoded dynamic clinical representation. The concatenation of the ALL patient's static features and the time-dependent representations were fed into a deep neural network followed by a support vector machine to carry out the final prediction. The prediction models were conducted in 5-fold cross-validation experiments and further evaluated using metrics of accuracy (ACC). Results The median leukemia free survival and median overall survival of these 513 ALL patients are 29.3 months and 61.9 months respectively. The outcome prediction models developed achieved accuracy of next 3-month relapse, relapse or mortality, and mortality prediction reached 86.2%, 74.9% and 64.3% respectively (Table 2). Conclusions Our team has demonstrated the BLSTM-DNN model's potential that is capable in taking multiple and longitudinal clinical measurements into integrated relapse or mortality prediction in AML in 2018 (ASH 2018 #2811) and we have observed the same potential in ALL relapse and mortality prediction. More external and multi-national center studies are needed to further improve the performance of such integrated outcome prediction models. Disclosures Ko: Roche: Honoraria.

An easy yet viable hydrothermal reaction is demonstrated to prepare ultra-small Zn2SnO4 nanoparticle with ~4.2 nm decorated reduced graphene oxide (RGO) nanocomposites (Zn2SnO4-RGO). The pure phase Zn2SnO4 nanoparticles with ~4.2 nm can be achieved during hydrothermal reaction, meanwhile, these nanoparticles uniformly dispersed on 2D graphene sheet, and graphene oxide was reduced. The photocatalytic performances of the as-prepared Zn2SnO4-RGO nanocomposites with different RGO mass ratio are investigated. The optimum synergetic effect is found when the RGO mass ratio is 7.11%, with the photocurrent and photodegradation efficiency improved by about 300% and 80% on methylene blue (MB), respectively, compared with the pure Zn2SnO4 nanoparticles.

Background Proper patient selection criteria for treatment of carotid chronic total occlusion (CTO) are unclear. This study was designed to predict procedure successful rate and 1-year patency after carotid artery stenting (CAS) for carotid CTO using pre-procedural CTA. Methods Patients with CTO detected on CTA who underwent recanalization within 3months were divided into those with occlusions at (or distal to) the clinoid segment of the internal carotid artery (group A) and those with occlusions proximal to the clinoid segment (group B) and outcomes were compared between groups. Results Technical success rates, major complications, and re-occlusions within 1-year were 52%, 22%, 91% in group A (N=23), and 89%, 0%, 0% in group B (N=19), respectively. Diabetes was more frequent in group A (43%) compared with group B (11%). Conclusion CTA may play a role in predicting successful rate and 1-year patency for endovascular recanalization in carotid CTO.

Objective: To analyze the relationship between hyperhomocysteinemia and carotid plaque features in high-risk stroke population. Methods: A total of 116 cases of high-risk stroke treated in our hospital from March 2014 to September 2015 were included in study and divided into stable plaque group 32 cases, unstable plaque group 45 cases and mixed plaque group 39 cases according to plaque features after carotid artery ultrasonography. Differences in serum levels of homocysteine (Hcy), adhesion molecule, hypersensitive C-reactive protein, lipid, cell fibronectin, and so on were compared among groups, and the correlation between serum Hcy and plaque feature-related indicators was further analyzed. Results: Serum Hcy, sVCAM-1, sICAM-1, hs-CRP, TC, TG, LDL-C and c-Fn values of unstable plaque group were significantly higher than those of stable plaque group and mixed plaque group, and HDL-C value was significantly lower than that of stable plaque group and mixed plaque group (P

Optical homogeneity of the optical materials directly affects the wavefront quality of the refractive optical system. There are some difficulties of testing large caliber optical materials, four-step method of Fizeau interferometry is a convenient and accurate method for measuring optical homogeneity. The article used the four-step method test the same quartz glass sample in a cavity with different precision and found that the optical homogeneity test results have a larger gap. While this article compared the effect of the fixed cavity surface shape and the changing cavity surface shape on 300 mm diameter quartz glass optical homogeneity test and analyzed the effect of different positions of fixed cavity distortion on optical homogeneity testing.

Synthetic quartz glass is an excellent transmittance material in ultraviolet (UV), visible and near-infrared bands. Homogenization process to synthetic quartz glass optical elements is done in order to make the composition and structure more uniform. The homogenization temperature is above 1800°C. After high-temperature homogenization, the UV transmittance of quartz glass appears a certain extent of decline in the edge partition., After high temperature homogenization process, it is found that the closer the layer to the graphite vessel, the stronger influence on UV transmittance will be. This conclusion is made by comparing the UV transmission of the quartz glass in different layers. Furthermore, the factors affecting the UV transmittance is also analyzed in this article.

In this study, Zn2SnO4 nanoparticles with different sizes are successfully synthesized by a very convenient hydrothermal process. To sufficiently characterize the as-synthesized Zn2SnO4 nanoparticles, XRD, FE-SEM, TEM, UV and BET analyses are carried out. Moreover, the photocurrent response and photocatalytic activity properties of various Zn2SnO4 nanoparticles have been studied. The results indicate that the photocurrent response and photodegradation efficiency behavior of the 3.3 nm Zn2SnO4 nanoparticles are significantly superior to that of the 4.2 nm and 6.2 nm nanoparticles. It is due to small particle size effects with large surface-to-volume ratios, which can increase the number of active surface sites.

A facile and efficient hydrolysis process was proposed to create graphene nanosheets-SnO 2 nanocomposites (GSCS). The hydrolysis reaction results in the formation of pure phase SnO 2 nanoparticle (∼10 nm in size) uniformly dispersed on graphene sheets while simultaneously cause reduction of graphene oxide. The as-prepared materials were characterized in detail by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Raman spectra and X-ray photoelectron spectroscopy (XPS). In the investigation of photocurrent response properties, it was found that the photocurrent of GSCS was 130 μA/cm 2 , which is about 4 times higher than that of SnO 2 .