Peptide-Mediated Liposomal Drug Delivery System Targeting Tumor Blood Vessels in Anticancer Therapy

Solid tumors are known to recruit new blood vessels to support their growth. Therefore, unique molecules expressed on tumor endothelial cells can function as targets for the antiangiogenic therapy of cancer. Current efforts are focusing on developing therapeutic agents capable of specifically targeting cancer cells and tumor-associated microenvironments including tumor blood vessels. These therapies hold the promise of high efficacy and low toxicity. One recognized strategy for improving the therapeutic effectiveness of conventional chemotherapeutics is to encapsulate anticancer drugs into targeting liposomes that bind to the cell surface receptors expressed on tumor-associated endothelial cells. These anti-angiogenic drug delivery systems could be used to target both tumor blood vessels as well as the tumor cells, themselves. This article reviews the mechanisms and advantages of various present and potential methods using peptide-conjugated liposomes to specifically destroy tumor blood vessels in anticancer therapy

Diagnostic metabolomic profiling of Parkinson\u27s disease biospecimens

BACKGROUND: Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson\u27s disease (PD) diagnosis. OBJECTIVE: To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD. METHODS: Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression. RESULTS: Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative. CONCLUSIONS: UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan-Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with outstanding 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care

地下水引發自由端順向坡土體滑動特性分析

Retrogressive sliding can be observed in groundwater-induced landslides. The major inducing factor of this phenomenon is the fluidization in the down-hillslope and the soil of the bottom-layers. Moreover, when the water content in the area around the groundwater input pipe surpasses 29%, sag in the upper soil layer can also occur. As the groundwater discharge increases in experiments with the same slope, the equilibrium time needed to achieve landslide equilibrium gets shorter and the durations of landslides also decrease. As the slope in experiments with the same level of groundwater discharge increases, the equilibrium time required to achieve landslide equilibrium gets shorter, the landsliding area in a specific time period gets larger and the total landsliding area also decreases.由地下水誘發自由端邊坡滑動現象，崩塌型態屬於後退型崩塌。主要誘發因子為滲透水分造成下層土壤流動化及地下水輸入管鄰近區域因土壤土體底層水分飽和度超過29%而產生土壤下陷現象。在相同坡度的邊坡滑動實驗中，地下水量越增加，達到邊坡滑動平衡的時間越短且發生滑動次數越少；而在相同輸入地下水量的邊坡滑動實驗中，坡度越增加則達邊坡滑動平衡時間越短、有越高比例的滑動面積集中在特定時段，且總滑動面積也會因此減少

Taiji Data Challenge for Exploring Gravitational Wave Universe

The direct observation of gravitational waves (GWs) opens a new window for exploring new physics from quanta to cosmos and provides a new tool for probing the evolution of universe. GWs detection in space covers a broad spectrum ranging over more than four orders of magnitude and enables us to study rich physical and astronomical phenomena. Taiji is a proposed space-based GW detection mission that will be launched in the 2030s. Taiji will be exposed to numerous overlapping and persistent GW signals buried in the foreground and background, posing various data analysis challenges. In order to empower potential scientific discoveries, the Mock LISA Data Challenge and the LISA Data Challenge (LDC) were developed. While LDC provides a baseline framework, the first LDC needs to be updated with more realistic simulations and adjusted detector responses for Taiji's constellation. In this paper, we review the scientific objectives and the roadmap for Taiji, as well as the technical difficulties in data analysis and the data generation strategy, and present the associated data challenges. In contrast to LDC, we utilize second-order Keplerian orbit and second-generation time delay interferometry techniques. Additionally, we employ a new model for the extreme-mass-ratio inspiral waveform and stochastic GW background spectrum, which enables us to test general relativity and measure the non-Gaussianity of curvature perturbations. Furthermore, we present a comprehensive showcase of parameter estimation using a toy dataset. This showcase not only demonstrates the scientific potential of the Taiji Data Challenge but also serves to validate the effectiveness of the pipeline. As the first data challenge for Taiji, we aim to build an open ground for data analysis related to Taiji sources and sciences. More details can be found on the official website at http://taiji-tdc.ictp-ap.org.Comment: 15 pages, 3 figure

Serum leptin is associated with cardiometabolic risk and predicts metabolic syndrome in Taiwanese adults

<p>Abstract</p> <p>Background</p> <p>Leptin is associated with cardiovascular disease (CVD); however, few studies have assessed its relationship with metabolic syndrome, especially in an Asian population. Therefore, the aim of the present study was to assess leptin levels and evaluate its association with CVD and metabolic syndrome.</p> <p>Methods</p> <p>In 2009, 957 subjects, who underwent a routine physical examination and choose leptin examination, were selected to participate. Participants (269 females and 688 males) were stratified according to leptin level quartiles. Metabolic syndrome was defined by NCEP ATP III using waist circumference cutoffs modified for Asian populations, and CVD risk was determined using the Framingham Heart Study profile.</p> <p>Results</p> <p>Leptin levels were correlated with CVD risk in men and women. With the exception of fasting plasma glucose, increased leptin levels were observed as factors associated with metabolic syndrome increased in both males and females. After adjusting for age, an association between leptin levels and metabolic syndrome was observed. After adjusting for age alone or with tobacco use, subjects in the highest leptin quartile had a higher risk of having metabolic syndrome than those in the lowest quartile (OR = 6.14 and 2.94 for men and women, respectively). After further adjustment for BMI, metabolic syndrome risk remained significantly increased with increasing leptin quartiles in men. Finally, increased leptin levels were a predictor of metabolic syndrome in men and women.</p> <p>Conclusions</p> <p>Serum leptin levels are correlated with CVD risk and metabolic syndrome. Analysis of leptin as part of routine physical examinations may prove beneficial for early diagnosis of metabolic syndrome.</p

Administration of Downstream ApoE Attenuates the Adverse Effect of Brain ABCA1 Deficiency on Stroke

The ATP-binding cassette transporter member A1 (ABCA1) and apolipoprotein E (ApoE) are major cholesterol transporters that play important roles in cholesterol homeostasis in the brain. Previous research demonstrated that specific deletion of brain-ABCA1 (ABCA1-B/-B) reduced brain grey matter (GM) and white matter (WM) density in the ischemic brain and decreased functional outcomes after stroke. However, the downstream molecular mechanism underlying brain ABCA1-deficiency-induced deficits after stroke is not fully understood. Adult male ABCA1-B/-B and ABCA1-floxed control mice were subjected to distal middle-cerebral artery occlusion and were intraventricularly infused with artificial mouse cerebrospinal fluid as vehicle control or recombinant human ApoE2 into the ischemic brain starting 24 h after stroke for 14 days. The ApoE/apolipoprotein E receptor 2 (ApoER2)/high-density lipoprotein (HDL) levels and GM/WM remodeling and functional outcome were measured. Although ApoE2 increased brain ApoE/HDL levels and GM/WM density, negligible functional improvement was observed in ABCA1-floxed-stroke mice. ApoE2-administered ABCA1-B/-Bstroke mice exhibited elevated levels of brain ApoE/ApoER2/HDL, increased GM/WM density, and neurogenesis in both the ischemic ipsilateral and contralateral brain, as well as improved neurological function compared with the vehicle-control ABCA1-B/-B stroke mice 14 days after stroke. Ischemic lesion volume was not significantly different between the two groups. In vitro supplementation of ApoE2 into primary cortical neurons and primary oligodendrocyte-progenitor cells (OPCs) significantly increased ApoER2 expression and enhanced cholesterol uptake. ApoE2 promoted neurite outgrowth after oxygen-glucose deprivation and axonal outgrowth of neurons, and increased proliferation/survival of OPCs derived from ABCA1-B/-B mice. Our data indicate that administration of ApoE2 minimizes the adverse effects of ABCA1 deficiency after stroke, at least partially by promoting cholesterol traffic/redistribution and GM/WM remodeling via increasing the ApoE/HDL/ApoER2 signaling pathway

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision.Comment: 14 pages, 5 figure

Scoring mechanisms of p16INK4a immunohistochemistry based on either independent nucleic stain or mixed cytoplasmic with nucleic expression can significantly signal to distinguish between endocervical and endometrial adenocarcinomas in a tissue microarray study

<p>Abstract</p> <p>Background</p> <p>Endocervical adenocarcinomas (ECAs) and endometrial adenocarcinomas (EMAs) are malignancies that affect uterus; however, their biological behaviors are quite different. This distinction has clinical significance, because the appropriate therapy may depend on the site of tumor origin. The purpose of this study is to evaluate 3 different scoring mechanisms of p16^INK4aimmunohistochemical (IHC) staining in distinguishing between primary ECAs and EMAs.</p> <p>Methods</p> <p>A tissue microarray (TMA) was constructed using formalin-fixed, paraffin-embedded tissue from hysterectomy specimens, including 14 ECAs and 24 EMAs. Tissue array sections were immunostained with a commercially available antibody of p16^INK4a. Avidin-biotin complex (ABC) method was used for antigens visualization. The staining intensity and area extent of the IHC reactions was evaluated using the semi-quantitative scoring system. The 3 scoring methods were defined on the bases of the following: (1) independent cytoplasmic staining alone (Method C), (2) independent nucleic staining alone (Method N), and (3) mean of the sum of cytoplasmic score plus nucleic score (Method Mean of C plus N).</p> <p>Results</p> <p>Of the 3 scoring mechanisms for p16^INK4aexpression, Method N and Method Mean of C plus N showed significant (<it>p-values </it>< 0.05), but Method C showed non-significant (p = 0.245) frequency differences between ECAs and EMAs. In addition, Method Mean of C plus N had the highest overall accuracy rate (81.6%) for diagnostic distinction among these 3 scoring methods.</p> <p>Conclusion</p> <p>According to the data characteristics and test effectiveness in this study, Method N and Method Mean of C plus N can significantly signal to distinguish between ECAs and EMAs; while Method C cannot do. Method Mean of C plus N is the most promising and favorable means among the three scoring mechanisms.</p