Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease.

OBJECTIVES: Predicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing. METHODS: We analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with 5-fold cross validation. RESULTS: Disease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the ß-diversity between groups (p=0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871. CONCLUSIONS: Identification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.

Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer.

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

Transcriptomic analysis of World Trade Center particulate Matter-induced pulmonary inflammation and drug treatments.

Over 400,000 people are estimated to have been exposed to World Trade Center particulate matter (WTCPM) since the attack on the Twin Towers in Lower Manhattan on September 11, 2001. Epidemiological studies have found that exposure to dust may cause respiratory ailments and cardiovascular diseases. However, limited studies have performed a systematic analysis of transcriptomic data to elucidate the biological responses to WTCPM exposure and the therapeutic options. Here, we developed an in vivo mouse exposure model of WTCPM and administered two drugs (i.e., rosoxacin and dexamethasone) to generate transcriptomic data from lung samples. WTCPM exposure increased the inflammation index, and this index was significantly reduced by both drugs. We analyzed the transcriptomics derived omics data using a hierarchical systems biology model (HiSBiM) with four levels, including system, subsystem, pathway, and gene analyses. Based on the selected differentially expressed genes (DEGs) from each group, WTCPM and the two drugs commonly affected the inflammatory responses, consistent with the inflammation index. Among these DEGs, the expression of 31 genes was affected by WTCPM exposure and consistently reversed by the two drugs, and these genes included Psme2, Cldn18, and Prkcd, which are involved in immune- and endocrine-related subsystems and pathways such as thyroid hormone synthesis, antigen processing and presentation, and leukocyte transendothelial migration. Furthermore, the two drugs reduced the inflammatory effects of WTCPM through distinct pathways, e.g., vascular-associated signaling by rosoxacin, whereas mTOR-dependent inflammatory signaling was found to be regulated by dexamethasone. To the best of our knowledge, this study constitutes the first investigation of transcriptomics data of WTCPM and an exploration of potential therapies. We believe that these findings provide strategies for the development of promising optional interventions and therapies for airborne particle exposure.

Gut microbiota composition can reflect immune responses of latent tuberculosis infection in patients with poorly controlled diabetes.

BACKGROUND: Diabetes mellitus (DM) is a major risk factor for tuberculosis (TB). Evidence has linked the DM-related dysbiosis of gut microbiota to modifiable host immunity to Mycobacterium tuberculosis infection. However, the crosslinks between gut microbiota composition and immunological effects on the development of latent TB infection (LTBI) in DM patients remain uncertain. METHODS: We prospectively obtained stool, blood samples, and medical records from 130 patients with poorly-controlled DM (pDM), defined as ever having an HbA1c > 9.0% within previous 1 year. Among them, 43 had LTBI, as determined by QuantiFERON-TB Gold in-Tube assay. The differences in the taxonomic diversity of gut microbiota between LTBI and non-LTBI groups were investigated using 16S ribosomal RNA sequencing, and a predictive algorithm was established using a random forest model. Serum cytokine levels were measured to determine their correlations with gut microbiota. RESULTS: Compared with non-LTBI group, the microbiota in LTBI group displayed a similar alpha-diversity but different beta-diversity, featuring decrease of Prevotella_9, Streptococcus, and Actinomyces and increase of Bacteroides, Alistipes, and Blautia at the genus level. The accuracy was 0.872 for the LTBI prediction model using the aforementioned 6 microbiome-based biomarkers. Compared with the non-LTBI group, the LTBI group had a significantly lower serum levels of IL-17F (p = 0.025) and TNF-α (p = 0.038), which were correlated with the abundance of the aforementioned 6 taxa. CONCLUSIONS: The study results suggest that gut microbiome composition maybe associated with host immunity relevant to TB status, and gut microbial signature might be helpful for the diagnosis of LTBI.

Inhibition of IRAK1 Is an Effective Therapy for Autoimmune Hypophysitis in Mice.

Autoimmune hypophysitis (AH) is an autoimmune disease of the pituitary for which the pathogenesis is incompletely known. AH is often treated with corticosteroids; however, steroids may lead to considerable side effects. Using a mouse model of AH (experimental autoimmune hypophysitis, EAH), we show that interleukin-1 receptor-associated kinase 1 (IRAK1) is upregulated in the pituitaries of mice that developed EAH. We identified rosoxacin as a specific inhibitor for IRAK1 and found it could treat EAH. Rosoxacin treatment at an early stage (day 0-13) slightly reduced disease severity, whereas treatment at a later stage (day 14-27) significantly suppressed EAH. Further investigation indicated rosoxacin reduced production of autoantigen-specific antibodies. Rosoxacin downregulated production of cytokines and chemokines that may dampen T cell differentiation or recruitment to the pituitary. Finally, rosoxacin downregulated class II major histocompatibility complex expression on antigen-presenting cells that may lead to impaired activation of autoantigen-specific T cells. These data suggest that IRAK1 may play a pathogenic role in AH and that rosoxacin may be an effective drug for AH and other inflammatory diseases involving IRAK1 dysregulation.

Densest subgraph-based methods for protein-protein interaction hot spot prediction.

BACKGROUND: Hot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots. RESULT: In this study, we develop three graph theory-based methods to predict hot spots from only a single residue interaction network. We detect the important residues by finding subgraphs with high densities, i.e., high average degrees. Generally, a high degree implies a high binding possibility between protein chains, and thus a subgraph with high density usually relates to binding sites that have a high rate of hot spots. By evaluating the results on 67 complexes from the SKEMPI database, our methods clearly outperform existing graph theory-based methods on recall and F-score. In particular, our main method, Min-SDS, has an average recall of over 0.665 and an f2-score of over 0.364, while the recall and f2-score of the existing methods are less than 0.400 and 0.224, respectively. CONCLUSION: The Min-SDS method performs best among all tested methods on the hot spot prediction problem, and all three of our methods provide useful approaches for analyzing bionetworks. In addition, the densest subgraph-based methods predict hot spots with only one residue interaction network, which is constructed from spatial atomic coordinate data to mitigate the shortage of data from wet-lab experiments.

The Development and Evaluation of a Smartphone-Aided Diagnosis Application to Measure Tympanic Membrane Perforations.

OBJECTIVES: Chronic otitis media is a long-term infection of the middle ear. It is characterized by persistent discharge from the middle ear through a perforated tympanic membrane. It is one of the most common causes of preventable hearing loss, especially in developing countries. Precise estimation of the size of tympanic membrane perforation is essential for successful clinical management. In this study, we developed a smartphone-based application to calculate the ratio of the area of tympanic membrane perforation to the area of the tympanic membrane. Twelve standardized patients and 60 medical students were involved to assess the area of tympanic membrane perforation, in particular, the percentage of perforation size. METHODS: In total, 60 student doctors (including year 5 and year 6 medical students, intern and post-graduate year training of doctors) were recruited during their rotation at the Otolaryngology department of Taipei Medical University Shuang-Ho Hospital. Twelve standardized patients with chronic otitis media were recruited through a single otology practice. Oto-endoscopic examination was performed for all patients by using a commercially-available digital oto-endoscope, and clinical images of the tympanic membrane perforation were obtained. To demonstrate the variability of perforation size estimation by different student doctors, we calculated the percentage of perforation using the smartphone-based application for 12 tympanic membranes objectively and compared the results with those visually estimated by the 60 student doctors subjectively. RESULTS: The variance in the visual estimation by the 60 student doctors was large. By contrast, variances in smartphone-based application calculations were smaller, indicating consistency in the results obtained from different users. The smartphone-based application accurately estimated the presence of perforation for tympanic membranes with high consistency. The differences in visual estimations can be considerably great and the variances can be large among different individuals. CONCLUSIONS: The smartphone-based application is a dependable tool for precisely evaluating the size of tympanic membrane perforation.

Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations.

BACKGROUND: While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. RESULTS: In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. CONCLUSION: With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.

Identification of pan-kinase-family inhibitors using graph convolutional networks to reveal family-sensitive pre-moieties.

BACKGROUND: Human protein kinases, the key players in phosphoryl signal transduction, have been actively investigated as drug targets for complex diseases such as cancer, immune disorders, and Alzheimer's disease, with more than 60 successful drugs developed in the past 30 years. However, many of these single-kinase inhibitors show low efficacy and drug resistance has become an issue. Owing to the occurrence of highly conserved catalytic sites and shared signaling pathways within a kinase family, multi-target kinase inhibitors have attracted attention. RESULTS: To design and identify such pan-kinase family inhibitors (PKFIs), we proposed PKFI sets for eight families using 200,000 experimental bioactivity data points and applied a graph convolutional network (GCN) to build classification models. Furthermore, we identified and extracted family-sensitive (only present in a family) pre-moieties (parts of complete moieties) by utilizing a visualized explanation (i.e., where the model focuses on each input) method for deep learning, gradient-weighted class activation mapping (Grad-CAM). CONCLUSIONS: This study is the first to propose the PKFI sets, and our results point out and validate the power of GCN models in understanding the pre-moieties of PKFIs within and across different kinase families. Moreover, we highlight the discoverability of family-sensitive pre-moieties in PKFI identification and drug design.

Furin and TMPRSS2 Resistant Spike Induces Robust Humoral and Cellular Immunity Against SARS-CoV-2 Lethal Infection.

An effective COVID-19 vaccine against broad SARS-CoV-2 variants is still an unmet need. In the study, the vesicular stomatitis virus (VSV)-based vector was used to express the SARS-CoV-2 Spike protein to identify better vaccine designs. The replication-competent of the recombinant VSV-spike virus with C-terminal 19 amino acid truncation (SΔ19 Rep) was generated. A single dose of SΔ19 Rep intranasal vaccination is sufficient to induce protective immunity against SARS-CoV-2 infection in hamsters. All the clones isolated from the SΔ19 Rep virus contained R682G mutation located at the Furin cleavage site. An additional S813Y mutation close to the TMPRSS2 cleavage site was identified in some clones. The enzymatic processing of S protein was blocked by these mutations. The vaccination of the R682G-S813Y virus produced a high antibody response against S protein and a robust S protein-specific CD8+ T cell response. The vaccinated animals were protected from the lethal SARS-CoV-2 (delta variant) challenge. The S antigen with resistance to enzymatic processes by Furin and TMPRSS2 will provide better immunogenicity for vaccine design.

CoMI: consensus mutual information for tissue-specific gene signatures.

BACKGROUND: The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures. RESULTS: Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis. CONCLUSIONS: Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.

Discovery of moiety preference by Shapley value in protein kinase family using random forest models.

BACKGROUND: Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). RESULTS: In this study, we used "merged moiety-based interpretable features (MMIFs)," which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. CONCLUSIONS: In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.

A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation.

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI .

Whole-Blood 3-Gene Signature as a Decision Aid for Rifapentine-based Tuberculosis Preventive Therapy.

BACKGROUND: Systemic drug reaction (SDR) is a major safety concern with weekly rifapentine plus isoniazid for 12 doses (3HP) for latent tuberculosis infection (LTBI). Identifying SDR predictors and at-risk participants before treatment can improve cost-effectiveness of the LTBI program. METHODS: We prospectively recruited 187 cases receiving 3HP (44 SDRs and 143 non-SDRs). A pilot cohort (8 SDRs and 12 non-SDRs) was selected for generating whole-blood transcriptomic data. By incorporating the hierarchical system biology model and therapy-biomarker pathway approach, candidate genes were selected and evaluated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, interpretable machine learning models presenting as SHapley Additive exPlanations (SHAP) values were applied for SDR risk prediction. Finally, an independent cohort was used to evaluate the performance of these predictive models. RESULTS: Based on the whole-blood transcriptomic profile of the pilot cohort and the RT-qPCR results of 2 SDR and 3 non-SDR samples in the training cohort, 6 genes were selected. According to SHAP values for model construction and validation, a 3-gene model for SDR risk prediction achieved a sensitivity and specificity of 0.972 and 0.947, respectively, under a universal cutoff value for the joint of the training (28 SDRs and 104 non-SDRs) and testing (8 SDRs and 27 non-SDRs) cohorts. It also worked well across different subgroups. CONCLUSIONS: The prediction model for 3HP-related SDRs serves as a guide for establishing a safe and personalized regimen to foster the implementation of an LTBI program. Additionally, it provides a potential translational value for future studies on drug-related hypersensitivity.

The inhibitory effects of PGG and EGCG against the SARS-CoV-2 3C-like protease.

The coronavirus disease (COVID-19) pandemic, resulting from human-to-human transmission of a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has led to a global health crisis. Given that the 3 chymotrypsin-like protease (3CLpro) of SARS-CoV-2 plays an indispensable role in viral polyprotein processing, its successful inhibition halts viral replication and thus constrains virus spread. Therefore, developing an effective SARS-CoV-2 3CLpro inhibitor to treat COVID-19 is imperative. A fluorescence resonance energy transfer (FRET)-based method was used to assess the proteolytic activity of SARS-CoV-2 3CLpro using intramolecularly quenched fluorogenic peptide substrates corresponding to the cleavage sequence of SARS-CoV-2 3CLpro. Molecular modeling with GEMDOCK was used to simulate the molecular interactions between drugs and the binding pocket of SARS-CoV-2 3CLpro. This study revealed that the Vmax of SARS-CoV-2 3CLpro was about 2-fold higher than that of SARS-CoV 3CLpro. Interestingly, the proteolytic activity of SARS-CoV-2 3CLpro is slightly more efficient than that of SARS-CoV 3CLpro. Meanwhile, natural compounds PGG and EGCG showed remarkable inhibitory activity against SARS-CoV-2 3CLpro than against SARS-CoV 3CLpro. In molecular docking, PGG and EGCG strongly interacted with the substrate binding pocket of SARS-CoV-2 3CLpro, forming hydrogen bonds with multiple residues, including the catalytic residues C145 and H41. The activities of PGG and EGCG against SARS-CoV-2 3CLpro demonstrate their inhibition of viral protease activity and highlight their therapeutic potentials for treating SARS-CoV-2 infection.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 µM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

A novel EGFR inhibitor suppresses survivin expression and tumor growth in human gefitinib-resistant EGFR-wild type and -T790M non-small cell lung cancer.

Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are currently used therapy for non-small cell lung cancer (NSCLC) patients; however, drug resistance during cancer treatment is a critical problem. Survivin is an anti-apoptosis protein, which promotes cell proliferation and tumor growth that highly expressed in various human cancers. Here, we show a novel synthetic compound derived from gefitinib, do-decyl-4-(4-(3-(4-(3-chloro-4-fluorophenylamino)-7-methoxyquinazolin-6-yloxy)propyl) piper-azin-1-yl)-4-oxobutanoate, which is named as SP101 that inhibits survivin expression and tumor growth in both the EGFR-wild type and -T790M of NSCLC. SP101 blocked EGFR kinase activity and induced apoptosis in the A549 (EGFR-wild type) and H1975 (EGFR-T790M) lung cancer cells. SP101 reduced survivin proteins and increased active caspase 3 for inducing apoptosis. Ectopic expression of survivin by a survivin-expressed vector attenuated the SP101-induced cell death in lung cancer cells. Moreover, SP101 inhibited the gefitinib-resistant tumor growth in the xenograft human H1975 lung tumors of nude mice. SP101 substantially reduced survivin proteins but conversely elicited active caspase 3 proteins in tumor tissues. Besides, SP101 exerted anticancer abilities in the gefitinib resistant cancer cells separated from pleural effusion of a clinical lung cancer patient. Consistently, SP101 decreased the survivin proteins and the patient-derived xenografted lung tumor growth in nude mice. Anti-tumor ability of SP101 was also confirmed in the murine lung cancer model harboring EGFR T790M-L858R. Together, SP101 is a new EGFR inhibitor with inhibiting survivin that can be developed for treating EGFR wild-type and EGFR-mutational gefitinib-resistance in human lung cancers.

Validation of a Personalized Hearing Screening Mobile Health Application for Persons with Moderate Hearing Impairment.

Hearing impairment is a frequent human sensory impairment. It was estimated that over 50% of those aged >75 years experience hearing impairment in the United States. Several hearing impairment-related factors are detectable through screening; thus, further deterioration can be avoided. Early identification of hearing impairment is the key to effective management. However, hearing screening resources are scarce or inaccessible, underlining the importance of developing user-friendly mobile health care systems for universal hearing screening. Mobile health (mHealth) applications (apps) act as platforms for personalized hearing screening to evaluate an individual's risk of developing hearing impairment. We aimed to evaluate and compare the accuracy of smartphone-based air conduction and bone conduction audiometry self-tests with that of standard air conduction and bone conduction pure-tone audiometry tests. Moreover, we evaluated the use of smartphone-based air conduction and bone conduction audiometry self-tests in conductive hearing loss diagnosis. We recruited 103 patients (206 ears) from an otology clinic. All patients were aged ≥20 years. Patients who were diagnosed with active otorrhea was excluded. Moderate hearing impairment was defined as hearing loss with mean hearing thresholds >40 dB. All patients underwent four hearing tests performed by a board-certified audiologist: a smartphone-based air conduction audiometry self-test, smartphone-based bone conduction audiometry self-test, standard air-conduction pure-tone audiometry, and standard bone conduction pure-tone audiometry. We compared and analyzed the results of the smartphone-based air conduction and bone conduction audiometry self-tests with those of the standard air conduction and bone conduction pure-tone audiometry tests. The sensitivity of the smartphone-based air conduction audiometry self-test was 0.80 (95% confidence interval CI = 0.71-0.88) and its specificity was 0.84 (95% CI = 0.76-0.90), respectively. The sensitivity of the smartphone-based bone conduction audiometry self-test was 0.64 (95% CI = 0.53-0.75) and its specificity was 0.71 (95% CI = 0.62-0.78). Among all the ears, 24 were diagnosed with conductive hearing loss. The smartphone-based audiometry self-tests correctly diagnosed conductive hearing loss in 17 of those ears. The personalized smartphone-based audiometry self-tests correctly diagnosed hearing loss with high sensitivity and high specificity, and they can be a reliable screening test to rule out moderate hearing impairment among the population. It provided patients with moderate hearing impairment with personalized strategies for symptomatic control and facilitated individual case management for medical practitioners.

Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.

Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein-protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.