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Nanoparticles enter tumours through endothelial cells, gaps or other mechanisms, but how they exit is unclear. The current paradigm states that collapsed tumour lymphatic vessels impair the exit of nanoparticles and lead to enhanced retention. Here we show that nanoparticles exit the tumour through the lymphatic vessels within or surrounding the tumour. The dominant lymphatic exit mechanism depends on the nanoparticle size. Nanoparticles that exit the tumour through the lymphatics are returned to the blood system, allowing them to recirculate and interact with the tumour in another pass. Our results enable us to define a mechanism of nanoparticle delivery to solid tumours alternative to the enhanced permeability and retention effect. We call this mechanism the active transport and retention principle. This delivery principle provides a new framework to engineer nanomedicines for cancer treatment and detection.Nanoparticle retention inside tumours has been associated with lymphatic vessel collapse. It is now shown that nanoparticles exit from solid tumours through lymphatic vessels in or surrounding the tumour by a nanoparticle-size-dependent mechanism.

Cancers of unknown primary (CUP) origin account for ∼3% of all cancer diagnoses, whereby the tumor tissue of origin (TOO) cannot be determined. Using a uniformly processed dataset encompassing 6756 whole-genome sequenced primary and metastatic tumors, we develop Cancer of Unknown Primary Location Resolver (CUPLR), a random forest TOO classifier that employs 511 features based on simple and complex somatic driver and passenger mutations. CUPLR distinguishes 35 cancer (sub)types with ∼90% recall and ∼90% precision based on cross-validation and test set predictions. We find that structural variant derived features increase the performance and utility for classifying specific cancer types. With CUPLR, we could determine the TOO for 82/141 (58%) of CUP patients. Although CUPLR is based on machine learning, it provides a human interpretable graphical report with detailed feature explanations. The comprehensive output of CUPLR complements existing histopathological procedures and can enable improved diagnostics for CUP patients. The original tumor location can be unclear for metastatic tumors. Here, the authors show that DNA sequencing of whole genomes can be used to classify metastatic tumors using a machine learning model, Cancer of Unknown Primary Location Resolver, in order to improve diagnosis and inform treatment decisions.

Nanoparticles for the detection and treatment of cancer have suffered from limited clinical translation. A key problem has been the lack of understanding of the mechanisms of nanoparticle delivery to solid tumours. The current delivery mechanism is called the enhanced permeability and retention effect, which states that nanoparticles passively enter the tumour through gaps between endothelial cells and are retained because of poor lymphatic drainage. However, nanoparticles designed according to the enhanced permeability and retention effect have limited delivery to solid tumours. An alternative mechanism proposes that nanoparticles enter the tumour through active endothelial transport processes, are retained in the tumour due to interactions with tumour components and exit the tumour through lymphatic vessels. This mechanism is called the active transport and retention principle. In this Review, we explore the contrasting views of these two mechanisms of nanoparticle delivery to solid tumours, explaining the underlying biological mechanisms and their effect on nanoparticle design for cancer applications. Defining the nanoparticle delivery mechanisms to solid tumours is crucial to the advancement and clinical translation of cancer nanomedicines and to determining how nanoparticles should be engineered for medical use.The mechanisms of nanoparticle delivery to solid tumours guide the engineering of nanoparticles for cancer applications. This Review discusses two contrasting nanoparticle delivery mechanisms, the enhanced permeability and retention effect and the active transport and retention principle, and their implications for the design of cancer nanomedicines.

Homologous recombination deficiency (HRD) results in impaired double strand break repair and is a frequent driver of tumorigenesis. Here, we develop a genome-wide mutational scar-based pan-cancer Classifier of HOmologous Recombination Deficiency (CHORD) that can discriminate BRCA1 - and BRCA2 -subtypes. Analysis of a metastatic ( n  = 3,504) and primary ( n  = 1,854) pan-cancer cohort reveals that HRD is most frequent in ovarian and breast cancer, followed by pancreatic and prostate cancer. We identify biallelic inactivation of BRCA1 , BRCA2 , RAD51C or PALB2 as the most common genetic cause of HRD, with RAD51C and PALB2 inactivation resulting in BRCA2 -type HRD. We find that while the specific genetic cause of HRD is cancer type specific, biallelic inactivation is predominantly associated with loss-of-heterozygosity (LOH), with increased contribution of deep deletions in prostate cancer. Our results demonstrate the value of pan-cancer genomics-based HRD testing and its potential diagnostic value for patient stratification towards treatment with e.g. poly ADP-ribose polymerase inhibitors (PARPi). Cancers deficient in homologous recombination can benefit from treatment with poly ADP-ribose polymerase (PARP) inhibitors. Here, the authors generated a classifier that can predict homologous recombination deficiency from genomic data and suggest several cancer types that may benefit from PARP inhibitor treatment.

There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.

Over the recent years, blockchain, a digitalization phenomenon, has leveraged its superior features to remodel the relationships of logistics partners. This cutting-edge technology has brought a faster, more transparent, and cost-effective logistics industry. This study, therefore, aims to investigate the behavioral intention to use the blockchain of individuals who work in logistics companies in Ho Chi Minh City (HCMC), Viet Nam, through the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended factors. Accordingly, non-probability sampling with convenience sampling has been chosen. A questionnaire was used to collect data from logistics workers before exploring and clarifying factors affecting the users' intention, namely performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). In addition, experience (EXP) was also expected to influence the relationships. Therefore, a multi-analytical hybrid structural equation modeling-artificial neural network (SEM-ANN) approach was used to evaluate the gathered data empirically. The expert panel examined the established questionnaire through face validity and content validity to ensure the validity and reliability of the survey instrument. The findings revealed the different positive impacts of factors on the intention to use blockchain. While the result of the PLS-SEM technique is a descending order impact of PE, FC, EE, SI, and EXP was found that have no meaningful effect on the relationships, the ANN approach produces a surprising conclusion when SI ranks first in the magnitude of influence.

The research goal is to identify the essential components of visitor satisfaction that influence future return intentions. The quantitative method and the deductive approach were implemented with a sample size of 300 tourists in Can Tho city, Vietnam after sending the survey questionnaire to 15 experts and receiving positive feedback within two weeks. The researchers used the main method to evaluate the data, which was structural equation modeling. The findings illustrated those three factors including cultural contact, perceived value, and green practices noticeably impact visitor satisfaction leading to their intention to visit again, with cultural contact being the most significant influence. At the same time, this helps researchers better understand the intention of tourists to return. Service quality and novelty seeking have negligible influence on tourists’ willingness to revisit the location. The valuable data of this study can inform tourism and hotel managers about their customer groups by encouraging visitors to engage in value-creating activities, cultural exposure and promotion of ideas related to green tourism. On the other side, this study contributes hospitality-related material resources for the following research and provides recommendations to create strategies for tourism regulators and hospitality businesses to promote visitor satisfaction and return.This study aims to identify the essential components of tourist satisfaction that encourage repeat visits. The questionnaire was reviewed and received good feedback from more than ten experts after which the survey was conducted with 300 tourists coming to Can Tho city, Vietnam. Through structural equation modelling, the findings illustrated those three factors including cultural contact, perceived value, and green practices noticeably impact visitor satisfaction leading to their intention to visit again, with cultural contact being the most significant influence. This also helps academics understand visitor return intentions. Tourists’ inclination to return is unaffected by service quality and novelty. This study’s findings can help tourism and hotel management understand their customers by encouraging value-creating events, cultural exposure, and green tourism initiatives. This study provides necessary resources for future research and recommends strategies for tourism regulators and hospitality firms to increase tourist happiness and repeat visits.

Multilayer graphene and its stacking order provide both fundamentally intriguing properties and technological engineering applications. Several approaches to control the stacking order have been demonstrated, but a method of precisely controlling the number of layers with desired stacking sequences is still lacking. Here, we propose an approach for controlling the layer thickness and crystallographic stacking sequence of multilayer graphene films at the wafer scale via Cu–Si alloy formation using direct chemical vapour deposition. C atoms are introduced by tuning the ultra-low-limit CH4 concentration to form a SiC layer, reaching one to four graphene layers at the wafer scale after Si sublimation. The crystallographic structure of single-crystalline or uniformly oriented bilayer (AB), trilayer (ABA) and tetralayer (ABCA) graphene are determined via nano-angle-resolved photoemission spectroscopy, which agrees with theoretical calculations, Raman spectroscopy and transport measurements. The present study takes a step towards the layer-controlled growth of graphite and other two-dimensional materials.Well-controlled multilayer graphene up to four layers thick with a defined stacking sequence is synthesized via SiC alloy formation on a Cu(111) substrate.

Marine plastic pollution is an increasing environmental threat. Although it is assumed that most marine plastics are transported from land to the ocean through rivers, only limited data on riverine plastic transport exists. Recently, new methods have been introduced to characterize riverine plastics consistently through time and space. For example, combining visual counting observations and plastic debris sampling can provide order of magnitude estimations of plastic transport through a river. In this paper, we present findings from multi-season measurement campaign in the Saigon River, Vietnam. For the first time, we demonstrate that macroplastic transport exhibits strong temporal variation. The monthly averaged plastic transport changes up to a factor five within the measurement period. As it is unclear what drives the variation in plastic transport, relations between rainfall, river discharge, presence of organic material and plastic transport have been explored. Furthermore, we present new findings on the cross-sectional and vertical distribution of riverine plastic transport. With this paper we present new insights in the origin and fate of riverine plastic transport, emphasizing the severity of the emerging thread of plastic pollution on riverine ecosystems.

The rapid upward thrust of the economy has harmed the environment, which has been compounded by pollution. More and more people are becoming concerned about their surroundings and looking for strategies to mitigate their unfavorable consequences. This is seen in governments' and enterprises' determination to expand and produce environmentally friendly products, as well as raise demand for green products. The purpose of this research is to uncover characteristics influencing green consumption behavior in Vietnam and to determine the most powerful motivator that drives customers to buy green products. The quantitative technique was applied in this investigation, with 231 samples obtained via an online survey. To analyze and assess the acquired data, several methods such as Cronbach's Alpha, EFA, SEM, One-way ANOVA, and Independent Sample T-test were used using SPSS and Amos software. According to the findings, three elements, including Attitudes, Social Norms, and Environmental Concerns, have a beneficial effect on green consumption behavior in Vietnam, with environmental concerns playing a particularly important role. The latest findings offered meaningful data about buyer purchasing characteristics, allowing businesses to expand green goods and devise marketing practices to get more customers to make use of them.