The role of multiplatform messaging applications in burns care and rehabilitation: A systematic review.

BACKGROUND: Multiplatform messaging applications also referred to as cross-platform instant messaging play an important role in delivery of healthcare and education with its low cost, ease of use and accessibility. AIM: To evaluate the existing evidence regarding the use of multiplatform messaging applications in facilitating consultations and decision-making processes in the context of burns care, as well as to assess the impact of such applications on burns care and rehabilitation. METHOD: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and PROSPERO protocol CRD42021265203. The CASP and JBI tools were used to evaluate the quality of the studies. Eight hundred fifty-three papers were retrieved from PubMed, CINAHL, Scopus, EMBASE and LILACS published up to July 2022 (updated August 2023) with no time restrictions applied. RESULTS: An analysis of the seven studies included in this review, inclusive of 16 Multiplatform messaging applications, revealed six themes. These encompassed the utilization of social media for directing and managing clinical practice, as a mode of communication, for evaluating the quality-of-care provision, for investigating available platforms and their technological features, measuring quality of life and for examining issues related to confidentiality. CONCLUSION: Multiplatform messaging applications offer a solution for individuals with burn injuries to stay in direct contact with burn specialist clinicians for their follow-up and subsequent rehabilitation phase of recovery.

Transferability of Technical Skills Across Robotic Surgery Platforms: A Scoping Review.

As the application of robotic approaches to surgery continues to broaden, new consoles have been introduced to the market. Due to the global utilization of a single platform, previously validated curricula have not been assessed on new robotic systems. Surgery by its nature occurs in a high-stakes environment, potentially exacerbated by non-standardized robotic systems. The aim of this review is to critique the evidence available regarding the transferability of technical skills across robotic platforms. A scoping review utilizing the Medline (Pubmed) and Cochrane Databases was conducted. Full texts were reviewed and appraised. Selected articles were eligible for inclusion if they investigated the ability or implications of the transfer of skill across robotic platforms. Data was extracted, coded inductively, and themes synthesized. NVIVO software was used as an adjunct for this qualitative analysis. Following the removal of duplicates a total of 278 papers were screened according to the eligibility criteria. Fifty full-text articles were reviewed and four met the criterion for inclusion. Novices' performance across platforms was comparable. Increasing levels of prior robotic experience revealed an improvement in technical performance on a novel robotic platform. Safety metrics appear comparable across systems. Quantifying learning curves across robotic platforms and their implications for the robotic surgeon in training remains to be determined. Future research needs to address the gaps in the literature by clearly defining the extent of technical skills transfer between robotic platforms. These factors will guide the next iteration of surgical training curriculums and regulations for robotic surgery.

Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research.

The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).

Fuelling the climate and science 'denial machine' on social media: A case study of the Great Barrier Reef's 2021 'in danger' recommendation on Twitter, YouTube and Facebook.

Australian climate policy has been stifled by a network of free-market and extractive industry-advocating actors, yet there is little empirical evidence to show how these actors and information flows behave in online communication spaces during Australian environmental conflicts. Focusing on the UNESCO 2021 'in danger' recommendation for the Great Barrier Reef for 6 weeks, this mixed-methods study of Twitter, Facebook and YouTube uses social network analysis, including cluster analysis and in-depth close reading. We find that a small, yet significant, mix of ideologically aligned partisan actors are fuelling the 'denial machine' in Australia by co-opting a scientific report's findings to argue that the Great Barrier Reef has recovered, and to contest the need for climate action. This article offers insights into the central actors and tactics that could erode public support for Australian climate policy, with similarities to strategies already established in the United States. It also contributes to furthering multi-platform analyses.

Clethra fimbriata hexanic extract triggers alteration in the energy metabolism in epimastigotes of Trypanosoma cruzi.

Chagas disease (ChD), caused by Trypanosoma cruzi, is endemic in American countries and an estimated 8 million people worldwide are chronically infected. Currently, only two drugs are available for therapeutic use against T. cruzi and their use is controversial due to several disadvantages associated with side effects and low compliance with treatment. Therefore, there is a need to search for new tripanocidal agents. Natural products have been considered a potential innovative source of effective and selective agents for drug development to treat T. cruzi infection. Recently, our research group showed that hexanic extract from Clethra fimbriata (CFHEX) exhibits anti-parasitic activity against all stages of T. cruzi parasite, being apoptosis the main cell death mechanism in both epimastigotes and trypomastigotes stages. With the aim of deepening the understanding of the mechanisms of death induced by CFHEX, the metabolic alterations elicited after treatment using a multiplatform metabolomics analysis (RP/HILIC-LC-QTOF-MS and GC-QTOF-MS) were performed. A total of 154 altered compounds were found significant in the treated parasites corresponding to amino acids (Arginine, threonine, cysteine, methionine, glycine, valine, proline, isoleucine, alanine, leucine, glutamic acid, and serine), fatty acids (stearic acid), glycerophospholipids (phosphatidylcholine, phosphatidylethanolamine and phosphatidylserine), sulfur compounds (trypanothione) and carboxylic acids (pyruvate and phosphoenolpyruvate). The most affected metabolic pathways were mainly related to energy metabolism, which was found to be decrease during the evaluated treatment time. Further, exogenous compounds of the triterpene type (betulinic, ursolic and pomolic acid) previously described in C. fimbriata were found inside the treated parasites. Our findings suggest that triterpene-type compounds may contribute to the activity of CFHEX by altering essential processes in the parasite.

Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP.

BACKGROUND: With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data. RESULTS: We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project at: https://github.com/HKU-BAL/Clair3-MP . CONCLUSIONS: These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

REM sleep deprivation induced by the modified multi-platform method has detrimental effects on memory: A systematic review and meta-analysis.

The modified multi-platform method (MMPM) is used to induce animal models of paradoxical sleep deprivation and impairs memory in rodents. However, variations in MMPM protocols have contributed to inconsistent conclusions across studies. This meta-analysis aimed to assess the variations of the MMPM and their effects on memory in rats and mice. A comprehensive search identified 60 studies, and 50 were included in our meta-analysis. Overall, the meta-analysis showed that the MMPM significantly reduced the percentage of time spent in target quadrants (I2 = 54 %, 95 % confidence interval [CI] = [-1.83, -1.18]) and the number of platform-area crossings (I2 = 26 %, 95 % CI = [-1.71, -1.07]) in the Morris water maze (MWM) and shortened the latency to entering the dark compartment in the passive avoidance task (I2 = 68 %, 95 % CI = [-1.36, -0.57]), but it increased the number of errors in the radial arm water maze (RAWM) (I2 = 59 %, 95 % CI = [1.29, 2.07]). Additionally, mice performed worse on the MWM, whereas rats performed worse on the passive avoidance task. More significant memory deficits were found in cross-learning and post-learning MMPM in the MWM and RAWM, respectively. This study provided evidence that the MMPM can be used in preclinical studies of memory deficits induced by paradoxical sleep deprivation.

Benchmarking multi-platform sequencing technologies for human genome assembly.

Genome assembly is a computational technique that involves piecing together deoxyribonucleic acid (DNA) fragments generated by sequencing technologies to create a comprehensive and precise representation of the entire genome. Generating a high-quality human reference genome is a crucial prerequisite for comprehending human biology, and it is also vital for downstream genomic variation analysis. Many efforts have been made over the past few decades to create a complete and gapless reference genome for humans by using a diverse range of advanced sequencing technologies. Several available tools are aimed at enhancing the quality of haploid and diploid human genome assemblies, which include contig assembly, polishing of contig errors, scaffolding and variant phasing. Selecting the appropriate tools and technologies remains a daunting task despite several studies have investigated the pros and cons of different assembly strategies. The goal of this paper was to benchmark various strategies for human genome assembly by combining sequencing technologies and tools on two publicly available samples (NA12878 and NA24385) from Genome in a Bottle. We then compared their performances in terms of continuity, accuracy, completeness, variant calling and phasing. We observed that PacBio HiFi long-reads are the optimal choice for generating an assembly with low base errors. On the other hand, we were able to produce the most continuous contigs with Oxford Nanopore long-reads, but they may require further polishing to improve on quality. We recommend using short-reads rather than long-reads themselves to improve the base accuracy of contigs from Oxford Nanopore long-reads. Hi-C is the best choice for chromosome-level scaffolding because it can capture the longest-range DNA connectedness compared to 10× linked-reads and Bionano optical maps. However, a combination of multiple technologies can be used to further improve the quality and completeness of genome assembly. For diploid assembly, hifiasm is the best tool for human diploid genome assembly using PacBio HiFi and Hi-C data. Looking to the future, we expect that further advancements in human diploid assemblers will leverage the power of PacBio HiFi reads and other technologies with long-range DNA connectedness to enable the generation of high-quality, chromosome-level and haplotype-resolved human genome assemblies.

Multi-Temporal Satellite Investigation of gas Flaring in Iraq and Iran: The DAFI Porting on Collection 2 Landsat 8/9 and Sentinel 2A/B.

The synergic use of satellite data at moderate spatial resolution (i.e., 20-30 m) from the new Collection 2 (C2) Landsat-8/9 (L8/9) Operational Land Imager (OLI) and Sentinel-2 (S2) Multispectral Instrument (MSI) provides a new perspective in the remote sensing applications for gas flaring (GF) identification and monitoring, thanks to a significant improvement in the revisiting time (up to ~3 days). In this study, the daytime approach for gas flaring investigation (DAFI), recently developed for identifying, mapping and monitoring GF sites on a global scale using the L8 infrared radiances, has been ported on a virtual constellation (VC) (formed by C2 L8/9 + S2) to assess its capability in understanding the GF characteristics in the space-time domain. The findings achieved for the regions of Iraq and Iran, ranked at the second and third level among the top 10 gas flaring countries in 2022, demonstrate the reliability of the developed system, with improved levels of accuracy and sensitivity (+52%). As an outcome of this study, a more realistic picture of GF sites and their behavior is achieved. A new step aimed at quantifying the GFs radiative power (RP) has been added in the original DAFI configuration. The preliminary analysis of the daily OLI- and MSI-based RP, provided for all the sites by means of a modified RP formulation, revealed their good matching. An agreement of 90% and 70% between the annual RPs computed in Iraq and Iran and both their gas-flared volumes and carbon dioxide emissions were also recorded. Being that gas flaring is one of the main sources of greenhouse gases (GHG) worldwide, the RP products may concur to infer globally the GHGs GF emissions at finer spatial scales. For the presented achievements, DAFI can be seen as a powerful satellite tool able to automatically assess the gas flaring dimension on a global scale.

Batch Correction and Harmonization of -Omics Datasets with a Tunable Median Polish of Ratio.

Large scale -omics datasets can provide new insights into normal and disease-related biology when analyzed through a systems biology framework. However, technical artefacts present in most -omics datasets due to variations in sample preparation, batching, platform settings, personnel, and other experimental procedures prevent useful analyses of such data without prior adjustment for these technical factors. Here, we demonstrate a tunable median polish of ratio (TAMPOR) approach for batch effect correction and agglomeration of multiple, multi-batch, site-specific cohorts into a single analyte abundance data matrix that is suitable for systems biology analyses. We illustrate the utility and versatility of TAMPOR through four distinct use cases where the method has been applied to different proteomic datasets, some of which contain a specific defect that must be addressed prior to analysis. We compare quality control metrics and sources of variance before and after application of TAMPOR to show that TAMPOR is effective at removing batch effects and other unwanted sources of variance in -omics data. We also show how TAMPOR can be used to harmonize -omics datasets even when the data are acquired using different analytical approaches. TAMPOR is a powerful and flexible approach for cleaning and harmonization of -omics data prior to downstream systems biology analysis.

Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data.

In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.

PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA.

Recent technological advances have made it possible to measure multiple types of many features in biomedical studies. However, some data types or features may not be measured for all study subjects because of cost or other constraints. We use a latent variable model to characterize the relationships across and within data types and to infer missing values from observed data. We develop a penalized-likelihood approach for variable selection and parameter estimation and devise an efficient expectation-maximization algorithm to implement our approach. We establish the asymptotic properties of the proposed estimators when the number of features increases at a polynomial rate of the sample size. Finally, we demonstrate the usefulness of the proposed methods using extensive simulation studies and provide an application to a motivating multi-platform genomics study.

Multiplatform untargeted metabolomics.

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Self-management system for postpartum women with hypertension disorders: an eHealth application intervention study.

BACKGROUND: Hypertension disorders are relatively common in pregnant women and often persist in the postpartum period. Few studies are available regarding the self-management of postpartum hypertension via the eHealth system. This study aimed to develop a self-management eHealth system for women with postpartum hypertension during the postpartum period. METHODS: We adopted a multi-platform system for this research, not only for use on the web interface but also on smartphones. The proposed system possessed three features: (1) the population was limited to postnatal women with hypertension; (2) a self-care record, which allowed postnatal women to keep track of their blood pressure, pulse, weight, medication record, exercise record, and risk factor assessment; and (3) through this system, nurse-midwives could keep track of postnatal women's health status maintaining the complete record and could communicate directly with the users if their health monitor values reach beyond normal range. RESULTS: Thirty-nine postnatal women with postpartum hypertension were recruited to the study. A survey to evaluate the usability and satisfaction of the proposed e-health application system was completed by these women. The usability rate of the system reached 92.4% (46.2% satisfied and 46.2% strongly satisfied), which suggested that the system was helpful to the users. The satisfaction rate of the system reached 94.9% (43.6% satisfied and 51.3% strongly satisfied), which suggested that the system was acceptable to the users. CONCLUSION: This proposed system has been developed completely with user experience and professional advice from experts. Postnatal women could gain important postpartum-related knowledge and access their related health records and other information easily via their smartphones or computers. During the postpartum period, an eHealth application system can effectively assist women with hypertension to manage their blood pressure and related postnatal healthcare issues.

Longitudinal, Multi-Platform Metagenomics Yields a High-Quality Genomic Catalog and Guides an In Vitro Model for Cheese Communities.

Microbiomes are intricately intertwined with human health, geochemical cycles, and food production. While many microbiomes of interest are highly complex and experimentally intractable, cheese rind microbiomes have proven to be powerful model systems for the study of microbial interactions. To provide a more comprehensive view of the genomic potential and temporal dynamics of cheese rind communities, we combined longitudinal, multi-platform metagenomics of three ripening washed-rind cheeses with whole-genome sequencing of community isolates. Sequencing-based approaches revealed a highly reproducible microbial succession in each cheese and the coexistence of closely related Psychrobacter species and enabled the prediction of plasmid and phage diversity and their host associations. In combination with culture-based approaches, we established a genomic catalog and a paired 16-member in vitro washed-rind cheese system. The combination of multi-platform metagenomic time-series data and an in vitro model provides a rich resource for further investigation of cheese rind microbiomes both computationally and experimentally. IMPORTANCE Metagenome sequencing can provide great insights into microbiome composition and function and help researchers develop testable hypotheses. Model microbiomes, such as those composed of cheese rind bacteria and fungi, allow the testing of these hypotheses in a controlled manner. Here, we first generated an extensive longitudinal metagenomic data set. This data set reveals successional dynamics, yields a phyla-spanning bacterial genomic catalog, associates mobile genetic elements with their hosts, and provides insights into functional enrichment of Psychrobacter in the cheese environment. Next, we show that members of the washed-rind cheese microbiome lend themselves to in vitro community reconstruction. This paired metagenomic data and in vitro system can thus be used as a platform for generating and testing hypotheses related to the dynamics within, and the functions associated with, cheese rind microbiomes.

Identifying subgroups of childhood obesity by using multiplatform metabotyping.

Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.

BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants.

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis.

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

Immunogenomic profiling of lung adenocarcinoma reveals poorly differentiated tumors are associated with an immunogenic tumor microenvironment.

OBJECTIVES: Pathologists have routinely observed distinct histologic patterns of growth in early-stage lung adenocarcinoma (LUAD), which have been suggested to be associated with prognosis. Herein, we investigated the relationship between LUAD patterns of growth, as defined by the updated international association for the study of lung cancer (IASLC) grading criteria, and differences in the tumor immune microenvironment to identify predictors of response to immunotherapy. METHODS: 174 resected stage I-III LUAD tumors were classified by histologic pattern of growth (i.e. solid, micropapillary, acinar, papillary, and lepidic) and then grouped as well differentiated, moderately differentiated, and poorly differentiated. Comprehensive multiplatform analysis including whole exome sequencing, gene expression profiling, immunohistochemistry, CIBERSORT, and T-cell receptor sequencing was performed and groups were compared for differences in genomic drivers, immune cell infiltrate, clonality, and survival. Finally, multivariate analysis was performed adjusting for pathologic stage and smoking status. RESULTS: Poorly differentiated tumors demonstrated a strong association with smoking relative to moderately differentiated or well differentiated tumors. However, unlike in prior reports, poorly differentiated tumors were not associated with a worse survival after curative-intent resection. Genomic analysis revealed that poorly differentiated tumors are associated with high tumor mutation burden but showed no association with oncogenic drivers. Immune analyses revealed that poorly differentiated tumors are associated with increased T-cell clonality, expression of PD-L1, and infiltration by cytotoxic CD8 T-cells, activated CD4 T-cells, and pro-inflammatory (M1) macrophages. Finally, multivariate analysis controlling for stage and smoking status confirmed independence of immune differences between IASLC grade groups. CONCLUSIONS: Poorly differentiated tumors, as defined by the updated IASLC grading criteria, are associated with a distinct immunogenic tumor microenvironment that predicts for therapeutic response to immune agents, including checkpoint inhibitors, and should be included in the clinical trial design of immunotherapy studies in early-stage lung adenocarcinoma.

Nitrogen-doped carbonized polymer dots activated by alkalinity of arginine for multicolor multi-platform sensor.

Among carbon-based materials, carbon dots (CDs) are popular because of their stable optical properties and good biocompatibility. Their fluorescence properties often are tailored by size, surface modification, or heteroatom doping with multiple precursors. In this paper, the alkalinity of Arginine (Arg) acts as an activator to promote carbonation and increase luminescence efficiency. Meanwhile, Arg acts as a tetrahedron and plays another three roles in preparing amino-modified nitrogen-doped carbonized polymer dots (SA-NCPDs), in which Arg serves as carbon source, nitrogen-doped nitrogen source, and surface modifier. The NCPDs based only on arginine display narrow full width at half maximum (FWHM) of 54 nm. The SA-NCPDs present dual emission distribution in the UV and visible blue regions, respectively. The SA-NCPDs present multicolor emission, especially crimson emission in water under 77 K in different solvents. Besides, SA-NCPDs in different solvents and the prepared polyvinyl alcohol (PVA) composite film have different phosphorescence and long afterglow at 77 K. The excellent biocompatibility and stability of the SA-NCPDs imply it is a potential material in biomedicine. In addition, a multiplatform multicolor ratiometric sensor and visualized colorimetric sensor with high selectivity were constructed successfully for detecting Cu2+, H2O2, and OPD based on self-absorption.