shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.

Sex-dependent differences in the genomic profile of lingual sensory neurons in naïve and tongue-tumor bearing mice.

Mechanisms of sex-dependent orofacial pain are widely understudied. A significant gap in knowledge exists about comprehensive regulation of tissue-specific trigeminal sensory neurons in diseased state of both sexes. Using RNA sequencing of FACS sorted retro-labeled sensory neurons innervating tongue tissue, we determined changes in transcriptomic profiles in males and female mice under naïve as well as tongue-tumor bearing conditions Our data revealed the following interesting findings: (1) FACS sorting obtained higher number of neurons from female trigeminal ganglia (TG) compared to males; (2) Naïve female neurons innervating the tongue expressed immune cell markers such as Csf1R, C1qa and others, that weren't expressed in males. This was validated by Immunohistochemistry. (3) Accordingly, immune cell markers such as Csf1 exclusively sensitized TRPV1 responses in female TG neurons. (4) Male neurons were more tightly regulated than female neurons upon tumor growth and very few differentially expressed genes (DEGs) overlapped between the sexes, (5) Male DEGs contained higher number of transcription factors whereas female DEGs contained higher number of enzymes, cytokines and chemokines. Collectively, this is the first study to characterize the effect of sex as well as of tongue-tumor on global gene expression, pathways and molecular function of tongue-innervating sensory neurons.

DepLink: an R Shiny app to systematically link genetic and pharmacologic dependencies of cancer.

Motivation: Large-scale genetic and pharmacologic dependency maps are generated to reveal genetic vulnerabilities and drug sensitivities of cancer. However, user-friendly software is needed to systematically link such maps. Results: Here, we present DepLink, a web server to identify genetic and pharmacologic perturbations that induce similar effects on cell viability or molecular changes. DepLink integrates heterogeneous datasets of genome-wide CRISPR loss-of-function screens, high-throughput pharmacologic screens and gene expression signatures of perturbations. The datasets are systematically connected by four complementary modules tailored for different query scenarios. It allows users to search for potential inhibitors that target a gene (Module 1) or multiple genes (Module 2), mechanisms of action of a known drug (Module 3) and drugs with similar biochemical features to an investigational compound (Module 4). We performed a validation analysis to confirm the capability of our tool to link the effects of drug treatments to knockouts of the drug's annotated target genes. By querying with a demonstrating example of CDK6, the tool identified well-studied inhibitor drugs, novel synergistic gene and drug partners and insights into an investigational drug. In summary, DepLink enables easy navigation, visualization and linkage of rapidly evolving cancer dependency maps. Availability and implementation: The DepLink web server, demonstrating examples and detailed user manual are available at https://shiny.crc.pitt.edu/deplink/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

BRCA1 deficiency in mature CD8+ T lymphocytes impairs antitumor immunity.

Women with BRCA1 germline mutations have approximately an 80% lifetime chance of developing breast cancer. While the tumor suppressor function of BRCA1 in breast epithelium has been studied extensively, it is not clear whether BRCA1 deficiency in non-breast somatic cells also contribute to tumorigenesis. Here, we report that mouse Brca1 knockout (KO) in mature T lymphocytes compromises host antitumor immune response to transplanted syngeneic mouse mammary tumors. T cell adoptive transfer further corroborates CD8+ T cell-intrinsic impact of Brca1 KO on antitumor adaptive immunity. T cell-specific Brca1 KO mice exhibit fewer total CD8+, more exhausted, reduced cytotoxic, and reduced memory tumor-infiltrating T cell populations. Consistent with the preclinical data, cancer-free BRCA1 mutation-carrying women display lower abundance of circulating CD8+ lymphocytes than the age-matched control group. Thus, our findings support the notion that BRCA1 deficiency in adaptive immunity could contribute to BRCA1-related tumorigenesis. We also suggest that prophylactic boosting of adaptive immunity may reduce cancer incidence among at-risk women.

Sex-dependent Differences in the Genomic Profile of Lingual Sensory Neurons in Naïve and Tongue-Tumor Bearing Mice.

Mechanisms of sex-dependent orofacial pain are widely understudied. A significant gap in knowledge exists about comprehensive regulation of tissue-specific trigeminal sensory neurons in diseased state of both sexes. Using RNA sequencing of FACS sorted retro-labeled sensory neurons innervating tongue tissue, we determined changes in transcriptomic profiles in males and female mice under naïve as well as tongue-tumor bearing conditions Our data revealed the following interesting findings: 1) Tongue tissue of female mice was innervated with higher number of trigeminal neurons compared to males; 2) Naïve female neurons innervating the tongue exclusively expressed immune cell markers such as Csf1R, C1qa and others, that weren't expressed in males. This was validated by Immunohistochemistry. 4) Accordingly, immune cell markers such as Csf1 exclusively sensitized TRPV1 responses in female TG neurons. 3) Male neurons were more tightly regulated than female neurons upon tumor growth and very few differentially expressed genes (DEGs) overlapped between the sexes, 5) Male DEGs contained higher number of transcription factors whereas female DEGs contained higher number of enzymes, cytokines and chemokines. Collectively, this is the first study to characterize the effect of sex as well as of tongue-tumor on global gene expression, pathways and molecular function of tongue-innervating sensory neurons.

Comparative Transcriptome Analysis of Superficial and Deep Partial-Thickness Burn Wounds in Yorkshire vs Red Duroc Pigs.

Hypertrophic scars are a common negative outcome of deep partial-thickness (DPT) burn wounds resulting in increased dermal thickness, wound area contracture, and inflammation of the affected area. The red Duroc and Yorkshire porcine breeds are common large animal models for studying dermal wounds due to their structural similarities to human skin; however, the porcine transcriptomic profiles of dermal burn wounds and healing process are not well known. In response, a longitudinal transcriptomic comparative study was conducted comparing red Duroc and Yorkshire superficial and DPT burn wounds to their respective control uninjured tissue. Using next-generation RNA sequencing, total RNAs were isolated from burn wound tissue harvested on 0, 3, 7, 15, 30, and 60 days postburn, and mRNA-seq and gene expression read counts were generated. Significant differentially expressed genes relative to uninjured tissue were defined, and active biological processes were determined using gene set enrichment analyses. Additionally, collagen deposition, α-smooth muscle actin (SMA) protein concentration, epidermal and dermal thickness measurements, and wound area changes in response to burn injury were characterized. Overall, the red Duroc pigs, in response to both burn wound types, elicited a more robust and prolonged inflammatory immune response, fibroblast migration, and proliferation, as well as heightened levels of extracellular matrix modulation relative to respective burn types in the Yorkshire pigs. Collectively, the red Duroc DPT burn wounds produce a greater degree of hypertrophic scar-like response compared with Yorkshire DPT burn wounds. These findings will facilitate future porcine burn studies down-selecting treatment targets and determining the effects of novel therapeutic strategies.

Predicting and characterizing a cancer dependency map of tumors with deep learning.

Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.

Androgen deprivation-induced elevated nuclear SIRT1 promotes prostate tumor cell survival by reactivation of AR signaling.

The NAD+-dependent deacetylase, Sirtuin 1 (SIRT1) is involved in prostate cancer pathogenesis. However, the actual contribution is unclear as some reports propose a protective role while others suggest it is harmful. We provide evidence for a contextual role for SIRT1 in prostate cancer. Our data show that (i) mice orthotopically implanted with SIRT1-silenced LNCaP cells produced smaller tumors; (ii) SIRT1 suppression mimicked AR inhibitory effects in hormone responsive LNCaP cells; and (iii) caused significant reduction in gene signatures associated with E2F and MYC targets in AR-null PC-3 and E2F and mTORC1 signaling in castrate-resistant ARv7 positive 22Rv1 cells. Our findings further show increased nuclear SIRT1 (nSIRT1) protein under androgen-depleted relative to androgen-replete conditions in prostate cancer cell lines. Silencing SIRT1 resulted in decreased recruitment of AR to PSA enhancer selectively under androgen-deprivation conditions. Prostate cancer outcome data show that patients with higher levels of nSIRT1 progress to advanced disease relative to patients with low nSIRT1 levels. Collectively, we demonstrate that lowering SIRT1 levels potentially provides new avenues to effectively prevent prostate cancer recurrence.

Campylobacter infection promotes IFNγ-dependent intestinal pathology via ILC3 to ILC1 conversion.

Innate lymphoid cells (ILCs) are a heterogeneous family of immune regulators that protect against mucosal pathogens but can also promote intestinal pathology. Although the plasticity between ILCs populations has been described, the role of mucosal pathogens in inducing ILC conversion leading to intestinal pathology remains unclear. Here we demonstrate that IFNγ-producing ILCs are responsible for promoting intestinal pathology in a mouse model of enterocolitis caused by Campylobacter jejuni, a common human enteric pathogen. Phenotypic analysis revealed a distinct population of IFNγ-producing NK1.1-T-bet+ILCs that accumulated in the intestine of C. jejuni-infected mice. Adoptive transfer experiments demonstrated their capacity to promote intestinal pathology. Inactivation of T-bet in NKp46+ ILCs ameliorated disease. Transcriptome analysis and cell-fate mapping experiments revealed that IFNγ-producing NK1.1-ILCs correspond to ILC1 profile and develop from RORγt+ progenitors. Collectively, we identified a distinct population of NK1.1-ex-ILC3s that promotes intestinal pathology through IFNγ production in response to C. jejuni infection.

Antibiotic Administration Routes and Oral Exposure to Antibiotic Resistant Bacteria as Key Drivers for Gut Microbiota Disruption and Resistome in Poultry.

Previous studies have identified oral administration of antibiotics and gut-impacting drugs as critical drivers for fecal antibiotic resistance (AR) and microbiome disruption in lab mice, but the practical implications of these findings have yet to be validated in hosts nurtured in conventional environment. Using ampicillin (Amp) as a way to extrapolate the general effect of antibiotics, this project examined the impact of drug administration routes on fecal microbiota and resistome using poultry raised in a teaching farm. AR genes were found to be abundant in the feces of young Leghorn chicks without previous antibiotic treatment. In chickens seeded with bla CMY-2 + Escherichia coli, 300 mg/kg body weight of Amp was orally administered for 5 days. This led to the fecal microbiota switching from Firmicutes occupied (95.60 ± 2.62%) and Lactobacillus rich, to being dominated by Proteobacteria (70.91 ± 28.93%), especially Escherichia/Shigella. However, when Amp was given via muscle injection, Firmicutes was mostly retained (i.e., from 83.6 ± 24.4% pre- to 90.4 ± 15.2% post-treatment). In control chickens without seeding with bla CMY-2 + E. coli, oral Amp also led to the increase of Proteobacteria, dominated by Klebsiella and Escherichia/Shigella, and a reduction of Firmicutes. Specifically within Firmicutes, Enterococcus, Clostridium, etc. were enriched but Lactobacillus was diminished. The fecal resistome including Ampr genes was more abundant in chickens receiving oral Amp than those treated with muscle injection, but the difference was primarily within 1 log. The data illustrated that both drug administration routes and pre-existing gut microbiota have profound impacts on gut microbiome disruption when antibiotic treatment is given. In hosts nurtured in a conventional environment, drug administration route has the most evident impact on gut microbiota rather than the size of the targeted bla CMY-2 + gene pool, likely due to the pre-existing bacteria that are (i) less susceptible to Amp, and/or (ii) with Ampr- or multidrug resistance-encoding genes other than bla CMY-2 +. These results demonstrated the critical interplay among drug administration routes, microbiota seeded through the gastrointestinal tract, AR, gut microbiota disruption, and the rise of common opportunistic pathogens in hosts. The potential implications in human and animal health are discussed.

Correction to: Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

Following publication of the original article [1], the authors provided an updated funding statement to the article. The updated statement is as follows.

Integrating resistance functions to predict response to induction chemotherapy in de novo acute myeloid leukemia.

OBJECTIVES: This study explored resistance functions and their interactions in de novo AML treated with the "7 + 3" induction regimen. METHODS: We analyzed RNA-sequencing profiles of whole bone marrow samples from 52 de novo AML patients who completed the "7 + 3" regimen and stratified patients into CR (n = 35) and non-CR (n = 17) groups. RESULTS: A systematic gene set analysis revealed significant associations between chemoresistance and mTOR (P < .001), myc (P < .001), mitochondrial oxidative phosphorylation (P < .001), and stemness (P = .002). These functions were independent with regard to gene contents and activity scores. An integration of these four functions showed a prediction of chemoresistance (area under the receiver operating characteristic curve = 0.815) superior to that of each function alone. Moreover, our proposed seven-gene scoring system significantly correlated with the four-function model (r = .97; P < .001) to predict chemoresistance to the "7 + 3" regimen. On multivariate analysis, a seven-gene score of ≥-0.027 (hazard ratio: 11.18; 95% confidence interval: 2.06-60.65; P = .005) was an independent risk factor for induction failure. CONCLUSIONS: Myc, OXPHOS, mTOR, and stemness were responsive for chemoresistance in AML. Treatments other than the "7 + 3" regimen need to be considered for de novo AML patients predicted to be refractory to the "7 + 3" regimen.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. RESULTS: We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. CONCLUSIONS: Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

An ancestry informative marker panel design for individual ancestry estimation of Hispanic population using whole exome sequencing data.

BACKGROUND: Europeans and American Indians were major genetic ancestry of Hispanics in the U.S. These ancestral groups have markedly different incidence rates and outcomes in many types of cancers. Therefore, the genetic admixture may cause biased genetic association study with cancer susceptibility variants specifically in Hispanics. For example, the incidence rate of liver cancer has been shown with substantial disparity between Hispanic, Asian and non-Hispanic white populations. Currently, ancestry informative marker (AIM) panels have been widely utilized with up to a few hundred ancestry-informative single nucleotide polymorphisms (SNPs) to infer ancestry admixture. Notably, current available AIMs are predominantly located in intron and intergenic regions, while the whole exome sequencing (WES) protocols commonly used in translational research and clinical practice do not cover these markers. Thus, it remains challenging to accurately determine a patient's admixture proportion without additional DNA testing. RESULTS: In this study we designed an unique AIM panel that infers 3-way genetic admixture from three distinct and selective continental populations (African (AFR), European (EUR), and East Asian (EAS)) within evolutionarily conserved exonic regions. Initially, about 1 million exonic SNPs from selective three populations in the 1000 Genomes Project were trimmed by their linkage disequilibrium (LD), restricted to biallelic variants, and finally we optimized to an AIM panel with 250 SNP markers, or the UT-AIM250 panel, using their ancestral informativeness statistics. Comparing to published AIM panels, UT-AIM250 performed better accuracy when we tested with three ancestral populations (accuracy: 0.995 ± 0.012 for AFR, 0.997 ± 0.007 for EUR, and 0.994 ± 0.012 for EAS). We further demonstrated the performance of the UT-AIM250 panel to admixed American (AMR) samples of the 1000 Genomes Project and obtained similar results (AFR, 0.085 ± 0.098; EUR, 0.665 ± 0.182; and EAS, 0.250 ± 0.205) to previously published AIM panels (Phillips-AIM34: AFR, 0.096 ± 0.127, EUR, 0.575 ± 0.290, and EAS, 0.330 ± 0.315; Wei-AIM278: AFR, 0.070 ± 0.096, EUR, 0.537 ± 0.267, and EAS, 0.393 ± 0.300). Subsequently, we applied the UT-AIM250 panel to a clinical dataset of 26 self-reported Hispanic patients in South Texas with hepatocellular carcinoma (HCC). We estimated the admixture proportions using WES data of adjacent non-cancer liver tissues (AFR, 0.065 ± 0.043; EUR, 0.594 ± 0.150; and EAS, 0.341 ± 0.160). Similar admixture proportions were identified from corresponding tumor tissues. In addition, we estimated admixture proportions of The Cancer Genome Atlas (TCGA) collection of hepatocellular carcinoma (TCGA-LIHC) samples (376 patients) using the UT-AIM250 panel. The panel obtained consistent admixture proportions from tumor and matched normal tissues, identified 3 possible incorrectly reported race/ethnicity, and/or provided race/ethnicity determination if necessary. CONCLUSIONS: Here we demonstrated the feasibility of using evolutionarily conserved exonic regions to infer admixture proportions and provided a robust and reliable control for sample collection or patient stratification for genetic analysis. R implementation of UT-AIM250 is available at https://github.com/chenlabgccri/UT-AIM250.

scdNet: a computational tool for single-cell differential network analysis.

BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. However, the highly sparse data matrices generated by the technology have posed an obstacle to the analysis of differential gene regulatory networks. RESULTS: Addressing the challenges, this study presents, as far as we know, the first bioinformatics tool for scRNA-Seq-based differential network analysis (scdNet). The tool features a sample size adjustment of gene-gene correlation, comparison of inter-state correlations, and construction of differential networks. A simulation analysis demonstrated the power of scdNet in the analyses of sparse scRNA-Seq data matrices, with low requirement on the sample size, high computation efficiency, and tolerance of sequencing noises. Applying the tool to analyze two datasets of single circulating tumor cells (CTCs) of prostate cancer and early mouse embryos, our data demonstrated that differential gene regulation plays crucial roles in anti-androgen resistance and early embryonic development. CONCLUSIONS: Overall, the tool is widely applicable to datasets generated by the emerging technology to bring biological insights into tumor heterogeneity and other studies. MATLAB implementation of scdNet is available at https://github.com/ChenLabGCCRI/scdNet .

Facile continuous production of soy peptide nanogels via nanoscale flash desolvation for drug entrapment.

A facile continuous production of soy peptide nanogels was demonstrated using a multi-inlet vortex micromixer for the entrapment of active ingredients. The full flow regime in the micromixer was systematically studied to understand the flow impact on nanogel size, drug encapsulation efficiency and drug loading efficiency. Ibuprofen was chosen as a model drug to demonstrate encapsulation capability. The study showed that the nanogel size, drug encapsulation efficiency and drug loading efficiency did not alter significantly as long as the flow rates were in transition and turbulent regimes. The driving force behind the folding of peptides within the microenvironment is kinetic mixing with high flow rates, which dominates in comparison to molecular diffusion, nucleation, and growth. Moreover, the hydrophilic-lipophilic balance of the soy peptides determined the drug encapsulation efficiency and the drug loading efficiency, which did not vary much under different manufacturing parameters. Both characteristics are beneficial to mass production of drug-entrapped peptide. In a 50% water-ethanol mixture, the encapsulation efficiency achieved 70% and the drug loading efficiency was up to 25% in transition and turbulent flow regimes. This work opens the avenues to continuous production of drug-entrapped soy peptide nanogels using a multi-inlet vortex micromixer.

An ultra-low-cost smartphone octochannel spectrometer for mobile health diagnostics.

With the rapid development and proliferation of mobile devices with powerful computing power and the ability of integrating sensors into mobile devices, the potential impact of mobile health (mHealth) diagnostics on the public health is drawing researchers' attention. We developed a Smartphone Octo-channel Spectrometer (SOS) as a mHealth diagnostic tool. The SOS has nanoscale wavelength resolution, is self-illuminated from the smartphone itself, and is ultra-low cost (less than $20). A user interface controls the optical sensing parameters and precise alignment. After calibrating and testing the SOS by quantifying protein concentrations, we clinically validated the SOS by comparing the diagnostic performance of our device with that of a clinical spectrophotometer. About 180 serum samples from de-identified patients with 4 types of autoantibodies were blindly read the ELISA results. The accuracy of the SOS achieved 100% across the clinical reportable range compared with the FDA-approved instrument. Furthermore, the self-illuminated SOS only requires about half of the light intensity of the FDA-approved instrument to achieve clinical-level sensitivity. The low-energy-consumption and low-cost SOS enables point-of-care spectrophotometric sensing in low-resource areas, and can be integrated into point-of-care diagnostic systems for rapid multiplex readout and analysis at patient bedside or at home.

Analytical validation of an ultra low-cost mobile phone microplate reader for infectious disease testing.

Most mobile health (mHealth) diagnostic devices for laboratory tests only analyze one sample at a time, which is not suitable for large volume serology testing, especially in low-resource settings with shortage of health professionals. In this study, we developed an ultra-low-cost clinically-accurate mobile phone microplate reader (mReader), and clinically validated this optical device for 12 infectious disease tests. The mReader optically reads 96 samples on a microplate at one time. 771 de-identified patient samples were tested for 12 serology assays for bacterial/viral infections. The mReader and the clinical instrument blindly read and analyzed all tests in parallel. The analytical accuracy and the diagnostic performance of the mReader were evaluated across the clinical reportable categories by comparison with clinical laboratorial testing results. The mReader exhibited 97.59-99.90% analytical accuracy and <5% coefficient of variation (CV). The positive percent agreement (PPA) in all 12 tests achieved 100%, negative percent agreement (NPA) was higher than 83% except for one test (42.86%), and overall percent agreement (OPA) ranged 89.33-100%. We envision the mReader can benefit underserved areas/populations and low-resource settings in rural clinics/hospitals at a low cost (~$50 USD) with clinical-level analytical quality. It has the potential to improve health access, speed up healthcare delivery, and reduce health disparities and education disparities by providing access to a low-cost spectrophotometer.

An ultra low-cost smartphone device for in-situ monitoring of acute organophosphorus poisoning for agricultural workers.

In this work, we present an ultra-low-cost smartphone device for in situ quantification of OP poisoning severity. The performance of the lens-less smartphone spectrum apparatus (LeSSA) is evaluated using standard human Interleukin-6 (IL-6) immunoassay kits. Upon dose-response curve fitting, LeSSA demonstrates an accuracy of 99.5%. The limit of detection (LOD) of LeSSA was evaluated through comparison of 6.4 pg/ml with standard laboratory grade UV-vis spectrophotometer at 5.5 pg/ml. Evaluating the capacity of LeSSA in spike solution by combining plasma cholinesterase (PChE) and human plasma shows consistency at agreement of 97.6% between LeSSA and the laboratory instrument. For application demonstration, the activity of PChE for 24 agricultural workers' plasma samples was measured with LeSSA, showing exceptional agreement (r2 = 0.92) with the laboratory instrument reference. In addition to near laboratory grade accuracy, the total manufacturing cost of LeSSA is only $20 USD highlighting it's affordability. With LeSSA, clinicians can evaluate OP poisoning severity without the need to transport patient samples to facilities at far distances. Utilizing LeSSA, immediate results can be used for administration of appropriate treatment.