Current Progress in CAR-T Cell Therapy for Solid Tumors

Cancer immunotherapy by chimeric antigen receptor-modified T (CAR-T) cells has shown exhilarative clinical efficacy for hematological malignancies. Recently two CAR-T cell based therapeutics, Kymriah (Tisagenlecleucel) and Yescarta (Axicabtagene ciloleucel) approved by US FDA (US Food and Drug Administration) are now used for treatment of B cell acute lymphoblastic leukemia (B-ALL) and diffuse large B-cell lymphoma (DLBCL) respectively in the US. Despite the progresses made in treating hematological malignancies, challenges still remain for use of CAR-T cell therapy to treat solid tumors. In this landscape, most studies have primarily focused on improving CAR-T cells and overcoming the unfavorable effects of tumor microenvironment on solid tumors. To further understand the current status and trend for developing CAR-T cell based therapies for various solid tumors, this review emphasizes on CAR-T techniques, current obstacles, and strategies for application, as well as necessary companion diagnostics for treatment of solid tumors with CAR-T cells

ODT Flow: Extracting, Analyzing, and Sharing Multi-Source Multi-Scale Human Mobility

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics

Detecting and phenotyping of aneuploid circulating tumor cells in patients with various malignancies

Circulating tumor cells (CTCs) have been exclusively studied and served to assess the clinical outcomes of treatments and progression of cancer. Most CTC data have mainly been derived from distinct cohorts or selected tumor types. In the present study, a total of 594 blood samples from 479 cases with 19 different carcinomas and 30 healthy samples were collected and analyzed by Subtraction enrichment method combined with immunostaining-fluorescence in situ hybridization (iFISH). Non-hematopoietic cells with aneuploid chromosome 8 (more than 2 copies) were regarded as positive CTCs. The results showed that none of CTCs was found in all 30 healthy samples. The overall positive rate of CTCs was 89.0% in diagnosed cancer patients (ranging from 75.0% to 100.0%). Average number of 11, 5, 8 and 4 CTCs per 7.5 mL was observed in lung cancer, liver cancer, renal cancer and colorectal cancer, respectively. Among 19 different carcinomas, the total number of CTCs, tetraploid chromosome 8, polyploid chromosome 8, CTM (Circulating tumor microemboli) and large CTCs in patients with stage Ⅲ and Ⅳ were statistically higher than patients with stage Ⅰ and Ⅱ (P < 0.05). Furthermore, EpCAM expression was more frequently found in most CTCs than vimentin expression, confirming that these CTCs were of epithelial origin. In addition, small and large CTCs were also classified, and the expression of vimentin was mostly observed in small CTCs and CTM. Our results revealed that there are higher numbers of CTCs, tetraploid, polyploid and large CTCs in patients with stage Ⅲ and Ⅳ, indicating that the quantification of chromosome ploidy performed by SE-iFISH for CTCs might be a useful tool to predict and evaluate therapeutic efficacy as well as to monitoring disease progression

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

Measuring Global Multi-Scale Place Connectivity Using Geotagged Social Media Data

Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook\u27s social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: (1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and (2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions

Social media data as a proxy for hourly fine-scale electric power consumption estimation

Accurate forecasting of electric demand is essential for the operation of modern power system. Inaccurate load forecasting will considerably affect the power grid efficiency. Forecasting the electric demand for a small area, such as a building, has long been a well-known challenge. In this research, we examined the association between geotagged tweets and hourly electric consumption at a fine scale. All available geotagged tweets and electric meter readings were retrieved and spatially aggregated to each building in the study area. Comparing to traditional studies, the usage of geotagged tweets is to reflect human activity dynamics to some degree by considering human beings as sensors, which therefore can be employed at the building level. High correlation is found between the human activity indicator and the power consumption as supported by a correlation coefficient level over 0.8. To the best of our knowledge, rare studies placed an emphasis on hourly electric power consumption using social media data, especially at such a fine scale. This research shows the great potential of using Twitter data as a proxy of human activities to model hourly electric power consumption at the building level. More studies are warranted in the future to further examine the effectiveness of the proposed method in this research

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four different sources. We further design a Responsive Index ($RI$) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the $RI$ between either two data sources, revealing their general similarity, albeit with varying Pearson’s $r$ coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The results suggest that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in $RI{\rm \; }$between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity

STIL facilitates the development and malignant progression of triple-negative breast cancer through activation of Fanconi anemia pathway via interacting with KLF16

Background: STIL is an important cell cycle-regulating protein specifically recruited to the mitotic centrosome to promote the replication of centrioles in dividing cells. However, the potential role of STIL in the regulation of the biological functions of triple-negative breast cancer remains still unclear. Methods: We screened for differentially expressed STIL in the Cancer Genome Atlas database. The expression of STIL protein in 10 pairs of breast cancer tissues and adjacent normal tissues was further assessed by western blotting. Functionally, the knockdown and overexpression of STIL have been used to explore the effects of STIL on breast cancer cell proliferation, migration, and invasion. Mechanistically, RNA-seq, dual-luciferase reporter assay, chromatin immunoprecipitation assay, mass spectrometry, immunoprecipitation assay, and DNA pull-down assay were performed. Results: Breast cancer tissues and cells have higher STIL expression than normal tissues and cells. STIL knockdown impairs breast cancer cell growth, migration, and invasion, whereas STIL overexpression accelerates these processes. STIL promotes breast cancer progression by regulating FANCD2 expression, and exploration of its molecular mechanism demonstrated that STIL interacts with KLF16 to regulate the expression of FANCD2. Conclusions: Collectively, our findings identified STIL as a critical promoter of breast cancer progression that interacts with KLF16 to regulate Fanconi anemia pathway protein FANCD2. In summary, STIL is a potential novel biomarker and therapeutic target for breast cancer

GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science:a systematic review

The launch of large language models (LLMs) like ChatGPT in late 2022 and the anticipated arrival of future GPT-x iterations have marked the beginning of the generative artificial intelligence (GAI) era. We conducted a systematic review of how to integrate LLMs including GPT and other GAI models into geospatial science, based on 293 papers obtained from four databases of academic publications – Web of Science (WoS), Scopus, SSRN and arXiv – 26 papers were eventually included for analysis. We statistically outlined the share of domains where LLMs and other GAI models, the type of data that have been used for these models, and the modelling tasks and roles that they play. We also pointed out the challenges and future directions for the next research agenda – along with which we could better position ourselves in the mainstream of science and the cutting-edge research paradigm as others leverage insights from the growing data deluge