Results 61 to 70 of about 517,907 (286)

PARP inhibition and pharmacological ascorbate demonstrate synergy in castration‐resistant prostate cancer

Molecular Oncology, EarlyView.
Pharmacologic ascorbate (vitamin C) increases ROS, disrupts cellular metabolism, and induces DNA damage in CRPC cells. These effects sensitize tumors to PARP inhibition, producing synergistic growth suppression with olaparib in vitro and significantly delayed tumor progression in vivo. Pyruvate rescue confirms ROS‐dependent activity.
Nicolas Gordon, Peter T. Gallagher, Orly I. Richter, Neermala Poudel Neupane, Amy C. Mandigo, Jennifer J. McCann, Emanuela Dylgjeri, Irina Vasilevskaya, Christopher McNair, Channing J. Paller, Wm. Kevin Kelly, Karen E. Knudsen, Matthew J. Schiewer, Ayesha A. Shafi   +13 more
wiley   +1 more source

Time series analysis and forecasting with ECOTOOL.

PLoS ONE, 2019
This paper presents ECOTOOL, a new free MATLAB toolbox that embodies several routines for identification, validation and forecasting of dynamic models.
Diego J Pedregal
doaj   +1 more source

Plecstatin inhibits hepatocellular carcinoma tumorigenesis and invasion through cytolinker plectin

Molecular Oncology, EarlyView.
The ruthenium‐based metallodrug plecstatin exerts its anticancer effect in hepatocellular carcinoma (HCC) primarily through selective targeting of plectin. By disrupting plectin‐mediated cytoskeletal organization, plecstatin inhibits anchorage‐dependent growth, cell polarization, and tumor cell dissemination.
Zuzana Outla, Jan Kosla, Magdalena Prechova, Lukas Frick, Patricia Bortel, Yasmin Borutzki, Andrea Bileck, Christopher Gerner, Samuel M. Meier‐Menches, Selma Osmanagic‐Myers, Martin Gregor   +10 more
wiley   +1 more source

Forecasting Hierarchical Time Series

, 2022
This paper addresses a common problem with hierarchical time series. Time series analysis demands the series for a model to be the sum of multiple series at corresponding sub-levels. Hierarchical Time Series presents a two-fold problem. First, each individual time series model at each level in the hierarchy must be estimated separately.
Sangari, Seema, Zhang, Xinyan
openaire   +2 more sources

Therapeutic strategies for MMAE‐resistant bladder cancer through DPP4 inhibition

Molecular Oncology, EarlyView.
We established monomethyl auristatin E (MMAE)‐resistant bladder cancer (BC) cell lines by exposure to progressively increasing concentrations of MMAE in vitro. RNA sequencing showed DPP4 expression was increased in MMAE‐resistant BC cells. Both si‐DPP4 and the DPP4 inhibitor sitagliptin suppressed the viability of MMAE‐resistant BC cells.
Gang Li, Shuichi Tatarano, Hirofumi Yoshino, Saeki Saito, Mitsuhiko Tominaga, Junya Arima, Ikumi Fukuda, Takashi Sakaguchi, Ryosuke Matsushita, Yasutoshi Yamada, Hideki Enokida   +10 more
wiley   +1 more source

Crucial parameters for precise copy number variation detection in formalin‐fixed paraffin‐embedded solid cancer samples

Molecular Oncology, EarlyView.
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris, Vasiliki Siozopoulou, Léon C van Kempen, Anne Sieben, Ella Roelant, Stig Hellemans, Elyne Backx, Laure Sorber, Koen De Winne, Senada Koljenović, Karen Zwaenepoel   +10 more
wiley   +1 more source

TRAIL‐PEG‐Apt‐PLGA nanosystem as an aptamer‐targeted drug delivery system potential for triple‐negative breast cancer therapy using in vivo mouse model

Molecular Oncology, EarlyView.
Aptamers are used both therapeutically and as targeting agents in cancer treatment. We developed an aptamer‐targeted PLGA–TRAIL nanosystem that exhibited superior therapeutic efficacy in NOD/SCID breast cancer models. This nanosystem represents a novel biotechnological drug candidate for suppressing resistance development in breast cancer.
Gulen Melike Demirbolat, Aslihan Kucuk, Omer Erdogan, Samed Ozer, Bensu Kozan, Tugba Cuceli, Erkan Gumus, Evrim Cevik, Ozge Cevik   +8 more
wiley   +1 more source

Forecasting Time Series from Clusters. [PDF]

Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting a large number of series that are logically connected in some way, the authors can first cluster them into groups of similar series.
Inder, B., Marahaj, E.A.
core  

ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs

Machine Learning and Knowledge Extraction
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models.
Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, Kang Qin   +5 more
doaj   +1 more source

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source