Modern therapy of acute myeloid leukemia (AML) began in 1973 with the first report of the successful combination of daunorubicin and cytarabine, which led to complete remission in approximately 45% of patients. Accurate AML diagnosis was dependent on morphology, aided initially only by cytochemistry. Unlike acute lymphoblastic leukemia (ALL), immunophenotyping offered little in the diagnosis of AML, at least during the 1970s and 1980s. The advent of reliable cytogenetics changed the entire prognostic outlook of AML. With karyotypic analysis, different groups of AML could be classified and stratified for various therapies. Unique mutational profiling was a major advance in further categorizing AML patients, aided by the immunophenotypic identification of antigenic markers on the cells. All these advances were occurring as the understanding of the importance of the tumor burden—known as minimal residual disease (MRD)—became crucial for the management of AML patients. The efficacy of MRD has rapidly progressed in the past decade, from a specificity of 10−3 with immunophenotyping to 10−4 with polymerase chain reaction (PCR), which is only appropriate for some patients with AML, and finally to 10−5 or even 10−6 cells with the extraordinary sensitivity of next-generation sequencing (NGS). All of these advances have promoted the concept of personalized medicine, which has led to the advent of targeted agents that can accurately be used for specific diagnostic subtypes. Responses can be predicted and measured accurately. Such targeted agents have now become a cornerstone in the management of AML, increasing efficacy and dramatically reducing toxicity. The focus of this review is on one of the most well-studied targeted agents in AML: the FMS-like tyrosine kinase 3 (FLT3) inhibitors, which have impacted the prognostication and therapeutics of AML. This review selectively discusses the FLT3 inhibitors in detail, as a model for the other burgeoning targeted agents that have already been approved, as well as those that are currently in development.
Early detection of hepatocellular carcinoma (HCC) while in its early stages is critical for reducing HCC mortality in high-risk patients. However, highly sensitive and specific surveillance biomarkers for early-stage HCC detection are still lacking. In recent years, great efforts have been made to research tumor-derived molecular features that are detectable in circulation, such as circulating tumor deoxyribonucleic
acid and circulating tumor ribonucleic acid, in order to explore their potential as non-invasive biomarker candidates in many tumor types. In this review, we summarize current studies on these new approaches and their application in early HCC detection.
Breast cancer is marked by large increases in the protein fibers around tumor cells. These fibers increase the mechanical stiffness of the tissue, which has long been used for tumor diagnosis by manual palpation. Recent research in bioengineering has led to the development of novel biomaterials that model the mechanical and architectural properties of the tumor microenvironment and can be used to understand how these cues regulate the growth and spread of breast cancer. Herein, we provide an overview of how the mechanical properties of breast tumor tissues differ from those of normal breast tissue and non-cancerous lesions. We also describe how biomaterial models make it possible to understand how the stiffness and viscosity of the extracellular environment regulate cell migration and breast cancer metastasis. We highlight the need for biomaterial models that allow independent analysis of the individual and different mechanical properties of the tumor microenvironment and that use cells derived from different regions within tumors. These models will guide the development of novel mechano-based therapies against breast cancer metastasis.
Intrahepatic cholangiocarcinoma (ICC) is the second most common liver cancer. Chemotherapy remains the main therapeutic strategy for advanced ICC patients, but chemosensitivity varies individually. Here, we applied cytometry by time-of-flight (CyTOF) to establish the immune profile of peripheral blood mononuclear cells (PBMCs) on the single-cell level at indicated time points before, during, and after chemotherapy. Multiplex immunofluorescence staining was applied to examine the spatial distribution of certain immune clusters. Tissue microarrays (TMAs) were used for prognostic evaluation. A total of 20 ICC patients treated with gemcitabine (GEM) were enrolled in our study, including eight cases with good response (R) and 12 cases with non-response (NR). Tremendous changes in PBMC composition, including an increased level of CD4/CD8 double-positive T cells (DPT), were observed after chemotherapy. Patients with higher level of CD4+CD45RO+CXCR3+ T cells before treatment had a favorable response to chemotherapy. Our study identified a positive correlation between the percentage of T cell subpopulations and clinical response after chemotherapy, which suggests that it is practical to predict the potential response before treatment by evaluating the proportions of the cell population in PBMCs.
Colorectal cancer (CRC) is the second leading cause of cancer related death worldwide. The 5-year survival rate of CRC patients depends on the stage at diagnosis, being higher than 80% when CRC is diagnosed in the early stages but lower than 10% when CRC is diagnosed in advanced stages. Autoantibodies against specific CRC autoantigens (tumor-associated antigens (TAAs)) in the sera of patients have been widely demonstrated to aid in early diagnosis. Thus, we herein aim to identify autoantigens target of autoantibodies specific to CRC that possess a significant ability to discriminate between CRC patients and healthy individuals by means of liquid biopsy. To that end, we examined the protein content of the exosomes released by five CRC cell lines and tissue samples from CRC patients by means of immunoprecipitation coupled with mass spectrometry analysis. A total of 103 proteins were identified as potential autoantigens specific to CRC. After bioinformatics and meta-analysis, we selected 15 proteins that are more likely to be actual CRC autoantigens in order to evaluate their role in CRC prognosis by Western blot (WB) and immunohistochemistry (IHC). We found dysregulation at the protein level for 11 of these proteins in both tissue and plasma exosome samples from patients, along with an association of nine of these proteins with CRC prognosis. After validation, all but one showed a statistically significant high diagnostic ability to distinguish CRC patients and individuals with premalignant lesions from healthy individuals, either by luminescence Halotag-based beads, or by a multiplexed biosensing platform involving the use of magnetic microcarriers as solid support modified with covalently immobilized Halotag fusion proteins constructed for CRC detection. Taken together, our results highlight the usefulness of the approach defined here to identify the TAAs specific to chronic diseases; they also demonstrate that the measurement of autoantibody levels in plasma against the TAAs identified here could be integrated into a point-of-care (POC) device for CRC detection with high diagnostic ability.
Rapid metastasis to vital organs such as the lung, liver, and brain is responsible for the vast majority of pancreatic cancer deaths. Liver metastasis of pancreatic cancer accounts for the high mortality rate in patients. Exosomes derived from pancreatic cancer cells tend to be enriched in proteins that are anchored to the cell membrane, supporting the reprogramming of the tumor microenvironment and the progression of distant metastatic lesions. For the first time, our study has demonstrated that CD44, a transmembrane glycoprotein delivered by exosomes, is involved in the metastatic process of pancreatic cancer. Moreover, CD44 was found to interact with integrin α6β4 to form a complex, thereby remodeling intracellular skeleton proteins, and to promote tumor cell motility through the activation of the Src and Ras signaling cascades. Notably, we also demonstrated that the CD44–α6β4 complex can be delivered to the target region via the paracrine effects of exosomes. The selective uptake of CD44-competent tumor exosomes by liver cells activated fibrotic pathways and generated a pre-metastatic niche by stimulating the cytokines, proinflammatory factors, and growth factors that ultimately support tumor metastasis. Our results suggest the potential application of exosomal CD44 as a biomarker for the clinical diagnosis of and therapy for pancreatic cancer.
Cancer cell separation is highly desirable for cancer diagnosis and therapy. Besides biochemical methods, engineered platforms are effective alternatives for sorting carcinoma cells from normal cells based on their unique properties in responding to the physical changes of the surrounding microenvironment. In this work, three-dimensional (3D) biomimetic scaffold platforms were developed to separate nasopharyngeal carcinoma 43 (NPC43) cells from immortalized nasopharyngeal epithelial 460 (NP460) cells based on precisely controlled design parameters including stiffness, number of layers, and structural layout. The migration characteristics of NPC43 and NP460 cells on the scaffold platforms revealed that NPC43 cells could squeeze into 10 µm wide, 15 µm deep trenches while NP460 cells could not. The different migration behavior was mainly due to cells having different interactions with the surrounding microenvironment. NPC43 cells had filopodia-like protrusions, while NP460 cells exhibited a sheet-like morphology. Using these 3D biomimetic platforms, 89% separation efficiency of NPC43 cells from NP460 cells was achieved on stiffer two-layer scaffold platforms with a 40/10 μm ridge/trench (R/T) grating on the top layer and a 20/10 μm R/T grid on the bottom layer. Moreover, the separation efficiency was further increased to 93% by adding an active conditioned medium (ACM) that caused the cells to have higher motility and deformability. These results demonstrate the capability to apply biomimetic engineered platforms with appropriate designs to separate cancer cells from normal cells for potential cancer diagnosis and treatment.
Rapid prototyping methods for the design and fabrication of polymeric labs-on-a-chip are on the rise, as they allow high degrees of precision and flexibility. For example, a microfluidic platform may require an optimization phase inwhich it could be necessary to continuously modify the architecture and geometry; however, this is only possible if easy, controllable fabrication methods and low-cost materials are available. In this paper, we describe the realization process of a microfluidic tool, from the computer-aided design (CAD) to the proof-of-concept application as a capture device for circulating tumor cells (CTCs). The entire platform was realized in polymethyl methacrylate (PMMA), combining femtosecond (fs) laser and micromilling fabrication technologies. The multilayer device was assembled through a facile and low-cost solvent-assisted method. A serpentine microchannel was then directly biofunctionalized by immobilizing capture probes able to distinguish cancer from non-cancer cells without labeling. The low material costs, customizable methods, and biological application of the realized platform make it a suitable model for industrial exploitation and applications at the point of care.
The coronavirus disease 2019 (COVID-19) pandemic has led to worldwide efforts to understand the biological traits of the newly identified human coronavirus (HCoV-19) virus. In this mass spectrometry (MS)-based study, we reveal that out of 21 possible glycosites in the HCoV-19 spike protein (S protein), 20 are completely occupied by N-glycans, predominantly of the oligomannose type. All seven glycosylation sites in human angiotensin I converting enzyme 2 (hACE2) were found to be completely occupied, mainly by complex N-glycans. However, glycosylation did not directly contribute to the binding affinity between HCoV-19 S protein and hACE2. Additional post-translational modification (PTM) was identified, including multiple methylated sites in both proteins and multiple sites with hydroxylproline in hACE2. Refined structural models of HCoV-19 S protein and hACE2 were built by adding N-glycan and PTMs to recently published cryogenic electron microscopy structures. The PTM and glycan maps of HCoV-19 S protein and hACE2 provide additional structural details for studying the mechanisms underlying host attachment and the immune response of HCoV-19, as well as knowledge for developing desperately needed remedies and vaccines.
It is difficult to identify suspected cases of atypical patients with coronavirus disease 2019 (COVID-19), and data on severe or critical patients are scanty. This retrospective study presents the clinical, laboratory, and radiological profiles, treatments, and outcomes of atypical COVID-19 patients without respiratory symptoms or fever at onset. The study examined ten atypical patients out of 909 severe or critical patients diagnosed with COVID-19 in Wuhan Union Hospital West Campus between 25 January 2020 and 10 February 2020. Data were obtained from the electronic medical records of severe or critical patients without respiratory symptoms or fever at onset. Outcomes were followed up to discharge or death. Among 943 COVID-19 patients, 909 (96.4%) were severe or critical type. Of the severe or critical patients, ten (1.1%) presented without respiratory symptoms or fever at admission. The median age of the ten participants was 63 years (interquartile range (IQR): 57–72), and seven participants were men. The median time from symptom onset to admission was 14 d (IQR: 7–20). Eight of the ten patients had chronic diseases. The patients had fatigue (n = 5), headache or dizziness (n = 4), diarrhea (n = 5), anorexia (n = 3), nausea or vomiting (n = 3), and eye discomfort (n = 1). Four patients were found to have lymphopenia. Imaging examination revealed that nine patients had bilateral pneumonia and one had unilateral pneumonia. Eventually, two patients died and eight were discharged. In the discharged patients, the median time from admission to discharge lasted 24 d (IQR: 13–43). In summary, some severe or critical COVID-19 patients were found to have no respiratory symptoms or fever at onset. All such atypical cases should be identified and quarantined as early as possible, since they tend to have a prolonged hospital stay or fatal outcomes. Chest computed tomography (CT) scan and nucleic acid detection should be performed immediately on close contacts of COVID-19 patients to screen out those with atypical infections, even if the contacts present without respiratory symptoms or fever at onset.
The reliable operation of flexible display devices poses a significant engineering challenge regarding the metrology of high barriers against water vapor. No reliable results have been reported in the range of 10-6 g∙(m2∙d)-1, and there is no standard ultra-barrier for calibration. To detect trace amount of water vapor permeation through an ultra-barrier with extremely high sensitivity and a greatly reduced test period, a predictive instrument was developed by integrating permeation models into high-sensitivity mass spectrometry measurement based on dynamic accumulation, detection, and evacuation of the permeant. Detection reliability was ensured by means of calibration using a standard polymer sample. After calibration, the lower detection limit for water vapor permeation is in the range of 10-7 g∙(m2∙d)-1, which satisfies the ultra-barrier requirement. Predictive permeation models were developed and evaluated using experimental data so that the steady-state permeation rate can be forecasted from non-steady-state results, thus enabling effective measurement of ultra-barrier permeation within a significantly shorter test period.
Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including electric vehicles, electric boats, electric trains, and electric airplanes. The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge (SOC) and capacity in real-time. This study proposes a multistage
model fusion algorithm to co-estimate SOC and capacity. Firstly, based on the assumption of a normal distribution, the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters. Secondly, a differential error gain with forward-looking ability is introduced into a proportional–integral observer
(PIO) to accelerate convergence speed. Thirdly, a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer (PIDO) to co-estimate SOC and capacity under a complex application environment. Fourthly, the convergence and anti-noise performance of the fusion algorithm are discussed. Finally, the hardware-in-the-loop platform is set up to verify the performance
of the fusion algorithm. The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2% and 3.3%, respectively.
Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper
extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.
The generation and controlled or uncontrolled release of hydrocarbon-contaminated industrial wastewater effluents to water matrices are a major environmental concern. The contaminated water comes to surface in the form of stable emulsions, which sometimes require different techniques to mitigate or separate effectively. Both the crude emulsions and hydrocarbon-contaminated wastewater effluents contain suspended solids, oil/grease, organic matter, toxic elements, salts, and recalcitrant chemicals. Suitable treatment of crude oil emulsions has been one of the most important challenges due to the complex nature and the substantial amount of generated waste. Moreover, the recovery of oil from waste will help meet the increasing demand for oil and its derivatives. In this context, functional nanostructured materials with smart surfaces and switchable wettability properties have gained increasing attention because of their excellent performance in the separation of oil–water emulsions. Recent improvements in the design, composition, morphology, and fine-tuning of polymeric nanostructured materials have resulted in enhanced demulsification functionalities. Herein, we reviewed the environmental impacts of crude oil emulsions and hydrocarbon-contaminated wastewater effluents. Their effective treatments by smart polymeric nanostructured materials with wettability properties have been stated with suitable examples. The fundamental mechanisms underpinning the efficient separation of oil–water emulsions are discussed with suitable examples along with the future perspectives of smart materials.