Initial Experience of CT-Based Online Adaptive Radiotherapy for Nasopharyngeal Carcinoma With a Novel Integrated Platform: A Case Report.

Intensity-modulated radiation therapy (IMRT) improves tumor control and reduces long-term radiation-induced complications of patients with nasopharyngeal carcinoma (NPC), contingent upon accurate contouring and precise delivery of treatment plans. Online adaptive radiotherapy (ART) involves real-time treatment plan modification based on the variations in targets and organs at risk (OARs) to uphold treatment planning accuracy. This study describes the first reported case of fan beam computed tomography (FBCT)-guided online ART for NPC using a novel integrated platform. Online ART was performed at the 25th fraction in this case, as tumors and the patient's anatomy were observed to regress inter-fractionally, necessitating adjustments to the contours based on the anatomy of the day. Online ART plan optimized target volume coverage while reducing doses to OARs. Notably, online ART significantly improved radiotherapy efficiency. This patient achieved a clinical complete response 12 weeks post-treatment, with Epstein-Barr virus DNA levels reduced to 0 copies/ml. Currently, the patient is alive without evidence of high-grade toxicity or local recurrence at approximately 10 months post-treatment. This case confirms the feasibility and dosimetric benefit of online ART for NPC using a novel integrated platform. Further research is needed to confirm its clinical benefits.

Multicolor emissive carbon dot-based fluorometric analysis platform for rapid quantification and discrimination of nitroimidazole antibiotic residues.

The development of efficient, rapid, portable, and accurate analysis of veterinary drug residues in food matrices is in great demand for food safety assessment. Here, we have developed a smartphone-integrated platform for fluorometric quantification of metronidazole (MNZ) residues and constructed a sensor array for discrimination of different nitroimidazole antibiotics (NIIMs). Multicolor CDs (B-CDs, C-CDs, Y-CDs, and R-CD) were prepared and showed different fluorescence response to MNZ. The fluorescence of C-CDs was quenched Because of the inner filter effect (IFE) between the C-CDs and MNZ, while that of R-CDs was enhanced due to the passivation of surface defects by MNZ. Based on the response pattern, the fluorometric quantification of MNZ based on the fluorescence images of C-CD + R-CD system (R/G values) was achieved with a low detection limit of 0.45 µM. By designing a smartphone-integrated platform, the analysis can be completed within 20 min. In addition, a fluorescence sensor array based C-CDs and R-CDs was also developed. The unique fingerprint of each NIIMs was obtained by linear discriminant analysis (LDA) of the response patterns, indicating an effective discrimination of five NIIMs. Moreover, the platform was used for quantification of MNZ in food samples and the recoveries were within 84.0-106.3 % with relative standard deviations 1.2-10.2 %. Therefore, the proposed method shows great potential as a universal platform for rapid detection of veterinary drug residues.

Integration of Magnetic-Field-Directed Self-Assembly-Based Cell Culture and Biosensing Platform for Improving hPSCs-Derived Neurons and Quantitative Detection of Neurotransmitter.

Despite the fact that human neural cell models have played significant roles in both research and cell replacement therapies for neurological diseases, the existing techniques for obtaining neurons from human pluripotent stem cells (hPSCs) are arduous and intricate. Additionally, the evaluation of neuron quality in the natural environment remains deficient. Consequently, we have developed a comprehensive platform utilizing magnetic-field-directed self-assembly (MDSA) of MXenes@Fe3O4 (M/F) nanocomposites. This platform facilitates the cultivation and in situ analysis of differentiated dopaminergic (DA) neurons. Our results showed that the introduction of M/F enhances neurite outgrowth and leads to the development of more intricate ramifications. Moreover, with the increase of magnetic field intensity, neurite outgrowth is further enhanced, and the proportion of differentiated mature neurons from hPSCs increases. This suggests that our platform promotes the maturation of neurons, emphasizing the crucial role of biophysical cues in expediting the differentiation process. The homogenization platform formed by MDSA of M/F nanocomposites exhibits high conductivity, leading to its exceptional performance in the real-time monitoring of the release of dopamine neurotransmitter from hPSC-derived DA neurons. Hence, this platform demonstrates significant potential for monitoring cell quality. In conclusion, our integrated platform, based on MDSA of M/F nanocomposites, offers a reliable and efficient means for the in vitro generation of human neurons with a controllable quality. The as-prepared platform holds potential for enhancing neuronal maturation and ensuring consistent cell quality, showing significant implications for in vitro biological research, disease modeling, and cell replacement therapy.

[Development and Application of Medical Imaging Analysis Platform Based on Radiomics and Machine Learning Technologies].

OBJECTIVE: In order to solve the technical problems, clinical researchers face the process of medical imaging analysis such as data labeling, feature extraction and algorithm selection, a medical imaging oriented multi-disease research platform based on radiomics and machine learning technology was designed and constructed. METHODS: Five aspects including data acquisition, data management, data analysis, modeling and data management were considered. This platform provides comprehensive functions such as data retrieve and data annotation, image feature extraction and dimension reduction, machine learning model running, results validation, visual analysis and automatic generation of analysis reports, thus an integrated solution for the whole process of radiomics analysis has been generated. RESULTS: Clinical researchers can use this platform for the whole process of radiomics and machine learning analysis for medical images, and quickly produce research results. CONCLUSIONS: This platform greatly shortens the time for medical image analysis research, decreasing the work difficulty of clinical researchers, as well as significantly promoting their working efficiency.

NeuTHOR Station-A Novel Integrated Platform for Monitoring BNCT Clinical Treatment, Animal and Cell Irradiation Study at THOR.

(1) Background: A well-established Boron Neutron Capture Therapy (BNCT) facility includes many essential systems, which are the epithermal neutron beam system, on-line monitoring system (OMS), QA/QC (quality assurance or quality control) system, boron concentration (BC) measurement system, and treatment planning system (TPS). Accurate data transmission, monitoring, and deposition among these systems are of vital importance before, during, and after clinical, animal, and cell BNCT irradiation. This work developed a novel integrated platform NeuTHOR Station (NeuTHORS) for BNCT at Tsing Hua Open-pool Reactor (THOR). Apart from the data of the OMS and QA/QC system, the data of BC and TPS can be loaded on NeuTHORS before BNCT clinical, animal, and cell irradiation. (2) Methods: A multi-paradigm computer programming language c# (c sharp) was used to develop the integrated platform NeuTHORS. The design of NeuTHORS is based on the standard procedures of BNCT treatment or experiment at THOR. Moreover, parallel testing with OMS-BNCT (the former OMS) and QA/QC of THOR was also performed for more than 70 times to verify the validation of NeuTHORS. (3) Results: According to the comparisons of the output, NeuTHORS and OMS-BNCT and QA/QC of THOR show very good consistency. NeuTHORS is now installed on an industrial PC (IPC) and successfully performs the monitoring of BNCT Treatment at THOR. Patients' f BC and TPS data are also input into NeuTHORS and stored on IPC through an internal network from BC measurement room and TPS physicist. Therefore, the treatment data of each patient can be instantaneously established after each BNCT treatment for further study on BNCT. NeuTHORS can also be applied on data acquisition for a BNCT-related study, especially for animal or cell irradiation experiments. (4) Conclusions: A novel integrated platform NeuTHOR Station for monitoring BNCT clinical treatment and animal and cell irradiation study has been successfully established at THOR. With this platform, BNCT radiobiology investigations will be efficiently performed and a thorough data storage and analysis system of BNCT treatments or experiments can thus be systematically built up for the further investigation of BNCT at THOR.

Recent advancement in nano-optical strategies for detection of pathogenic bacteria and their metabolites in food safety.

Pathogenic bacteria and their metabolites are the leading risk factor in food safety and are one of the major threats to human health because of the capability of triggering diseases with high morbidity and mortality. Nano-optical sensors for bacteria sensing have been greatly explored with the emergence of nanotechnology and artificial intelligence. In addition, with the rapid development of cross fusion technology, other technologies integrated nano-optical sensors show great potential in bacterial and their metabolites sensing. This review focus on nano-optical strategies for bacteria and their metabolites sensing in the field of food safety; based on surface-enhanced Raman scattering (SERS), fluorescence, and colorimetric biosensors, and their integration with the microfluidic platform, electrochemical platform, and nucleic acid amplification platform in the recent three years. Compared with the traditional techniques, nano optical-based sensors have greatly improved the sensitivity with reduced detection time and cost. However, challenges remain for the simple fabrication of biosensors and their practical application in complex matrices. Thus, bringing out improvements or novelty in the pretreatment methods will be a trend in the upcoming future.

Fluorescence Super-Resolution Imaging Chip for Gene Silencing Exosomes.

Tumor cell-derived extracellular vesicles and their cargo of bioactive substances have gradually been recognized as novel biomarkers for cancer diagnosis. Meanwhile, the PD-L1 (Programmed Death-Ligand 1) protein, as an immune checkpoint molecule, is highly expressed on certain tumor cells and holds significant potential in immune therapy. In comparison to PD-L1 monoclonal antibodies, the inhibitory effect of PD-L1 siRNA (small interfering RNA) is more advantageous. In this article, we introduced a microfluidic chip integrating cell cultivation and exosome detection modules, which were intended for the investigation of the gene silencing effect of PD-L1 siRNA. Basically, cells were first cultured with PD-L1 siRNA in the chip. Then, the secreted exosomes were detected via super-resolution imaging, to validate the inhibitory effect of siRNA on PD-L1 expression. To be specific, a "sandwich" immunological structure was employed to detect exosomes secreted from HeLa cells. Immunofluorescence staining and DNA-PAINT (DNA Point Accumulation for Imaging in Nanoscale Topography) techniques were utilized to quantitatively analyze the PD-L1 proteins on HeLa exosomes, which enabled precise structural and content analysis of the exosomes. Compared with other existing PD-L1 detection methods, the advantages of our work include, first, the integration of microfluidic chips greatly simplifying the cell culture, gene silencing, and PD-L1 detection procedures. Second, the utilization of DNA-PAINT can provide an ultra-high spatial resolution, which is beneficial for exosomes due to their small sizes. Third, qPAINT could allow quantitative detection of PD-L1 with better precision. Hence, the combination of the microfluidic chip with DNA-PAINT could provide a more powerful integrated platform for the study of PD-L1-related tumor immunotherapy.

Emerging nanozyme-based multimodal synergistic therapies in combating bacterial infections.

Pathogenic infections have emerged as major threats to global public health. Multidrug resistance induced by the abuse of antibiotics makes the anti-infection therapies to be a global challenge. Thus, it is urgent to develop novel, efficient and biosafe antibiotic alternatives for future antibacterial therapy. Recently, nanozymes have emerged as promising antibiotic alternatives for combating bacterial infections. More significantly, the multimodal synergistic nanozyme-based antibacterial systems open novel disinfection pathways. In this review, we are mainly focusing on the recent research progress of nanozyme-based multimodal synergistic therapies to eliminate bacterial infections. Their antibacterial mechanism, the synergistic antibacterial systems are systematically summarized and discussed according to the combination of mechanisms and the purpose to improve their antibacterial efficiency, biosafety and specificity. Finanly, the current challenges and prospects of the multimodal synergistic antibacterial systems are proposed.

New Digital Health Technologies for Insulin Initiation and Optimization for People With Type 2 Diabetes.

OBJECTIVE: The health and economic burden of type 2 diabetes is of global significance. Many people with type 2 diabetes eventually need insulin to help reduce their risk of serious associated complications. However, barriers to the initiation and/or optimization of insulin expose people with diabetes to sustained hyperglycemia. In this review, we investigated how new and future technologies may provide opportunities to help overcome these barriers to the initiation and/or optimization of insulin. METHODS: A focused literature search of PubMed and key scientific congresses was conducted. Software tools and devices developed to support the initiation and/or optimization of insulin were identified by manually filtering >300 publications and conference abstracts. RESULTS: Most software tools have been developed for smartphone platforms. At present, published data suggest that the use of these technologies is associated with equivalent or improved glycemic outcomes compared with standard care, with additional benefits such as reduced time burden and improved knowledge of diabetes among health care providers. However, there remains paucity of good-quality evidence. Most new devices to support insulin therapy help track the dose and timing of insulin. CONCLUSION: New digital health tools may help to reduce barriers to optimal insulin therapy. An integrated solution that connects glucose monitoring, dose recording, and titration advice as well as records comorbidities and lifestyle factors has the potential to reduce the complexity and burden of treatment and may improve adherence to titration and treatment, resulting in better outcomes for people with diabetes.

A Self-Established "Machining-Measurement-Evaluation" Integrated Platform for Taper Cutting Experiments and Applications.

Taper-cutting experiments are important means of exploring the nano-cutting mechanisms of hard and brittle materials. Under current cutting conditions, the brittle-ductile transition depth (BDTD) of a material can be obtained through a taper-cutting experiment. However, taper-cutting experiments mostly rely on ultra-precision machining tools, which have a low efficiency and high cost, and it is thus difficult to realize in situ measurements. For taper-cut surfaces, three-dimensional microscopy and two-dimensional image calculation methods are generally used to obtain the BDTDs of materials, which have a great degree of subjectivity, leading to low accuracy. In this paper, an integrated system-processing platform is designed and established in order to realize the processing, measurement, and evaluation of taper-cutting experiments on hard and brittle materials. A spectral confocal sensor is introduced to assist in the assembly and adjustment of the workpiece. This system can directly perform taper-cutting experiments rather than using ultra-precision machining tools, and a small white light interference sensor is integrated for in situ measurement of the three-dimensional topography of the cutting surface. A method for the calculation of BDTD is proposed in order to accurately obtain the BDTDs of materials based on three-dimensional data that are supplemented by two-dimensional images. The results show that the cutting effects of the integrated platform on taper cutting have a strong agreement with the effects of ultra-precision machining tools, thus proving the stability and reliability of the integrated platform. The two-dimensional image measurement results show that the proposed measurement method is accurate and feasible. Finally, microstructure arrays were fabricated on the integrated platform as a typical case of a high-precision application.

Photonic Matrix Computing: From Fundamentals to Applications.

In emerging artificial intelligence applications, massive matrix operations require high computing speed and energy efficiency. Optical computing can realize high-speed parallel information processing with ultra-low energy consumption on photonic integrated platforms or in free space, which can well meet these domain-specific demands. In this review, we firstly introduce the principles of photonic matrix computing implemented by three mainstream schemes, and then review the research progress of optical neural networks (ONNs) based on photonic matrix computing. In addition, we discuss the advantages of optical computing architectures over electronic processors as well as current challenges of optical computing and highlight some promising prospects for the future development.

Ontology Driven Smart Health Service Integration.

BACKGROUND AND OBJECTIVE: The massive increase, in the Internet of Things applications, has greatly evolved technological aspects of human life. The drastic development of IoT based smart healthcare services have layout the smart process models to facilitate all stakeholders (e.g. patients, doctors, hospitals etc.) and made it an important social-economic concern. There are variety of smart healthcare services like remote patient monitoring, diagnostic, disease specific remote treatments and telemedicine. Many trending Internet of Health Things research and development are done in a very disjoint and independent fashion providing solutions and guidelines for variant diseases, medical resources and remote services management. These expositions work over many shared resources such as health facilities for patient and human in healthcare system. METHODS: This research discusses the ontology for merging methods to form an integrated platform with shared knowledge of smart healthcare services. The proposed process model creates an ontological framework of integrated healthcare services, which are firstly defined using ontologies and lately integrated over similarities, differences, dependencies and other semantic relations. The data and process requirements for service integration facility is derived from various smart healthcare services. RESULTS: The proposed model is evaluated using two-step ontological modeling testing method, applied at the ontological framework of integrated smart health services. First evaluation step has targeted the model consistency validation using reasoning tool while querying tools are used to validate the retrieved data entities and relations among them for predefined use-cases. CONCLUSIONS: The research concluded with a novel approach for smart health service integration using ontological modeling and merging techniques. The model efficiency enhancement and query optimization methods are listed in future tasks of the research.

Fully integrated protein absolute quantification platform for analysis of multiple tumor markers in human plasma.

In this study, we developed a fully integrated protein absolute quantification platform for simultaneous analysis of multiple tumor markers in human plasma, by which multiple target proteins (alpha-fetoprotein, prostate-specific antigen, carcino-embryonic antigen and mucin-1) were firstly enriched by aptamers immobilized capillary column using graphene oxide modified polymer microsphere as the separation matrix, and then the eluted target proteins were online denatured, reduced, desalted and digested by our developed fully automated sample treatment device (FAST), finally the resulting peptides were analyzed by parallel reaction monitoring (PRM) on LTQ-orbitrap velos mass spectrometry. Compared to traditional ELISA assay, the platform exhibited significant advantages such as short analysis time, low limit of detection, and ease of automation. Furthermore, our developed platform was also applied in the absolute quantification of tumor markers from clinical human plasma samples, and the results were comparable to those obtained by clinical immunoassay. All the results demonstrated that such a platform could provide a promising tool for achieving high sensitivity, high accuracy, and high throughput detection of disease related protein markers in the routine physical examination and clinical disease diagnosis.

Circulating tumor cell detection and single-cell analysis using an integrated workflow based on ChimeraX® -i120 Platform: A prospective study.

Circulating tumor cell (CTC) analysis holds great potential to be a noninvasive solution for clinical cancer management. A complete workflow that combined CTC detection and single-cell molecular analysis is required. We developed the ChimeraX® -i120 platform to facilitate negative enrichment, immunofluorescent labeling, and machine learning-based identification of CTCs. Analytical performances were evaluated, and a total of 477 participants were enrolled to validate the clinical feasibility of ChimeraX® -i120 CTC detection. We analyzed copy number alteration profiles of isolated single cells. The ChimeraX® -i120 platform had high sensitivity, accuracy, and reproducibility for CTC detection. In clinical samples, an average value of > 60% CTC-positive rate was found for five cancer types (i.e., liver, biliary duct, breast, colorectal, and lung), while CTCs were rarely identified in blood from healthy donors. In hepatocellular carcinoma patients treated with curative resection, CTC status was significantly associated with tumor characteristics, prognosis, and treatment response (all P < 0.05). Single-cell sequencing analysis revealed that heterogeneous genomic alteration patterns resided in different cells, patients, and cancers. Our results suggest that the use of this ChimeraX® -i120 platform and the integrated workflow has validity as a tool for CTC detection and downstream genomic profiling in the clinical setting.

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

Development of coupling controlled polymerizations by adapter-ligation in mate-pair sequencing for detection of various genomic variants in one single assay.

The diversity of disease presentations warrants one single assay for detection and delineation of various genomic disorders. Herein, we describe a gel-free and biotin-capture-free mate-pair method through coupling Controlled Polymerizations by Adapter-Ligation (CP-AL). We first demonstrated the feasibility and ease-of-use in monitoring DNA nick translation and primer extension by limiting the nucleotide input. By coupling these two controlled polymerizations by a reported non-conventional adapter-ligation reaction 3' branch ligation, we evidenced that CP-AL significantly increased DNA circularization efficiency (by 4-fold) and was applicable for different sequencing methods but at a faction of current cost. Its advantages were further demonstrated by fully elimination of small-insert-contaminated (by 39.3-fold) with a ∼50% increment of physical coverage, and producing uniform genome/exome coverage and the lowest chimeric rate. It achieved single-nucleotide variants detection with sensitivity and specificity up to 97.3 and 99.7%, respectively, compared with data from small-insert libraries. In addition, this method can provide a comprehensive delineation of structural rearrangements, evidenced by a potential diagnosis in a patient with oligo-atheno-terato-spermia. Moreover, it enables accurate mutation identification by integration of genomic variants from different aberration types. Overall, it provides a potential single-integrated solution for detecting various genomic variants, facilitating a genetic diagnosis in human diseases.

New voyages to explore the natural product galaxy.

Natural products are a large family of diverse and complex chemical molecules that have roles in both primary and secondary metabolism, and over 210,000 natural products have been described. Secondary metabolite natural products are of high commercial and societal value with therapeutic uses as antibiotics, antifungals, antitumor and antiparasitic products and in agriculture as products for crop protection and animal health. There is a resurgence of activity in exploring natural products for a wide range of applications, due to not only increasing antibiotic resistance, but the advent of next-generation genome sequencing and new technologies to interrogate and investigate natural product biosynthesis. Genome mining has revealed a previously undiscovered richness of biosynthetic potential in novel biosynthetic gene clusters for natural products. Complementing these computational processes are new experimental platforms that are being developed and deployed to access new natural products.

Integrated platform with combination of on-line protein digestion, isotope dimethyl labeling and multidimensional peptide separation for high-throughput proteome quantification.

In recent years, various enzymatic microreactors and on-line enzyme digestion strategies have been widely applied in high throughput proteome analysis. However, the incomplete and irreproducible digestion would introduce some unexpected variations in comparative proteome quantification when the samples are digested and then chemically isotope labeled in different aliquots. To address these problems, we developed an integrated platform for high throughput proteome quantification with combination of on-line low miss-cleavage protein digestion by an ultra-performance immobilized enzymatic reactor, on-line dimethyl labeling onto a C18 precolumn, peptide separation by two-dimensional nano liquid chromatography and MS detection. Compared to traditional off-line method, such a platform exhibits obvious advantages such as high sensitivity, throughput, accuracy, precision and ease of automation. All these results demonstrated that such a platform might become a promising technique for the quantitative proteome analysis.

Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping.

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

REmote SUpervision to Decrease HospitaLization RaTe. Unified and integrated platform for data collected from devices manufactured by different companies: Design and rationale of the RESULT study.

The number of patients with heart failure implantable cardiac electronic devices (CIEDs) is growing. Hospitalization rate in this group is very high and generates enormous costs. To avoid the need for hospital treatment, optimized monitoring and follow-up is crucial. Remote monitoring (RM) has been widely put into practice in the management of CIEDs but it may be difficult due to the presence of differences in systems provided by device manufacturers and loss of gathered data in case of device reimplantation. Additionally, conclusions derived from studies about usefulness of RM in clinical practice apply to devices coming only from a single company. An integrated monitoring platform allows for more comprehensive data analysis and interpretation. Therefore, the primary objective of Remote Supervision to Decrease Hospitalization Rate (RESULT) study is to evaluate the impact of RM on the clinical status of patients with ICDs or CRT-Ds using an integrated platform. Six hundred consecutive patients with ICDs or CRT-Ds implanted will be prospectively randomized to either a traditional or RM-based follow-up model. The primary clinical endpoint will be a composite of all-cause mortality or hospitalization for cardiovascular reasons within 12 months after randomization. The primary technical endpoint will be to construct and evaluate a unified and integrated platform for the data collected from RM devices manufactured by different companies. This manuscript describes the design and methodology of the prospective, randomized trial designed to determine whether remote monitoring using an integrated platform for different companies is safe, feasible, and efficacious (ClinicalTrials.gov Identifier: NCT02409225).